Thursday, October 20, 2015
1:00 pm, MRB 202
Dr. Carlos Lopez
Assistant Professor of Cancer Biology, Vanderbilt University School of Medicine
A computational modeling framework for cell-decision processes
A mechanistic understanding of biological signaling networks is typically hindered by the complexity associated with measuring all relevant parameters at multiple time points. Mathematical representations are central to understanding these complex biological signaling processes but, as signaling networks increase in size and complexity, it becomes increasingly challenging to manage biological knowledge in mathematical form and link these models to experimental data. Here we show a novel paradigm whereby we leverage the power of rule-based languages to encode biological models of signaling processes as Python programs thorough our PySB models-as-programs framework. We show how our modeling approach can be used to explore network topologies and guide experiments to understand how cells commit to either apoptosis or necroptosis forms of programmed cell death. We also demonstrate how our approach can be used to explore parameter uncertainties in complex kinetic models and identify the key parameters that drive dynamics in COX-2 kinetics. We showcase how PySB models, as Python programs, can leverage tools and practices from the open-source software community, substantially advancing our ability to collaborate, disseminate knowledge, and manage the testing of biochemical hypotheses. Our modeling paradigm guides experiments and makes the theory-and-experiments cycle easily accessible to users.