Theoretical and computational tools to model multistable gene regulatory networks.
Federico Bocci1,2, Dongya Jia3, Qing Nie1,2
1The NSF-Simons Center for Multiscale Cell Fate Research, Irvine, CA 92697, United States of America.
Summary
This review overviews computational models for gene regulatory networks, detailing methods from statistical physics and non-linear dynamics. It provides tutorials and examples for understanding cell population dynamics and heterogeneity.
Area of Science:
- Systems Biology
- Theoretical Biology
- Computational Biology
Background:
- Complex gene regulatory networks (GRNs) dynamics are increasingly modeled computationally.
- Multistability and cell heterogeneity arise from these complex interactions.
- Theoretical physics concepts are widely applied to biological systems.
Purpose of the Study:
- To provide an overview of methodologies for modeling gene regulatory networks.
- To highlight current and future challenges in the field.
- To offer practical tutorials and examples for researchers.
Main Methods:
- Review of theoretical and computational modeling approaches.
- Application of concepts from statistical physics, non-linear dynamics, and network theory.
- Simulation of archetypal biological system models.
Main Results:
- A comprehensive overview of key methodologies in GRN modeling.
- Identification of challenges and future research directions.
- Practical guidance through tutorials and literature examples.
Conclusions:
- Theoretical modeling is crucial for understanding complex biological systems.
- Interdisciplinary approaches enhance insights into gene regulation and cell behavior.
- The field offers significant opportunities for physicists and mathematicians.
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