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Computationally modelling cell proliferation: A review
M I A Barbosa1, J Belinha2, R M Natal Jorge3
1Institute of Science and Innovation in Mechanical and Industrial Engineering (INEGI), University of Porto, Porto, Portugal.
Summary
Mathematical models help understand cell proliferation, a key process in development and disease. This review compares different modeling approaches to reveal their strengths and limitations in studying cell cycle regulation.
Area of Science:
- Computational Biology
- Cell Biology
- Biomathematics
Background:
- Cell proliferation is essential for human development and homeostasis.
- Errors in cell cycle regulation can lead to diseases like cancer.
- Mathematical and computational models are increasingly used to study biological processes.
Purpose of the Study:
- To review and compare mathematical models of cell proliferation.
- To focus on models relevant to mammalian or generic eukaryotic cells.
- To highlight the capabilities, shortcomings, and future challenges of these models.
Main Methods:
- Review of previously published continuum, discrete, and hybrid modeling approaches.
- Comparison of different modeling strategies for cell proliferation.
- Analysis of assumptions and simplifications in existing models.
Main Results:
- Various modeling approaches exist for cell proliferation, each with specific assumptions.
- Continuum, discrete, and hybrid models offer different perspectives on cell cycle regulation.
- Understanding model limitations is crucial for accurate biological interpretation.
Conclusions:
- Mathematical models are valuable tools for understanding complex biological processes like cell proliferation.
- A comparative analysis reveals the strengths and weaknesses of different modeling techniques.
- Further research is needed to refine models and address remaining challenges in cell cycle research.
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