9/3 1:30 - 4:00, Hewlett 201
Rule-Based Kinetic Modeling of Signal Transduction Networks
University of Connecticut Health Center.
Purpose provide a background and hands-on experience on how to develop detailed kinetic models of signaling networks that account comprehensively for the full spectrum of chemical species and reactions implied by the user-specified rules.
Assembly of quantitative models of large complex networks brings about several challenges. One of them is combinatorial complexity, where relatively few signaling molecules can combine to form thousands or millions of distinct chemical species. A receptor that has several separate phosphorylation sites can exist in hundreds of different states, many of which must be accounted for individually when simulating the time course of signaling. When assembly of protein complexes is being included, the number of distinct molecular species can easily increase by a few orders of magnitude. Validation, visualization, and understanding the network can become intractable. In this tutorial, we will describe how to deal with the problem of combinatorial complexity using rule-based approach. This approach allows systematic incorporation of site-specific details about protein-protein interactions into a model. We will describe how to use rule-based modeling using a 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 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 BioNetGen@VCell), describing several published models and discussing how they can be extended in the future.
Tutorial material the tutorial will be based on the previous version given at ICSB-2007. The materials are available at http://vcell.org/bionetgen. All registered participants will be provided with tutorial materials in electronic form in advance and hard-copy of tutorial materials on site. All participants will be provided with accounts for the web-based and downloadable versions of the software well in advance, so they will have a chance to install BioNetGen software that we will use during the tutorial. The preliminary software installation is not required as the tutorial will be given using the web-version that does not require installation.
Models that will be described during tutorial
- M. L. Blinov, J. R. Faeder, B. Goldstein, and W. S. Hlavacek (2006) A Network Model of Early Events in Epidermal Growth Factor Receptor Signaling That Accounts for Combinatorial Complexity. BioSystems, 83, 136-151.
- D. Barua, J. R. Faeder, and J. M. Haugh (2007) Structure-based Kinetic Models of Modular Signaling Protein Function: Focus on Shp2. Biophys. J., 92, 2290-2300.
- J. R. Faeder, W. S. Hlavacek, I. Reischl, M. L. Blinov, H. Metzger, A. Redondo, C. Wofsy, and B. Goldstein (2003) Investigation of early events in FcεRI-mediated signaling using a detailed mathematical model. J. Immunol., 170, 3769-3781.
- W. S. Hlavacek et al. (2006) Rules for modeling signal-transduction systems. Sci. STKE., 2006, re6.
- M. L. Blinov et al. (2006) Graph theory for rule-based modeling of biochemical networks. Lect. Notes Comput. Sci., 4230, 89-106.
Registration Registered attendees please register for this Tutorial.