Children's Hospital Informatics Program at Harvard-MIT
Division of Health Sciences and Technology
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Children's Hospital Informatics Program at Harvard-MIT Division of Health Sciences and Technology |
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About ChIP | HMS |
MIT Health Sciences & Technology | Center for Brain Science |
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Biological systems are evolved complex systems. This has important implications for how we understand the organization of the parts that constitute all living organisms, from the genes that orchestrate cellular processes to tissues to the most complex system that we know of in the universe, the human brain. Research in our lab is driven by biological problems and is done in collaboration with experimental biologists. We use computational methods to analyze data and to try to understand how cellular function is organized. Our primary scientific focus is on how complex structure determines function: the molecular pathways that accomplish cellular functions and the organization of neurons that ulimately result in complicated behavior and thought. In our systems biology work, we seek to develop and use intelligent hybrid systems (IHS) algorithms for reasoning about biological dynamics and integrating data over scales from proteins and genes to clinical manifestations of disease. In neuroinformatics we are also applying methods from IHS to analysis exciting new data about neural morphology and connectivity. In rather curious recursive way, future progress in IHS methods depend on advances in our understanding of neural circuits and neural computation, while progress in neuroscience requires algorithms based on IHS for data processing and systems analysis. We are interested in pushing back the frontiers on both fronts. Neuroinformatics: Image Processing and Quantitative Neuroanatomy This is a developing collaboration with the Jeff Lichtman Lab. The goal is to use computational methods together with optical imaging and bioinformatics tools to understand how neural structure develops normally and how abnormal neural structure results in disease. 
Neurons are uniquely interesting among all cells because in large networks they form higher levels of organization that give rise to the brain. A major interest in neuroscience concerns how specific neural structures cause specific brain functions, or, from a clinical perspective, how abnormal structure results in disease. Exciting new developments in optical imaging and various forms of functional imaging create new opportunities for measuring neural morphology and network structure and correlating this with function. A key technology for making use of these new data sources will be computational algorithms for image processing and quantification of anatomical structure, from axons and dendritic arbors of single neurons to networks of neurons. Our interest is in developing needed computational methods to enable a deeper understanding of the fundamental principles that organize the brain. Current work involves processing of data from brainbow mice. Computational and Systems Biology The exit from mitosis is the last critical decision a cell has to make during a division cycle. A complex regulatory system has evolved to evaluate the success of mitotic events and control this decision. Whereas outstanding genetic work in yeast has led to rapid discovery of a large number of interacting genes involved in the control of mitotic exit, it has also become increasingly difficult to comprehend the logic and mechanistic features embedded in the complex molecular network. We attempt to examine these design features from the perspective of evolutionary design and complex system engineering using computer modeling, lab experimentation, and in vivo imaging of yeast. This work is being done in close collaboration with Rong Li and her lab at the Stowers Institute for Medical Research (Rong Li Lab).  Evolutionary Design of Complex Systems Evolutionary mechanisms, whether in nature or in computer algorithms, are powerful optimizers. Systems that result from evolutionary mechanisms tend to be quite different from the kinds of systems that humans design using standard engineering approaches. We would like to understand evolutionary mechanisms better through computational evolution of biological system models. This work is currently being developed in conjunction with our work in cell cycle regulation with the Rong Li Lab. |
