Pysb: A Modeling Framework to Explore Biochemical Signaling Processes and Cell-Decisions

Abstract

Experiments often result in observations that suggest conflicting biochemical mechanisms in signaling networks. Mathematical modeling of biological systems could be used to probe knowledge derived from experimental observations. However, probing multiple mechanistic hypotheses in biological modeling often involves the instantiation of complex systems of equations that can make model revision, extension, and sharing challenging. To address these modeling barriers, we have developed a modeling framework that brings a program-based approach to biological modeling. In our approach, biological models are written as Python programs that encode biological functions as executable code. I will discuss the development and implementation of these methods to explore intracellular signaling pathway crosstalk and response to external cues in the context of programmed cell death and life/death decision processes in cancer biology will be presented. Our approach to model calibration to experimental data, extracting important knowledge from biochemical signaling networks, and developing tools to relate models to experiment will be discussed. We will also highlight how a programming language for biological modeling facilitates model tracking, sharing, and dissemination.View Large Image | View Hi-Res Image | Download PowerPoint Slide