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      Logical Modeling and Dynamical Analysis of Cellular Networks


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          The logical (or logic) formalism is increasingly used to model regulatory and signaling networks. Complementing these applications, several groups contributed various methods and tools to support the definition and analysis of logical models. After an introduction to the logical modeling framework and to several of its variants, we review here a number of recent methodological advances to ease the analysis of large and intricate networks. In particular, we survey approaches to determine model attractors and their reachability properties, to assess the dynamical impact of variations of external signals, and to consistently reduce large models. To illustrate these developments, we further consider several published logical models for two important biological processes, namely the differentiation of T helper cells and the control of mammalian cell cycle.

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          Most cited references88

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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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            Minimum information requested in the annotation of biochemical models (MIRIAM).

            Most of the published quantitative models in biology are lost for the community because they are either not made available or they are insufficiently characterized to allow them to be reused. The lack of a standard description format, lack of stringent reviewing and authors' carelessness are the main causes for incomplete model descriptions. With today's increased interest in detailed biochemical models, it is necessary to define a minimum quality standard for the encoding of those models. We propose a set of rules for curating quantitative models of biological systems. These rules define procedures for encoding and annotating models represented in machine-readable form. We believe their application will enable users to (i) have confidence that curated models are an accurate reflection of their associated reference descriptions, (ii) search collections of curated models with precision, (iii) quickly identify the biological phenomena that a given curated model or model constituent represents and (iv) facilitate model reuse and composition into large subcellular models.
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              Dynamical analysis of a generic Boolean model for the control of the mammalian cell cycle.

              To understand the behaviour of complex biological regulatory networks, a proper integration of molecular data into a full-fledge formal dynamical model is ultimately required. As most available data on regulatory interactions are qualitative, logical modelling offers an interesting framework to delineate the main dynamical properties of the underlying networks. Transposing a generic model of the core network controlling the mammalian cell cycle into the logical framework, we compare different strategies to explore its dynamical properties. In particular, we assess the respective advantages and limits of synchronous versus asynchronous updating assumptions to delineate the asymptotical behaviour of regulatory networks. Furthermore, we propose several intermediate strategies to optimize the computation of asymptotical properties depending on available knowledge. The mammalian cell cycle model is available in a dedicated XML format (GINML) on our website, along with our logical simulation software GINsim (http://gin.univ-mrs.fr/GINsim). Higher resolution state transitions graphs are also found on this web site (Model Repository page).

                Author and article information

                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                31 May 2016
                : 7
                [1] 1Computational Systems Biology Team, Institut de Biologie de l'Ecole Normale Supérieure, CNRS UMR8197, INSERM U1024, Ecole Normale Supérieure, PSL Research University Paris, France
                [2] 2INESC-ID/Instituto Superior Técnico, University of Lisbon Lisbon, Portugal
                [3] 3Instituto Gulbenkian de Ciência Oeiras, Portugal
                [4] 4Faculty of Medicine, Joint Research Centre for Computational Biomedicine, RWTH Aachen University Aachen, Germany
                [5] 5Department of Biochemistry, University of Nebraska-Lincoln Lincoln, NE, USA
                Author notes

                Edited by: Rui Alves, Universitat de Lleida, Spain

                Reviewed by: Monika Heiner, Brandenburg Technical University Cottbus-Senftenberg, Germany; Noriko Hiroi, Keio University, Japan

                *Correspondence: Claudine Chaouiya chaouiya@ 123456igc.gulbenkian.pt

                This article was submitted to Systems Biology, a section of the journal Frontiers in Genetics

                Copyright © 2016 Abou-Jaoudé, Traynard, Monteiro, Saez-Rodriguez, Helikar, Thieffry and Chaouiya.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                Page count
                Figures: 8, Tables: 2, Equations: 5, References: 110, Pages: 20, Words: 15056

                regulatory and signaling networks,logical modeling,discrete dynamics,attractors,reachability analysis,simulation,t cells activation and differentiation,cell cycle control


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