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Lang, P.F.* ; Penas, D.R.* ; Banga, J.R.* ; Weindl, D. ; Novak, B.*

Reusable rule-based cell cycle model explains compartment-resolved dynamics of 16 observables in RPE-1 cells.

PLoS Comput. Biol. 20:e1011151 (2024)

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The mammalian cell cycle is regulated by a well-studied but complex biochemical reaction system. Computational models provide a particularly systematic and systemic description of the mechanisms governing mammalian cell cycle control. By combining both state-of-the-art multiplexed experimental methods and powerful computational tools, this work aims at improving on these models along four dimensions: model structure, validation data, validation methodology and model reusability. We developed a comprehensive model structure of the full cell cycle that qualitatively explains the behaviour of human retinal pigment epithelial-1 cells. To estimate the model parameters, time courses of eight cell cycle regulators in two compartments were reconstructed from single cell snapshot measurements. After optimisation with a parallel global optimisation metaheuristic we obtained excellent agreements between simulations and measurements. The PEtab specification of the optimisation problem facilitates reuse of model, data and/or optimisation results. Future perturbation experiments will improve parameter identifiability and allow for testing model predictive power. Such a predictive model may aid in drug discovery for cell cycle-related disorders.

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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Systems Biology; Nuclear Import; Dna-damage; S-phase; Feedback; Localization; Transitions; Progression; Activation; Hysteresis

ISSN (print) / ISBN 1553-734X

e-ISSN 1553-7358

Journal PLoS Computational Biology

Quellenangaben Volume: 20, Issue: 1, Article Number: e1011151

Publisher Public Library of Science (PLoS)

Publishing Place 1160 Battery Street, Ste 100, San Francisco, Ca 94111 Usa

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute of Computational Biology (ICB)

Grants MCIN/AEI