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Title: Stochastic simulation of Boolean rxncon models: Towards quantitative analysis of large signaling networks
Authors: Mori, Tomoya  kyouindb  KAKEN_id  orcid (unconfirmed)
Flöttmann, Max
Krantz, Marcus
Akutsu, Tatsuya  kyouindb  KAKEN_id  orcid (unconfirmed)
Klipp, Edda
Author's alias: 阿久津, 達也
Keywords: Signal transduction
Systems biology
Probabilistic Boolean modeling
Bipartite Boolean
Issue Date: 11-Aug-2015
Publisher: BioMed Central Ltd.
Journal title: BMC Systems Biology
Volume: 9
Thesis number: 45
Abstract: Background: Cellular decision-making is governed by molecular networks that are highly complex. An integrative understanding of these networks on a genome wide level is essential to understand cellular health and disease. In most cases however, such an understanding is beyond human comprehension and requires computational modeling. Mathematical modeling of biological networks at the level of biochemical details has hitherto relied on state transition models. These are typically based on enumeration of all relevant model states, and hence become very complex unless severely - and often arbitrarily - reduced. Furthermore, the parameters required for genome wide networks will remain underdetermined for the conceivable future. Alternatively, networks can be simulated by Boolean models, although these typically sacrifice molecular detail as well as distinction between different levels or modes of activity. However, the modeling community still lacks methods that can simulate genome scale networks on the level of biochemical reaction detail in a quantitative or semi quantitative manner. Results: Here, we present a probabilistic bipartite Boolean modeling method that addresses these issues. The method is based on the reaction-contingency formalism, and enables fast simulation of large networks. We demonstrate its scalability by applying it to the yeast mitogen-activated protein kinase (MAPK) network consisting of 140 proteins and 608 nodes. Conclusion: The probabilistic Boolean model can be generated and parameterized automatically from a rxncon network description, using only two global parameters, and its qualitative behavior is robust against order of magnitude variation in these parameters. Our method can hence be used to simulate the outcome of large signal transduction network reconstruction, with little or no overhead in model creation or parameterization.
Rights: © 2015 Mori et al. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.
DOI(Published Version): 10.1186/s12918-015-0193-8
PubMed ID: 26259567
Appears in Collections:Journal Articles

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