Evidence map›Paper›PMID 42680997›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2026

A Dynamic Boolean Model of the Mammalian Cell Cycle: Enhanced Robustness and Mutational Analysis.

Mohammad Abdallah Alsharaiah, Sandhya Samarasinghe, Don Kulasiri

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Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Mohammad Abdallah AlsharaiahKing Abdullah II School for Information Technology, Department of Information Technology, The University of Jordan, Amman, Jordan.
Sandhya SamarasingheComplex Systems, Big Data, Informatics Initiative (CSBII), Lincoln University, Christchurch, New Zealand. sandhya.samarasinghe@lincoln.ac.nz.
Don KulasiriComplex Systems, Big Data, Informatics Initiative (CSBII), Lincoln University, Christchurch, New Zealand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The mammalian cell cycle is a tightly regulated process driven by cyclins, their regulators. While previous Boolean models captured certain aspects of cell cycle dynamics, they suffered from incomplete coverage of key regulatory proteins and limited temporal realism. Here, we present an improved Boolean model of the mammalian cell cycle core control system. The model incorporates a complete set of 13 essential elements, including cyclins (D, E, A, B), their transcription factors (Myc, TFE, TFB), inhibitors (p27, Rb), ubiquitin-mediated degraders (SCF, CDH1, CDC20A, CDC20B). We also introduce synchronous, asynchronous updating schemes with realistic time delays for protein activation, enabling a more accurate temporal representation of cell cycle progression. Simulation results reveal two principal attractors: a stable G0 attractor, a 20-state limit cycle attractor representing realistic phase transitions of the cell cycle. Robustness, sensitivity analyses demonstrate that the system is highly resilient, while mutation studies highlight vulnerabilities-particularly that overexpression of certain regulators halts cell cycle progression more frequently than knockouts. This chapter provides detailed modeling steps, Boolean logic formulations, simulation procedures, and mutation experiments. Possible extensions of the model are also provided, along with some guidance on implementing and troubleshooting Boolean models for cell cycle analysis.

Indexed as

Cell CycleModels, BiologicalMutationAnimalsCell Cycle ProteinsComputer SimulationCyclinsDNA Mutational AnalysisHumansMammalsCell Cycle ProteinsCyclinsAttractorsBoolean networksCell cycle modelingComputational modelingCyclinsMutation analysisRobustnessSensitivitySystems biologyTemporal realism

Identifiers

PMID42680997

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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.