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10.1016/j.biosystems.2016.03.002 http://scihub22266oqcxt.onion/10.1016/j.biosystems.2016.03.002 C5149109!5149109!26965665     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biosystems 2016 ; 142-143 (ä): 15-24 Nephropedia Template TP {{tp|p=26965665|t=2016. Relative Stability of Network States in Boolean Network Models of Gene Regulation in Development |pdf=|usr=}}{{26965665}} gab.com Text Relative Stability of Network States in Boolean Network Models of Gene Regulation in Development JO: Biosystems http://www.kidney.de/pget.php?&tw=1&pmid=26965665 #FOAvip Progress in cell type reprogramming has revived the interest in Waddington?s concept of the epigenetic landscape. Recently researchers developed the quasi-potential theory to represent the Waddington?s landscape. The Quasi-potential U(x), derived from interactions in the gene regulatory network (GRN) of a cell, quantifies the relative stability of network states, which determine the effort required for state transitions in a multi-stable dynamical system. However, quasi-potential landscapes, originally developed for continuous systems, are not suitable for discrete-valued networks which are important tools to study complex systems. In this paper, we provide a framework to quantify the landscape for discrete Boolean networks (BNs). We apply our framework to study pancreas cell differentiation where an ensemble of BN models is considered based on the structure of a minimal GRN for pancreas development. We impose biologically motivated structural constraints (corresponding to specific type of Boolean functions) and dynamical constraints (corresponding to stable attractor states) to limit the space of BN models for pancreas development. In addition, we enforce a novel functional constraint corresponding to the relative ordering of attractor states in BN models to restrict the space of BN models to the biological relevant class. We find that BNs with canalyzing/sign-compatible Boolean functions best capture the dynamics of pancreas cell differentiation. This framework can also determine the genes' influence on cell state transitions, and thus can facilitate the rational design of cell reprogramming protocols. Twit Text FOAVip Relative Stability of Network States in Boolean Network Models of Gene Regulation in Development ????Biosystems http://www.kidney.de/pget.php?&tw=1&pmid=26965665 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26965665 #FOAvip English Wikipedia <ref>{{Cite pmid|26965665}}</ref> Relative Stability of Network States in Boolean Network Models of Gene Regulation in Development #MMPMID26965665 Zhou JX; Samal A; d?Hérouël AF; Price ND; Huang S Biosystems 2016[Apr]; 142-143 (ä): 15-24 PMID26965665show ga Progress in cell type reprogramming has revived the interest in Waddington?s concept of the epigenetic landscape. Recently researchers developed the quasi-potential theory to represent the Waddington?s landscape. The Quasi-potential U(x), derived from interactions in the gene regulatory network (GRN) of a cell, quantifies the relative stability of network states, which determine the effort required for state transitions in a multi-stable dynamical system. However, quasi-potential landscapes, originally developed for continuous systems, are not suitable for discrete-valued networks which are important tools to study complex systems. In this paper, we provide a framework to quantify the landscape for discrete Boolean networks (BNs). We apply our framework to study pancreas cell differentiation where an ensemble of BN models is considered based on the structure of a minimal GRN for pancreas development. We impose biologically motivated structural constraints (corresponding to specific type of Boolean functions) and dynamical constraints (corresponding to stable attractor states) to limit the space of BN models for pancreas development. In addition, we enforce a novel functional constraint corresponding to the relative ordering of attractor states in BN models to restrict the space of BN models to the biological relevant class. We find that BNs with canalyzing/sign-compatible Boolean functions best capture the dynamics of pancreas cell differentiation. This framework can also determine the genes' influence on cell state transitions, and thus can facilitate the rational design of cell reprogramming protocols. äRelative Stability of Network States in Boolean Network Models of Gene(epmc).pdf DeepDyve Pubget Overpricing