Evidence map›Paper›PMID 29529037›Full record

ArticlePLoS computational biology2018

Relatively slow stochastic gene-state switching in the presence of positive feedback significantly broadens the region of bimodality through stabilizing the uninduced phenotypic state.

Hao Ge, Pingping Wu, Hong Qian, Xiaoliang Sunney Xie

Abstract read
In one paragraph

Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Fluctuating-rate model with multiple gene states.Journal of mathematical biology · 2020
    Article
  3. Absolutely robust controllers for chemical reaction networks.Journal of the Royal Society, Interface · 2020
    Article
  4. Article
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

4 authors.

Hao GeBiodynamic Optical Imaging Center (BIOPIC), Peking University, Beijing, P.R.China.ORCID 0000-0003-0404-5268
Pingping WuSchool of Mathematical Sciences and Centre for Computational Systems Biology, Fudan University, Shanghai, P.R.China.
Hong QianDepartment of Applied Mathematics, University of Washington, Seattle, Washington, United States of America.ORCID 0000-0002-5466-8650
Xiaoliang Sunney XieBiodynamic Optical Imaging Center (BIOPIC), Peking University, Beijing, P.R.China.

Funding

NIH Director's Pioneer Award (RMI)DP1OD000277 · OD · HARVARD UNIVERSITY · PI XIE, XIAOLIANG SUNNEY · 2004 to 2008
$4.0M
NIH HHS 1DP1OD000277NIH HHS DP1 OD000277
6 · The paper itself

Abstract

Within an isogenic population, even in the same extracellular environment, individual cells can exhibit various phenotypic states. The exact role of stochastic gene-state switching regulating the transition among these phenotypic states in a single cell is not fully understood, especially in the presence of positive feedback. Recent high-precision single-cell measurements showed that, at least in bacteria, switching in gene states is slow relative to the typical rates of active transcription and translation. Hence using the lac operon as an archetype, in such a region of operon-state switching, we present a fluctuating-rate model for this classical gene regulation module, incorporating the more realistic operon-state switching mechanism that was recently elucidated. We found that the positive feedback mechanism induces bistability (referred to as deterministic bistability), and that the parameter range for its occurrence is significantly broadened by stochastic operon-state switching. We further show that in the absence of positive feedback, operon-state switching must be extremely slow to trigger bistability by itself. However, in the presence of positive feedback, which stabilizes the induced state, the relatively slow operon-state switching kinetics within the physiological region are sufficient to stabilize the uninduced state, together generating a broadened parameter region of bistability (referred to as stochastic bistability). We illustrate the opposite phenotype-transition rate dependence upon the operon-state switching rates in the two types of bistability, with the aid of a recently proposed rate formula for fluctuating-rate models. The rate formula also predicts a maximal transition rate in the intermediate region of operon-state switching, which is validated by numerical simulations in our model. Overall, our findings suggest a biological function of transcriptional "variations" among genetically identical cells, for the emergence of bistability and transition between phenotypic states.

Indexed as

FeedbackComputer SimulationEscherichia coliGene Expression RegulationGene Expression Regulation, BacterialGene Regulatory NetworksKineticsLac OperonModels, BiologicalOperonPhenotypeStochastic Processes

Identifiers

PMID29529037
PMCPMC5864076

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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.