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School of Science
Research in the School of Science spans the space from string theory to cognitive science. The students and faculty of the School of Science carry out their research within six departments (Biology, Brain and Cognitive Sciences, Chemistry, EAPS [Earth, Atmospheric, and Planetary Sciences], Mathematics, and Physics), and a number of Laboratories and Centers (Center for Cancer Research, Center for Learning and Memory, Center for Space Research, Laboratory for Nuclear Science, Spectroscopy Laboratory). Much of this is interdisciplinary, including many collaborations with faculty in the departments in the School of Engineering.
Instead of giving a general overview of the research in the School, I would like to highlight the research of our younger faculty, who have recently begun their academic careers here at MIT, and who are the future stars of their disciplines. Unfortunately I cannot do justice to the research of all the young faculty, so I have chosen a small, representative group to highlight. I apologize to the others, any one of whom could have been chosen.
Angelika Amon, in the Biology Department, has been studying mitosis, an essential step in chromosome duplication and segregation. Early in mitosis, each chromosome pairs with its replicated sister chromosome and then becomes attached to one or other pole of the mitotic spindle. As mitosis proceeds, the connections between sister chromosomes are severed and motor proteins pull one chromosome of each pair to opposite ends of the cell. The final stage ensures that each daughter cell receives one copy of each chromosome. If cell division occurs before the chromosomes divide, diseased cells can result. How does the cell machinery prevent this from happening? Amon discovered that the interaction of two proteins, one bound to the spindle-pole body and the other localized in the daughter cell, were required to activate the last step of mitosis. This mechanism prevents cell division from occurring until nuclear segregation is complete.
Using computational methods, Chris Burge and his group in the Biology Department have successfully predicted the function of molecular sequences in messenger RNA (mRNA). Messenger RNA molecules typically contain strings of genetic material called exons, which code for proteins, and introns, which do not. Introns, like film outtakes, are removed from mRNA by a splicing mechanism that joins exons together. Surprisingly, the exons make up only a small percent of the genetic material in human cells. RNA splicing determines which segments in the lengthy stream of genetic material that makes up a gene end up being expressed and which do not. The new computational method can predict which sequences of genetic material get spliced out and which end up as the blueprint for life. They have found a way to predict which mutations in a gene's exons are likely to cause the exons to be skipped by the splicing machinery. Skipping of exons typically results in inactivation of the gene's product, which can lead to disease.
David Mohrig's research, in EAPS, focuses on elucidating the geomorphic and hydrological processes involved in the evolution of terrestrial and submarine landscapes over 10,000 years or more. His approach involves integration of information from field studies of modern and ancient sedimentary systems, three-dimensional seismic surveys of subsurface structures, laboratory experiments on sediment-transporting flows, and numerical studies. His research leads to an understanding of how the processes governing tectonics and mass transport have changed the earth over geologic time. It has great significance to oil and gas exploration because some of these structures serve as large reservoirs for hydrocarbons.
Kate Scholberg, a young physicist, is working in two large international collaborations, one (AMS) to place a particle detector in the international space station and the other (K2K) studying whether neutrinos have mass. These particles were assumed to have zero mass, but recent experiments and theories have suggested that they may indeed have mass. There are three kinds of neutrinos and the experiment measures the oscillation among these three by sending a beam of neutrinos a distance of 250 km through the earth from Tsukuba, Japan to the Super K detector. The discovery of non-zero neutrino mass is perhaps the most exciting discovery in particle physics in the past several years.
In the Chemistry Department, Peter Seeberger and his students have perfected a way to synthesize complex oligosaccharides, i.e., sugars, and to automate the process. Seeberger created an oligosaccharide synthesizer, which cuts the time required to produce extremely complex carbohydrate molecules by a factor of 100. The device has opened the door to a flood of potential applications for new research and disease treatments. Recently, he and his students used this approach to prepare a complex oligosaccharide that is structurally similar to the toxic carbohydrate found in the single-celled parasites that cause malaria. Injection of this synthetic toxin elicited an immune response in mice, making it an excellent candidate for clinical evaluation in humans. Application to related problems in human health, such as West Nile virus, can be envisioned.
Pawan Sinha, in the Department of Brain and Cognitive Sciences, is investigating how the brain accomplishes its remarkable feats of recognition, such as identifying distant faces. Besides being a fundamental challenge in neuroscience, this question is of great practical significance for creating artificial systems that can interpret their environment. Pawan is addressing two basic problems: What is the nature of object representations, and how are these representations learned? Using highly degraded inputs, Pawan and his students have determined some of the critical pieces of information that subserve recognition. Based on these results, they are formulating computational models that mimic human performance. To explore object learning, Pawan is studying children in India who have recovered sight several years after being born blind. This reveals how a brain that has just been provided access to visual information, begins to create representations for the task of recognition. This unique project promises to provide fundamental insights about brain plasticity and learning.
Dan Spielman is a young mathematician who studies theoretical computer science. Recently, he introduced smoothed analysis, a new framework in which to analyze algorithms, and demonstrated that the good practical performance of the simplex method, which has been effectively used since the 1950s to solve optimization problems in numerous industrial applications, could be understood in this framework. In smoothed analysis, one analyzes the performance of an algorithm assuming there is slight imprecision in its input. This assumption is reasonable in many real-world applications in which data is derived from experimental measurements. While an algorithm with a good worst-case analysis will perform well on all inputs, an algorithm with a good smoothed analysis will perform well on almost all inputs in every small neighborhood of inputs. One surprising corollary of this work is that experimental error in the data input to an algorithm can actually improve an algorithm's performance.
These and the other young faculty who have begun their careers at MIT in the past few years, are changing the way we think about science. They are the future and make MIT the exciting intellectual community it is.
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