Principles of model building: an experimentation-aided approach to development of models for signaling networks

Abstract

Living cells continuously probe their environment and respond to a multitude of external cues. The information about the environment is carried by signaling cascades that act as "internal transducing and computing modules," coupled into complex and interconnected networks. A comprehensive understanding of how cells make decisions therefore necessitates a sound theoretical framework, which can be achieved through mathematical modeling of the signaling networks. In this chapter, we conceptually describe the typical workflow involved in building mathematical models that are motivated by and are developed in a tight integration with experimental analysis. In particular, we delineate the steps involved in a generic, iterative experimentation-driven model-building process, both through informal discussion and using a recently published study as an example. Experiments guide the initial development of mathematical models, including choice of appropriate template model and parameter revision. The model can then be used to generate and test hypotheses quickly and inexpensively, aiding in judicious design of future experiments. These experiments, in turn, are used to update the model. The model developed at the end of this exercise not only predicts functional behavior of the system under study but also provides insight into the biophysical underpinnings of signaling networks.