Technology Day 2005 - "Bioengineering: Building Bridges Between the Sciences, Engineering and Medicine"

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VINCENT: Folks, my name is Doug Vincent, chair of the Tech Day committee. I'm really excited today. It's a beautiful day outside. There's not a lot of things that would draw me inside today. But the program we have for you today is one of those things. We have had so much fun putting this program together, working with these five outstanding faculty members. Couldn't be happier than to share that with you all today. We have a little bit different format this year.

We've delayed lunch till after 12:45, so our five speakers will all be before lunch. We'll hear from three of them, have a break, including some fruit and granola, then rejoin here for the last two. And at the end, we'll have some questions and answers. President Hockfield will moderate that. We have a live feed downstairs, to Little Kresge, and we'll go through some more of the details for questions and answers a little bit later in this program.

The goal of that is so this afternoon, folks are free to explore the campus, participate in other class activities, and so forth. So bear with us for a little bit lengthened morning program. We think you will find it worth the while. I need to say thank you to the Alumni Association for the faith that they continue to have in the Tech Day committee. Thank you to the committee. Outstanding year. And of course, thank you to Lou Alexander. Outstanding. Just a pleasure to work with you.

If folks would reach to wherever they have their cell phones right now and find the button on it that says off and turn that off, I appreciate that. Thank you. Just like to introduce now Beth Garvin, Executive Vice President and CEO of the MIT Alumni Association. Beth?


GARVIN: Good morning. It's a pleasure to see so many familiar faces, and even more exciting to see so many unfamiliar faces. I hope I get a chance to say hello to each of you personally and welcome you back to MIT. Doug and I were just chatting before the program started and I said, don't give me much of an introduction, because nobody wants to hear from you or me, Doug. They're here to hear from the president and to hear from these marvelous faculty.

He noted, however, that, yeah, we're just the glue that holds it together. And I'd like to acknowledge that glue in the Technology Day committee and all the reunion committee volunteers, the reunion gift committee volunteers, reunion row volunteers, tech challenge game volunteers, all the hundreds of people who have helped make Tech Reunions 2005 a fabulous success from all accounts.

It's been my joy this year to be able to introduce our 16th president, Susan Hockfield, to all of you, and to introduce the alumni to her. It has been just a remarkable journey as we first announced Susan's naming. And any of you with an email address that we know have heard from me multiple times about how I think this has been such a wonderful choice for MIT and what a wonderful fit Dr. Hockfield is for MIT at this point in time. So without any further ado, all of the background is in your program book. And I'm sure you've already read of Susan's remarkable accomplishments, so I do nothing more than turn it over to President Hockfield.


HOCKFIELD: Thank you. Thank you. It's a great pleasure to be here this morning. Thank you, Beth. It's been a real treat for me to travel the country with you and to have you by my side introducing me to alumni from coast to coast. One of the things that has impressed me enormously about MIT is the kind of energy that just emanates from the place. You walk down the corridors and the place is just bursting with energy. But that energy is carried inside the people of the place.

And so as I travel the country with Beth and her band of just tireless troopers, having them by my side and having them introduce me to many of you over the last, what is it now, almost six months, has just been an incredible delight and a treat. The MIT alumni are, indeed, a very special bunch, special in so many ways, special that you could have finally brought spring to Cambridge. We thank you for that. It's been a grim spring, but this weekend is the first real burst of sunshine. It makes us feel optimistic that we actually might have a summer here.

It's just been wonderful to sample the treats of this reunion weekend. My only regret-- people have asked me what I regret. What are the things I don't like about my job? And my response is always the thing I don't like is only having 24 hours a day. And that's the way I feel about these several days. I mean, I wish I could live every hour of these days three times over so that I could be at all of the events and meet more of you than I'm going to unfortunately have a chance to meet this weekend.

I've met many of you, and I will be meeting several more of you, many more of you, over the next several hours through the end of this reunion weekend. But I promise you in the months and years to come, I will be getting around to meeting all of you. So it's great to be here. This morning's program is wonderful. I can take absolutely no credit for it. There are many happy coincidences that I've seen over the last six months. And certainly the topic for this morning's program is a very happy coincidence.

Those of you who read or heard my inauguration address know that one of the things I drew out-- among the very, very many exciting opportunities and challenges for MIT going forward-- is this fantastic convergence between the life sciences and engineering. And this morning's program exemplifies that in just startling ways. In addition, the content of this program that you will hear exemplifies the intellectual rigor and the work at the frontiers of knowledge that really do define MIT as an institution.

I know that the devotion of alumni is one of our absolutely greatest strengths. And keeping MIT strong and vibrant is a team effort that involves all of you as well as all of us on campus. But this great community of MIT that reaches across the country, across the world, is perfectly in line with the Institute's fundamentally collaborative spirit. At MIT, something that I've learned is that we have a gift for learning from one another. We excel at the kinds of intellectual interaction that push conventional boundaries and establish new fields of study, new industries, and new ways of thinking about the world.

The collaborations that MIT fosters depend on our remarkable culture of openness. I've been struck by how the very architecture of the main group symbolizes this. The main group was imagined and then constructed, imagined by Bosworth, as one huge but interconnected building with corridors that link the entire expanse of these enormous buildings. Our historic campus embodies the vision of an institution without internal boundaries, a place where people who are interested in similar problems can work together freely across what might otherwise be disciplinary divides.

In its design, the main group has actually helped invent MIT and helped MIT invent the future. In the years ahead, we will continue to capitalize on our spirit of openness to create new partnerships across our own schools and departments, and with other institutions in the public and private sectors. Collaboration is absolutely essential if we are to fulfill MIT's mission of bringing knowledge to bear on the world's great problems.

The challenges facing us today from climate change to the future of Social Security to the startling spread of contagious diseases are inherently multidisciplinary. Now, MITs distinct ability to catalyze work at the interfaces of existing disciplines also depends on a single uniform standard of excellence. Over the course of this year, I've found an uncompromising dedication to excellence in all of our departments and schools. We see it in the faculty, in the students, and also in the staff around the Institute.

I think this morning's program will demonstrate what I mean. Today's faculty presenters are absolutely stellar researchers. But they're also stellar teachers and members, devoted members, generous members of the MIT community. And they will be talking about some of our era's most exciting advances in science and technology. This generation is bearing witness to a fascinating convergence of engineering and the life sciences. This convergence holds the promise of transforming our lives. But this kind of convergence is not unprecedented. It's a convergence that we saw not so long ago, and we know this precedent.

70 years ago, president Karl Compton insisted that the physical sciences must play a critical role in education and research at MIT. And the result of bringing in the physical sciences into conversation with engineering produced nothing less than a new era for engineering which MIT pioneered. To appreciate the power of the kinds of collaborations that Compton helped spawn through the 20th century's big convergence, you only have to think about the radiation lab that helped develop radar and helped us win World War II.

Today engineering is making the same kind of fertile connections with the life sciences. And I believe we can expect equally revolutionary results. Combining MIT's historic strength and engineering and our newer strengths in biology in the brain and cognitive sciences, we're already opening up unprecedented opportunities for educational innovation, for invention, and for discovery. We're pioneering new educational areas as well as new research directions.

We have biological variants of several of the engineering majors. And next fall, we're going to be initiating a new major in biological engineering. I think this Tech Day program will show you how MIT is leading the way in this new field of all fields, just as we have led the way in disciplines that define the information age. I think it also demonstrates the tremendous contributions MIT can and will be making to this nation and the world. I've been looking forward to hearing these faculty presenters talk about their research for months, and I am delighted to have the luxury of sitting in the audience with all of you today. I'm anticipating an exciting program, and I'm sure that MIT will prove as it has done over the last several months, that my anticipation cannot even come close to what we are actually going to experience.

So enjoy the morning and I will return-- I promise you it's only going to be the morning. But I'm going to return at the end to moderate the question period. So enjoy this morning's presentations. Thank you.


VINCENT: This is great. Every once in a while, I think a group like the Tech Day committee gets lucky. And we selected last fall, bioengineering, and then to be honored with the president Hockfield's first time presiding over today, sometimes you get lucky. And about that, last fall when we were looking at this topic, trying to understand how do we construct this program, we sat down with Professor Doug Lauffenburger, and he just gave us an education.

And we were so thrilled and learned so much, we said, geez, what do you think? Can you do that on a broader scale? He said absolutely. Remarkably, he thanked us this morning for being a part of it, when in fact, really, we thank he and all the faculty for their time to put together today's program. Doug is the director of MIT bioengineering division. And at this point, just turn it over to Doug.


LAUFFENBURGER: Thank you very much, Doug, and it's a privilege to be part of this today with all the alumni and friends coming back. We'll see if there's any continuing adjustment that needs to be done. And I'll be darned, there was. What I want to give you is not the research excitement itself. I have to lay out the context for what's happening at MIT and why it's happening, why it's happening now, why it's happening here. And that's the purpose of this talk.

So I'm going to talk about the landscape of bioengineering at MIT and how we're breaking new ground in this landscape. Now, those of you who haven't been back for a while know that this landscape has been changing dramatically, too. Breaking new ground all over the local blocks with new buildings and new excitement. And so that's been changing, as well. But that's not the landscape that I mean.

I want to talk about the intellectual landscape of the interface between engineering and biology. Now, there has been a traditional biomedical engineering landscape, both at MIT and around the country. And this has been around for about 40 years. There's been a field known as biomedical engineering, bioengineering, medical engineering at many places. And what it's been all about is essentially, the marriage of the classical engineering disciplines in which students are trained in mathematics and physics and chemistry, married with medicine to solve medical problems in ways that technology is based on, mathematics and physics and chemistry could.

Now, one important feature of this is that it's a multidisciplinary application field. All the different engineering fields could bring what they had to bear in contact with medicine and solve problems from their own unique disciplinary backgrounds. Accordingly, from the MIT point of view, this was never appropriate as an undergraduate major degree of study because you were better off studying in any of the other classical engineering disciplines and applying your interests to medicine, if you so wished.

At the graduate level, of course, since you were taking these other engineering disciplinary backgrounds and using them to solve medical problems, that was a very appropriate type of graduate study to do within any of the MIT engineering departments-- electrical engineering and computer science, mechanical engineering, material science, chemical engineering, and so forth. And many of these programs benefited from interaction with the health sciences, technology and its connection to Harvard Medical School.

The other feature is that the bio in the bioengineering in the biomedical engineering has, for those 40 years in these programs, essentially been organ level physiology, which is, of course, crucial for medicine, but is essentially descriptive that's not the mechanisms by which living systems operate. So you could describe what was going on in engineering terms and try to work at it from the outside, but you couldn't control the underlying mechanism.

So that's what's been around for 40 years. And it's very important, had very valuable contributions, and will continue to have very valuable contributions, and will continue at MIT within all these other departments. These kind of contributions have been manifested in a variety of career opportunities carried out by MIT alums. Probably many of you have been working in these very fields, medical devices of all sorts, diagnostics like imaging and prosthetics, implants, extracorporeal devices, maybe the pharmaceuticals industry and processing, manufacturing, and delivery.

And in all these very important contributions of biomedical engineering careers, the one interesting feature is that training of engineers in the actual biological science fundamentals really wasn't crucial. You have to be really good at your math and your physics and your chemistry and interface with a physiological problem. That was what was crucial, rather than the biological science itself.

All right, with that as a 40 year backdrop, now what is new besides many buildings going up? What is new in the bioengineering landscape? Well, as president Hockfield told you, we have now created something brand new, and in fact, I will assert, unique-- a biology based engineering discipline that we call biological engineering. Instead of being an engineering discipline rooted solely in math and physics or math and physics and chemistry, this one is rooted in the molecular life sciences. What's at the core of it is genetics, biochemistry, molecular biology, cell biology.

This engineering discipline is rooted in the mechanisms by which living systems operate, the very molecules and cells that make things happen for good or ill. But these sciences are fused with engineering and analysis and synthesis, just like all the other engineering disciplines fuse analysis and synthesis with physics and chemistry and so forth. So it's focused on taking these life sciences and understanding them in a quantitative and systems oriented way and designing new technologies based on these biological, molecular, and cellular components and mechanisms.

So once you have a discipline now that's just like electrical engineering, mechanical engineering, chemical engineering, but is now rooted in a different science, now MIT has agreed that this is appropriate for standalone degree programs, both a new PhD program that we started in 1999, and what's quite exciting is a new bachelor's degree program that starts this very fall.

Right. But why has this change happened? Why was this traditional landscape for 20 years and now something very radically new is happening? Why is engineering and biology interaction changing so dramatically? Because the biological science itself has changed so dramatically. The engineering can't change in a revolutionary way unless the science is changed in a revolutionary way. And that's what's happened.

In the past 20 years, there have been two major revolutions in biology-- the molecular revolution roughly in the '80s, the genomic revolution roughly in the '90s. So if you took biological science before the '80s, you did not learn these types of biological science. This has all happened in the last 20 years. I went to college in the 1970s. And in the 1970s, I did not learn this kind of biological science. So what was the engineering biology interface, quote, then, before these revolutions, back when I took biological science in the 1970s?

It honestly was not amenable to engineering analysis and synthesis and design. Engineers could not access biology before these revolutions because biological sciences didn't understand the actual mechanisms. Few of the actual components, the parts, had been identified. You could only quantify things at these higher organ levels of hierarchy, which is why engineers worked at the organ physiology level. You certainly couldn't manipulate any of the molecular components. Even if you identified them, there was nothing you could do about them. A gene was a gene and a protein was a protein.

There wasn't enough data to build models and design. And it was hard to generalize from humans to mice to rats to worms and so forth. So the modern era of bioengineering really began with the molecular biology revolution in, essentially, the 1980s, because that permitted mechanistic components to be identified and manipulated. And you can't engineer anything unless you know what the components are and you can manipulate them.

And I just show this for illustration. This is a bone. And for all the traditional years of biomedical engineering, bone could be perceived and dealt with only on the basis of being this macroscopic load bearing mechanical object. For instance, my mother has had six hip replacements. Many years ago she crushed her hip falling off a bicycle. And what could biomedical engineers do about that?

Well, pre the revolution, all they could do was replace that hip as a mechanical load bearing substitute. First wood, crutches, then metals, the implants became metals. Then plastics. The implants became plastics. And none of them worked very long for a variety of reasons, mainly that they really weren't bone. They didn't interact with the surrounding tissue and the immune response. They didn't respond properly to stress and so forth.

But with the molecular biology revolution, now we know this isn't just some macroscopic load bearing object. What has been identified as the cells that are dynamically breaking it down, building it up. The proteins by which the cells are stimulated to break down bone and build it up and the genes that encode for the proteins that tell the cells what to break down and to build up. And so now we have the hope of regenerating not wood, metal or plastic, but regenerating living bone. And you'll hear a little bit of that from Professor Griffith in a minute. So the very fact of identifying what the mechanistic components are and being able to do something to manipulate them was crucial.

Now, this modern era has been catalyzed by the genomic biology revolution in which, just depicted here are a large number of sequencers cranking out the identities of genes and your chromosomes and what kind of proteins they might encode for and so forth, because this has accelerated the rate at which molecular compounds can be identified. Instead of one by one in a hard slog, we can now find tens and hundreds and thousands of them at once. So it's the ability of finding the parts has just been dramatically accelerated.

The concomitant has been showing even how much more complex these mechanisms are than are previously appreciated. People had hoped with the onset of molecular biology that if you could find maybe one gene or one protein, you could just fix something and a disease would go away. The genomic revolution looking at tens and hundreds of thousands of the components and their interactions at once now have made us realize that's going to be really difficult. These are very complex networks and machines.

So these 20 years of two biology revolutions have led us to a now engineering biology interface that's just night and day different. Not only is biology amenable to engineering analysis and synthesis-- we have parts, we can manipulate them-- but again, I would assert that biology benefits from and maybe even requires the engineering analysis and synthesis approach. Now molecular components are being identified. The mechanisms involved in molecular cell actions are being elucidated. Quantitative analysis is possible all the way from DNA structure, protein DNA interactions, up through molecular networks to cell functions to tissue functions. You can quantitatively analyze every step along the way, not just at the organ level.

And molecular components can now be easily manipulated. It's very simple to go in and change a gene, change a protein, change any piece you want. What's the hard part? The hard part is to understand and predict what happens when you're going to do that. Right now it's almost trial and error. And that's what we've got to get beyond. And that's where engineering comes in.

So we believe that this is the time for a new fusion of biology with engineering because of these two revolutions. The molecular biology revolution finally, for the first time, permitted engineers to access the science of biology. And the genomic revolution now requires biology to be addressed by engineering. So we now have the parts. We can manipulate them. And it's darn complex and engineers really can get involved and have to get involved.

Just as a few illustrations, if we think it's something like cells migrating the cells of your immune response seeking out cancer cells or viruses or healing a wound, how would you make that happen? How would you enhance the rate at which wounds were healed or immune response operated? Or if this was a tumor cell metastasizing to an improper organ, how would you stop that? Well, we have to understand how this cell migration works in terms of what's depicted here schematically, it's a machine.

This is the cell and the membrane and it's connected molecularly to the extracellular matrix and allows the cells to crawl and pull their way through. And the cell has motors inside that are generating the forces in just the right places. It's an exquisite molecular machine. And we need to understand it in terms of molecular structures and properties and biophysics and biomechanics. And these machines aren't autonomous. They're not spontaneous. They're highly regulated.

The cells will migrate under some conditions and not under other conditions. They'll proliferate under some conditions and not other conditions. What's regulating them? Well, there's a fascinating set of biomolecular signal processing circuits that take information from the environment and say, what should I do now? Should I migrate? Should I not? Should I proliferate? Should I not? Should I die from a stem cell? Should I differentiate into something else? How do I decide?

So they take their information from their surroundings and process those signals and change their gene expression, metabolism inside a skull, and so forth, and carry out different functions. They do signal processing. So we need to understand this regulation of the machines by these signal processing networks the way, perhaps, electrical engineers, computer scientists and so forth might think about regulatory circuits.

And a fascinating thing that's really becoming realistic to think about-- especially at MIT because we realistically think about things that most places really only will speculate about-- there's an opportunity of a field called synthetic biology, where folks on this campus and others are trying to design these machines and networks from scratch. Instead of just intervening in them one molecule here or there, they're saying, can we design our own DNA sequences that will produce the right proteins that will interact in the right ways to build a network or a machine with the right sort of topology then that would carry out a dynamic function of one sort or another?

People are building their own machines and networks from designing their own DNA sequences from scratch. A fascinating field and it's starting here. There's a young professor named Drew [? Wendy ?] who's really the-- it's very interesting. He's assistant professor and he's known as the father of the field. So that doesn't happen very often. Now at the same time, we start with these mechanistic underpinnings. We say, for an engineer to access biology, you've got to have the parts. You've got to know what the parts are. You've got to be able to manipulate the parts.

But of course, you don't do that in a vacuum. You wanted to think about what higher level functions you're aiming at, whether it's physiology, pathology, therapeutic interventions, brand new devices, like I suggested on the previous slide, brand new materials. Professor Belcher will talk about brand new materials from biomolecular mechanisms. [INAUDIBLE] will talk about new types of therapeutics from these molecular mechanisms.

So you've got to think about the complexity of biology in one dimension, which is many, many components at once in another dimension of all the information you know about it-- sequence, structure, thermodynamics, kinetics, mechanics. And in the other dimension of the physiological complexity-- individual cells, cells and population, cells and tissues, and so forth. So the underlying mechanisms govern the higher level functions.

And to an engineer, this is the way we think. You think you're thinking about higher level system function and how you're going to design that or how you're going to intervene in it from the molecular and cellular components. So there is something that we call this discipline of biological engineering that is both the discipline important for science and for technology. And it looks a lot, in these first few bullets, it should look just like any of the other engineering disciplines maybe you studied-- chemical engineering, mechanical engineering, electrical engineering, and so forth-- because you and it will analyze complicated many component hierarchical systems.

You do that in all those other engineering disciplines. You'll synthesize designed technologies, not trial and error. We don't build any other technologies by trial and error. It's time that we really stop doing medicine by trial and error. And I think Martha Gray will talk about that, as well. And all engineers operate with this paradigm. There's different ways to pose it. But measure and model and manipulate with design principles and parameters, computational models, and so forth, construct your own components to have the right properties.

The only difference between biological engineering and all the other engineering disciplines is what are our components and mechanisms? In other cases, it might be organic chemistry, inorganic chemistry, solid state physics. In our case, it's biological molecules, biological machines and networks, and the cells that comprise, essentially, the organizing principles for these. So just like any other engineer, except the science is different. But the science is still quantitative manipulable components.

So it's clear to us that this is time to have created a new biology-based engineering discipline to sit alongside its sibling disciplines in the School of Engineering. And the idea of creating a new engineering discipline isn't new. MIT does this every few decades. Electrical engineering, mechanical engineering, at one point in time didn't exist. At one point in time they were established as now physics based engineering disciplines. 100 years ago, chemical engineering didn't exist. Materials science and engineering really didn't exist.

And they came into being as chemistry based engineering disciplines. So now we are in the 21st century creating another one. It just happens to be a biology based engineering discipline. And the biology is molecular life sciences. And it comprises both technology and science facets. We are designing and developing biology based technologies, as you'll see in the ensuing talks. And at the same time, I think we're facilitating the advance of the science itself, turning engineering back to the science and how can we understand it better? So it's a two way street between the science and the technology, as any good engineering discipline, really, is.

I won't go into this in much detail, but we are very excited about the new biological engineering major that starts this fall. Professor Griffith, who speaks next, was really the principal architect of this. He spent an immense amount of time constructing this in partnership with many other departments. And all I want to show you is the science core beyond the typical freshman year. It requires organic chemistry, biochemistry, molecular cell biology, genetics, just as I said, along with the mathematics that one would require.

And then what's listed here are nine brand new courses. We didn't just sort of cobble together, oh, let's borrow a course from-- let's borrow a subject from this department, a the subject from that department, a subject from that department. These are nine new courses all designed from scratch saying, if we're going to understand and manipulate this kind of biology of genetics and biochemistry and molecular cell biology, what is the way an engineer would do that? What's the right thermodynamics to study this science? What's the right mechanics to study this science? What's the right signal processing or fields or control or computation?

You can't just borrow the same old and sew them together. It's brand new engineering, because it's brand new science. So these are brand new subjects. And a lot of faculty effort, but the faculty are very enthusiastic about this. What's the point of learning this new discipline? We're very convinced and we have lots of confident input, likewise, from our friends out in industry that there's going to be new types of places that engineers have never been hired before because engineers haven't been capable of knowing this kind of a science. So, yes, medical devices, but instead of building the boxes these folks are going to design and manipulate the interactions of these devices with their environment.

So maybe you still have a prosthetic hip. Maybe we can't quite regenerate my mother's hip from cells and genes and proteins quite yet. But her hip may fail less if we can figure out how to interface that synthetic material with the immune response, the inflammatory response, the surrounding tissue and so forth so that it doesn't fail there. Medical diagnostics but now will be in terms of these biological mechanisms. Analysis will be at the gene and molecule and cell levels, the appropriate information technology.

We can imagine cell and tissue based therapeutics. Engineers will be involved in the pharmaceutical and biotech industries, not just to manufacture and to deliver, but to actually design and to develop, to discover. These kind of biological engineers will help facilitate the discovery of where the right targets should be, what the right therapeutics should be, because of their ability to think in a quantitative systems oriented manner about these very, very complex systems, and all the way along the way to predict pharmacology effects, toxicology effects, and so forth.

And finally, but maybe even more importantly, in terms of the scope. Biological engineers will not only contribute to medicine and human health in a very powerful new way. They, we believe, will transform many of the other non health care associated fields, creating new types of materials and devices in a perhaps environmentally benign manner with much more controlled properties, things you'll hear from Professor Belcher, understanding better toxicology environmental health, the pathogens, the toxins, carcinogens in our environment, national defense, new types of biological sensors and actuators that can carry out functions in far more exquisite ways than physics and chemistry might by themselves.

So let me end the sort of pictorially. The traditional biology medicine engineering landscape looks something like this. You can imagine problems driven by applications in health care-- maybe clinical, hospital, aerospace, military, maybe the pharmaceutical device diagnostics industries. And I'm speaking only about the engineering disciplines here, certainly the science disciplines would be analogous situation. You could take any one of these engineering disciplines-- chemical engineering, electrical engineering, mechanical engineering, material science-- based on chemistry, physics, and math, the way it has been for 40 years, interface with medicine be a wonderful biomedical engineer and solve problems in the health care field.

That's been the traditional pathway and it's an important pathway. You can also contribute to biotechnology perhaps in the processing manufacturing and delivering or so forth. So what's new, what's new-- and some of you over here won't be able to see this-- there will be something new showing up in the bottom left hand corner, which is the sibling discipline of biological engineering, that according to the curriculum I just showed you a couple of slides ago now takes as a central science based genetics biochemistry molecular biology, cell biology and can very powerfully interface with medicine and solve biomedical engineering problems for the health care industry, can also be applied to novel types of biotechnologies, both for the health care industry in terms of therapeutics and in other types of diverse industries as new materials, the environment, and so forth. And so we're just placing this new discipline alongside the old ones because it has a new science space.

You notice in parentheses in the middle I have bioengineering because at MIT what we've decided to do is called bioengineering the whole scope. So biomedical engineering, medical engineering, you can think about as the precise application, regardless of the engineering discipline itself, to medical problems. Biological engineering is this new biology based discipline. And then bioengineering just covers the whole scope. So you can major in electrical engineering, minor in biomedical engineering. You can major in biology, minor in biomedical engineering. Or now you can major in biological engineering per se and have a whole world of applications open to you.

What do I want to say about the landscape, this is another landscape that's changed. These are the faculty that are affiliated with the biological engineering department at this point in time. And if you can see the colors, you can see how the landscape has changed. The names in blue, many of them who might be familiar to you, were members of the department when we started in 1998. The names in green have all been hired since then. And they give great credit to the president and the provost and the deans of these last few years of investing in this brand new opportunity for MIT, which I truly believe were at the forefront of, were defining, and the Institute has been investing in in terms of its most precious asset, which is people.

Finally, I may say, to go back to this landscape, that it's crucial to emphasize that we do this in partnership for the Department of Biology. We have a wonderful-- I won't say unique, but it's really unusual-- partnership between biological engineering in the school of engineering and biology in the school of science in terms of faculty members who have appointments in both, courses we've co-developed and co teach, parts of the undergraduate curriculum that are now transparent research programs and so forth. And so biology is mainly located in this brown box. Biological engineering is mainly located in this blue box.

But we have this wonderful handshake partnership. And I really think this could have happened only at MIT, because of the world class biology department, because of its own role in defining the biology of the molecular biology revolution and the genomic biology revolution that they could appreciate what we wanted to do from the engineering school in having a similar revolution in biological engineering. So I thank you for your patience with this overview. The real excitement is going to come from the next few people because they're going to show you the fascinating things that we're actually doing. But I hope this painted you a picture of what is new, why we're doing it, how we got there, and why this is really such an extraordinary point in time. Thank you very much.


VINCENT: Wow. That's pretty incredible. What a remarkable turn of events these last few years. And what a great setup. Thank you, Doug, for really teeing up the rest of the excitement this morning. I mean, it is a remarkable story about the courage, the vision, the leadership, to look to do this. And this is I think what I know I certainly love about MIT, and I hope this sort of thing continues. Next we're going to hear from Professor Linda Griffith. As Doug talked about, Professor Griffith was instrumental in pulling together a new undergraduate major here, bioengineering, at MIT. She's also going to share with us some of the really fascinating work in tissue engineering.


GRIFFITH: I've been giving a lot of talks at MIT in the past year, mainly about our new education program. And it's really wonderful to be able to share with you some of the research that drove a lot of faculty at MIT to try to create the kind of student who could push these research frontiers forward. I'd like to acknowledge at the beginning before I start the research talk the wonderful support we've gotten from the Alumni Association in helping develop our curriculum.

Several of the faculty received a class of 1960 Teaching Award for some of the very early ideas that we had in moving toward this kind of program that would bring biology and engineering together. And it allowed us to work with students a lot more effectively. And we're very, very grateful for that support for the education program.

What I'd like to do in describing a little view of the field of tissue engineering today is give you two sort of vignettes. One is where the field is going in terms of what we classically think of of tissue engineering in building organs and tissues to treat patients. And so that's the bench to bedside part of the talk. In the second part of the talk, I want to give you a glimpse into what may seem like a more mundane, but ultimately, I think, is a far more powerful application of tissue engineering, and that's coming back to the bench and using tissue engineering for a range of applications and drug development that will hopefully help us put all the surgeons who do organ transplants out of business someday. Except for, of course, my postdoc mentor.

So to get us oriented, let's think about what generated enormous excitement in the field starting almost a couple of decades ago. And I call this tissue engineering version 1. A common paradigm in the field is that we have some kind of source of cells that might be used to regrow a tissue. And in this case, we could imagine a patient who's lost the outer part of their ear could have some cells taken from their rib cartilage if only we could grow them into the proper shape to put in the patient.

So this is just an example of how you might do that and make a scaffold that's made of a porous biodegradable polymer, actually common surgical polymer, combine it with cells. And here this is a demonstration that you can implant this kind of cell polymer scaffold beneath the skin and have the tissue grow from the cells that were implanted onto the polymer. This was an experiment done using a mouse model. And you can see the shape of a human ear growing on the back of this mouth after a couple of months.

And so this kind of application generated lots of excitement because it actually works, and it works fairly well. And so these kinds of things are now moving into the clinic. We can ask the question, though, if it works for us an application like that which is fairly constrained in the number of patients, will it work for applications where there are lots of patients and more serious problems? So the answer, happily, is yes. This shows a radiograph from the patient of one of my collaborators, George Marshall, at the Cleveland Clinic. This patient has a very large tumor in his bone. You can see the diffuse area here is cancer of the bone. And it needs to be removed.

So what will happen in a patient like this, and happen in this patient, typically, is this part of the bone gets cut out. And what's done right now is, because it's a very large defect in the bone, a piece of cadaver bone will be placed there in order to give the patient support following the surgery. Well we all can imagine that cadaver bone doesn't heal so well. There are potential problems with infection. It's not quite the same as having the real bone.

Another application area that some of you may be facing arises from injuries to joints. You can see, perhaps, pretty clearly that this soccer player here is experiencing an injury that will later lead to great problems with his joint. And, in fact, people who experience injuries like this and even lesser ones will go on offense to develop osteoarthritis. That's a wearing away of the surface of the cartilage that lines the joint, in this case, in the knee.

And osteoarthritis is a leading cause for hip implants. My mother also just had a hip implant, and it was due to the wearing away of the cartilage in her knee. So wouldn't it be better than having a joint replacement that goes on to lead to complications later and never works perfectly as the original joint? Wouldn't it be better if we could somehow repair this lesion, and grow cartilage back, and, have it secured firmly to the underlying bone just as it is in the normal tissue?

So one of the great areas of activity at MIT, at my lab and and many others, is trying to combine these concepts of understanding the facets that govern cell behavior, the underlying molecular mechanisms that cause cells to migrate into a wound bed and cause, say, stem cells that are present there to proliferate and turn into bone or turn into cartilage. We're trying now to combine these factors the molecular factors that govern the properties of cells with engineering approaches that would create environments that cells could be transplanted into these sites and go on to form new tissues. So in work in our lab, we said, okay, what we really need are two aspects of this.

We need to be able to build a large scaffold that would be like the house that these cells would live in. And at the same time, we need to be able to give those cells cues for what they should be doing when we put them in an implant site. So a common situation in the clinic is an orthopedic surgeon would have available, if he's doing a joint replacement or wants to repair a joint or repair a bone, he can harvest bone marrow from that patient. And the bone marrow has in its stem cells that can go on to form bone.

So we ask the question, can we build a scaffold that will help those cells go on to form bone. And so let me just take you through one part of solving this problem. In order to build a device that could be used to put the cells on, we had to invent a whole new way to process materials that would be appropriate for implanting into people.

So when I started out doing this, materials processing wasn't my particular area of strength. I knew a lot about the cells and how to design polymers to interact with cells, but making those into a big device wasn't something that I was that talented at. So it turns out that MIT, as we all know, is a place where you run into people all the time from different disciplines. I ran into a mechanical engineer who had invented a process called three dimensional printing.

And he'd invented it to do rapid prototyping of airplane engine parts, essentially. So what we decided to do together is say, gee, when you're making airplane engine parts, you need to make complicated shapes, you need to build them from some kind of computer model. It sounds like that's the same kind of challenge we face in building scaffolds for bone regeneration. We might want to start with an MRI image of a patient's jaw, for example, and build a new jaw just exactly to match the other half of the patients jaw. So can we take a process that's computer controlled and that builds complex three dimensional objects up from scratch, can we take that and adapt it to tissue engineering?

So starting about 10 years ago, this is exactly what we did. So we could start with an idea saying, okay, we have a computer model for what we want to build. We want to build a scaffold that has lots of pores and channels that will facilitate tissue in growth. And we want to build it in a particular shape. And the way that we can do that is to build it up as a series of very thin, two dimensional layers, step by step.

So the three dimensional printing process is one of many kinds of manufacturing and prototyping processes that are now endemic in many areas that are called solid freeform fabrication methods. They build up complex objects as a series of thin two dimensional slices. So you start, in this case by spreading, a powder of the material you want to build an object from, in our case, a bio material.

You spread a powder on top of the piston, so you just roll out a very thin layer of powder. Into that powder, using a computer model of what you want to build, you print a binder or glue that holds the powder particles together everywhere you want a solid part of the object to be formed, so then you can build many, many devices at once. You drop the piston and-- You finish printing, you drop the piston down. And that gives you the chance then to roll out another layer of powder.

So this has now been licensed and is being commercialized. There's actually some early stage bone regeneration products and patients now. But some of the frontiers are actually solving those extreme challenges, such as joint repair.

So Therics, one of the companies that licenses that technology, teamed up with a tissue engineering company, Advanced Tissue Sciences, to design and test a scaffold for joint repair that takes into account the number of challenges you have in building a complex scaffold that will allow a region to have both bone grow and to have cartilage grow. So the idea here is that we can, at the top part of the scaffold, build a region that will favor the seeding of cartilage cells into the scaffold and their growth into cartilage tissue.

And we can do that by arranging the pores and channels in a way that let the cartilage cells easily get seeded in here. And then we can create a second region that has different materials and different structure that facilitates the end growth of bone. So you can take these in the lab. And it's quite straightforward, actually to build these.

And this shows a joint. In this case, this was done in sheep as a test for how this process would work. So the scaffold was seeded and cultured with cartilage cells, implanted into the joint.

And you could later see that the cartilage formed in vitro and allowed the joint not to move. So these sorts of applications where we combine cells and scaffolds are really moving into the clinic now in somewhat incremental ways. But you're starting to see them appear in applications, particularly, in orthopedics and in places where we have a connective tissue repair that are needed. But you can imagine, with the power of combining cells and scaffolds, what if we could build organs that would replace much more life threatening functions?

So if we can do this for tissues like bone and cartilage, could we do it for diseases that are truly life threatening? So you could ask the question, well, gee, we have an incredible shortage of organs for heart transplants, for liver transplants, for kidney transplants. Can we take this same concept and maybe take one donor organ, one liver, and use it to cure 100 patients who need an organ transplant?

Could we do that by taking a tissue engineering approach, and taking the cells from one donor organ, and constructing a whole series of smaller organs that could be transplanted into patients? So many people in the field have had a vision that since liver is such an amazingly regenerative organ-- I think that some of you may remember from your high school mythology class the story of Prometheus. And he stole fire from the gods, so he got chained to the rocks.

And the eagle would come every day and eat his liver out. And then it would grow back overnight. And the whole process would repeat the next day. So even long ago, it was known that liver regenerates.


So all we need is the eagle to come and have Prometheus chained to the rock, and we can solve the organ transplant problem. Well, maybe not. No, but that makes people think, especially transplant surgeons who see organs grow back after you excise a tumor.

They ask a question, could you then say, okay, maybe we could get regeneration and culture if we had a very complex scaffold that would let cells assemble into a liver-like structure with an artery and a vein? Maybe what we could do is take one organ and make a bunch of mini organs that we could then transplant into patients. And so this captured the imagination of a lot of people.

And it's something that I think is still envisioned as possibly one day coming to fruition. But that one day is pretty far in the future. I think saying 10 years is incredibly optimistic, 20 years is optimistic. And keeping the attention of NIH and so forth to fund this kind of research for that long may be the biggest challenge.

So we can take a step back and say, gee, where will we be in a couple of decades with this kind of organ replacement? And option 1, and one that certainly gets the attention of popular press, is that we're going to be able to have more organ transplants. So all those people now who die because they don't get a transplant will be able to get liver in a box.

Hopefully, it won't be made in my lab, but MIT isn't a good manufacturing process approved place, I don't think. But the idea is that you would have these organs available for transplant. So that's one option and one that I think a lot of people get excited about. It would certainly be very dramatic. You get brought back from the brink of death by ordering your liver off or whatever, and there it is, made just for you.

Option 2 doesn't give the press to have so many dramatic lifesaving stories. Option 2 is that we just get rid of transplants altogether and, maybe, only do them under unusual circumstances so that we put all those organ transplant surgeons out of work. And now they get to do other things, like golf or whatever they would maybe be doing if they had more time.

So we could ask ourselves-- I actually see a show of hands. Who would prefer option 1 if you're the patient? Okay, option 2, that we cure the disease much earlier so people actually never even, maybe, have to go to the hospital?

They may go to their doctor, get diagnosed and take a drug, and be able to cure the disease that would ultimately lead to the organ transplant. So we can ask, what are the bottlenecks in realizing option 2? Why aren't we there with more diseases and more organs?

And I'll say that my own epiphany about this vision of why aren't we pursuing option 2 harder was really brought about by the creation of the biological engineering division. In fact, several faculty who became an integral part of the division were focused on the area of toxicology. And when we think about toxicology, a lot of us want to maybe yawn.

I certainly felt that way initially, but then there was the aha moment. Preventing things is much better than fixing them later on when they're much more serious. And so we really started to take a hard look at what it was that prevented us from going and preventing diseases.

We certainly do have a lot of disease models that we use very, very productively to understand and help develop treatments for human disease. So Bob Horvitz and MIT won the Nobel Prize, in part, for showing that c elegans, a nematode worm, could be used to understand some facets of cancer to understand how cells undergo programmed cell death. And we certainly can use yeast to understand aspects of our own biology. Mice and all of these animals here can reveal different facets of human disease and help us enormously in developing therapies and cures for these diseases.

But despite the utility of all these models, we know that mice are not little people. Worms are certainly not little people. Mice are not little people, and even chimpanzees and orangutans are not people. And what we would be really excited to have, if you're excited-- the "we" being perhaps a drug company-- is a model of a human that could be used to really understand a complex disease or use to predict how a human will respond to a drug.

So we can do pretty good right now in looking at the individual molecular interactions that govern the cell behavior. As Doug described earlier, we're starting to put together the molecular interactions that transmit signals from outside the cell to inside the cell that govern gene regulation, for example. And we're somewhat good at understanding how different cell types interact with each other to create a more complex response. But as we get up to the tissue level and certainly the organ level, humans start to diverge enormously from animals in how they respond in most cases.

What we would love to do is have readily accessible models, therefore, of how human cells and tissues interact to come up with a complex response. So about seven years ago, we started to really refocus efforts in our lab toward this goal. We still continue to develop some exciting approaches in treating connective tissue diseases by tissue engineering. But we decided that trying to build models of humans so that we could study disease, and hopefully cure it, would be an enormous contribution ultimately, to health care.

And certainly, liver probably exemplifies the need for this approach more than any other organ. So you-- I don't know any of you who know anyone who has hepatitis C. It turns out, hepatitis C was discovered around the same time as AIDS, but there really aren't great therapies for hepatitis C right now. There are therapies, and there's some new ones coming out, but it's been incredibly hard to study this disease, because it only affects humans and chimps, and it targets liver cells.

And it turns out that when you take liver cells out and put them in culture, they become refractory to infection by hepatitis C. So you can't really study the whole viral lifecycle in culture. And guess what? Hepatitis C is the single-leading cause of liver transplants in the Western world. So if you eliminated hepatitis C, you would have a lot more organs available for other people who need transplants.

And in fact, if you take this approach and say, gee, what are some of the other causes of transplants? Well, there's toxicity of drugs. Even Tylenol kills about 100 people a year in the US. And right now, one of the leading causes for drugs to fail in phase I clinical trials is liver toxicity. Very difficult to predict liver toxicity, because human livers metabolize drugs, in most cases, different than animals you use to test those drugs. So there are all of these problems that we face for which there are no really good accessible models to study the human condition.

And so why is that? We know we can take cells out and put them in culture and get them to behave, in some cases, like they do in the body. And so with liver, we can do that. We can take liver tissue, dissociate it down into the individual cells, and put them in culture, and get them to look very beautiful.

So this is just an example. If we take-- in our lab, we use rats for these kinds of studies, because they're commonly used in toxicology. So we can isolate the cells from a rat liver by digesting it with enzymes, and put it in something that makes it think that it's a little bit-- that it's like it's back in the body. We use an extracellular matrix. And then we can take a picture of these cells a couple of days after they're in culture, and they look very beautiful.

Their nuclei are very round, they have a cytoskeleton organized, and they look quite happy. But looks are deceiving. When we go in and start to measure the level of functions that those cells have that are liver specific, it turns out, they really aren't acting like liver anymore. So this just illustrates that. So those same cells I just showed you-- if we go in now and break them open and get their messenger RNA out and do a microarray analysis to look at all the levels of most of their genes using a microarray experiment, we see that the cells are losing function quickly.

So let me orient you. So this is a scale that everything above the line means that the gene expression level has gone up. Everything below the line means that it's gone down relative to liver in the body. And this is over a few days in culture.

So what you can see here is we are looking at the genes involved in drug metabolism. So there's about 100 or so of these probes on this particular microarray chip. And what's really obvious is that, really soon after cells are placed in culture, they lose their liver-specific gene expression. And this is what bedevils the efforts of the drug industry to do a better job predicting toxicology. It's loss of these kinds of genes that bedevil our efforts to culture hepatitis in culture, and to study many facets of liver disease.

So what can we do? We asked the question, can we recapitulate function of liver by recapitulating the structure of liver? Instead of doing a 2D cell culture, maybe what we should be doing is trying to create environment in culture through tissue engineering that really replicates this 3D microscopic environment.

So for example, this is the liver capillary bed. It's the business end of the liver. It's about 1/2 a millimeter across. And in that little environment, hepatocytes, the cells that carry out most of the functions of liver, are in constant communication with other cells. And in fact, there are mechanical forces on these cells arising from flow of blood that give rise to signals that tell the cells yes, you're in the right place. Keep doing all the things that liver does.

And in fact, David Darnell won a special Lasker Award for showing that when you take cells out of this environment, they lose their message level and all the things that make them like liver. So I'm going to fast forward through all the development we did to come up with a design for how we could create this three-dimensional structure, and show you what we're doing now in our efforts to build, essentially, liver on a chip and use it for a number of applications. So what we reasoned is that if we could create a little microenvironment that would give the cells some cues, they could organize themselves into a capillary bed-like structure.

And it turns out, using silicon microfabrication technology was a very rapid route to doing that. We have a wonderful Microsystems Technology Lab at MIT that lets you prototype different designs. So we went over there. And in a period of about three years with a lot of support from DARPA, who was interested in using little livers to detect viruses that may be warfare agents, what we did is we came up with a design based on silicon chip technology that would foster the formation of three-dimensional tissue structure from isolated liver cells.

So what you see here is a chip that has bored into it a whole bunch of holes. So this chip is about 2/10 of a millimeter thick. And when we bore holes in it, and then into those holes, we seed liver cells. And the dimension here is 3/10 of a millimeter. And so what happens is the liver cells attach to the walls of the holes in the chip. Now, we modify the walls of the holes in this chip with molecules that the liver cells recognize, and they form these tissue-like structures. The structures are stained in green, and that actually indicates the cells are healthy and viable.

And so what you see is formation of tissue-like structures. That happens very rapidly, and then the tissue appears stable over time. The whole thing lives in a bioreactor, so we place that chip, the scaffold with the cells, in here. And then we can flow fluid through the whole system. So you can see a set of pumps here that are pumping fluid through, and we essentially have liver on a chip.

Now, this kind of arrangement allows the cells to capture features of what liver architecture looks like. So you can see vessels here, endothelium-- and in fact, we can follow this using what's called two-photon microscopy that lets us optically section throughout the tissue. So what we're seeing here is sequential sections through the tissue over 10 days.

And what's labeled here are just the blood vessel lining cells. The blood vessel lining cells are green against-- the rest of the cells aren't labeled. You can see that over a period of a couple of weeks, they're forming vessel structures within this chip so that we truly are creating a three-dimensional tissue that could be used for a lot of applications.

So now we've got this tissue, and we've gone through a lot of different kinds of assays to show that it has a very high level of function. What can we use it for? Well, for one thing, we can use it to try to predict drug toxicity.

And what Doug Lauffenburger didn't show on his campus changes were the things associated with the MIT campus changes-- Novartis Research Headquarters, Pfizer Research Technology Center. All these pharmaceutical companies are looking to MIT for new methods that can help with drug development. And in fact, one of the huge applications of this is to build little livers that could be used in high-throughput screening of new drugs.

One other application-- there are a number of applications listed here. I'm just going to show you briefly some results from one that we're working on in the Biotechnology Process Engineering Center. And that is this issue of why gene therapy hasn't quite panned out as much as everyone hoped about 10 years ago. There was, a little over five years ago, a very significant event in the whole field of gene therapy, and that was the death of a patient, Jesse Gelsinger at the University of Pennsylvania. He was undergoing a trial to correct a genetic defect in liver, and he died from the effects of the gene therapy vector, which happened to be an adenovirus.

So one of the huge challenges in predicting how well these kinds of gene therapy vectors work is that what you see in a 2D culture then doesn't get replicated in animals. Very huge differences between what you see in mice and what you see in cell culture when you're designing these vectors. And then huge differences between what you see in a mouse and what you see in a person when you go to use it clinically.

So we can now use this new tool of a three-dimensional liver to start asking questions on why these kind of gene therapy vectors are toxic, and try to build in safeguards against that. And address those as we design new vectors. So just as an example, we can use the classic adenoviral vector-- in fact, this is a vector we get from the University of Pennsylvania-- and monitor the whole process in a model way by having cells express a green fluorescent protein.

So just to give you an idea of how this pans out, we can measure many, many things about how these gene therapy vectors interact with real 3D tissues in this new system. So here, we're showing a 3D reconstruction of a single capillary bed in our little unit. So again, the dimensions here are about 3/10 of a millimeter, and this is stained. It's a 3D reconstructed image from optical sectioning.

So we've stained it so that cells expressing the gene that we've transfected in are green. So these cells have taken up the gene and are expressing it. All of the cells are stained blue, and cells that have been killed are stained red. So now we can start to build quantitative models of how we should design gene therapy vectors to be effective and actually also safe. And we're working with a lot of other people in that project.

Another application area that is hugely challenging is understanding how to stop cells at the very earliest stages of tumor metastasis. So often, we can't see a metastasis until it's about a centimeter in diameter. The resolution methods are getting better, but what we really would love to do is figure out, if you remove a primary tumor, how do you keep the cells that have moved to distant organs, and our single cells invading the tissue from growing into larger tumors?

So we can use this little microreactor system. Because we control the fluid flow locally, we can build a model of how that tumor cell actually grows into a tumor, and we can watch the whole thing as it happens. So this is just an example of putting prostate tumor cells into the liver. And so this is the silicon chip, this is the tissue, and you can just see it there.

And if we look a couple of weeks later, we've just put a few tumor cells in. They've grown into large tumors that we can see. You can actually see them with your naked eye. These little white areas are tumors, and they've been growing very large, because there's fluid flow through controlled at the microscale level.

And so I'll finish up here just showing this is, again, imaging to show the tumor taking over. The liver starts out as green, and you can see the tumor taking over in a period of a couple of weeks. The tumor is red in this case, and it's being invaded by connective tissue from the underlying cells.

So all of this is great, and I have a lot of graduate students in the lab who are working very hard with our prototype system. But clearly, what we need if we're going to move this thing to high-throughput assays, is to adapt this design and these design concepts into a high-throughput format. So we're working with a local pharmaceutical company to do exactly that, and figure out how we can take this concept and put it in a multiwell plate format.

And this little movie just shows you how we can use microfluidics now to replace that whole big reactor system. Now we've crunched it down. Actually, this plate has 24 little reactors. This one's got five. And you can see the fluid being pumped through here using microfluidics.

And so we're trying to move this now out of the lab and actually have people use it so that we can have physiological models of humans, and actually put those transplant surgeons out of business. And even though it's not as exciting for the news media, what we hope is that we'll be able to develop drugs better and faster, and cure diseases that right now, we really don't have a great handle on. So the parting thoughts are that-- two big messages.

There are some great applications of tissue engineering moving into the clinic. They mostly have to do with connective tissues. It's very hard to move into the-- go from animals up to people. But that the big future of tissue engineering, though it sounds a lot more mundane, not quite as scintillating, is actually to have a human body on a chip so that we can ultimately predict how humans will respond and cure diseases. So with that, I'll close. And I'll talk to you at the panel discussion.


DOUG VINCENT: Thank you. Let's see. I've used "wow," so I guess I'll go for "spectacular" this time. Just terrific, terrific stuff. And you guys having fun?



DOUG VINCENT: Yeah. This is neat, neat stuff. Professor Angela Belcher is up next. Now, Angela, the last fall, was one of MIT's newest MacArthur Foundation Genius Grant recipients. Pretty exciting news. Delighted to share that with you this morning.

And she's going to spend some time with us now, as she's working to develop materials on a nanoscale, and how she's using nature as a guide. Angela? Thank you.


ANGELA BELCHER: Okay, thanks. Well, thank you very much. I really appreciate the invitation to come here and speak to you today and tell you a little bit about what we're thinking about at the Biological Engineering interface.

And as Professor Lauffenburger said in the first talk, that this is a relatively new discipline. So I came to MIT because I thought, this is the place where it's all going to happen. And it's a really exciting place to be.

But one thing that I think is a little bit different is that I think that biological engineering has been around for quite a long time, and I brought an example with me today. This is an abalone shell that was a marine gastropod that was grown off the coast of Santa Barbara. This is an organism that has an incredible ability to make materials at the nanoscale.

What it does is it uses materials from its environment. It uses the chemicals it has in the ocean to build this really exquisite structure. This structure is very, very tough and very strong. It's 3,000 times tougher than its geological counterpart. I didn't tell you, it's made out of calcium carbonate, which is basically chalk. But the organism figured out how to make it in a way that was incredibly strong.

The organism also figured out a way to make it using non-toxic processing. And so I think that one of the keys is to look at nature and how nature has constructed materials over millions of years, and see what we can learn from that. And what can we apply it to, what we like to say, nature hasn't had the opportunity to work with yet?

During the Precambrian geological time period, when the chemicals in the ocean started changing, the organisms had to learn how to deal with the changes in the chemicals in the ocean. And being clever, they actually decided to use it to make materials. So the kinds of materials that nature has worked with are really limited to what's found in the ocean, and we'll talk about that today. This structure also was built based on millions of years of evolution.

At MIT, the lifespan of a graduate student is about four to five years, and so we have to figure out how to develop materials on the time scale-- not geological time scale, but the time scale of an MIT graduate student. So starting with that, I want to show my really incredible group that I have at MIT. This is a really multidisciplinary group. We have students from Biological Engineering, Biology, Chemistry, Chemical Engineering, Material Science and Engineering, and Electrical Engineering and Physics. We're all working together to see if we can understand how nature makes materials and convince organisms to work with materials that they haven't already worked with before.

So about a year ago, I was asked by the National Nanoscience Initiative to say what I thought the biggest challenges in material science were today. And I took this very seriously, and so I put together a list of what I thought the biggest challenges were. Maybe we can actually make that fit a little better.

I said, I like a material that can actually self assemble. I like a material that's self-correcting. So if you have a computer component and it breaks, it can fix itself-- or self-healing. I'd like to have a material that can grow its own template, that can recycle its own template, and only goes to a desired length in diameter, and then stops.

I'll show you a little bit about how abalone's shells grow, but the abalone shell-- the structure that makes it so exquisite and makes this color-- you can come see it afterwards-- this color so beautiful-- this is the same color of pearls-- is that the thickness of this inorganic material-- the thickness of this chalk is controlled at the genetic level to be precise. And that precise control gives it many wonderful properties. So I'd also like to have a material that's environmentally benign and uses environmentally benign precursors and solvents.

It would be great to have a material that is grown at room temperature and pressure-- that generates little waste. The organisms in the ocean, they don't produce a lot of toxic waste, because that would be bad for the organisms. We'd like to span multiple length scales.

For the most part I'm a self-assembling, self-organizing material that is organized from the nanoscale to the macro length scale. So we'd like to be able to take those kinds of properties and be able to apply it to other kinds of materials. Interface-- organic and inorganic interfaces-- interface with biology.

But my dream is to have a material that's genetically controllable and genetically tunable. I'd like to have a DNA sequence that codes for the production of any kind of material you want. You want a solar cell? Here's the DNA sequence for it. You want a battery? Here's the DNA sequence for it. I'd also like one that's responsible to external cues, inexpensive, of course, and scalable.


So it's a pretty long list, and your response is an appropriate response. But at the same time, biology has already figured out how to master many of these properties. And so, us as material scientists and biological engineers, we think it's our job to figure out how they solve these problems and guide them to work on other problems.

And so what we'd like to do is order, with genetic control, the atomic scale to the macroscopic scale to where we can control atoms and unit cells and lattices to basically self-assemble devices. And I'll show you a couple devices today. I'll show you the first virus-assembled nanoelectrode. I'll show you the first virus-assembled rechargeable battery as two examples.

So as I said before, biology has already figured out how to do a lot of this. They can communicate within a cell. They can correct themselves. They have genetic code, which codes for the synthesis of everything they need. This is a eukaryotic cell, and this is a prokaryotic cell. We'd like to have the same kinds of properties.

The way that we're going to do this is, we're going to actually evolve organisms to work with materials we want them to work with. We're going to force organisms to live with semiconductor materials, and force them to live with electronic materials, so that they can start to use them and process them. But that's pretty challenging.

When you think about biological organisms, which are soft, and they're in aqueous conditions-- they're in water, and they're basically squishy organisms, how are you going to think about integrating it into a semiconductor fab line? Or something that's done under really controlled atmospheric conditions. So there's robustness questions.

How robust is it? Do you have to work with the conditions under which organisms normally live? And then if you want to evolve these organisms, how far can you push them? And where do you start?

When we first started thinking about this problem, we thought about antibodies, which are natural proteins in the body that bind small molecules. These small molecules can be organic molecules, or they could be other proteins. And they do it with an incredible amount of specificity, so these proteins that are in your body can recognize an addition of a carbon to a molecule. Or they can recognize chirality of a molecule. We said, we want that. We want a protein that can recognize an electronic component-- that can recognize a unit cell of an inorganic material.

Here's another. I never really knew snowflakes until I moved to New England, but this is an incredible self-assembling system. You think of this really exquisite structure. And in the middle, it has a nucleus that allows for the nucleation of this really complex structure-- really exquisite structure. So what we'd like to do is have a nucleus that codes for, or tells an electronic component how to grow.

This is a slide that talks about scale. And so we're interested in nanoscale, and I had a-- a reporter asked me yesterday. Did you always know you wanted to be a nanotechnologist? And-- [STAMMERS]


I can tell you that I haven't thought a day about it. What I thought was biology makes incredible materials. What length scale does biology choose to grow materials? For the most part, it chooses the nanoscale. So it's worked well for biology. Let's see if we can harness that.

And so here's a-- this is a length of a typical component here of a piece of inside of your computer. Now, here's a drawing-- a schematic of the length scale of certain biological molecules. And so this is a DNA strand here. This is a ribosome.

It's a molecular machine that processes all the proteins in your body. And here's some other kinds of proteins as well. So we said, if you want to make things small, why don't you start with the building blocks of biological molecules that can process these?

Again, the fact that there's these already naturally existing molecular machines that have evolved to be very specific-- this is our diagram of what a ribosome looks like. Basically, the biological machine that reads messenger RNA and builds proteins. We said, wouldn't it be great if, as it's going along and reading its messenger RNA, instead of building a protein, why don't you have it build a semiconductor?

Is that possible? Can you have each tRNA molecule that is specific for an amino acid have a codon that's specific for electronic material? And we've recently accomplished that in a relatively small way in the lab this year.

Well, there's many kinds of naturally occurring biomaterials. This is a coccolithophorid. It's a unicellular algae. It's made out of calcium carbonate. This is an abalone shell, like the one that I showed before. This is a fracture of an abalone shell.

And looking at it in scanning electron in a micrograph, you can see these tablets, which are basically chalk tablets. But they stack on top of each other to form this brick wall-like structure. Now, proteins that are coded at the DNA level decide what kind of material to grow-- what thermodynamic phase of this material to grow, how thick it is, and how it stacks up to make this really tough structure.

This is a diatom. It's made out of SiO2. I'll show you a little bit more about it. It's basically made out of glass. And these are magnetotactic bacteria, which have inside of them small, single-domain magnets which are used for navigation. They make these really incredible magnets. All of these are processed in the ocean, basically at ocean temperatures, using nontoxic materials, and pressures in the ocean.

So what's the key? How does this work? Well, this is a mantle epithelial-- basically, epithelial tissue from the abalone shell. An abalone is a gastropod. It basically has a big foot and a big piece of tissue that comes across here.

These cells are the cells that are pushed right up against the abalone shell, and their job is to secrete proteins and to secrete ions have actually builds the shell. So in between this tissue and this shell is basically a little reaction vessel that grows the shell. And the key is is that there's proteins in these cells that are pumped out into this space that guide the growth of the inorganic materials.

Here's a diagram of how it works. Proteins are made out of amino acids. Amino acids differ from each other based on chemical functionality. These particular proteins are very negatively charged, and their job is to grab calcium ions out of solution, out of the ocean, and to start to use them to build structures.

And so they do this in a very exquisite way. The distance between the negative charges is very important. It causes the distances between the inorganic material-- the chalk, basically. It causes it to grow up in this particular way.

This is just showing some more pictures. This is the abalone shell here. These are individual tablets. This is the top. This is actually a part of a pearl that it looks like it hasn't grown out its full length. But this is an organic component. This is the protein that actually directs it and helps it grow these kinds of materials.

And this is a mechanism. Basically, you have these proteins in solution. They're binding calcium, binding calcium carbonate, starting to build these nanostructure materials that start to grow up to be a full shell or a full pearl. And this is just a fun picture, because this down here is the older shell. And this is the new components that are just growing, and haven't grown out to their full length yet.

These are diatoms made out of silica. This is a scanning electron micrograph, and this is on the micron scale. But the reason I show you this is that diatoms are-- there's at least 10,000 different species of diatoms, and they're classified based on their morphology. And you can see how different this morphology is.

These are made out of glass, but they're all this-- they're different species. And so what it says-- they also have all the same reaction vessel, which is the ocean. So it says that this overall macroscopic structure of these organisms is coded at the DNA level.

So if you look at the kinds of-- here's this periodic table. And if you look at the kinds of elements that nature has to work with, it mostly has calcium in the form of calcium carbonate, like shells. It has calcium phosphate, like bones. It has the silicon that I showed you for silica, and it has iron.

But what about the rest of the periodic table? Why hasn't nature used the whole periodic table to build materials out of? And the answer to that our group is that just, they haven't had the opportunity yet. Let's give the organisms opportunity to work in this part of the periodic table. Let's give them the opportunity to make semiconductors. Let's give them the opportunity to make materials for magnetic storage and materials for batteries.

So let's take the idea of a protein-protein interaction or protein-antibody interaction, and let's combine it with fabrication to make what we call evolved hybrid materials. And the question is, how are you going to think about doing that? If we can take proteins from the shell, and we can extract them out, and we can start making synthetic shell in the lab.

But how do you figure out which sequences to use to grow a semiconductor? Because we don't have any data for that. You could probably do modeling, but Professor Lauffenburger says they don't use a lot of trial and error. We actually use-- sorry, Doug. We use a lot a lot of trial and error to begin with. And then we take what we learned from that, and then we try to make it better.

And so we didn't know how to come up with these sequences, so we decided to borrow an idea from the drug discovery industry that's called phage display, which uses a virus. This is a virus that is a non-toxic virus. It actually has a bacterial host.

As a material scientist, I look at this is an object that's about one micron long in this direction, and about six nanometers across in this direction. And it only has a couple of genes that code for a couple of proteins. So it's actually easy to manipulate. We can do additions in the DNA sequence that code for the additions in the protein on one end of the virus. Why do we want to do this?

Well, we want to take a billion possibilities simultaneously as a library, and we want to search through this library to see if we can find any chemical groups on the protein that match the semiconductor of interest. Just like the abalone shell found a way to grow calcium carbonate, we want to use a virus because it's quick and easy, and it takes only a couple of weeks to go through this whole process and use this in our lab. So here's the idea.

The idea is, now you have a library of viruses that are all genetically identical to each other. They only differ from each other based on a small amino acid protein on-- a small protein on each end. Now, in about a one microliter sample, you can have a billion different viruses. And you can force them to interact with a semiconductor wafer.

Most of them won't have any chemical specificity, and you wash those off. Some will have a chemical specificity, and you want to keep those. No, they can't make copies of themselves. And so you have to infect them into their bacterial host, which now acts as a factory and makes a million copies for you. And now you have a population of these proteins that have some affinity for this surface.

Now, we think of us as kind of a Darwinian process. We want to take that population and force them to interact again, and go through this process about five to seven times, looking for the survival of the fittest-- the one that works best with a semiconductor. And here's a movie that my students made that was on MSNBC, where you have this population of viruses, and you're throwing it at a semiconductor. And you're looking for the ones that have a chemical functionality that matches the material.

Most of them don't, and they're washed away. This is also done to music, but I didn't play the music. And then you can manipulate and change the surface charge on these, and remove them from the surface. And then you can infect them into their bacterial host. The bacteria now makes a million copies for you, and then gives them back again. And so this is our amplification.

So now we've worked with about 40 different kinds of materials in my group. And it takes about two to three weeks to get to the answer-- get to the sequence that can control a material. So we look at a virus as just a material that has DNA that's easy to manipulate, that you can actually change the DNA and change the coat of the virus-- the major protein coat of the virus-- to have it be specific for anything you want it to be specific for.

Here's a list of the kinds of materials we've worked with in my group. These are semiconductor materials over here that we're looking at for laser technology. Right now, we're really interested in gallium nitride-- a laser material that might be a very efficient material for solar cells. We've worked in this part of the periodic table here for magnetic storage. I'll show you some materials over here that we're using to make rechargeable batteries. And over here, that we're using to make nanoelectrodes.

So this is just in a-- this is the real data. This is an example of how you can select a virus to be specific for something like a gallium arsenide-- a semiconductor wafer. What we did was, through this process of throwing the viruses at the material and asking them to bind and getting rid of the ones that weren't specific, we came up with some that bound this material gallium arsenide, which is a semiconductor material that biological organisms would have never had to come in contact with and learn to live with, or to use. And then--

So this is a gallium arsenide wafer. These are 1-micron lines with 4-micron spaces. And we basically use these viruses as just a tag to-- we put an organic molecule-- a dimolecule on it. We dipped our wafer in the solution of the virus and we pulled it out. And what you can see is that it pulled out just the gallium arsenide. It didn't recognize the material that it wasn't evolved to recognize.

Well, we've been doing-- that work was first published-- we first thought about this in 2000, and we published this. But we've got-- we want to go down that rest of the list that I told you, so we can get an organism to work with a material it normally doesn't work with. But so what? We want to grow the material. We want to use it for some kind of important technology.

And this is a work by a graduate student of mine, Asher, who had this very clever idea. It was a very bold idea, which was, we'd already shown that we could select viruses to be specific for different kinds of semiconductors. But he said, let's select viruses to find mistakes in semiconductors. Let's find them to find defects in semiconductors.

And so what he did was he took a population of viruses and forced them to interact with defects in semiconductors-- single atomic defects in semiconductors. And then he negatively selected them to not recognize non-defects. And so then what he does is now he has a fluorescent signal where you can take a device or you can take a wafer, and you can dip it in your virus. And it can count the number of mistakes on that, and you can optically read it out, which is pretty interesting.

But now we're looking at applying this to other materials. We do a lot of work with the Army. We're looking at applying this to other materials where you might be able to find defects in instrumentation in the field. So what we want to do is basically have a solution that you spray onto something, like the wing of an airplane, where the organism-- a virus, or in this case, it's a yeast-- actually sticks to it and gives out an optical signal.

So if you're able to have a biological organism that recognizes a defect, it'll light up. And so you can just spray it on. Yes, there's a defect in this. You need to pull the plane over. No, there's not a defect in this. You're okay to deploy.

So in addition, we're looking at being able to grow things. We can tag things. We can look for defects. But what if you wanted to self-assemble a battery from scratch? How are you going to do that?

This is some real sequence. This is protein sequence that comes from third round, fourth round, and fifth round selection in part of your evolutionary process. And what you can see-- these are color coded for the functional groups. What I hope you can see is that there's a lot of similarities in the colors of groups. But eventually, as you take it through additional rounds, the sequences fall out. They come to what's called a consensus sequence. These are the best sequences to use.

So before, we tagged the material, but we'd like to grow a material. Shells do it. Bones-- we do it. Bacteria do it.

Here's our cartoon of what it would look like if a virus could grow four semiconductor particles on its head. This was our idea. So our idea was to have the proteins on the tip of a virus-- have a sequence that allows them to grow a semiconductor. And this is a low-resolution TEM image of a virus that's grown a collection of semiconductor particles called quantum dots on its head. And this is magnified about 800,000 times, showing you the lattice-- showing you the atomic structure of that semiconductor.

So this is where we used a virus. Through genetic engineering and selection, we convinced it to grow five semiconductor particles on its head. So we thought that was very interesting, but I'm not sure how practical that is.

And so we decided to go back into the genome of the virus and do more engineering. So that now, instead of growing things on just five copies on one end of the virus, let's grow them all across the length of a virus. So let's make viruses that are electronic components-- semiconductors and metal materials that could be used for wiring up a device.

And here's a picture. So the idea now is to have the virus act as a scaffold to grow lots of little semiconductor particles along its length, like this picture. And I forgot to mention to you that we lucked out when we picked this organism to work with, because the code of the virus, the major body of the virus, actually self-assembles itself. And it self-assembles itself in a crystalline manner so that all the proteins are all crystographically related to each other.

So we picked a perfect scaffold already made by nature. And what we say is, now let's manipulate it to do something we want it to do. Let's have it grow semiconductor wires. And this is a special kind of transmission electron micrograph. This is a one-micron scale bar here of the first virus-grown semiconductor wires. This is an elemental map that shows you that most of the wires, the inorganic materials, the zinc and the sulfur, actually map back to this particular semiconductor.

So our idea was that if you could get a virus to grow lots of different particles, and you could get it do it in high packing, could you then get rid of the biological organism and be left with nice wires that are all the same length and all the same diameter that could be used in electronics? And here's some of our first examples of those. These are very high-quality semiconductor wires that are all grown based on the genetic engineering of a virus. This is a 200-nanometer length scale here.

We've been doing a lot of further engineering. I'll show you the first virus-based nanotube. This is a silver-based nanotube. This part here is all inorganic material. It's all silver. This part here-- this is where the DNA is in the virus. And the virus actually has the code to build this silver wire.

And we've been using this technology. This is work by a very talented student of mine, Ki Tae Nam, to see if you can direct viruses to build electrodes for batteries. And so this is going towards the first virus-based battery.

The thing that we think is good about this is that we could try to grow it at room temperature. We could try to use nontoxic materials. We could make it flexible. And we think that by adding the nanoscale regularity that nature already gives you, that we could take advantage of that. And so this was Ki Tae's dream about six months ago.

And this is the first virus-based lithium ion rechargeable battery. It was made in our group, and this was done in collaboration with Professor Paula Hammond in Chem E and Professor Yet-Ming Chang in Materials. So over here, you basically have a flask that has the sequences encoded to grow the anode material-- the electrode for the battery.

You grow it up in the lab. You pour in the precursors. The organism grows it. You put it on a surface. And we were able to actually reach the theoretical capacity for energy density based on this material, and it's all made very rapidly.

It's made at room temperature. The thing that's really nice is that the viruses actually self-organize themselves as well. And so we're looking at this as a flexible, high-energy density battery that could be made very inexpensive.

We're also looking at actually integrating it into textiles. We do a lot of work with Natick Soldier Center, which actually tries to make textiles and better uniforms for the soldiers in the field. And a good portion of the weight of a soldier is actually batteries, and so we're seeing, can we make these flexible batteries into textiles, and weave them into the soldier's uniform directly?

Now, you can do other fun things with viruses. Like you can engineer the head of a virus to bind to the tail of a virus to make virus-based nanorings. That's mostly just for fun.

And we've been working on trying to assemble these into electronic components-- either nanoelectrodes that are self-assembling. We want to have a sequence that tells the electronic component where to sit, what to grow, and how to grow it back in case it gets damaged. And this is a work by Chiang and Yu Muang in my group that have been trying to figure out new ways of engineering to have multiple kinds of functionalities.

And here are some examples of the first virus-based nanoelectrodes grown in our lab. We convinced the virus to bind gold on this side, gold on this side. So this is six nanometers in this direction. I covered up the scale bar, but this must be 500 nanometers across.

And then we poured in the solution and the viruses grew the gold wires. These are very small wires, actually, which have all made contact and have some reasonable electronic properties. They're definitely not perfect yet, but the organism has the ability to bind the material on this side, bind the material on this side, and grow the material in between, all coded at the DNA level.

This is going towards our first virus-based transistor, where we've encoded the organism to bind to two electrodes on each side, and then encoded it to bind a semiconductor in between to connect these two electrodes. So this is a semiconductor material grown between two gold electrodes. I think I have a few more minutes here.

So I've reached some of those goals that I told you that we'd like to be able to do, but we have a long way left to go. But the other thing in picking this organism-- again, we're really lucky in the fact that biology chooses things to be actually of defined lengths. If you have an enzyme in your body that's too short, if an important part is cleaved off, then it could be non-functional and you could have a disease.

Well, the same thing with viruses. They always build their structures at the same length, and so we can predict that and we know how they are. But the thing that's really nice about having materials all at the same length is that they can assemble themselves.

And so think about taking a box with crayons, and you throw a couple of crayons in and you shake the box, and they're randomly oriented relative to each other. But the more you throw in and keep shaking it, they actually start to self-assemble. Well, viruses do this too.

And they actually make liquid crystals. And so in this case, what we've done is we've engineered our virus to grow a material of interest, and then self-assemble itself into a liquid crystal for things like liquid crystal displays. And so here's the movie for that.

So the idea is through this genetic selection, you've selected these organisms to be specific to grow a semiconductor. You put in the precursors, just like happens in the ocean. They grow the materials. You keep adding more viruses. And they actually start to assemble themselves into very defined structures.

They line up. So virus, semiconductor, virus, semiconductor, virus, semiconductor. And they form really exquisite patterns. And if you look at them in a cross-polarized microscopy, you can see that they're actually liquid crystal and materials.

The thing that's really fascinating about these materials, though-- if you take these liquid crystals, these high concentrations of viruses and you basically plate them out on a surface, they actually form films. This is a structure that looks like a piece of tape. You can pick it up with forceps.

And it's 99% virus, 1% semiconductor. And you can pick it up with forceps and you can move it around. So we look at this as maybe having materials that are like tape rolls, where you could actually roll out your electronic materials, or roll out different kinds of materials based on this ability to self-assemble.

This is just showing you-- it's interesting to think of a virus as a unit cell. And these are the unit cells that actually stack on top of each other. They have really interesting structures, which we'll skip over. But you can actually engineer and put anything you want on them-- a gold, an enzyme, an organic molecule, and have them self-assemble into these film kind of structures.

I just want to introduce one more idea, which is looking to nature again and thinking about how spiders spin silk. Silk is a material that's obviously naturally evolved. What happens is that the organisms-- the spiders take a high concentration of protein, and that's a liquid crystal phase. And they push it through a small hole called the spinneret, and they spin out silk fibers.

So our idea was, wow. If you can make liquid crystals out of viruses, and spiders spin silk out of liquid crystals, maybe we can spin viruses. And we were able to do that. These are viruses that you can grow for meters in length that are basically using the same principles of how spiders spin silk. But we spin viruses.

Now, we spin viruses that have optical materials in them, and magnetic materials, and semiconductor materials in them, so they could be used for things like optical fibers. We're using them for tags for integrating them into soldier uniforms, and we're using them for biodetectors, as well. And this shows you that you can actually make mats out of them, and we're trying to use them for non-woven fabrics.

There's other organisms you could use, too. We chose viruses. Yeast are incredible materials. We like yeast because we work with Professor Wittrup, who's a yeast expert. Because we used the same yeast that you use to make beer and bread, and we try to use them to make semiconductors. And so you know from the beer industry and the beverage industry that yeast are scalable. And so we look at yeast as factories as a possibility of growing materials. And so instead of Budweiser, we think of Nano-weiser factories.

Okay. So I'm out of time, but I'll leave you with that. And I thank you for your attention.


DOUG VINCENT: --started up here again. Thank you for working quickly back to your seats. So are you guys still having fun? You ready to hear some-- two more of the outstanding faculty here--

AUDIENCE: Can't hear you.

DOUG VINCENT: --at MIT? You can't hear me? Let's see. Are we getting any better with the acoustics?


DOUG VINCENT: No? We can get more volume? They're saying they can. Oh, there we go. Hello. Okay. I think we've got it.

We're going to get going. We have two more presentations, and we may be a little bit late for lunch. My sense of the energy in the room is that folks will, in fact, want to have the panel Q&A. We'll gauge that a little bit later.

What I'd like to do is get moving with Professor Ram Sasisekharan, extraordinarily talented researcher that has won numerous awards for his creative ways that he's had some innovation here in the Life Sciences. Going to talk a little bit about the emerging field of glycomics.


RAM SASISEKHARAN: Thank you very much. I'd like to begin with saying that it's really a pleasure and privilege to be here sharing with you some of our work here at MIT. And I'd like to specifically say that the Alumni Association is one that I've paid attention to, because one of the past presidents of the Alumni Association, Brian Hughes, actually did in part support some of the areas that I'm going to briefly touch upon today. So I really know how important it is in terms of how the alumni give back to MIT.

But having said that, the title of my talk today is "Glycomics-- the Sweet Science." I know all of you took a break right now and you had some sugar. And in the simplest form, glycomics is the study of sugars. But not the sugars that you or I are talking about right now in terms of the one that you put in your coffee.

CREW: I've got to stop you here for a second.

RAM SASISEKHARAN: Okay. Now, that's the energy level at MIT. And so the fascinating thing is that life has exploited simple sugars in the most extraordinary way to generate energy that you and I know the reason why we take a break and get some carbs. But that's not what I'm talking about, because I know there's good and bad things about carbs, and so it's not the low-carb diet.

But I'm going to tell you about how life systems have actually exploited carbohydrates, going beyond the energy system in some fundamental ways, that it's been both a challenge in many ways in terms of the science. Doug talked about the two revolutions-- the molecular revolution and the genomic revolution. The field of carbohydrate-- studying carbohydrates to understand its place in biology was a struggle.

But engineering, in many ways, with the help of technology, has demystified that. And part of what I'm going to do today is to share with you some of those exciting technological innovations that have really opened up this field. So it's not about Atkins diet.

So I'd like to connect to a point that Doug made earlier, which is, much of biology then focused on how you could look at a cell, or how a cell regulates variety of cellular processes. And it was driven by the central dogma, the Bible of life, how the blueprint DNA makes RNA. RNA makes protein, and everything was beautiful with life.

And technology, in terms of ability to sequence DNA and proteins, and the recombinant DNA technology that you just heard Angie Belcher talk about-- how you can manipulate DNA to basically create any kinds of systems that will enable you to address in a cell. And historically, everything outside the cell-- and you heard some of my colleagues refer to this word "extracellular matrix."

In very simple terms, you can view that as a junkyard, or the backyard of the house. You throw all the garbage. You ignore it. Sometimes you have somebody come and take care of it. The outside of the cell is no different. And in large part, what it is made up of is actually these complex glycans, complex carbohydrates, where the simple sugars are taken further into much higher level of complexity than I'm going to get to.

And in many ways, how the cells perceive its environment, regulates signal, so that the cells now know they need to divide, migrate, or die, is controlled by this external environment. And the biology then ignored this, and many tools that were developed to study proteins in DNA actually were developed in a way that you could butcher the sugars, because that was the stuff that you wanted to get rid of, because you wanted to access things here. Things have changed now.

We're now into a very exciting area of how we're trying to take a more systems or an integrated view. It's no longer the cell in isolation. Cells are in the context of this microenvironment. There's the cell, the extracellular matrix, how the cells come together to form a tissue so that you have a liver cell that's coming to form a liver tissue, or the lung cells coming to form as a lung tissue.

And each of these cells package themselves in unique and different ways to give you the property associated with the tissue. And in that is this very important space-- the extracellular environment that brings these pieces together. And the polysaccharides, or the carbohydrates, hydrate this environment so a liberalized variety of signaling molecules that come from the outside and then send signals to the cells.

In many ways, the reason why we're able to access these things are to several high-throughput technologies, such as the genomics and proteomics. It's not looking at one single component. It's looking at the component in a concerted way so that you can not only understand how a cell behaves, but how the cells behave in the context of the tissue and the microenvironment, to give you the property of the organ or the organ system leading the organisms.

And in many ways, we did need the help of genetics-- the whole organism genetics, once you knocked out a gene or knocked in a gene-- to really understand how that particular gene transmitted information, not only in the context of a cell, but all the way into the tissue to the organism level so as to appreciate the complexity of biology. And with regard to glycomics, the study of sugar, this became a very important piece. Because when you knocked out genes that made these carbohydrates, at the single cellular level, you cannot not really demystify that. We needed whole organism genetics.

And what was very interesting, historically, is that when people studied, whether it's a fly or a mouse or any other sort of animal model system, that when they saw some very strange phenotypes-- the wings in the wrong place, the eyes in the wrong place, or the wrong kinds of limbs, the lengths of the limbs, so on and so forth-- they eventually found out that several of the genes that were affecting these processes going beyond the cell into the tissue level actually were related to enzymes that made these complex sugars.

So in very simple terms, the way I'd look at it as, you can view every-- every cell that is in our body has a sugar coat on them. It's like the way we wear jackets. In winter, we wear a fur coat or a thick jacket. In summer, we wear a t-shirt. In simple way, the way you could look at how a cell functions is that the sugar coat on the cell dramatically influences how the cell behaves and how the cellular processes are regulated hence. So I want to be able to go back and put it in the context of the-- am I okay?

CREW: Yeah. One second.

RAM SASISEKHARAN: So sorry about that. So I want to be able to go back to the molecular picture-- the molecular revolution that Doug talked about, and put sugars in context, and then take you through this journey of how it translates to the more complex systems. As I said earlier, the dogma was that DNA made RNA made proteins that truly dictated the principles of life. We now know that's no longer the only part of the story.

These proteins are seriously and heavily decorated by these sugars, or glycans, that dramatically influence the behavior and the properties of these proteins. So in many ways, what you can get is a huge amount of functional diversity in terms of this one gene to one protein. And with this diversity, you're able to take a small set of proteins and get an extraordinary amount of functional possibilities.

So if you take one particular protein system-- this is erythropoietin, which is actually a very important biotech drug which is used for anemic patients. This particular protein has sugars decorated on them so that one particular protein has greater than 150 unique sugar structures that can give you a plethora of biological properties. And part of what we're trying to do is to truly demystify that-- understand how do these distinct sugar structures on this protein influence function. And therefore, get to the heart of what we call as structure function relationships.

So in many ways, the dogma has been revisited. Because if you truly get from the genotype to the phenotype, we need to really understand what are the principles of these sugars in terms of how they regulate the functions of these proteins. Therefore, we can understand biology in the truest sense.

So if I were to use one slide to drive home the point about the importance of glycans, it would be this. Where the only difference, in many ways, has been the kinds of sugars that are displayed on these cells that I talked about, to just give you an opposite phenotype in terms of the same locus. So this is a very important field, as you can see, but it has had its challenges.

In large part, it's because of the fact that there have been lack of tools to study these molecules-- the point that I made earlier. Several of the tools that we developed was actually developed in terms of getting rid of sugars. So in many ways, sugars were a hassle to deal with. They were more this inert material that was outside the cell. Probably was, by and large, useless.

But we now know that there are numerous functions in search of what I call a structure or sequence activity relationship. In a large part, it is because of the fact that there are these heterogeneous, polydispersed, but highly information contained material that has really stymied the field. And in very simple terms, I can say that they come in two flavors.

They are these linear sugars and then these sugars that branch. And that structural diversity truly enables us to get this very diverse set of functions that are possible. And I'll give you some specific examples to go through that.

Another important point, to put it in context with regard to DNA and proteins, is that you can amplify DNA and you can amplify proteins. You saw my colleague, Dr. Belcher, talk about how you can basically take a virus and get the bacteria to copy many numbers, but that's not the case with sugars. You cannot amplify them.

So what you isolate them from these cells are tissues, is what you have. So you really need innovation that enables you to access this kind of low amounts of material. And keep in mind, they're not only low amounts, but you have all this polydispersed, highly complex set of structures.

Another thing that complicate the analysis is that, unlike DNA and proteins where there's a template-- there's a reading frame that enables you to make these copies-- they're a non-template complex biosynthesis. A bunch of enzymes come together and decorate the cells or the proteins with these sugars. So in many ways, it complicates our ability to understand how some of the structure activity relationship truly drive the biological processes.

And if I were to step back and put it in the context of a broader framework, you can view sugar as a regulating function in an analog fashion. That is, it's not a switch of a turn on and off of a light. It's like a rheostat. It's like a dimmer, where there's a plethora of structures that give you a diversity of functions.

And when I go through these examples, I'll illustrate what that analog function means. But the important thing is the fact that, with the complexity and the way they are made, the lack of a template for biosynthesis, you truly need a more complex systems approach, because it's right at the cell-tissue interface, and it's not one structure. It's a set of structures, or an ensemble of structures, that I'll come to in a minute. So it is a challenging problem. But nonetheless, an exciting one from the point of view of how a technologist or an engineer will look at it.

So I just would like to very quickly introduce the two families at a higher level-- the branch sugars. They're very complex structures with a core, with several radiating chains. Virtually all therapeutic proteins-- about 80% to 90% of them have sugars attached to them-- several proteins in our body. By and large, the reason is that you can view this as the most extensive posttranslational modification that a protein undergoes.

And in many ways, they perform the function of recognition-- how two proteins can interact the way the sugars regulate that, the way the proteins fold, the way you target proteins. How does an EPO know, once it's made in the bone marrow, to go to the brain? Or how do proteins know where they need to go? What kinds of information are there? There's a lot of information that's coded in the glycans that tell them how they get targeted.

And very important part-- when you move away from a cell and look at it in the context of an organism, is the half-life. How long is the drug in the blood? How long is the particular protein in the blood? The half-life of these molecules are very dramatically regulated by the sugars that are attached to them. And obviously, so on and so forth in terms of stability and specificity.

So I'd like to very quickly introduce, using the schema, the other family of sugars, which are the linear sugars. These are the sugars that virtually coat every eukaryotic human cell. And here's the schema of a cell.

These ant-like structures are the proteins to which the sugar chains radiate. Our own president has worked on some of these proteins in the nervous system in terms of how the neurons grow. These are fundamental protein molecules, as I said, that regulate how extracellular molecules or signaling molecules bind to cognate receptors on the cell surface so that they get the necessary information in terms of what happens.

So in many ways, you could view them as they're found on the cell ECM interface. They act as a reservoir to store, sequester, and present several of these signaling molecules to the cells so that the cells know what to do. And also, when you zoom in to these ant-like structures, here are the sugar chains that are attached. They modulate the signaling molecules in a very specific way.

In other words, you can have these long chains to which proteins attach. And that's presented to a surface receptor on the cell to initiate signals or not. And what's fascinating is a point that I'll come back to, is several pathogens-- viruses, bacteria, protozoans-- use these glycans on the cell surface in our bodies as a way to infect and achieve these troposomes. How a flu virus infects-- in a very specific way, it affects the upper airways as against hepatitis, which affects the liver cells, for instance.

So I'll touch upon that briefly. But this is a high level introduction to how the linear sugars look like. And if you actually zoom in, the way it's able to perform these diverse functions in terms of regulating how a signaling molecule binds to these sugars to be presented to a cognate receptor, is the fact that there's numerous sequences that are possible. As an example, when you have four bases that make DNA and 20 amino acids that make proteins, there are about 48 different building blocks that gets made in a linear fashion to give you this complexity.

Therefore, you can get the diversity that you need to bind to a whole host of proteins to mediate the signaling processes. So what I'm going to do is to, through an example, give you the idea of, when you look at these structures-- I said there are these ensemble of structures that are present-- how do you look at this problem? And how do you demystify the sugar structure sequence through the function? And I'd like to use one specific example that's going to be relevant, but then I'll go through a variety of applications.

So with regard to the technologies to understand how sugars look, if you will, on the surface of a protein or a cell, first you need to understand what does ensemble means, and how do you go about doing that? And this one particular, very relevant, practical problem that basically frames this question, which is several biotech drugs. After the small molecule pharmaceutical drugs, such as the statins, which are about 11 Angstroms in size, several of the protein-based drugs, like erythropoietin, insulin, human growth hormone, and so on, which have been a product of the biotech revolutions, are pretty complex proteins.

But what's fascinating is that when you look at the top 10 proteins that are used therapeutically in the clinic, nine of the 10 are glycosolated proteins. So the sugars on these molecules dramatically influence the biological properties of these. And it's expected that these protein-based therapeutics are going to be the mainstay of the pharmaceutical biotech kinds of drugs that are going to come out.

So to name a few, such as the Epogen and Factor VIII and so on. And part of the challenge that we took on was to try to understand, how do we take this to the next level? We looked at the proteins. Now, these glycoproteins on sugars, and how do they affect the biological functions of these molecules?

And I just want to put it in the context of the story that I said earlier, is that sugars have been around, and historically, people have viewed it as something that it's a hassle to deal with. Get rid of it. In fact, one of the reasons that I stayed on here at MIT-- actually graduated from the Harvard MIT division of Health Sciences and Technology program.

But the energy level that was there at MIT, I was basically told not to work in this area. They said this is carbohydrates. This is the last thing you want to touch. It's the most difficult things to do.

But a point that I'll come back to, I drew upon the energy that was in this environment. I actually played tennis with a chemical engineer. And it was the fusion of the biology and the engineering in terms of the way we were thinking about the problem that enabled us to tackle of how to sequence sugars in a way that people were able to sequence DNA and proteins so that we could really open the Pandora's box, if you will, in trying to understand what sugars do.

So with proteins, the mindset that people had when I started working in this area is, forget it. Just get rid of it. It's not important, because it's useless. But now we know that they are not impurities. They affect several important properties, particularly the way these drugs behave.

So the question to me then was-- this is very fascinating. How do you look at the system? How do you look at this ensemble of structures? Because it is the polydispersed, heterogeneous ensemble of structures that we need to visualize.

We can visualize DNA. We can visualize proteins. But how do we look at the sugar coat on a protein or in a cell to really understand what's going on? And I'd like to use this analogy to illustrate the idea.

In many ways, looking at sugars on a protein or in a cell is like looking at image processing. It's looking at, how do you look at an image? And to me, the analogy that I've used as the six blind men and the elephant. Everybody was trying to figure out what this was, and each person got it wrong. But the idea was, you really need to understand what that whole picture looked like.

And here is the elephant. So what we did was, we brought in several different orthogonal analytical techniques. Each technique had its strengths, but there are limitations or weaknesses in terms of studying sugar, because you're looking, as I said, as an ensemble or a set of structures.

Then we'd make measurements associated with these. Then if you do not link these measurements in a meaningful way, and allow this to just slap on raw data during image processing, if the data doesn't talk to each other, you get a blurry picture. The more you're able to integrate the various measurements, you're able to then get, there's an entire picture.

This is the concept behind it. What would we really do is we took a variety of different analytical techniques-- mass spectrometry, NMR, and different kinds of other analytical techniques-- exploited the strengths of these techniques, understood the limitations and weaknesses of those in such a fashion that we then developed a number-based approach to code information associated with the various building blocks. Why is this important? The point that I made earlier.

I really needed an electrical engineer, a chemical engineer, a biologist, a carbohydrate chemist as a team to really take on this problem in a very interdisciplinary fashion. And what we were then able to do is solve this as a puzzle, where we were able to look at not just one structure, but a set of structures that existed either on a protein or on a cell. And we were able to build beyond that.

And once we got the sugar structures, we eventually linked it to the genome and the proteome database. So what we're now able to do-- the example that I used earlier about EPO-- here's the EPO molecule with the four sugar sites in it. And I said earlier that they affect folding, they affect the way you look at the half-life of it.

What's also interesting is, if you let these cells recombinantly making EPO, the human cells that make even process condition dramatically affect the kinds of sugars that the EPO molecules display on their surface. So part of our approach is to take a very integrated way of understanding, what are the different sugars that attach to what sites of these proteins? How much are they present [INAUDIBLE] that is their abundance and their location?

You can begin to see one equal molecule now has been translated into these 150 different unique structures with the sugars that are attached, and what they do. So the next question obviously is going to be, once you have this information, what are you going to do? How do you understand the biology? And I'll come back to that in a minute.

So this is giving you a flavor with regard to how we did this for the branch sugars. Then we also took on the problem of trying to demystify existing sugar drugs in the market. One that you might know-- heparin and low-molecular-weight heparin. It's the drug of choice as an anticoagulant, especially for things like deep vein thrombosis, which is DVT, where you want to make sure that you don't clot.

But what's also interesting is, this is the drug that's used as the mainstay for anybody who goes into the hospital with a chest pain. Anything to do with acute coronary syndrome, in terms of ACS. But what's fascinating is that this drug, though it's been around for the last 85 years, it's the [INAUDIBLE]. Essentially, it's a complex mixture that's been so poorly characterized, it's-- the analogy is like what was the case with insulin in the early '30s.

When insulin was isolated from the pancreatic organ from a pig, it was always used as a mixture. And then people saw this beneficial effect, and it was a serendipitous discovery. The story with heparins and low-molecular-weight heparins is no different. And what's fascinating is, until we approached this problem in using the technology, as we said, it was still viewed as this complex mixture. It had these very interesting set of biological activities.

But the fact remained that this was a big product in the pharmaceutical industry with poor understanding. The FDA really did not know how to look at the system and how to really understand the complex set of structures correlating to its function. And again, to put it into context, here's a small molecule drug, like your statins or your Cox 2 inhibitors and so on. Just to put it in context, here is your heparins and low-molecular--weight heparins.

There are thousands of chains with many, many structures and sequences that really pose this challenge in terms of how you understand that. So part of what we did was-- the proof of the pudding was in the eating in the sense of, could we take the technology that we developed, do the so-called image processing, and try to be able to give you a picture of what this complex mixture looked like? Therefore, you can begin to understand how these structures correlate to functions.

So what I'm now going to do is change gears and walk you through some applications. We took these set of different technologies we developed. I'm going to walk you through a handful of very specific examples of what we did. And to put it in context with regard to why sugars are important, going beyond the energy molecule, that is, that I talked about.

So one interesting thing we found was, once we could start sequencing these sugars, we could understand what was going on with regard to the sugar structures. Is that tumor cells, on their surface, contain sugar sequences that either kept the tumors dormant so that they were not rapidly dividing-- the point that I had made earlier that how they respond to the environment with regard to signaling. Or they could displace sugar structures that actually enhance their ability to respond to growth signals, and then rapidly start dividing.

And it was in balance. So the red sugar sequences kept these cells quiet and would not permit signals to go into the cells. But the blue sequences could activate signals and enable these tumor cells to rapidly divide. And this balance was critical.

And then what happens is that during the process of tumor progression, you get more of the green stuff growing on these cells, so that they can more rapidly respond to these signaling molecules and take off in terms of their growth. So part of what we then began doing is, once we began to understand the biology behind it, we began generating various of these sugar fragments to try to really understand, could we then take these red guys, understand them in terms of mechanisms not only at the cellular level, but in terms of animal models?

And we have generated various different kinds of sugar compounds, and a long list of these, one of them which is actually plant-derived that I talked about Brian Hughes being very excited to support. And the other ones that are in the general heparin families, and they are going under phase I testing right now. The fact that sugars change on these cells then begs the question-- could this be a marker for distinct states that a particular cell type is in?

So here's a set of healthy cells. But then as you progress towards the disease-- in this case, it's cancer. We now know-- a simple example I can give you is PSA for prostate cancer. We all know that it's a very controversial thing. Just looking at the protein levels of PSA doesn't really tell you what stage you are in in this cancer progression. What we found, and we and others did, was the fact that the sugars on PSA actually subtly change and give you a beta handle or a signature to the distinct state that you are in during the progression of prostate cancer.

So what we're now doing is we're dealing with several human samples to really try to translate what we found in the lab to something clinically meaningful. And there's a whole diagnostic platform that's being developed to really harness or leverage and understand, could sugars be a better marker? Because there are enough evidences to support that. The sugar structures change much more subtly than those analog regulation of function that I talked about earlier.

One of the fascinating things is the point that I made earlier about how pathogens infect. So if you look at epithelial cells, these are the cell lines that line our upper airways, our gut, the blood-brain barrier. These are the cells that are exposed to the outside, and the way the body sees the outside.

And here's a typical epithelial cell. It's polarized. It has a head and a tail. Very simple. It's an apical side and a basolateral side. And here's the blood vessel. Normally, molecules are transported across in an active way. There's a receptor that binds and then things are transported through what's called as a transcellular transport.

What's fascinating is that several normal biological processes exploit what's called as a paracellular transport. Between cells, there are these junctions that hold these cells together. And then solutes and proteins and others from this side go into the other side, and there's a way that there's a gatekeeping mechanism going on.

What we found is the fact that the apical glycans in a very specific way regulate whether these junctions open or shut, and therefore what goes in or what doesn't. In many ways, the analogy is like the Alibaba and the 40 thieves. You need the right card to swipe, and if you do that, you have access in.

And what's even fascinating is the fact that these glycans can be used as ways to open and close these junctions. But pathogens-- several of these not only bind to these glycans, but use it as a way to mimic the system and open these functions as a way to get into the system, if you will. So we're beginning to see several bacterial and virus species actually use the sugars on the cell surface as a way to get entry into distinct cellular compartments.

And you can begin to see that then you have these glycans which are different for the upper airways and the blood-brain barrier and the low gut. The kinds of sugars that are there in these cells uniquely regulate what gets accessed in and not, and how the access is regulated. So this is a pretty exciting area.

So the point that I made earlier that, when you have these sugars that regulate proteins on the outside, they store them, they release them, the kinetics become important-- what we began to do is to address the issue of combination therapy. We said, when you look at a cell-- here's a tumor cell, there's the blood vessels around them. The endothelial cells grows in one way. The tumor cells grow in one. The sugars are different. The way they regulate this compartment is different.

So if you really want to understand what's going on as tumors grow with regard to the blood vessels, then you need to understand how these two compartments relate to each other. And if you take off-the-shelf drugs-- off-the-shelf chemotherapy drugs, off-the-shelf anti-angiogenesis drug. And so just combining them in the standard way of mixing A and B together, part of our thinking was, if there is a very important component of how these sugar molecules regulate the entire microenvironment in very distinct ways, then drugs need to be delivered, or drugs act in similar ways.

To cut a long story short, what we then decided to do was took off-the-shelf drugs and decided to develop two compartments. We call this delivery device a nanocell. There's an inner core and an outer core. The inner core has the chemotherapeutic drug and the outer core has the anti-angiogenesis drug. In many ways, like the smart bomb approach, where you want to put the chemotherapeutic agent inside so that it doesn't escape, and the anti-angiogenesis. And

The most important thing is the fact that you need to regulate the kinetics of the release of these two compartments in distinct ways which reflect back to the way these cells grow in terms of how the tumor cells grow and the endothelial cells grow. So cutting a long story short-- and I don't want to give you too much of a data dump here. But what we found is when we looked at the ability for these tumor cells to grow in animals, the number of days here, and compared it with not only the controls, but the various just physically mixed combination therapy-- it's like taking just a chemotherapy, and an anti-angiogenesis as two different regimens.

But then doing it in this controlled release fashion, so that you're affecting the kinetics, really. What we found was some very dramatic effects in terms of the survival outcomes. This is the normal, so the controls drop dead in 20 days. The standard current combination therapies of taking combined effects give you a little bit extra survival.

But a strategy where you're beginning to look at this spatial temporal release kinetics and the way cells come together to form tissues and organs, and how these glycans conceptually play a role, you can then begin to really not only tease apart the system in an integrated way, but you could leverage it in terms of these kinds of drug delivery strategies. And where we're basically taking this is trying to figure out how we can use combination therapy in the area of treating stroke. In the interest of time, I'm not going to go through that, but what I wanted to show you is the fact that we are really leveraging this to the extent that we possibly can, in terms of novel approaches to treatment of diseases.

The last point that I want to quickly touch upon is how this field has integrated itself to the genomics and proteomics area. As I said, since glycans are very important, we now have large high-throughput data in the area of glycomics. MIT is one of the centers for an international effort for the consor-- it's called the Consortium for Functional Glycomics, where we basically house the largest datasets that's there in this field.

We have all the different glycan structures. We go all the way from the molecule to the mouse, or the molecule of the human, and correlate the link between the genome, proteome, and glycome, and generate different kinds of data, whether it's data from the cells to the target organ systems. And then our goal was to really be able to use relational databases and use a simple port, like a molecule page that truly integrates these diverse kinds of information.

So I think this field-- this is emerging to be an important one where it has had its challenges from the molecular revolution the genomic revolution-- has been possible because of the technology. And the way that we have looked at this, the more systems fashion, but it has begun to address some of the several complex and challenging issues that we have had. So the summary.

It's an emerging field. Fundamental roles in biological processes. We're limited by several tools that were really needed. Two kinds-- the branched and the linear. Complex and information dense. And we've developed several different tools to address, at the end of day, how structure correlate to activity and function with many different exciting applications.

And last but not the least, funding from NIH. And this is my group. Several different members of my group were obviously responsible for this, but I did touch upon some of the collaborators that we have had. And the question of "so what?" Could we really take this and go beyond?

What we were able to do was really leverage these different technologies that were developed at MIT and take on looking at these glycoproteins and low-molecular-weight heparin. And a company has been spun off that has really taken this problem in terms of trying to really translate it into the real world. Thank you very much.


DOUG VINCENT: I think you guys can see why we had so much fun putting this panel together. I'm running out of adjectives. Holy cats? Something like that. This is not science fiction. This is just hardcore science and engineering taking place right here.

I'd like to introduce to you Martha Gray. Martha is the director of the HST Program-- the Health, Science, and Technology Program-- a joint alliance that has existed for the last 35 years between Harvard and MIT. In that role as director, she is part of and helped guiding some marvelous research that's taking place there, to take much of what we've seen here today and to help translate that into medical practice. So-- Martha?


MARTHA GRAY: He was just fiddling with my mic. Can you hear me? Sound all right?

So this is most likely what George's coronary artery looked like when he was admitted to the hospital. And when you consider that where that dotted line is is where the edge of the artery normally should be, I think you can appreciate that this extra tissue inside there most likely caused a reduction in the amount of blood that could flow through that artery. If I can figure out how to turn this on.

And that was the ultimate cause of his symptoms. Now, what I remember most about George is not what his diseased coronary artery looked like. And it's not even that both his father and his elder brother died of sudden cardiac death. What I remember is this big, almost jolly guy with a very deep, booming voice who was willing-- excited, even-- to be a guinea pig for me while I was learning to do a history and physical.

Now, I remember that history and physical so well. It took me more than two hours. I'd like to think it's not because I was new and slow, but because he was so busy asking me questions. But I met him when I was doing my clinical experience at Mount Auburn hospital while I was a graduate student at MIT.

I went to see George the day he left the hospital, and he handed me a card with his address on it and said, you need to let me know what happens over the years. He was very taken with the idea of putting engineers in a clinical setting-- having them have hands-on experience in medicine. And he thought that "you techies," you guys at MIT, will figure out what to do for guys with hearts like his.

Now, I'm embarrassed to say, I have no idea where that card is now. And it's also the case that I haven't done anything directly to help guys with hearts like his. But many of our faculty and students have, and I wanted to share some of those stories with you, as well as some other stories. And I'll dedicate these to George, and if I knew where he was, he could come. This would be the card.

So the therapeutic concept in his case is really conceptually straightforward. You remove the blockage, and the blood flows. Now in practice, it doesn't work like that.

Now I've got to figure out how to use this pointer. Can somebody help me? Well, I'm sure there's a button here somewhere. Ah, here it is. A day of sitting here, I found it. [LAUGHS]

The nerves get you everywhere. Okay, so you probably have the idea by now. There's a catheter that's threaded up through the groin to the heart. And in the region of the lesion or of the blockage, which you can see there in yellow, a balloon is expanded.

And in this particular instance, there's a metal casing-- a wire mesh around that balloon. It's called a stent. And that holds the vessel open. And George's era, actually, was before the days of these stents. And it was in the early days of what's called balloon angioplasty, where just the balloon was put in place. Though sometimes there was the problem of it re-collapsing on itself.

Now, the good news is that this Roto-Rooter approach works pretty well. Initially, because of that collapse, the balloon would fail under 10% the time. And with stents, it's really relatively rare. The bad news is, in about one to six months, these things both fail. And with by failure here, I mean needs another angioplasty or needs a coronary artery bypass graft.

So some of the work I'm going to describe is work of Professor Elazer Edelman, who is an alum. And he focused on, why is it that those vessels become blocked again after they're opened? And can the procedure and the devices be improved?

Now, if you look at a vessel after this six month period-- this is one such example. This is the edge of the artery. These little black marks in this example are where the stent was, and you can see there's this massive regrowth into the lumen. And that, again, blocks blood vessels.

And the thinking at the time was, this response was really due to the fact that you've taken this diseased artery, blown it open, all kinds of biological things happen, and that's it. He said, well, that's probably all true, but what about the stent itself? And could that play a role in why these things fail?

So these are the first generation stents and the so-called slotted tube-- or that's his name for them. And if you put those in an artery and look at the lining of the vessel, 60% of the cells that line that vessel were scraped off, if you will, or denuded from the vessel wall. So that can't be good.

And the possible reasons for them are things like, the stent, as it expanded, the way it was constructed, it would shorten. And you can see, it buckles a bit here. And also, there's balloon contact with the vessel wall. And any of those, and perhaps more things, could induce the biological response that led to restenosis.

Fast forward some years with some new designs that were developed based on some data I won't show you. The corrugated ring design differs in that there is no shortening, no buckling-- or at least, not measurable buckling-- and much, much less balloon contact, though it has exactly the same amount of wire. Now, the biological measures are what you need to look at.

And if you compare, or if he compared, the slotted tube to the corrugated ring on every measure, and these are four of them, there was a marked improvement with the corrugated ring, like the denudation, the clotting, and so forth. These were the first data to show that the stent design itself, never mind the disease and everything else-- the stent design itself played an important role in the response to this therapy. It also changed the intellectual property or commercial landscape, because knowing that the stent design mattered gave companies a different axis with which to compete.

Now, you've probably heard recently, and hopefully not experienced directly, drug eluting stents. And there, the concept is-- well, another way in which you could prevent this regrowth is to put a drug on the stent. Use a drug that prevents the proliferation of those cells that grow in.

And looking at some clinical data, people have tried this now for a number of drugs. And without naming the drugs, if you look at clinical data and compare the restenosis rate relative to a bare stent, you can see that some drugs work and some don't. The thought is, well, this is the difference in biological activity. It's the difference in what happens in clearance in the bloodstream and so forth.

These three drugs are equally effective in vitro-- in the test tube, if you will, at preventing proliferation. Again, that was our challenge to the paradigm. It's like, well all that might be true, but we should look at really, where does that drug go? What's it interacting with? And maybe we can get some insight into how to best both build these things and tell a priori whether these devices will work.

And so this is one example of one drug that's fluorescently labeled, and you can see at this time point that the drug is very close to where the stent is. And I'm not showing you, you can also look at the depth of the tissue. And it turns out, if you look at all this data-- and again I'm collapsing many years-- that there's a good correspondence between the properties of the drug and its interaction with extracellular matrix-- that stuff that Ram was talking about-- and the transport of that drug into the depths of the tissue that corresponds very well with the clinical outcome.

So over the past 20 years, 30 years since George, there's been a dramatic improvement in therapies. That really came about from challenging the paradigm and understanding the tissue device and drug interaction. That's restoring blood flow.

In the realm of treating cardiac problems, another basic therapeutic concept is maintaining electrical signaling, or restoring electrical signaling. And one exemplar of this issue is the problem of sudden cardiac death. Out of every seven of you, one will die of sudden cardiac death. That's more than will die of cancer.

And the cause of that death is arrhythmia in the ventricles. You might have underlying cardiac disease, but what kills you is the arrhythmia that prevents the heart from beating properly. There is a treatment that's extraordinarily effective-- an implantable defibrillator. The problem is, who do you give the defibrillator to?

If you gave it to everybody who had cardiac risk, only 4% or so would actually need it, and that's a very invasive therapy for that kind of population. And I should say that the-- as the slide says, sometimes the first indication of cardiac disease is sudden death. And obviously, it's too late by then.


So I'm sharing a little bit of the work of Professor Richard Cohen, who's been thinking about this issue in various dimensions for several decades. He's also an alum of MIT. And he's looked at stochastic behavior of heart muscle and experimental models, and is really focused on strategies for predicting the risk of arrhythmia.

Now, what you can actually measure at the surface of the body is an electrocardiogram. And what that's detecting is a whole collection of a whole bunch of small currents within the heart muscle itself. And if you were to be able to visualize current in a working muscle, there's a very coordinated pattern that allows this heart to beat effectively.

And the question was, can you extract from this information something that tells you there's a little blip over here? And the answer is, you can. I wouldn't show the example if it weren't that true. And at a microvolt level, at a level that you can't even see here, it turns out that there's something called T-Wave alternans that can show up-- a microvolt change if your heart rate is reasonably high.

And this has been tested in a number of clinical trials, and this is one. And in this group of patients, those that had a negative T-Wave alternans test in the 18 months had no significant cardiac event, whereas a quarter of those with a positive test had a significant cardiac event. And this is the only statistically significant risk predictor of a risk of sudden cardiac death.

And this technology has been commercialized by MIT to a company called Cambridge Heart. It's been recently approved for Medicare funding. And I would point out that this is one early example of the idea of being able to predict who you should treat when the population is very large.

It's the personalized medicine problem. How do you know how to give a very effective treatment? Which people should receive it?

The third fundamental therapeutic concept-- if blood flow isn't the problem, or electrical isn't the problem, or you can't do anything about it, restoring muscle function. And that's the third example I want to give, and it's work of Gordana Vunak-Novakovic and Lisa Freed. And they're in the tissue engineering area, and they're focused on principle design of tissue engineering-- along the lines that Linda was talking about earlier.

And here's the conceptual concept. There's an area of the heart that's damaged and not functioning properly. Could you actually put a patch on there that works and restore function that way? And the idea was to take a polymer, seed it in the experiment. I'm going to show you rat heart cells.

They penetrate the polymer. You let them seed and take hold for a few days, and then put in a dish and stimulate it with a pacemaker, actually-- a real pacemaker. And so that during the time it's developing, it's contracting in a way that's analogous to a heart. And after eight days in culture, you actually get a tissue that's beating.

These are silicon spacers. Here is the electrode. You can't see the other electrode very well. You can get individual muscle cells. They'll contract spontaneously.

This is the first time anybody's been able to create a tissue they can beat in an organized way. And so this-- there's millions of cells cutting across there. And if you look at the [? altered ?] structure, the engineered tissue both in electron microscope level and a light microscope level, look very similar to native tissue.

So these have really been transforming advances in technology-- improved therapy for blood flow that came about from understanding cell tissue interaction, trying to predict the risk for electrocardiac events, and at least as a potential, restoring muscle function. And this was on the MIT website this February. So if I saw George now and I had his address, I'd send him a Valentine's Day card with this on it and try to describe what I've just described to you.

Now what I have worked on is not the heart, but on problems related to arthritis. And the work I'll describe for you is work I've done with another alum, Professor Deb Burstein. Arthritis is, according to the Arthritis Foundation, the leading cause of disability in the United States. One in three of you probably have it. So very, very common.

And the treatment is really one of two things. One is pain relief, either by over-the-counter or by prescription kinds of drugs. And then finally, the last resort treatment is replacing the joint.

Now, it's easy to say oh, what a crude treatment. But this was invented only in the late '60s, and this has kept many, many people out of wheelchairs. Now, they last about 20 years. So this has been an extraordinarily high impact innovation. But again, as Linda noted, the ideal would be to try to prevent this from happening so you didn't even get to that stage.

And there are several new therapies on the horizon. Some are nutraceuticals. Virtually every drug company I'm aware of has a program in trying to develop arthritic therapies. There's new surgical strategies coming down the pike and that have been suggested and used.

And the challenge is, so you've got this thing that works in an animal or works in a test tube or a Petri dish. How do you really know if they work, particularly in humans? And the current way you can look at either development of disease or progression or the efficacy of a treatment is x-ray.

So this is an x-ray of one of my student's patient's knee joints. And this is the thigh bone, the femur, the tibia, the calf bone. And you can see the space between the joints. And if you're used to looking at x-rays, you'd say this space is narrower than usual.

But you can't actually see cartilage by x-ray, so the fact that this is narrow means that the cartilage that would normally be between the joints must be missing. And in fact, this is the knee of this patient. And this yellow stuff should be covering the whole joint. You can see it's been completely stripped of its cartilage. And I think you can appreciate how insensitive this kind of approach is.

Magnetic resonance imaging has been a dramatic improvement in the first instance, because you can actually now see the cartilage tissue. You're not just looking at space between bone. And what's been emerging is actually molecular imaging of cartilage, looking at, as I'll describe briefly in a second, the sugars that are really functionally very important inside the cartilage. And it gives you information that you can't visualize on an anatomic measure.

And so if you can actually see very early stage changes, then you have the potential to ask questions, like does the treatment work? Who gets OA? How fast does it happen? And so forth. And I wanted to give an example-- one example. Autologous chondrocyte transplantation popularized in this country by Genzyme.

And the idea is that if you have a local defect in your cartilage, you go in surgically, clean it out, remove it, fill it with cartilage cells, and then cover it with a piece of tissue. Now, how do you know if this idea works? What did Genzyme have to do for the FDA?

Well, they had to ask subjects to agree to what's called a second-look biopsy, second-look arthroscopy, which means they make an incision in your knee joint, put a light tube in, and actually put a device in and remove a small piece of your cartilage. Then they stain at histologically. You've taken it out of the body, you slice it very thin-- six microns thin-- and you stain it. And this purple color, the darker the purple, the better the cartilage.

This is much bigger than a biopsy. It would be much smaller. Here's bone. Here's cartilage. The biopsy wouldn't go down to the bone normally. It would just be a small piece here.

This has at least two problems. One is, if you're doing well as a patient, the last thing you want is somebody go in and take a piece of cartilage out that you've just tried to fix. And the second thing is is the sampling problem. If, let's say, this were it, where exactly do you sample? And how do you interpret that single sample from something that's a very heterogeneous piece?

So it has all kinds of problems, but that's the best that had been available. And what we can do now for the very first time is take-- now using the same example. Using imaging, you can actually get a surrogate for this histology, where the darker blue is like dark blue here. Light purple turns red in this diagram.

But you can see there's good correspondence between this. Yet this did not require that you take it and slice it into six microns thick sections. This block is a few centimeters by a few centimeters. And this is a thin imaging MR slice.

So you can do this in vitro. It turns out you can also do this in vivo, and this is one of the first images showing that you can make these measurements in vivo. This is from somebody who had the autologous chondrocyte transplantation in this region of the tissue. And two months after she had had that operation, you can see that there's tissue there, but it's colored red.

And here's the scale. Red means there's not much of that important molecule. Yellow and blue means there is more. And so this region is filled, but presumably not functional yet. And the x-ray and the other MRs for this, you could not distinguish those patterns.

For a different patient, one and 1/2 years after the operation, this was the region of her transplant. And you can't distinguish the transplant region from the rest of the tissue. Now, this isn't to say that autologous chondrocyte transplantation works. The study wasn't designed in a randomized way to follow people over time. That's ongoing. But what it does say is that this measurement is actually sensitive to the kinds of changes you'd want to be able to detect.

And a number of drug companies have now begun to adopt this for their arthritis clinical trials. And a number of groups have begun to adopt it, to begin to ask some of the more fundamental physiology and biology questions, like who gets arthritis? How does it develop?

And I wanted to share one of those with you from Leif Dahlberg's group in Sweden, who adopted this technique about six years ago. And he looked at subjects who had complaints of knee pain, but had normal x-rays-- normal in every other regard. So each of these bars is a-- think of them as a person, and they're just ranked according to the molecular index to that color that I was showing you. And you can see there's a broad distribution.

It's now six years later, and he's gone back to them, and they've had repeat x-rays. And seven of them actually have evidence on the x-ray of osteoarthritis. And one of them has already had a total joint replacement, and they tend to be bunched towards the low end. So this is early indication that this may have some predictive value about what might happen in arthritis.

So one of the things that was eye opening, if you will, about the imaging things I just showed you-- it just allows you to see things you couldn't see before, which allows you to ask questions you couldn't ask before. And I wanted to highlight one last set of stories, the Athinoula A. Martinos Center for Biomedical Imaging that was established about five years ago in 1999, and their work in brain imaging. And Bruce Rosen is the director of the Center, Greg Sorensen the Associate Director, and George Bush is one of the faculty there.

Now, about 20 years ago, if you were to look at-- or, 25 years ago-- brain images. Again, this was transformative in its time. You could see the outline of the brain. This is a cross-section taken, if you will, that way. You can get a sense of structures, but I'm sure you can immediately discern how much more detail in the kinds of images that we can get today.

And just from that kind of images and the kinds of computational technologies around a sequence of images taken through the brain, you can get a three-dimensional representation. And what our pioneering advances is you can take this very convoluted structure, blow it up like a raisin turning into a grape, and begin to see what anatomic structures might be next to one another. And again, so you can begin to ask physiological questions. Or where do the different pathways in the brain go?

And I'm not going to talk about any use of these for any medical application or neuroscience application, but I think you can appreciate the kinds of questions you could ask that you simply couldn't ask before. That was focusing on measuring structures in anatomy. What you'd really love to be able to do in addition is to be able to tell where the brain is activated. And this is one of the pioneering images called functional magnetic resonance imaging that Bruce Rosen was one of the first inventors of.

And this little dot here shows that there is a difference, after a particular task, in blood oxygenation. And blood oxygenation is used as a surrogate for electrical function. So presumably, when you think you use an energy source and it reduces the amount of oxygen in the blood-- that's the concept.

So I want to show you how a functional magnetic resonance, fMRI, study is done, in running an experiment with you. On the next series of slides, you're going to see a word. It's going to be written in a font, and that font is colored. And what I want you to do is yell out as fast as you can-- and your time to lunch depends on this.


As fast as you can, what color the font is. You got it? Okay.





MARTHA GRAY: With practice, you get better. And I didn't give you a chance to practice. That's called a Stroop test, and it's one of the tests that's done in looking at people with attention deficit hyperactivity disorder.


I know. My friends tell me that to be prevalent here, and I didn't want to use that joke. But you obviously thought of it yourself. [LAUGHS]

So as you can-- quite obviously from this picture, if you say, which regions of the brain light up? It's very, very different in kids with ADHD. And so again, we're opening up a whole new vista. Who knows what that means biologically or diagnostically? This is early data. But you can see how it's just opening up the door to all kinds of different questions.

Now, I said that measured brain activity. You can do it through measuring blood oxygenation. Now, brain activity really happens on a millisecond time scale, not on a second time scale of MR. Magneto-encepholography is a technique that allows you to measure electrical activity of the brain. The problem is, you can't localize it. You can't tell when you measure its surface of the brain exactly where it came from.

And the folks at the Martinos Center said, well, what if we combine the best of both worlds? Use the MRI to constrain the inverse problem. And then, if you will, make a movie of activation. And this is one example of that. Again, in this case they're reading novel words.

And when you see the thing restart, you'll see it starts at the posterior of the brain and it moves itself up to the frontal lobe, back and forth. So you can see this fleeting thought is very complicated in terms of going back to front, to back to front. And it gives you a whole new appreciation for the complexities of what systems neuroscience is likely to mean when you think about what does it mean to read a word and remember it.

So I've given you a number of vignettes-- heart, mind, arthritis, and of things that have really over the past 20 years gone, if you will, from bench to bedside. They've all been things that I think every single person on that list has gotten a grant proposal back saying, it's impossible. It will never work. And I've showed you that these things, in fact, have worked and had tremendous impact.

And each of these advances was enabled by a true advance in technology. Another important common theme is, though I told you the head investigator-- in fact, if you were to go into any of their laboratories or talk to them about their work, they won't say oh, it was just my engineering background that did it. Actually, they had teams of people within the labs that include physical scientists and engineers, biologists, physicians, and so forth.

So that's the case for all of the stories I told you, and they really attribute the advances I've shown you to the ability to create that integration and the ratcheting that you get by having the multiple disciplines talking to one another. The theme you've heard, I think, throughout this morning. Which brings me to Health Sciences and Technology, that a number of people has mentioned.

I just wanted to give you a sense of the landscape in 1970 and to fill out what's here at MIT now. Health Sciences and Technology was established in 1970. It's, if you will, a joint department that's owned by Harvard and MIT. It's done interdisciplinary education and research programs, and it really focuses on the integration, the translation from bench to bedside, and of course, as everybody at MIT, innovation. And we use this to talk about equal footing of the different disciplines, and really integrating the different professions so we can both identify the critical unmet medical needs and bring the solutions to the bedside. And industry is an important player in all of that.

We've just passed 1,000 alums, and we have 450 students enrolled, including PhD students, MD students, MS MBA students, and so forth. And we have 50 primary and joint faculty. Like Doug mentioned in the Biological Engineering, this group has grown in recent years.

I put this up here to say I've selected a very small set of what's happening here. I said, okay, well, for Tech Day, why don't I pick the alums who are faculty who are doing something that's already reached the clinic? So that means that I haven't been able to talk to you about the genomics, or the bioinformatics, or the stuff that's dealing with predicting outcome in space through our active work with NASA about quantum dots to bring molecules-- target molecules for cancer, and on and on. So hopefully, this will intrigue you and you can come visit us sometime.

Doug talked about the map. And I think one of the things HST does is bring under the MIT umbrella the hospitals and Harvard Medical School, so that the landscape for Bio and Medical Engineering at MIT actually brings in the local, very vibrant teaching hospitals and medical school and industry. Which leads me to my closing point, which is, we are extraordinarily well-positioned, I think, at MIT, to have just a huge impact over the next decades on advancing human health around the world.

And when you think about the number of disciplines, such as biological engineering that's starting in the fall, through biology, which has been here for a long time, the number of centers that have formed, plus the fact that with HST, we've got Harvard under the same umbrella, there is no other institution that I'm aware of in the world that has the strength and the collection that we have here at MIT. So we're delighted that Susan's excited about this as an area, and I think we hopefully come back in 35 years for the next Tech Day and have equally transformative advances. Thank you.


DOUG VINCENT: That was great.


DOUG VINCENT: That was perfect. Perfect. Okay, I think we're going to still have some questions, but a little bit abbreviated time. So if Professor Hockfield and Professors, if you can join up here on stage.

There are two microphones in the aisles, folks. Downstairs in Little Kresge, you'll need to come up. We'll try to-- again, I apologize. We don't have the full half hour. If we can line up questions at those microphones, and we'll get our faculty ready to go.

One thing while we're getting set here-- hopefully all of you collected blank surveys as you came in the door. If you would be so kind to complete those surveys and leave them as you exit, they're very important for the Tech Day committee that's-- if you've enjoyed what's happened here today, very much so, it's a result of feedback that we've gotten on those surveys. So please, please in fact do take the moment to complete those. It's very helpful to us.

And thanks, too, for all the patience as we run a little bit over. We'll get you to lunch in time, we promise. They told me that nobody's going to eat the food until we get there, so-- okay.


DOUG VINCENT: Yes. I think we're good. Thank you.

SUSAN HOCKFIELD: Go ahead and start the questions.

AUDIENCE: Since sugars have such an important influence on the operation of the body, what sort of risks are you taking when you eat a bar of candy?


RAM SASISEKHARAN: I think the short answer is they're two unrelated events, but I think we're trying to really get to the heart of how much sugar we take eventually is used in the synthesis of these more complex sugars. But I think the short answer is, I think you shouldn't worry about it. Except your diet.

AUDIENCE: Hi. With the brain imaging and all, what impact does that have on mental health, like schizophrenia?

MARTHA GRAY: There's--

CREW: Mic one.

MARTHA GRAY: There's a number of emerging--

AUDIENCE: Can't hear.

AUDIENCE: Can't hear you.



MARTHA GRAY: I don't know what number I am. Can you hear me yet?



SUSAN HOCKFIELD: You can get a handheld mic.


MARTHA GRAY: All right. Well, let me talk through Angela's. So there are a number of emerging analysis techniques. The schizophrenia is an extraordinarily difficult problem. There does look like there is some anatomic differences that you can see between schizophrenics and non-schizophrenics.

I'm not aware of yet anything that's showing the kind of functional differences, though. There are groups looking at that. But if you're interested offline, I could give you the names of some people to talk to.

AUDIENCE: Oh, thank you. yes, please.


MARTHA GRAY: And yeah, you would know.

SUSAN HOCKFIELD: Can I just jump in?

AUDIENCE: Can't hear you.

AUDIENCE: Can't hear you.

SUSAN HOCKFIELD: My mic-- am I on? Okay.


SUSAN HOCKFIELD: What? Can you hear me?


SUSAN HOCKFIELD: Yes. Terrific. Many of you have seen the new building going up just across the street from the new Stata Center-- the Brain and Cognitive Science Project, which is going to be the new home for all of our neuroscientists.

There is tremendous interest among the neuroscientists to make the kinds of connections that we believe can be made almost nowhere except at MIT between the basic biology, the great engineering, and the implications for not just neurological disease, but also psychiatric disease. So there are a lot of people interested in it. And working with the Martinos Center, we really think we're going to be able to open up this area we have never opened up before.

AUDIENCE: I used to do cardiac tissue culture, and you showed something that I thought was impossible. You showed cells that had differentiated into mature cardiac myocytes, with their myofilaments all lined up parallel from cell to cell. And they seem to have grown that way in vitro. How did you do that? Was that just the electrical stimulation, or did you add some magic growth factors?


MARTHA GRAY: Well-- [LAUGHS] first of all, I want to make it clear that I didn't do it. Lisa Freed and Gordana Vunak-Novakovic did that. But I can't speak to any magic formulation growth factors, but they explored in some empirical way and some strategic ways what population of cells.

It turns out just using myocytes alone didn't work very well. You had to have a spectrum of myocytes. And they had to wait three days before stimulating, otherwise the stimulation itself would prevent the right connections from happening between the different cell that you'd need to create that kind of organized structure that you see.

I don't know how many days out-- what it would look like if you had looked after two days of stimulation versus the eight days that I showed you. But I believe they explored that, and they also looked at the kind of stimulation they were doing. Not an exhaustive search, but they were wanting to find something that would work that you could imagine could work in vivo. So using-- that's one of the reasons they used a pacemaker.

AUDIENCE: You talked about autologous chondrocyte transplants for putting cartilage in knees. Can that also be used for cartilage in vertebrae for people with back problems?

MARTHA GRAY: No, probably not. At this point in a back problem, there's a number of issues. And if you're referring to disk disease, there are people that have artificial disks that are-- I don't know if anything's been FDA approved, but--

LINDA GRIFFITH: It's FDA approved.



MARTHA GRAY: It is FDA approved. So Linda may have more information on that.

LINDA GRIFFITH: No. I just read The Wall Street Journal.



And remembers what she reads. But the problem with using that technique and putting it between a load-bearing joint like you might have there is, usually in the disk thing, you'd have to remove most of the disk. In this case, they take advantage of the fact that most of the cartilage is there, and replace the defect. But the properties of that defect could never support your body weight. And so trying to imagine a little hole in a disk is not really the way the disk degenerates.

AUDIENCE: Okay. Thank you.

AUDIENCE: We saw that looking at things from the engineering perspective, all of a sudden we start realizing, wait a minute. We need to do glycomics. And as I understand it, if we start at the cell level, then the glycomics are getting immediate interconnection between the cells. But then there's still quite a distance between there, even to the organ level. And I'm wondering, is there something similar that appears to be interesting as a possible additional discipline that's just above the level of glycomics, at the next level of interconnection?

SUSAN HOCKFIELD: Who wants to pick that one up? Doug?


DOUG LAUFFENBURGER: Yeah, I'll say something about that. I can't imagine another discipline there. Because to form a new engineering discipline, you have to have, I think, a new science, quite honestly. And I think what you're talking about with that is just an extension of the sciences, as the things Ram was working on integrate things from the molecular and cell level and are able to then explain what happens when they come together and operate as a tissue. So I think it's a natural extension of a discipline based on the molecular and cellular sciences.

AUDIENCE: Dr. Griffith and Dr. Belcher both talked a lot about structure creating function and how to really create something new. I'd like to have you just have a chance to expand, having heard some of the more practical things of the second half, where you think some of the future design products will actually evolve into? What you think will actually hit the marketplace to help people themselves.

LINDA GRIFFITH: Well, I think probably you're directing that more to Angie. But I can say, for what we're doing, we have defined some approaches that would potentially greatly speed the drug development process and make it much safer. We're in the process of translating those into pharmaceutical companies right now.

And so we just got interviewed on NPR last week, and they wanted to know the same thing. When is it going to be in the market? Not by Christmas, so you can't--


--get this. But we're really trying to get it in, in a research way, into some pharmaceutical company laboratories. And I think this idea of having the human body on a chip, where you have these things that can screen against how humans really respond, will be trickling into pharmaceutical research labs over the next five years. And more pervasively, maybe, in 10 years.


ANGELA BELCHER: So for-- we haven't been focusing that much on medical applications, but more for electronics applications. And the applications that we're most interested in are making efficient solar cells-- large area solar cells. We're very interested in energy. We're interested in batteries and different kinds of alternative energy sources.

Some of the first things that-- I have a company based on this technology. They're looking, or are processing things like large area displays, where you wouldn't make the whole display biologically, but you would make certain components of it in a self-assembling way. So I think this technology has the possibility to reach many different kinds of applications.

We're talking to people about medical applications as well. But we're really-- we're interested in disruptive technology, so we're interested in trying to figure out ways that you may not be able to make a material any other way. How to make inexpensive large area self-assembling solar cells and such.

AUDIENCE: As a follow up question, I was just wondering, even though you might be developing this in one particular area, like in health or in energy, I could see it being translated into clothing, where you want to change your outfit, you just change the charge on your clothing and you've got a whole different display going on.


AUDIENCE: Or make it invisible, if you wanted to do something else.


ANGELA BELCHER: Well, we're--


ANGELA BELCHER: We're looking into textiles, but mostly for army applications. But also interesting to have paints that are reversible paints, as well, so you could change the color of your car.



AUDIENCE: With regard to glycosolation issues that we just heard about earlier, does measured immunoactivity, where you're actually just measuring levels of peptide, ever give you an exact correlation with bioactivity? If you're really with absolute peptide levels, if you're not necessarily-- for example, within endocrinology, TSH-producing tumors with relatively low levels of TSH, you get a tremendous stimulation of the thyroid, apparent