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      Logic-Based Models for the Analysis of Cell Signaling Networks†

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          Abstract

          Computational models are increasingly used to analyze the operation of complex biochemical networks, including those involved in cell signaling networks. Here we review recent advances in applying logic-based modeling to mammalian cell biology. Logic-based models represent biomolecular networks in a simple and intuitive manner without describing the detailed biochemistry of each interaction. A brief description of several logic-based modeling methods is followed by six case studies that demonstrate biological questions recently addressed using logic-based models and point to potential advances in model formalisms and training procedures that promise to enhance the utility of logic-based methods for studying the relationship between environmental inputs and phenotypic or signaling state outputs of complex signaling networks.

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          Most cited references41

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          Nature, nurture, or chance: stochastic gene expression and its consequences.

          Gene expression is a fundamentally stochastic process, with randomness in transcription and translation leading to cell-to-cell variations in mRNA and protein levels. This variation appears in organisms ranging from microbes to metazoans, and its characteristics depend both on the biophysical parameters governing gene expression and on gene network structure. Stochastic gene expression has important consequences for cellular function, being beneficial in some contexts and harmful in others. These situations include the stress response, metabolism, development, the cell cycle, circadian rhythms, and aging.
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            Modeling and simulation of genetic regulatory systems: a literature review.

            In order to understand the functioning of organisms on the molecular level, we need to know which genes are expressed, when and where in the organism, and to which extent. The regulation of gene expression is achieved through genetic regulatory systems structured by networks of interactions between DNA, RNA, proteins, and small molecules. As most genetic regulatory networks of interest involve many components connected through interlocking positive and negative feedback loops, an intuitive understanding of their dynamics is hard to obtain. As a consequence, formal methods and computer tools for the modeling and simulation of genetic regulatory networks will be indispensable. This paper reviews formalisms that have been employed in mathematical biology and bioinformatics to describe genetic regulatory systems, in particular directed graphs, Bayesian networks, Boolean networks and their generalizations, ordinary and partial differential equations, qualitative differential equations, stochastic equations, and rule-based formalisms. In addition, the paper discusses how these formalisms have been used in the simulation of the behavior of actual regulatory systems.
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              Probabilistic Boolean Networks: a rule-based uncertainty model for gene regulatory networks.

              Our goal is to construct a model for genetic regulatory networks such that the model class: (i) incorporates rule-based dependencies between genes; (ii) allows the systematic study of global network dynamics; (iii) is able to cope with uncertainty, both in the data and the model selection; and (iv) permits the quantification of the relative influence and sensitivity of genes in their interactions with other genes. We introduce Probabilistic Boolean Networks (PBN) that share the appealing rule-based properties of Boolean networks, but are robust in the face of uncertainty. We show how the dynamics of these networks can be studied in the probabilistic context of Markov chains, with standard Boolean networks being special cases. Then, we discuss the relationship between PBNs and Bayesian networks--a family of graphical models that explicitly represent probabilistic relationships between variables. We show how probabilistic dependencies between a gene and its parent genes, constituting the basic building blocks of Bayesian networks, can be obtained from PBNs. Finally, we present methods for quantifying the influence of genes on other genes, within the context of PBNs. Examples illustrating the above concepts are presented throughout the paper.
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                Author and article information

                Journal
                Biochemistry
                bi
                bichaw
                Biochemistry
                American Chemical Society
                0006-2960
                1520-4995
                12 March 2010
                20 April 2010
                : 49
                : 15
                : 3216-3224
                Affiliations
                []Center for Cell Decision Process and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
                [§ ]Department of Systems Biology, Harvard Medical School, Boston, Massachusetts 02115
                Author notes
                [* ]To whom correspondence should be addressed. Phone: (617) 252-1629. Fax: (617) 258-0204. E-mail: lauffen@ 123456mit.edu .
                Article
                10.1021/bi902202q
                2853906
                20225868
                24a8a4e4-c8eb-438f-a3da-106e915195bc
                Copyright © 2010 American Chemical Society

                This is an open-access article distributed under the ACS AuthorChoice Terms & Conditions. Any use of this article, must conform to the terms of that license which are available at http://pubs.acs.org.

                History
                : 23 December 2009
                : 11 March 2010
                : 25 March 2010
                : 20 April 2010
                : 12 March 2010
                Funding
                National Institutes of Health, United States
                Categories
                Current Topic
                Custom metadata
                bi902202q
                bi-2009-02202q

                Biochemistry
                Biochemistry

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