Literature DB >> 26304616

Scale invariance analysis for genetic networks applying homogeneity.

Emmanuel Bernuau1, Denis Efimov2,3,4, Wilfrid Perruquetti5,6.   

Abstract

Scalability is a property describing the change of the trajectory of a dynamical system under a scaling of the input stimulus and of the initial conditions. Particular cases of scalability include the scale invariance and fold change detection (when the scaling of the input does not influence the system output). In the present paper it is shown that homogeneous systems have this scalability property while locally homogeneous systems approximately possess this property. These facts are used for detecting scale invariance or approximate scalability (far from a steady state) in several biological systems. The results are illustrated by various regulatory networks.

Keywords:  34D20; 37C75; 37C80; 92D25

Mesh:

Year:  2015        PMID: 26304616     DOI: 10.1007/s00285-015-0923-y

Source DB:  PubMed          Journal:  J Math Biol        ISSN: 0303-6812            Impact factor:   2.259


  6 in total

1.  A synthetic oscillatory network of transcriptional regulators.

Authors:  M B Elowitz; S Leibler
Journal:  Nature       Date:  2000-01-20       Impact factor: 49.962

Review 2.  Modeling transcriptional control in gene networks--methods, recent results, and future directions.

Authors:  P Smolen; D A Baxter; J H Byrne
Journal:  Bull Math Biol       Date:  2000-03       Impact factor: 1.758

Review 3.  Scale invariance in biology: coincidence or footprint of a universal mechanism?

Authors:  T Gisiger
Journal:  Biol Rev Camb Philos Soc       Date:  2001-05

4.  Transient dynamic phenotypes as criteria for model discrimination: fold-change detection in Rhodobacter sphaeroides chemotaxis.

Authors:  Abdullah Hamadeh; Brian Ingalls; Eduardo Sontag
Journal:  J R Soc Interface       Date:  2013-01-04       Impact factor: 4.118

5.  A characterization of scale invariant responses in enzymatic networks.

Authors:  Maja Skataric; Eduardo D Sontag
Journal:  PLoS Comput Biol       Date:  2012-11-01       Impact factor: 4.475

6.  The Goodwin model: behind the Hill function.

Authors:  Didier Gonze; Wassim Abou-Jaoudé
Journal:  PLoS One       Date:  2013-08-01       Impact factor: 3.240

  6 in total

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