专业描述

The MIT Computational and Systems Biology Initiative (CSBi) is a campus-wide education and research program that links biologists, computer scientists and engineers in a multi-disciplinary approach to the systematic analysis of complex biological phenomena. CSBi places equal emphasis on computational and experimental methods and on molecular and systems views of biological function. Multi-investigator research in CSBi is supported through a sophisticated research infrastructure, the CSBi Technology Platform. CSBi includes about eighty faculty members from over ten academic units across MIT's Schools of Science and Engineering, the Sloan School of Management, and the Whitehead Institute for Biomedical Research. Research and Technology Goals OverviewThe overall goal of CSBi is to foster links among biology, engineering, and computer science and to create interdisciplinary, multi-investigator teams to undertake the systematic analysis of complex biological phenomena. CSBi places equal emphasis on computational and experimental research and on molecular and systems-level views of biological function. CSBi retains a fundamental commitment to an academic tradition placing graduate students and postdoctoral fellows at the forefront of scientific inquiry. At the same time, CSBi recognizes the increasing dependence of biological research on multidisciplinary teams and sophisticated technologies. One of CSBi's primary research objectives is the development of methods and devices that can measure, in a systematic and precise manner, the biochemical properties of biomolecules in cells, tissues, and whole organisms. We expect many of these measurement devices to incorporate novel technology and micro-fabricated components. A second CSBi objective is building mathematical models of biological systems that link mechanistic information on molecular function to systems-wide understanding of networks and interactions. Like models in mature fields of engineering, systems biology models will be able to capture empirical information as it accumulates and will have the ability to predict experimental outcomes. It is models, not databases, that represent the most effective way to store and propagate knowledge. Biological Complexity The chemical and physical processes in living cells are extremely complex. The great strength of molecular genetics, a research paradigm that has dominated life sciences for over 50 years, is its ability to unravel biological problems one gene (or protein) at a time. Paul Ehrlich noted, in his 1908 Nobel lecture, that this method "does not solve the secret of life itself, which may be compared with the complicated organism of a mechanical work of art, but nevertheless the possibility of taking out individual wheels and studying them exactly signifies an advance compared with the old method of breaking into pieces the whole work and then trying to deduce something from the mixture of broken pieces." It is becoming increasingly clear, however, that component-by-component analysis will not suffice in the study of signal transduction, oncogenic transformation, neurobiology, and other processes in which many genes interact. Biological systems are characterized by distinct types of complexity that define a multi-dimensional landscape. On one axis, the complexity of the system increases as the number of molecular species under investigation rises from one to a complete genome's worth. On a second axis, mechanistic complexity increases as the type of data changes from sequence to structure, to subcellular localization, and then to time-dependent changes in protein activity in cells. On a third axis, the complexity of the biology increases from cells to tissues, to organisms, and then to populations. The early phases of systems biology have been dominated by an emphasis on studying ever more genes in simple settings and using simple types of data. This focus has been necessary because a trade-off exists between greater complexity and our ability to extract meaningful insight. A major goal of systems biology research is to develop tools to tackle multiple sources of biological complexity at the same time in an effective and rigorous fashion.