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Center for Nonlinear Studies |
Experiments often result in observations that suggest conflicting biochemical mechanisms in signaling networks. Mathematical modeling of biological systems is a method that could be used to probe our knowledge derived from experimental observations and provide a consensus among seemingly discordant observations. Our main goal is the formulation of hypotheses, testable through experiments, about the interactions among signaling pathways and their role in cancer biology. However, probing multiple mechanistic hypotheses in biological modeling often involves the instantiation of complex systems of equations, which despite their usefulness, makes model revision, extension, and sharing extremely 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 programs in the Python programming language that encode biological functions as sets of modular pieces of software. I will discuss the development and implementation of these novel methods to explore multi-pathway signaling events that lead to intracellular signaling pathway crosstalk, cellular decision-making, and response to external cues. I will present the exploration of multiple mechanistic hypotheses and the validity of proposed signaling crosstalk mechanisms using theoretical approaches to complement experimental data. For this reason, attention will be devoted to model calibration to experimental data as a central aspect of this work. Model tracking, model sharing, and its connection with experiments will be discussed through the use of software developed in the laboratory for model dissemination and outreach.
Host: William Hlavacek