Theoretical and computational tools to model multistable gene regulatory networks.
Arxiv
|February 24, 2023
Summary
This review overviews theoretical and computational models for gene regulatory networks, explaining how these models can create complex cell behaviors. It provides practical examples and tutorials for researchers in systems biology.
Area of Science:
- Systems Biology
- Theoretical Biology
- Computational Biology
Background:
- Recent years show increased use of theoretical and computational models to understand gene regulatory networks.
- These models help explain how complex interactions lead to multistable and heterogeneous cell populations.
- Mathematical and physical concepts from statistical physics, non-linear dynamics, and network theory are increasingly applied to biology.
Approach:
- This review provides a comprehensive overview of key methodologies in theoretical modeling of biological systems.
- It highlights current challenges and future directions in the field.
- Includes practical tutorials for simulating archetypal biological system models.
Key Points:
- Explores the application of statistical physics, non-linear dynamics, and network theory to gene regulatory networks.
- Compares and contrasts biological networks with classical systems in statistical and quantum mechanics.
- Offers concrete examples from existing literature for researchers.
Conclusions:
- This review serves as a guide for theoreticians entering the dynamic field of biological systems modeling.
- It bridges the gap between theoretical physics and complex biological systems.
- Facilitates understanding and simulation of gene regulatory network dynamics and cell population heterogeneity.
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