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Leveraging Modeling Approaches: Reaction Networks and Rules

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Book cover Advances in Systems Biology

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 736))

Abstract

We have witnessed an explosive growth in research involving mathematical models and computer simulations of intracellular molecular interactions, ranging from metabolic pathways to signaling and gene regulatory networks. Many software tools have been developed to aid in the study of such biological systems, some of which have a wealth of features for model building and visualization, and powerful capabilities for simulation and data analysis. Novel high-resolution and/or high-throughput experimental techniques have led to an abundance of qualitative and quantitative data related to the spatiotemporal distribution of molecules and complexes, their interactions kinetics, and functional modifications. Based on this information, computational biology researchers are attempting to build larger and more detailed models. However, this has proved to be a major challenge. Traditionally, modeling tools require the explicit specification of all molecular species and interactions in a model, which can quickly become a major limitation in the case of complex networks – the number of ways biomolecules can combine to form multimolecular complexes can be combinatorially large. Recently, a new breed of software tools has been created to address the problems faced when building models marked by combinatorial complexity. These have a different approach for model specification, using reaction rules and species patterns. Here we compare the traditional modeling approach with the new rule-based methods. We make a case for combining the capabilities of conventional simulation software with the unique features and flexibility of a rule-based approach in a single software platform for building models of molecular interaction networks.

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Author information

Authors and Affiliations

  1. Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, CT, USA

    Michael L. Blinov & Ion I. Moraru

Authors
  1. Michael L. Blinov
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  2. Ion I. Moraru
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Corresponding author

Correspondence to Michael L. Blinov .

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Editors and Affiliations

  1. School of Informatics, University of Edinburgh, Crichton Street 10, Edinburgh, EH89AB, UK

    Igor I. Goryanin

  2. School of Biological Sciences, University of Edinburgh, Mayfield Road, Edinburgh, EH93JR, UK

    Andrew B. Goryachev

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Blinov, M.L., Moraru, I.I. (2012). Leveraging Modeling Approaches: Reaction Networks and Rules. In: Goryanin, I.I., Goryachev, A.B. (eds) Advances in Systems Biology. Advances in Experimental Medicine and Biology, vol 736. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7210-1_30

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