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Innovations of the Rule-Based Modeling Approach

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Systems Biology

Abstract

New modeling approaches are needed to tackle the complexity of cell signaling systems. An emerging approach is rule-based modeling, in which protein-protein interactions are represented at the level of functional components. By using rules to represent interactions, a modeler can avoid enumerating the reachable chemical species in a system, which is a necessity in traditional modeling approaches. A set of rules can be used to generate a reaction network, or to perform simulations with or without network generation. Although the rule-based approach is a relatively recent development in biology, it is based on concepts that have proven useful in other fields. In this chapter, we discuss innovations of the rule-based modeling approach, relative to traditional approaches for modeling chemical kinetics. These innovations include the use of rules to concisely capture the dynamics of molecular interactions, the view of models as programs, and agent-based computational approaches that can be applied to simulate the chemical kinetics of a system characterized by a large traditional model. These innovations should enable the development of models that can relate the molecular state of a cell to its phenotype, even though vast and complex networks bridge perturbations at the molecular level to fates and activities at the cellular level. In the future, we expect that validated rule-based models will be useful for model-guided studies of cell signaling mechanisms, interpretation of temporal phosphoproteomic data, and cell engineering applications.

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Abbreviations

BEM:

Bond electron matrix

BNGL:

BioNetGen Language

ODE:

Ordinary differential equation

SBGN:

Systems Biology Graphical Notation

SBML:

Systems Biology Markup Language

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Acknowledgments

We thank Michael L. Blinov, James R. Faeder, David J. Klinke II, Jean Krivine, and Carlos F. Lopez for helpful discussions.

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Authors and Affiliations

  1. Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, 14853, USA

    Lily A. Chylek

  2. Clinical Translational Research Division, Translational Genomics Research Institute, Phoenix, AZ, 85004, USA

    Edward C. Stites & Richard G. Posner

  3. Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, 86011, USA

    Richard G. Posner

  4. Department of Biology, University of New Mexico, Albuquerque, NM, 87131, USA

    William S. Hlavacek

  5. Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA

    William S. Hlavacek

Authors
  1. Lily A. Chylek
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  2. Edward C. Stites
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  3. Richard G. Posner
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  4. William S. Hlavacek
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Corresponding author

Correspondence to William S. Hlavacek .

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Editors and Affiliations

  1. Chemical and Biological Engineering, Vanderbilt University, Nashville, TN, USA

    Aleš Prokop

  2. Research Group on Process Network Engineering, Kaposvár University, Kaposvár, Hungary

    Béla Csukás

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Chylek, L.A., Stites, E.C., Posner, R.G., Hlavacek, W.S. (2013). Innovations of the Rule-Based Modeling Approach. In: Prokop, A., Csukás, B. (eds) Systems Biology. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6803-1_9

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