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modeling (1473) systems biology (81) translational medicine (21) whole-cell (3) jonathan karr (1)
Biology is complex. Even the simplest organisms such as M. genitalium are amazingly complex, containing hundreds of genes and an untold number of molecular interactions. Humans beings are unimaginably complex. Humans have all of the complexity of individual cells as well as the complexity of tissues, organs, and entire organisms.
Despite decades of experimental and computational research, we still don't have an integrated understanding of how phenotypes emerge from the level of individual molecules. Novel computational techniques which integrate heterogeneous data and mathematics are desperately needed to tackle the overwhelming complexity of biology.
Our goal is to understand and reverse engineer the complexity of biology to enable (1) personalized and predictive medicine and (2) rational bioengineering. As a stepping stone in this direction, we've recently focused on two specific questions: How are complex cellular behaviors such as growth controlled at the molecular level? Which enzymes exert the most control over growth? How fast can a species be engineered to grow? What gene expression distribution maximizes cellular growth? What are the minimal req
BpForms: a toolkit for concretely describing non-canonical DNA, RNA, and proteins
BcForms: a toolkit for concretely describing macromolecular complexes
ObjTables: Toolkit for creating and reusing high-quality spreadsheets
WholeCellViz :: Mycoplasma genitalium