A Programmatic Modeling Approach to Explore Alternative Hypothesis of Mitochondrial Regulation in Extrinsic Apoptosis Signaling

Abstract

Experiments often result in observations that can lead to conflicting interpretations of biochemical mechanisms in signaling networks. We aim to use mathematical modeling of extrinsic apoptosis signaling networks to address such experimental observations and provide a theoretical explanation to seemingly discordant interpretations. Probing multiple mechanistic hypotheses in biological modeling often involves the instantiation of complex systems of equations, which despite their usefulness can make model revision, extension, and sharing extremely challenging. To address these modeling barriers, we have developed a modeling framework that allows biological models to be written as native Python programs that encode biological functions. Our modeling framework, PySB, offers access to a large set of existing numerical and programming methods to biological systems modeling. We discuss the implementation of our approach and show how it can be used to explore multiple hypotheses to describe the regulation of mitochondrial outer membrane permeabilization among the Bcl-2 family of proteins. We use our modeling framework to systematically explore proposed mechanisms, both from the literature and from our own experimental work, resulting in the instantiation, comparison, and calibration of multiple model topologies for numerical exploration. Our preliminary results, based on simulations calibrated to experimental data, suggest that the so-called indirect mechanism does not accurately reproduce experimental observations. Preliminary simulations of the so-called direct, and embedded mechanisms, with different topological variants, suggest that the latter model has better relation with experimental data and combinations of these hypothetical mechanisms could more likely account for the experimental observations. We will also aim to provide a programmatic foundation to probe biochemical model topology, physicochemical parameter-space exploration, and model likelihood with the goal of developing novel testable hypotheses about the interactions among these proteins and their role in apoptosis cancer biology.