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Dynamic processes of fate decision in inducible bistable systems
Sijing Chen1, Yanhong Sun1, Fengyu Zhang2
1The State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics, Peking University, Beijing, China.
Biophysical Journal
|October 31, 2024
Summary
Delays in gene regulatory networks and external induction timescales significantly impact biological fate decisions. Considering these dynamic processes, not just steady states, is crucial for understanding differentiation trajectories and designing synthetic biology systems.
Area of Science:
- Systems Biology
- Computational Biology
- Molecular Biology
Background:
- Gene regulatory networks (GRNs) govern biological fate decisions through complex interactions.
- Mathematical models using ordinary differential equations and steady-state analysis offer insights but often miss transient biological phenomena.
- Existing models frequently omit dynamic processes, leading to discrepancies with experimental observations.
Purpose of the Study:
- To investigate the influence of delays in gene regulatory steps on fate decisions in inducible bistable systems.
- To analyze the impact of external induction timescales on dynamic differentiation processes.
- To highlight the importance of dynamic modeling over solely steady-state analysis in biological systems.
Main Methods:
- Mathematical modeling of inducible bistable gene regulatory networks.
- Analysis of the effects of unequal delays in biochemical interactions.
- Simulation of varying external induction timescales.
Main Results:
- Steady-state parameters define the overall fate decision landscape.
- Unequal delays and induction timescales cause deviations in differentiation trajectories.
- New, persistent transient distributions emerge due to dynamic process omissions.
Conclusions:
- Dynamic processes, including delays and induction timescales, are critical for accurately modeling biological fate decisions.
- Relying solely on steady-state analysis can lead to incomplete understanding of transient phenomena.
- Findings guide improved interpretation of experimental data and future dynamical modeling in synthetic biology.
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