Tutorial: Rule-based modeling using BioNetGen and the Virtual Cell

Michael Blinov (UCHC)

Abstract

Cell signaling, the process by which cells sense and respond to their environment, involves a large number of proteins and other biomolecules whose interactions define a vast response network. A key feature of these systems is that the molecules involved have a modular structure that allows each molecular component of the network to interact with a large number of other elements. Modeling the dynamics of such complex systems poses a number of challenges, but is critical for developing a mechanistic understanding of biological signal transduction and the ultimate goal of controlling pathological responses to cure and prevent disease. In this tutorial, we will describe how to develop and simulate kinetic models of signaling networks using a simple yet powerful language (BioNetGen language, BNGL) and software (BioNetGen) we have developed. BNGL allows explicit representation of the individual elements that mediate the interactions among proteins and other signaling molecules. For example, molecules are represented as structured objects in which the functional elements are sites that may bind to other sites of the same or different molecules and which may have an associated internal state that represents either conformation or covalent modification. The model is built by defining rules that govern how molecules interact to form complexes, modify internal states, and degrade or produce new molecules. The application of rules to a seed set of molecules is used to generate a reaction network, freeing the user from the intense bookkeeping that would be required to enumerate such a network by hand and greatly reducing the barrier to exploring how alternate formulation of the rules would affect model behavior. We will describe a number of options for simulating network kinetics, including ODE’s and kinetic Monte Carlo using the popular Gillespie algorithm. We will demonstrate how exporting models in the Systems Biology Markup Language (SBML) provides compatibility with a large number of additional simulation tools and methods. We will show how to define macroscopic variables, which represent quantities that can be directly compared with experimental data, such as Western blots and co-immunoprecipitation. The tutorial will provide hands-on experience on how to model and simulate portions of signaling pathways (using the web-version of BioNetGen), describing several published models and discussing how they can be extended in the future. We will discuss how the rule-based description could be used as a way to represent knowledge about the interactions present in signaling networks and how it could provide the basis for a collaborative framework aimed at developing comprehensive models of signaling pathways.

References

1. W. S. Hlavacek et al. (2006) Rules for modeling signal-transduction systems. Sci. STKE., 2006, re6.

2. M. L. Blinov et al. (2006) Graph theory for rule-based modeling of biochemical networks. Lect. Notes Comput. Sci., 4230, 89-106.

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