After receiving Killian Award, Richard Schrock reflects on a life in chemistry

F.G. Keyes Professor Richard Royce Schrock holds many titles, not the least of which is Nobel laureate. An organometallic chemistry pioneer, Schrock received the Nobel for his contributions to the development of the olefin metathesis reaction (now used for the efficient and more environmentally friendly production of important pharmaceuticals, fuels, and other products) on Oct. 5, 2005. That momentous afternoon, a crowd packed into Huntington Hall to witness Schrock deliver his Nobel lecture. “It was an impromptu talk,” Schrock recalls. “No slides, and no preparation.” This year, on Feb. 15, another crowd will congregate in the very same room, this time to watch Schrock give the 2017–2018 Killian Lecture, “Adventures in Organic Chemistry and Catalysis.”

Schrock was named the winner of the 2017–2018 James R. Killian Jr. Faculty Achievement Award this past May. In terms of the honor and excitement that came with receiving the award, “The Killian Prize is equal to [the Nobel],” says Schrock. The Killian Prize was established in the spring of 1971 as a permanent tribute to James R. Killian Jr., former MIT president (1948–1959) and chairman of the Corporation (1959–1971). The award is given in recognition of extraordinary professional achievement by MIT faculty members and aims to communicate their accomplishments to members of the Institute community. The Department of Chemistry is proud to have had several of its faculty members receive this honor since the award’s inception: Stephen J. Lippard, JoAnne Stubbe, George H. Büchi, John S. Waugh, and Alexander Rich were all Killian Lecturers. 

Before being named a Killian Lecturer or a Nobel laureate, and even before a type of metal carbene was named in his honor, an 8-year-old Schrock was influenced by an older brother, five years his senior. “My brother Ted was very good in chemistry; he went on to be a surgeon,” says Schrock. “For some reason, I guess because he loved chemistry, he gave me a chemistry set for Christmas, and there was pretty good stuff in it.” Schrock acquired some of his brother’s old high school chemistry books, and went ahead to procure the chemicals required to conduct some of his first experiments — sweet-smelling esters such as ethyl acetate.

A watershed in chemistry

Schrock received his PhD from Harvard University in 1971. Following a fellowship at Cambridge University, he found himself working in the Central Research and Development Department of DuPont. It was there that Schrock experienced a watershed moment in his life.

“There’s a notebook page from when I was at DuPont — dated July 27, 1973,” Schrock recalls. “It was witnessed by someone in my lab, and I signed it on August 7, 1973, about 10 days later. It must have been that day when I went home and told my wife, ‘I think I’ve done something important.’”

Schrock had discovered a double bond compound, which had never been suspected — the reaction had never been observed. The compound was well-behaved and one of a kind. The discovery of this compound ultimately led Schrock to become the first to elucidate the structure and mechanism of so-called black box olefin metathesis catalysts.

The future of chemistry

As for the future — Schrock believes we continue to push back the frontiers.

“Everything is chemistry,” says Schrock. “We can explain so much if we understand the basics and the applications of chemistry. Whether it’s life sciences, different kinds of polymers, electronic conducting materials, organic conducting materials — all the things that are being done now in the chemical industry and beyond, really rely on chemistry.” Schrock believes in the limitless potential of discovery, and that the answers and cures to the currently incurable conundrums exist. It’s up to researchers to have that breakthrough moment, he says.

“Some people say, ‘Oh, everything’s been discovered,’” Schrock muses. “But people said that at the end of the 19th century, before they even knew what an atom was.”

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Eric Schmidt to join MIT as visiting innovation fellow

Today, MIT President L. Rafael Reif announced that Eric Schmidt, who until January was the executive chairman of Google’s parent company, Alphabet, will join MIT as a visiting innovation fellow for one year, starting in Spring.

Schmidt will figure prominently in MIT’s plans to bring human and machine intelligence to the next level, serving as an advisor to the newly launched MIT Intelligence Quest, an Institute-wide initiative to pursue hard problems on the horizon of intelligence research.

“I am thrilled that Dr. Schmidt will be joining us,” says MIT President L. Rafael Reif. “As MIT IQ seeks to shape transformative new technologies to serve society, Eric’s brilliant strategic and tactical insight, organizational creativity, and exceptional technical judgment will be a tremendous asset. And for our students, his experience in driving some of the most important innovations of our time will serve as an example and an inspiration.”

In his role as a visiting innovation fellow, Schmidt will work directly with MIT scholars to explore the complex processes involved in taking innovation beyond invention to address urgent global problems. In addition, Schmidt will engage with the MIT community through events, lectures, and individual sessions with student entrepreneurs.

“We are privileged to have Eric at MIT,” says MIT Provost Martin Schmidt. “The Institute aims to unlock innovations that today’s entrepreneurs have not even begun to dream about. Eric can help us move those efforts forward: his experience helping turn Google into the company it is today has given him insight of extraordinary power and value.”

A global influencer

While Schmidt was CEO of Google from 2001 to 2011, the company developed a reputation for innovation. It released products and services such as Google News, Blogger, Google Books, Gmail, Google Earth, and Google Maps. It also acquired YouTube and launched the Chrome web browser and the Android mobile operating system.

In 2011, Schmidt became Google’s executive chairman. He grew relationships with businesses, universities, and governments; Forbes described him as “Google’s global ambassador.” In 2015, Schmidt was appointed executive chairman of Alphabet. He resigned from that role last month but will remain an Alphabet board member and technical advisor.

Schmidt studied electrical engineering at Princeton before earning advanced degrees in computer science from the University of California, Berkeley. He honed his software skills at the legendary Xerox Palo Alto Research Center in California. As chief technology officer at Sun Microsystems, Schmidt helped develop the Java programming language before becoming CEO of Novell, and, finally, Google.

In a survey released in January by US News & World Report, Schmidt was ranked the most respected CEO among world business leaders. He remains vocal on the world stage; recently, he has advocated for increased government and private-industry investment in fundamental research.

“I have spent much of my career building corporations that could scale. AI represents the next great scalability platform,” says Schmidt. “Think of a platform of knowledge about everything. If you can’t use that to make the world a better place, you’re not paying attention.”

Tapping into the intelligence of MIT students, Schmidt says, will be a priority during his appointment. During a campus visit last spring, he advised students to find ways to pioneer innovations in emerging spaces in AI. “I want you to run at the next challenges,” he said. “I want you to figure out new algorithms, new technical approaches, and new hardware architecture.” He compared his early days at Xerox PARC with the atmosphere at MIT today. “When you get these collections of people in the same place with enough funding, they can make incredible things happen.”

MIT Dean of Engineering Anantha Chandrakasan looks forward to Schmidt’s arrival. “The Intelligence Quest will depend for its success on its ability to bring together extraordinary talent from academia and industry, and to energize it. We are very excited to have Eric help up as we define and drive new directions.”

The most recent visiting innovation fellow was former US Secretary of Defense Ash Carter. Other previous fellows have included Ethernet co-inventor and 3Com co-founder Bob Metcalfe ’68 and Former Massachusetts Governor Deval Patrick.

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When numbers started counting

Odds are, you’ve tried to win arguments by citing statistics. Who has been the greater player, LeBron James or Michael Jordan? Which health care policy is right? Where are the best schools? Which city has the worst morning traffic? If you can find the numbers, then maybe — maybe — you can resolve these matters.

But have you ever wondered: When did people start using numbers in politics or other public debates, anyway? Did the Egyptians have quantitative arguments about pyramid policy? Or is it a very recent phenomenon, due to the spread of data and electronic communications?

In a new book, William Deringer, an assistant professor at MIT, offers an answer: In the English-speaking world, people started using numbers in political debates in Britain around 1688, and the practice took firm hold over the next few decades.

Why then? England had just concluded its “Glorious Revolution,” in which William and Mary usurped the throne, deposing James II, while Parliament gained a stronger hold on state affairs. That rise of parliamentary power, along with polarized political parties and the growth of the press, contributed to a public culture of debate and dispute — one in which numbers increasingly became a form of ammunition.

“It was part of a larger phenomenon,” says Deringer, who is the Leo Marx Career Development Assistant Professor of Science, Technology, and Society. “Issue after issue, you had two sides arguing intensely. This turned out to be a political context in which numbers functioned really well.”

Moreover, by 1720, when the infamous episode of global financial speculation known as the South Sea Bubble reached a crisis point, quantitative arguments became even more embedded in civic life, given the junction of politics and economics. Really, the advent of numerical arguments in politics dates to the whole period from 1688 to at least 1720, and even a bit beyond that.

As Deringer suggests in the book, the influence of this change has been immense. The practices of British political culture thoroughly informed American colonial politics and in a sense created the means for quantitative reasoning to gain authority in the modern U.S. state.

“The developments of the 17th and 18th centuries created cultural conditions that continue to influence us today,” Deringer says.

Deringer’s book, “Calculated Values: Finance, Politics, and the Quantitative Age,” is being published this week by Harvard University Press.

Fiscal duty and free speech

To be clear, Deringer’s historical claim is not that numbers or mathematics were wholly ignored in civic life before the late 17th century. From the ancient Greeks who discoursed upon the moral value of mathematics, to late-Medieval Venetians who used double-entry bookkeeping to change commerce, mathematics mattered in many ways. The English themselves compiled the famous Domesday Book around 1086 to keep track of land and income.

What Deringer is tracing, however, is a new era in which “fighting with numbers,” as he writes in the book, became “a regular part” of politics. In the modern world, we look to statistics to help resolve public issues and give quantitative evidence considerable weight.

This new practice in politics, Deringer believes, stems crucially from the expansion of parliamentary powers in Britain, in the years after 1688. Those powers, in a series of parliamentary acts, limited the monarch’s ability to control courts and elections, ensured the right of free speech in Parliament, and, significantly, included the “financial settlement” in which the monarch had to keep reapplying to Parliament for state funds.

In a short time, then, Parliament became increasingly active in controlling Britain’s purse strings, and it tolerated increasingly vocal debate on the subject — conditions in which statistics gained authority.

“People were using calculations as a way of making criticism,” Deringer says. The polarized politics, with Tories and Whigs at odds, and the growing press meant that this was “an environment remarkably hospitable to numerical calculation as a mode of thinking and arguing.”

Indeed, a fair amount of our language for such things dates to this time period; the phrase “facts and figures” is first found in 1727, for instance.

Critics with a cause — and calculations

Deringer’s findings also cut against the grain of theoretical work that regards the state as an overwhelming source of repressive power. In contrast to this notion, the emergence of statistics in British politics did not help the state subjugate anyone. It helped both sides in politics make claims, and actually helped outsiders and antiestablishment critics gain credibility for their assertions.  

“One of the things I found fascinating about this period is that [state power] could not have been the only explanation” for the advent of statistics in politics, Deringer says. “The state was not as functional as it could have been. The state didn’t know what the public thought it should know.”

That applies to the debates about the South Sea Bubble as well, he observes. In the book, Deringer chronicles the public saga of one Archibald Hutcheson, a critic of the South Sea Company, who felt its stock was overinflated and engaged in quantitative financial detective work to prove his point.

“The people who were doing the most intensive calculations about the bubble were consistently people who were critical of this scheme,” Deringer says. The collapse of the South Sea Bubble, he writes, “was probably the greatest political triumph for calculation in the entire 18th century.”

Of course, simply wielding numbers is no guarantee of winning a political debate, something apparent in contemporary times as well. Sometimes entrenched interests override solid numerical reasoning; many other times, statistics depend on debatable assumptions or yield results open to multiple interpretations.

In many cases, Deringer says, “Calculations can be really flexible. There’s a lot of give in some numbers. If you change a couple of assumptions, there will be a very different conclusion.”

The use of numbers in politics, he thinks, also creates a heightened skepticism of quantitative claims — which can be a good thing if it creates more critical thinking and sharpens our analysis of complicated issues. Having numbers on hand can be good; asking questions about them can be better. And that has remained constant, from 1688 through the present day.

“I think these things go together in a two-sided relationship,” Deringer says. “There is something healthy about that.”

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A deep dive into the effects of trade

Take a moment to consider what the following facts have in common. In India, migrants from within the country continue to consume the favored foods of their home regions — even though the relatively high prices mean they eat less. In Mexico, more 9th graders drop out of school when a local manufacturing plant opens to export goods. And in Egypt, rug weavers earn 20 percent more in profits when orders come from foreign buyers. Three countries, three quite different issues. What are the links?

First, these circumstances have all been created, one way or another, by economic trade. And second, these facts were all uncovered by MIT economist David Atkin, a scholar whose empirical, tightly focused studies of trade and development have helped experts better understand trade’s effects.

With this work, Atkin plays a valuable role in the world of trade policy. Global trade agreements have powerful backers and staunch opponents. What the subject could use, really, is a few more observers who simply study its effects in close detail. Furthermore, in academia, trade scholarship has often been heavy on theory and large-scale metrics, and much lighter on localized studies that tell us something about daily life.

“I focus on situations where we can learn something we can be more sure about,” Atkin says. “Typically that means focusing on more specific cases.” Or, as he adds, his work explores “settings where you can really dive in deep” in order to “pin down causal relationships” in society.

The hidden impact of trade policy

Take our three facts. As Atkin documented in meticulous studies, trade liberalization among regions within India may not quite have the textbook results policymakers might anticipate. Because people have persistent food tastes and prefer the cuisines of their home regions, migrants pay more for imported home-area foods. (Atkin has found they endure a “caloric tax” of 5 to 7 percent of their daily intake.) Therefore the potential economic gains people might realize from the increased trade of food will be limited due to the powerful cultural persistence of dietary habits.

Meanwhile, in Mexico, Atkin found, for every 25 new export manufacturing jobs in a given location, one 9th grader dropped out of school. In so doing, the children were opting to earn money in the factory in the short run but not helping their earnings over the long term. That’s a questionable trade-off — and a trade issue, too. The North American Free Trade Agreement (NAFTA) opened up Mexico to more export manufacturing (which often requires less education), but having a less-educated work force was an unintended consequence.

The weavers in Egypt who earned more for their goods participated in an experiment set up by Atkin and some colleagues, which showed that their productivity and profits surged “due to information about specific weaving techniques and quality standards that flowed from overseas buyers to firms,” as he has written. Global trade, in this case, provided technical information that helped manufacturers in poorer settings.

“Understanding the impact trade reforms might have on nutrition or on educational choices will hopefully help policymakers incorporate those concerns into their discussions, and help put policies in place that will ameliorate those concerns,” Atkin says.

Getting on the train

Atkin, who grew up in London, claims he has “no great origin story” concerning his interest in economics and trade, but says it was present from his school days onward and enhanced by some of his travels abroad while growing up.

“I was always extremely interested in why things were different in different countries, why poor countries were poor, why rich countries were rich, and how markets or institutions worked differently wherever I would go,” Atkin says.

Atkin attended Cambridge University, where he studied economics at St. John’s College, earning his degree in 2002. “I did economics as an undergraduate and didn’t get off the train,” Atkin says.

Atkin had something of a whistle stop at the London School of Economics, where he earned a master’s degree in a year; he then had a longer layover at Princeton University, where he received his PhD in 2009. He held junior faculty positions at Yale University and then the University of California at Los Angeles before joining MIT in 2015. Atkin was granted tenure at MIT in 2017.

As Atkin’s career has progressed, he has also constructed a variety of studies slightly more focused on development issues in poorer countries, apart from trade. For instance: About 40 percent of the world’s soccer balls are manufactured in Sialkot, a city in Pakistan. To see if the industry could be made more productive, Atkin and some colleagues actually created an improved, faster technology for soccer ball manufacture — only to watch as just a small fraction of the city’s firms adopted it.

Why was it not adopted more widely? Because of how the workers were paid, the researchers realized. One firm that quickly adopted the new technology paid workers by the hour, but most of the rest paid workers by the ball — and the workers did not want to take the time to learn the new procedure, which would have slowed down their production rates.

The junction of trade and development

Whether studying trade, development, or the combination of the two, Atkin says he is most comfortable examining how poorer parts of developing countries can be affected by new economic activity.

“There [is] a lot of important work that needs to be done on the intersection between trade and development, thinking about trade from the perspective of developing countries,” Atkin says. “I’ve thought that is where I should devote my energies.”

At MIT, he has a lot of intellectual company in these areas. The Institute has become a renowned center for development economics and is the home of the Abdul Latif Jameel Poverty Action Lab (J-PAL), co-founded in part by MIT professors Esther Duflo and Abhijit Banerjee.

In addition to being affiliated with J-PAL, Atkin is part of a burgeoning group of trade scholars in the MIT Department of Economics. That includes Arnaud Costinot, an MIT economist since 2008, and David Donaldson, a co-author of papers with Atkin who also works on trade and development issues and is rejoining the MIT faculty. (MIT labor economist David Autor, who has studied the effects of trade agreements on U.S. jobs, is also among current Institute faculty who has closely researched trade issues.)

Atkin says he hopes that with its growing critical mass of trade experts, MIT will again become recognized as a key hub of research and teaching on the interaction of trade and development. 

“It’s wonderful, with all of the expertise MIT has in development, to be able to marry that with a very strong trade group with Arnaud and Dave,” Atkin says. “It’s just a wonderful place to be thinking about these issues.”

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Automating materials design

For decades, materials scientists have taken inspiration from the natural world. They’ll identify a biological material that has some desirable trait — such as the toughness of bones or conch shells — and reverse-engineer it. Then, once they’ve determined the material’s “microstructure,” they’ll try to approximate it in human-made materials.

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory have developed a new system that puts the design of microstructures on a much more secure empirical footing. With their system, designers numerically specify the properties they want their materials to have, and the system generates a microstructure that matches the specification.

The researchers have reported their results in Science Advances. In their paper, they describe using the system to produce microstructures with optimal trade-offs between three different mechanical properties. But according to associate professor of electrical engineering and computer science Wojciech Matusik, whose group developed the new system, the researchers’ approach could be adapted to any combination of properties.

“We did it for relatively simple mechanical properties, but you can apply it to more complex mechanical properties, or you could apply it to combinations of thermal, mechanical, optical, and electromagnetic properties,” Matusik says. “Basically, this is a completely automated process for discovering optimal structure families for metamaterials.”

Joining Matusik on the paper are first author Desai Chen, a graduate student in electrical engineering and computer science; and Mélina Skouras and Bo Zhu, both postdocs in Matusik’s group.

Finding the formula

The new work builds on research reported last summer, in which the same quartet of researchers generated computer models of microstructures and used simulation software to score them according to measurements of three or four mechanical properties. Each score defines a point in a three- or four-dimensional space, and through a combination of sampling and local exploration, the researchers constructed a cloud of points, each of which corresponded to a specific microstructure.

Once the cloud was dense enough, the researchers computed a bounding surface that contained it. Points near the surface represented optimal trade-offs between the mechanical properties; for those points, it was impossible to increase the score on one property without lowering the score on another.

That’s where the new paper picks up. First, the researchers used some standard measures to evaluate the geometric similarities of the microstructures corresponding to the points along the boundaries. On the basis of those measures, the researchers’ software clusters together microstructures with similar geometries.

For every cluster, the software extracts a “skeleton” — a rudimentary shape that all the microstructures share. Then it tries to reproduce each of the microstructures by making fine adjustments to the skeleton and constructing boxes around each of its segments. Both of these operations — modifying the skeleton and determining the size, locations, and orientations of the boxes — are controlled by a manageable number of variables. Essentially, the researchers’ system deduces a mathematical formula for reconstructing each of the microstructures in a cluster.

Next, the researchers use machine-learning techniques to determine correlations between specific values for the variables in the formulae and the measured properties of the resulting microstructures. This gives the system a rigorous way to translate back and forth between microstructures and their properties.

On automatic

Every step in this process, Matusik emphasizes, is completely automated, including the measurement of similarities, the clustering, the skeleton extraction, the formula derivation, and the correlation of geometries and properties. As such, the approach would apply as well to any collection of microstructures evaluated according to any criteria.

By the same token, Matusik explains, the MIT researchers’ system could be used in conjunction with existing approaches to materials design. Besides taking inspiration from biological materials, he says, researchers will also attempt to design microstructures by hand. But either approach could be used as the starting point for the sort of principled exploration of design possibilities that the researchers’ system affords.

“You can throw this into the bucket for your sampler,” Matusik says. “So we guarantee that we are at least as good as anything else that has been done before.”

In the new paper, the researchers do report one aspect of their analysis that was not automated: the identification of the physical mechanisms that determine the microstructures’ properties. Once they had the skeletons of several different families of microstructures, they could determine how those skeletons would respond to physical forces applied at different angles and locations.

But even this analysis is subject to automation, Chen says. The simulation software that determines the microstructures’ properties can also identify the structural elements that deform most under physical pressure, a good indication that they play an important functional role.

The work was supported by the U.S. Defense Advanced Research Projects Agency’s Simplifying Complexity in Scientific Discovery program.

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Understanding and treating disease

In 2006, a discovery opened up a new world of possibility for treating diseases. For the first time, researchers created stem cells without using embryos. Adult skin cells were reprogrammed into induced pluripotent stem cells, or iPSCs, that could differentiate into specialized cells for use in almost any part of the body — from the liver to the heart or brain, and everywhere in between. Areas of the body damaged by disease could be made healthy again.

But after more than a decade of research on iPSCs, the process of creating them is still incredibly inefficient. “We have been puzzled that after 10 years of intense research in that direction, the efficiency of iPSC reprogramming is still only about 0.1 percent,” says Associate Professor Domitilla Del Vecchio. “It’s not really at the point that you can use it for clinical purposes.”

Del Vecchio and her colleagues are hoping to change that. Currently, researchers develop iPSCs by delivering synthetic DNA to the nucleus of a somatic cell, such as a skin cell. This synthetic DNA produces high levels of select proteins — known as transcription factors — with the aim of “pushing” the somatic cell to reprogram into a stem cell. But overloading a cell with such a high level of transcription factors leads to a highly inefficient process. “If you have a mechanical system, such as a car or a robotic manipulator, and you give it an arbitrary push, you should not expect that the system will end up exactly in the configuration you want,” says Del Vecchio.

To fix this problem, Del Vecchio and her team are adding accelerators and brakes to the process. Using mathematical analysis, they can demonstrate that with an appropriate balance the pluripotent stem cell state can be reached. With the help of small molecules, the synthetic DNA delivered via a virus can produce tunable levels of transcription factors based on a target configuration. This method — called a synthetic genetic feedback controller — can be used to steer the concentration of transcription factors in the cell to the point at which it can become a stem cell.

The applications of Del Vecchio’s work may have far-reaching implications for the way diseases are treated. Researchers could quickly create healthy heart cells for patients with a heart condition or beta cells for diabetic patients. 

Del Vecchio’s eyes light up when discussing the possibilities. “It’s clearly high risk,” she admits. “But we want to proceed in this direction because if it works, it will be highly impactful for society and hence extremely rewarding for us.”

Giving doctors the ability to quickly create stem cells could change the way many diseases are treated. The research Del Vecchio’s team is conducting represents just one example of how mechanical engineering researchers across a diverse range of specialties are developing new and innovative ways to deepen our understanding of disease and unlock new therapies to treat it. 

Diagnosing disease

Understanding disease starts with the cell. Assistant Professor Ming Guo, who serves as the d’Arbeloff Career Development Professor, is interested in decoding the differences between the mechanical properties of a healthy cell and a diseased cell as a way to develop a diagnostic tool. “Just by looking at a healthy cell compared to a diseased cell, you can tell that they’re different mechanically,” says Guo.

Stiffness in particular is a key trait in distinguishing what kind of disease a cell has — for example cancer cells have been shown to be soft while asthma cells are often stiff. Currently, this information is probed by contact-only methods such as atomic force microscopes or optical tweezers, which use either a mechanical tip or a focused laser beam on a patient’s tissue to measure cell properties. Guo and his colleagues have now developed a safer, less invasive method of analyzing the mechanics of a cell to help formulate a diagnosis by simply taking a small sample of cells and watching them using a standard optical microscope.

“We came up with the method by simply observing the movement of organelles in the cell,” says Guo. The team took videos of cells under a microscope. They tracked the movements of individual organelles or particles within the cell at frequencies of 10 frames per second and higher. Then, by plugging the value of these movements into a generalized form of the Stokes-Einstein equation, they were able to calculate the exact stiffness of a cell. 

“We found that high frequency fluctuation can help us gauge cell stiffness and understand its mechanics,” says Guo. Understanding these mechanical properties can help doctors diagnose diseases on the spot. Guo has begun collaborating with doctors at Massachusetts General Hospital on applying this method to cancer and asthma cells. The hope is that doctors and researchers can test drug efficacy by measuring a cell’s mechanics before and after treatment.

Tracking disease

While improving diagnostic methods could help catch a disease early, tracking how diseases grow could be key to developing new therapeutic interventions. Roger Kamm, the Cecil and Ida Green Distinguished Professor, and his lab use a device that’s roughly the size of a quarter to track tumor cells as they leave the vascular network and eventually grow into tumors. These tiny microfluidic devices can help us understand how cancer metastasizes.

“We’ve developed a 3-D vascularized network in which we can track cancer cells inside a capillary,” says Kamm. “It’s about understanding how cell populations interact. We can watch the tumor cells escape from the vessel to invade the surrounding tissue.” 

The microfluidic device consists of two media channels on either side with 3-D hydrogel in the center. The gel is seeded with endothelial cells that form capillaries where the tumor cells are introduced. From there, Kamm and his team use microscopic imaging to watch every single movement the tumor cell makes. From intravasation — when a cancer cell enters the bloodstream — to extravasation — when a tumor cell leaves the blood stream and becomes metastatic — the cell’s path is studied with painstaking precision.

“Using microfluidics, we can follow this process over time,” explains Kamm. “After the cell enters the metastatic organ, we can see how a single tumor cell starts to multiply over days and how it begins to form a metastatic tumor.”

Microfluidic devices allow Kamm to analyze the forces that inform the cancer cell’s behavior. Understanding whether tumor cells push or pull when they leave the capillary and the force interactions between endothelial cells and tumor cells represent potential therapeutic targets for preventing or minimizing metastasis.

“We can look at all these different tumor cell lines, treat the different cells, and see how that affects the rate at which the tumor cells escape from the vasculature and grow,” says Kamm. This knowledge could open up new opportunities in treating cancer and even developing new immunotherapies.

Treating disease

Armed with more knowledge of how diseases grow and spread, researchers are better able to develop new ways to treat, and in some cases cure, disease. Among them is Assistant Professor Ellen Roche, who is taking a unique dual approach to treating heart disease using both mechanical and biological therapies.

“The idea is to mechanically assist the heart,” says Roche, who also serves as Helmholtz Career Development Professor at MIT’s Institute for Medical Engineering and Science. “Rather than take over its function we just assist and augment it using a biomimetic approach.”

Roche uses new techniques like soft robotics to develop devices that mimic both the tissue properties and the motion of the heart. One such device is a sleeve that wraps around the heart to assist with pumping. Soft robots like this sleeve use elastomeric materials and fluidic actuation to mimic an organ’s movement. “By smartly designing simple fluidics channels and reinforcing soft materials in just the right way, you can achieve very complex motion with just elastomeric changes, and pressurized air or water,” says Roche.

While working on mechanical therapies to treat things like congenital heart disease and heart attacks, Roche is also looking at how biological therapies can help in treating these diseases. She and her team are developing smart devices that provide localized drug delivery instead of systemic drug delivery.

“One of my main goals is to combine these mechanical and biological therapies and see how they interplay with each other,” says Roche. Understanding how these different therapies interact could help determine the best timing sequence for maximum efficacy. With the help of collaborators in the cardiac surgery group at Boston Children’s Hospital, Roche is creating and testing models for these therapies. “We really want to see if we can treat disease and recover function using polytherapy rather than just a mechanical or biological approach.”

Rehabilitation from disease

In instances when disease is not detected or treated in time, researchers are developing tools that assist in the recovery process. From optimizing the design of prosthetic feet or building cheaper wheelchairs, mechanical engineers are finding ways to improve the quality of life for those living with the aftermath of disease. This work also includes tools and devices that can be used in physical rehabilitation. One such tool is the MIT-MANUS — a robot developed by Professor Neville Hogan to help stroke victims recover and regain mobility.

“They say no two snowflakes are alike, well no two stroke patients are alike either,” says Hogan. “That makes the problem spectacularly complicated.” Hogan has collaborated with neurologists and neuroscientists on understanding the process of recovery and basic motor control in the brain. He used this knowledge to develop robots that interact with stroke patients and help them regain control of their movements.

MIT-MANUS was originally designed to help restore motor function in stroke patients’ shoulders and elbows. Patients strap their forearm into a brace attached to a robotic arm and grasp onto a controller connected to a video screen. On the screen, a video game provides patients with prompts to move their arm and wrist. If the patient is unable to fully move their arm on their own, MIT-MANUS provides guidance and assists their movements. The robot then tracks and stores this data for physical therapists and specialists to analyze.

“In clinical trials of MIT-MANUS we found that there was a reduction of impairment in joints exercised through use of the robot,” says Hogan. Over the years the scope of this robot-aided therapy for stroke victims has grown beyond hands and arms. Hogan and his collaborators have put together a robotic gym that helps deliver localized therapy to various limbs and joints throughout the body.

Whether it’s constructing large robots like the MIT-MANUS to help rehabilitate stroke victims, tracking the miniscule movements of organelles in the cell, or using genetic circuits to create stem cells, mechanical engineers are shaping both our fundamental understanding of disease and the way in which doctors approach treatments and therapies. 

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3 Questions: Transforming our electric power systems

The MIT Energy Initiative continues to develop and expand its eight Low-Carbon Energy Centers, which facilitate multidisciplinary collaboration among MIT researchers, industry, and government to advance research in technology areas critical to addressing climate change. Francis O’Sullivan and Christopher Knittel, co-directors of the Center for Electric Power Systems Research, collaborated on answers to three pressing questions about transforming the energy system.

Q: Why is research into electric power systems necessary to reduce carbon emissions worldwide?

A: Fueling global economic development and powering the lives of billions who lack access to modern energy sources will require a dramatic expansion of the world’s electricity system. At the same time, efforts to mitigate global climate change depend on making drastic carbon dioxide emissions cuts — moving even larger portions of the transportation, heating and cooling, and industrial sectors away from fossil fuels and toward cleaner power sources that generate electricity.

Accommodating these changes will require the electric power sector to undergo an unprecedented transformation.

The sector is already highly complex, requiring the precise integration of hardware, operations, and market and regulatory structures. Going forward, the deployment of renewable and increasingly distributed energy resources such as wind, solar, storage, and demand response will challenge the planning and reliable operation of the system. Simultaneously, the much-expanded digitalization of power systems necessary to support a much more decentralized grid will result in increasing cyber risks.

Transforming the sector will require cross-disciplinary research spanning engineering, science, economics, and policy, as well as real-world input from stakeholders in industry, government, and nongovernmental organizations. This is the work of the MIT Energy Initiative’s Low-Carbon Energy Center for Electric Power Systems Research.

Q: How is the electric power systems center addressing these research challenges?

A: The center draws upon MIT’s extensive existing research capability in a broad range of relevant fields — from power system modeling to market and regulatory design, and from cyber security to power systems technology — to advance a system-level understanding of the power sector and the transformation it is undergoing.

The center develops new methodological approaches, in-depth policy evaluations, and advanced modeling and analysis tools to represent the complex and dynamic behaviors of power systems. The goal is to make justified, insightful assessments of how such systems will evolve over time and to determine how regulatory and policy innovations can facilitate the transformation to a decarbonized power sector.

Q: Can you provide an example of the kind of research currently under way at the center?

A: The Utility of the Future report, released in December 2016, is a great example of the in-depth research going on here. Developed over several years in collaboration with the Institute for Research in Technology at Comillas Pontifical University, the report provides a toolkit for businesses, policymakers, and regulators to navigate the unfolding changes in electric power systems and develop robust, efficient alternatives.

The study paired research in quantitative economic and engineering modeling with a sophisticated understanding of the complex interactions that characterize the electric power industry. The team included MIT faculty with decades of experience in advising governments, corporations, and institutions on regulation and market design. In addition, we tapped industry stakeholders and other market participants to contribute insights from their real-world experience.

The research revealed that in order to ensure that distributed and centralized energy resources are integrated efficiently, electric power systems in the United States, Europe, and other parts of the world will need major regulatory, policy, and market overhauls.

Going forward, the center will be analyzing potential policy and regulatory changes while also tackling many of the other impacts and opportunities likely to emerge from the greater decarbonization, decentralization, and digitization of the power system. These include the challenge of understanding how new and emerging technologies can be effectively integrated into existing power structures. Since wind and solar aren’t entering the system in the same way in Massachusetts as in New Delhi, for example, we are looking at the system’s evolution within a plethora of contexts.

We are also developing a variety of technical and economic modeling tools as well as new market theories to address the system’s extraordinary complexity. In sum, we are working to devise strategies that will enable cleaner, more reliable, and more cost-effective power system solutions in the future.

This article appeared in the Autumn 2017 issue of Energy Futures, the magazine of the MIT Energy Initiative.

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