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Mathematics and Computers in Simulation
Article . 2017 . Peer-reviewed
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Article . 2017
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https://dx.doi.org/10.48550/ar...
Article . 2014
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Graph-theoretic analysis of multistationarity using degree theory

Authors: Carsten Conradi; Maya Mincheva;

Graph-theoretic analysis of multistationarity using degree theory

Abstract

Biochemical mechanisms with mass action kinetics are often modeled by systems of polynomial differential equations (DE). Determining directly if the DE system has multiple equilibria (multistationarity) is difficult for realistic systems, since they are large, nonlinear and contain many unknown parameters. Mass action biochemical mechanisms can be represented by a directed bipartite graph with species and reaction nodes. Graph-theoretic methods can then be used to assess the potential of a given biochemical mechanism for multistationarity by identifying structures in the bipartite graph referred to as critical fragments. In this article we present a graph-theoretic method for conservative biochemical mechanisms characterized by bounded species concentrations, which makes the use of degree theory arguments possible. We illustrate the results with an example of a MAPK network.

Keywords

Molecular Networks (q-bio.MN), Systems biology, networks, Applications of graph theory, biochemical mechanisms, multistationarity, Dynamical Systems (math.DS), mass-action kinetics, FOS: Biological sciences, bipartite graph, FOS: Mathematics, Quantitative Biology - Molecular Networks, Mathematics - Dynamical Systems, Kinetics in biochemical problems (pharmacokinetics, enzyme kinetics, etc.), MAPK network

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    popularity
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Top 10%
Average
Average
Green
bronze