(89c) Computational Challenges in Systems Biology

There are two paradigms in computational systems biology: the iterative cycle of biochemical model—mathematical model—computational model, and integration of novel data and legacy knowledge to develop context-specific biochemical, mathematical, and computational models. In this talk, I will address the challenges associated with these tasks. In the first part, I will provide a perspective on the development of contextual models from large scale measurements of genes, proteins and metabolites. These include statistical methods for network reconstruction, pruning of models in the context of biology and most importantly analysis of time-series measurements. In the second part of the talk, I will focus on challenges in mathematical modeling of networks using physics and equation driven approaches. In integrating data-driven and equation-driven models, our view is that we transition from heuristic models to mathematical models to computational models in an iterative cycle till the quantitative models yield predictable outcomes that can be experimentally validated. We will use exemplars from cellular systems to illustrate the modeling approaches.

Acknowledgements:

This work was supported by the National Science Foundation grant STC-0939370; by National Institutes of Health (NIH) Grants U01 DK097430, U01 CA200147, U01 CA198941, U19 AI090023, R01 HL106579, R01HL108735, R01 HD084633, R01 DK109365.