||Kim Lab of Computational Evolutionary Biology|
|Public Private Project1 Project2 Project3 Project4 Archive|
School of Arts and Sciences
University of Pennsylvania
A key property of living objects is that each object, whether they are proteins, cells, or whole organisms, has an associated generating process, that is, a decoding process whereby stored information is converted into a complex functioning biological object. For example, generating a protein involves translation and folding; generating an organism involves a cascade of gene regulatory and cell biological processes. We are interested in such bio-generative processes and understanding the temporal control and architectural constraints of these processes.
Questions include how to infer the organizational structure of such generative processes from available data, the evolution of control processes, and how the relationship between generative dynamics, variability, and the final form interact to determine the evolution of the biological object. Two central projects in our lab are using comparative transcriptome profiling of single cells to understand individual cellular variation and interactions in a multicellular organisms and using computational analysis of non-coding RNAs to understand the evolution of sub-cellular processes in neurons.
Since 2007, Jim Eberwine (Pharm) and I have been engaged in multiple joint projects concerning genomics of cell differentiation and cell diversity. Our labs collaborate in all kinds of projects where we bounce ideas off each other, design and carryout experiments together, and design analysis of data together. Many of the projects described below, especially in neuroscience are joint projects between our two labs.
In addition to these theoretical problems, we work on a wide range of collaborative projects and computational biology projects. Currently, these collaborations involve molecular control of neurons, functional prediction of sequence elements for genes involved in synaptic transmission, novel technologies for functional genomics, statistical analysis of whole-genome expression profiling, as well as software engineering bioinformatics analysis platforms. We employ a variety of techniques including discrete algorithms,simulations, statistical learning, dynamical systems and algebraic geometry, molecular biology, functional genomics, and single-cell genomics.
© 2004, J. Kim
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