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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
Published on: December 7, 2021
A general modeling strategy for gene regulatory networks with stochastic dynamics
Andre Ribeiro1, Rui Zhu, Stuart A Kauffman
1Institute for Biocomplexity and Informatics, Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, Canada. ARibeiro@ucalgary.ca
Summary
Time delays in gene regulatory networks significantly reduce fluctuations, enabling gene coexistence. This study proposes a flexible modeling strategy for complex networks using the Gillespie algorithm for ensemble analysis.
Area of Science:
- Systems Biology
- Computational Biology
- Molecular Biology
Background:
- Gene regulatory networks (GRNs) exhibit stochastic behavior influencing cellular functions.
- Understanding stochastic kinetics is crucial for modeling complex biological systems.
Purpose of the Study:
- To investigate the impact of time delays on gene expression fluctuations in a simple GRN.
- To propose a general modeling strategy for simulating complex GRNs using an ensemble approach.
Main Methods:
- Developed a stochastic genetic toggle switch model with two mutually repressive genes.
- Employed the Gillespie algorithm incorporating time delays for protein production.
- Designed a flexible modeling strategy for complex GRNs, allowing multimer formation and varied regulatory elements.
Main Results:
- Time delays were found to significantly weaken global fluctuations in gene expression.
- Mutually repressive genes demonstrated prolonged coexistence due to these delays.
- The proposed strategy accommodates complex regulatory interactions, including activators/inhibitors and multiple regulatory sites.
Conclusions:
- Time delays are a critical factor in stabilizing gene expression dynamics.
- The developed ensemble modeling strategy offers a versatile tool for simulating diverse and complex real-world genetic networks.
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