Evidence map›Paper›PMID 41256887›Full record

ArticleArXiv2025

Bifurcations and multistability in inducible three-gene toggle switch networks.

Rebecca J Rousseau, Rob Phillips

Abstract readPreprint
In one paragraph

Article in ArXiv, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

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

5 · Who and what money

Authors and funding

2 authors.

Rebecca J RousseauDepartment of Physics, California Institute of Technology, Pasadena, CA 91125.
Rob PhillipsDepartment of Physics, California Institute of Technology, Pasadena, CA 91125.

Funding

The Principles of Regulatory, Conformational and Evolutionary AdaptationR35GM118043 · NIGMS · CALIFORNIA INSTITUTE OF TECHNOLOGY · PI ROB PHILLIPS · 2016 to 2026
$7.9M
NIH Director's Pioneer Award (RMI)DP1OD000217 · OD · CALIFORNIA INSTITUTE OF TECHNOLOGY · PI PHILLIPS, ROB · 2004 to 2008
$4.0M
NIGMS NIH HHS R35 GM118043NIH HHS DP1 OD000217
6 · The paper itself

Abstract

Control of transcription presides over a vast array of biological processes including through gene regulatory circuits that exhibit multistability. Two- and three-gene network motifs are often found to be critical parts of the repertoire of metabolic and developmental pathways. Theoretical models of these circuits, however, typically vary parameters such as dissociation constants, transcription rates, and degradation rates without specifying precisely how these parameters are controlled biologically. In this paper, we examine the role of effector molecules, which can alter the concentrations of the active transcription factors that control regulation, and are ubiquitous to regulatory processes across biological settings. We specifically consider allosteric regulation in the context of extending the standard bistable switch to three-gene networks, and explore the rich multistable dynamics exhibited in these architectures as a function of effector concentrations. We then study how the conditions required for tristability and more complex dynamics, and the bifurcations in dynamic phase space upon tuning effector concentrations, evolve under various interpretations of regulatory circuit mechanics, the underlying activity of inducers, and perturbations thereof. Notably, the biological mechanism by which we model effector control over dual-function proteins transforms not only the phenotypic trend of dynamic tuning but also the set of available dynamic regimes. In this way, we determine key parameters and regulatory features that drive phenotypic decisions, and offer an experimentally tunable structure for encoding inducible multistable behavior arising from both single and dual-function allosteric transcription factors.

Identifiers

PMID41256887
PMCPMC12622193

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Registered trials

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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.