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Molecular Factors Affecting Cell Division

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Cell size is a significant factor impacting cellular design, function, and fitness. There exists some internal coordination by which cells double their masses before division, thus, achieving homeostasis. Coordination between cell growth and proliferation depends on the checkpoints in between cell cycle phases. Loss of coordination or failure in the checkpoint mechanism can drive the cell to uncontrolled growth and loss of cellular function. Like dividing cells that coordinate cellular growth,...

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Related Experiment Video

Updated: May 23, 2026

Finite Element Modelling of a Cellular Electric Microenvironment

Finite Element Modelling of a Cellular Electric Microenvironment

Published on: May 18, 2021

Computational modelling of cell identity.

Woo Jun Shim¹, Chris Siu Yeung Chow¹, Shaine Chenxin Bao¹

¹Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.

The Biochemical Journal

|May 22, 2026

Summary

Computational models predict cell identity, overcoming experimental limitations in profiling molecular diversity. This review guides biologists in selecting appropriate computational methods for cell type classification and discovery.

Keywords:

bioinformatics cell identity computational models gene expression and regulation machine learning

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Last Updated: May 23, 2026

Finite Element Modelling of a Cellular Electric Microenvironment

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Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Experimental cell profiling captures limited molecular diversity.
Computational modeling predicts cell states beyond experimental reach.
Consortium data advances genomic feature learning for cell identity.

Purpose of the Study:

To review computational methods for cell identity inference.
To provide a practical guide for biologists.
To outline method rationales, applications, and limitations.

Main Methods:

Examined 43 computational methods.
Included methods using genomic regulatory elements, marker genes, and reference atlases.
Covered traditional machine learning and transformer-based approaches.

Main Results:

Identified diverse computational strategies for cell identity inference.
Highlighted the role of genomic features in model generalizability.
Assessed biological rationales, applications, and limitations.

Conclusions:

Computational models are essential for comprehensive cell identity analysis.
Understanding method specifics aids in choosing the right tools.
This review empowers biologists with informed method selection for cell state research.