{"database":"BioModels","file_versions":[],"scores":null,"additional":{"submitter":["Quentin Thommen"],"curationStatus":["Non-curated"],"modellingApproach":["differential equation model"],"levelVersion":["L3V2"],"full_dataset_link":["https://www.ebi.ac.uk/biomodels/MODEL2608180003"],"isPrivate":["false"],"repository":["BioModels"],"modelFormat":["SBML"],"omics_type":["Models"],"tokenised_name":["Heat shock response network model from Guilbert et al. 2020"],"publication_year":["2020"],"submissionId":["MODEL2608180003"],"publication_authors":["Marie Guilbert, François Anquez, Alexandra Pruvost, Thommen Q, Emmanuel Courtade"],"first_author":["Marie Guilbert"],"publication":["10.1111/febs.15297,\n                            Cell-to-cell variability in stress response is a bottleneck for the construction of accurate and predictive models which could guide clinical diagnosis and treatment of certain diseases, for example, cancer. Indeed, such phenotypic heterogeneity can lead to fractional killing and persistence of a subpopulation of cells which are resistant to a given treatment. The heat shock response network plays a major role in protecting the proteome against several types of injuries. Here, we combine high-throughput measurements and mathematical modeling to unveil the molecular origin of the phenotypic variability in the heat shock response network. Although the mean response coincides with known biochemical measurements, we found a surprisingly broad diversity in single-cell dynamics with a continuum of response amplitudes and temporal shapes for several stimulus strengths. We theoretically predict that the broad phenotypic heterogeneity is due to network ultrasensitivity together with variations in the expression level of chaperones controlled by the transcription factor heat shock factor 1. Furthermore, we experimentally confirm this prediction by mapping the response amplitude to chaperone and heat shock factor 1 expression levels.. 24, 287.\n                            UMR 8523, PhLAM - Physique des Lasers Atomes et Molécules, CNRS, Université de Lille, France."],"submitter_mail":["quentin.thommen@univ-lille.fr"],"publication_doi":["10.1111/febs.15297"],"submitter_affiliation":["Univ. Lille"],"additional_accession":[]},"is_claimable":false,"name":"Heat shock response network model from Guilbert et al. 2020","description":"The model describes the mammalian heat shock response network using four dynamical variables: temperature, misfolded proteins (MFP), HSP mRNA, and heat shock proteins (HSP). Heat stress increases protein denaturation, generating MFPs that compete with HSF1 for binding to HSP chaperones. This sequestration mechanism releases HSF1, which activates HSP transcription and thereby generates a negative feedback restoring proteostasis. The model also includes HSP-dependent translation and provides an analytical estimate of HSF1 activation, (F_{\\mathrm{Th}}), corresponding to the fraction of HSF1 associated with nuclear stress bodies.","dates":{"last_modification":"2026-08-18","publication":"2026-09-02","submission":"2026-08-18"},"accession":"MODEL2608180003","cross_references":{"doi":["10.1111/febs.15297"]}}