Literature DB >> 20961750

Logical and symbolic analysis of robust biological dynamics.

Leon Glass1, Hava T Siegelmann.   

Abstract

Logical models provide insight about key control elements of biological networks. Based solely on the logical structure, we can determine state transition diagrams that give the allowed possible transitions in a coarse grained phase space. Attracting pathways and stable nodes in the state transition diagram correspond to robust attractors that would be found in several different types of dynamical systems that have the same logical structure. Attracting nodes in the state transition diagram correspond to stable steady states. Furthermore, the sequence of logical states appearing in biological networks with robust attracting pathways would be expected to appear also in Boolean networks, asynchronous switching networks, and differential equations having the same underlying structure. This provides a basis for investigating naturally occurring and synthetic systems, both to predict the dynamics if the structure is known, and to determine the structure if the transitions are known.
Copyright © 2010 Elsevier Ltd. All rights reserved.

Mesh:

Year:  2010        PMID: 20961750     DOI: 10.1016/j.gde.2010.09.005

Source DB:  PubMed          Journal:  Curr Opin Genet Dev        ISSN: 0959-437X            Impact factor:   5.578


  4 in total

1.  Estimating Attractor Reachability in Asynchronous Logical Models.

Authors:  Nuno D Mendes; Rui Henriques; Elisabeth Remy; Jorge Carneiro; Pedro T Monteiro; Claudine Chaouiya
Journal:  Front Physiol       Date:  2018-09-07       Impact factor: 4.566

2.  Network model of immune responses reveals key effectors to single and co-infection dynamics by a respiratory bacterium and a gastrointestinal helminth.

Authors:  Juilee Thakar; Ashutosh K Pathak; Lisa Murphy; Réka Albert; Isabella M Cattadori
Journal:  PLoS Comput Biol       Date:  2012-01-12       Impact factor: 4.475

3.  A Spherical Phase Space Partitioning Based Symbolic Time Series Analysis (SPSP-STSA) for Emotion Recognition Using EEG Signals.

Authors:  Hoda Tavakkoli; Ali Motie Nasrabadi
Journal:  Front Hum Neurosci       Date:  2022-06-29       Impact factor: 3.473

4.  Majority rules with random tie-breaking in Boolean gene regulatory networks.

Authors:  Claudine Chaouiya; Ouerdia Ourrad; Ricardo Lima
Journal:  PLoS One       Date:  2013-07-26       Impact factor: 3.240

  4 in total

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