Evidence map›Paper›PMID 39195248›Full record

ArticleCells2024

Multiscale Modeling of Bistability in the Yeast Polarity Circuit.

Siarhei Hladyshau, Kaiyun Guan, Nivedita Nivedita, Beverly Errede, Denis Tsygankov, Timothy C Elston

Abstract read
In one paragraph

Article in Cells, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Polarization as a Process: The Potential of Process Ontology for Understanding Cellular Symmetry Breaking.BioEssays : news and reviews in molecular, cellular and developmental biology · 2026
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Siarhei HladyshauSchool of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA.ORCID 0000-0003-2906-8503
Kaiyun GuanCurriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.ORCID 0000-0002-1559-1275
Nivedita NiveditaDepartment of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.ORCID 0000-0001-9710-9683
Beverly ErredeDepartment of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.ORCID 0000-0002-1557-4440
Denis TsygankovWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.ORCID 0000-0002-1180-3584
Timothy C ElstonDepartment of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.ORCID 0000-0002-4601-2117

Funding

Mathematical modeling of cellular signaling systemsR35GM127145 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Timothy C Elston · 2018 to 2026
$3.9M
Coordinated Actin Regulation in Directed Neural Crest Cell MigrationR01GM136892 · NIGMS · GEORGIA INSTITUTE OF TECHNOLOGY · PI NIE, SHUYI · 2020 to 2024
$1.6M
NIGMS NIH HHS R01 GM136892NIGMS NIH HHS R35 GM127145NIH, NSF NIH R01 GM136892, NIH R35 GM127145, NSF CMMI 1942561
6 · The paper itself

Abstract

Cell polarity refers to the asymmetric distribution of proteins and other molecules along a specified axis within a cell. Polarity establishment is the first step in many cellular processes. For example, directed growth or migration requires the formation of a cell front and back. In many cases, polarity occurs in the absence of spatial cues. That is, the cell undergoes symmetry breaking. Understanding the molecular mechanisms that allow cells to break symmetry and polarize requires computational models that span multiple spatial and temporal scales. Here, we apply a multiscale modeling approach to examine the polarity circuit of yeast. In addition to symmetry breaking, experiments revealed two key features of the yeast polarity circuit: bistability and rapid dismantling of the polarity site following a loss of signal. We used modeling based on ordinary differential equations (ODEs) to investigate mechanisms that generate these behaviors. Our analysis revealed that a model involving positive and negative feedback acting on different time scales captured both features. We then extend our ODE model into a coarse-grained reaction-diffusion equation (RDE) model to capture the spatial profiles of polarity factors. After establishing that the coarse-grained RDE model qualitatively captures key features of the polarity circuit, we expand it to more accurately capture the biochemical reactions involved in the system. We convert the expanded model to a particle-based model that resolves individual molecules and captures fluctuations that arise from the stochastic nature of biochemical reactions. Our models assume that negative regulation results from negative feedback. However, experimental observations do not rule out the possibility that negative regulation occurs through an incoherent feedforward loop. Therefore, we conclude by using our RDE model to suggest how negative feedback might be distinguished from incoherent feedforward regulation.

Indexed as

Cell PolarityModels, BiologicalSaccharomyces cerevisiaeSaccharomyces cerevisiae ProteinsSignal TransductionSaccharomyces cerevisiae Proteinsbiochemical networksCdc42 Rho GTPasehysteresisTuring instability

Identifiers

PMID39195248
PMCPMC11352540

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.