Literature DB >> 31609384

BioSwitch: a tool for the detection of bistability and multi-steady state behaviour in signalling and gene regulatory networks.

Pencho Yordanov1, Joerg Stelling1, Irene Otero-Muras2.   

Abstract

MOTIVATION: Multi-steady state behaviour, and in particular multi-stability, provides biological systems with the capacity to take reliable decisions (such as cell fate determination). A problem arising frequently in systems biology is to elucidate whether a signal transduction mechanism or a gene regulatory network has the capacity for multi-steady state behaviour, and consequently for a switch-like response to stimuli. Bifurcation diagrams are a powerful instrument in non-linear analysis to study the qualitative and quantitative behaviour of equilibria including bifurcation into different equilibrium branches and bistability. However, in the context of signalling pathways, the inherent large parametric uncertainty hampers the (direct) use of standard bifurcation tools.
RESULTS: We present BioSwitch, a toolbox to detect multi-steady state behaviour in signalling pathways and gene regulatory networks. The tool combines results from chemical reaction network theory with global optimization to efficiently detect whether a signalling pathway has the capacity to undergo a saddle node bifurcation, and in case of multi-stationarity, provides the exact coordinates of the bifurcation where to start a numerical continuation analysis with standard bifurcation tools, leading to two different branches of equilibria. Bistability detection in the G1/S transition pathway of Saccharomyces cerevisiae is included as an illustrative example.
AVAILABILITY AND IMPLEMENTATION: BioSwitch runs under the popular MATLAB computational environment, and is available at https://sites.google.com/view/bioswitch.
© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Year:  2020        PMID: 31609384     DOI: 10.1093/bioinformatics/btz746

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  1 in total

1.  Computational quantification of global effects induced by mutations and drugs in signaling networks of colorectal cancer cells.

Authors:  Sara Sommariva; Giacomo Caviglia; Silvia Ravera; Francesco Frassoni; Federico Benvenuto; Lorenzo Tortolina; Nicoletta Castagnino; Silvio Parodi; Michele Piana
Journal:  Sci Rep       Date:  2021-10-01       Impact factor: 4.379

  1 in total

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