Evidence map›Paper›PMID 42608618›Full record

ArticleJournal of mathematical biology2026

Positive equilibria in mass action networks: geometry and bounds.

Murad Banaji, Elisenda Feliu

Abstract read
In one paragraph

Article in Journal of mathematical biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

2 authors.

Murad BanajiSchool of Mathematical Sciences, Lancaster University, Lancaster, UK. m.banaji@lancaster.ac.uk.ORCID http://orcid.org/0000-0002-4983-0377
Elisenda FeliuDepartment of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark.ORCID http://orcid.org/0000-0001-7205-6511

Funding

H2020 European Research Council 101044561
6 · The paper itself

Abstract

Any mass action network gives rise to a parameterised family of polynomial equations whose positive solutions are the positive equilibria of the network. Here, we consider alternative systems of equations, whose solutions are in smooth, one-to-one correspondence with positive equilibria of the network, and capture degeneracy or nondegeneracy of the corresponding equilibria. The construction leads us to consider partitions of networks in a natural sense, and we explore the implications of choosing different partitions. The alternative systems are in some situations simpler than the original mass action equations, which allows us to rapidly identify various algebraic and geometric properties of the positive equilibrium set. This includes the characterisation of toricity and local toricity, bounds on the number of positive nondegenerate equilibria on stoichiometric classes, semialgebraic descriptions of the parameter regions for multistationarity, and the study of bifurcations. After discussing the construction of the alternative systems, various consequences for particular classes of networks and numerous examples are presented. We also develop additional techniques specifically for quadratic networks, the most common class of networks in applications, and use these techniques to derive strengthened results for quadratic networks.

Indexed as

Models, BiologicalAlgorithmsComputer SimulationHumansMathematical ConceptsPopulation Dynamicsbifurcationsmass action networksmultistationarityparameterisations of equilibriapolynomial dynamical systems

Identifiers

PMID42608618
PMCPMC13481394

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.