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Published on: December 7, 2021
A multi-objective differential evolutionary approach toward more stable gene regulatory networks
Afshin Esmaeili1, Christian Jacob
1Department of Computer Science, University of Calgary, Alberta, Canada. a.esmaeili@ucalgary.ca
Bio Systems
|October 27, 2009
Summary
This study enhances gene regulatory network stability using random Boolean networks and Differential Evolution. The evolutionary approach optimizes network properties for increased resilience and structured state spaces.
Area of Science:
- Computational Biology
- Systems Biology
- Evolutionary Computation
Background:
- Mathematical and computational models of genetic regulatory networks are crucial for understanding living systems.
- Biological systems exhibit stability and resilience due to evolutionary fine-tuning.
- Random Boolean networks (RBNs) serve as abstract models for gene regulatory systems.
Purpose of the Study:
- To investigate the use of Differential Evolution (DE) for enhancing the stability of RBNs.
- To develop an evolutionary approach for producing gene regulatory models with specific properties, particularly high stability.
- To explore the transition from chaotic regimes to structured state spaces in RBNs.
Main Methods:
- Utilized random Boolean networks (RBNs) as a model for gene regulatory systems.
- Applied Differential Evolution (DE), an optimization technique, to evolve network properties.
- Evaluated network stability using parameters like network sensitivity, attractor cycle length, and number of attractor basins.
Main Results:
- Successfully produced RBNs with increased stability from chaotic regimes using DE.
- Demonstrated that the evolutionary approach can generate networks with homogenous Boolean functions.
- Showcased the creation of highly structured state spaces in the evolved networks.
Conclusions:
- Differential Evolution is an effective method for optimizing gene regulatory network models for enhanced stability.
- Evolutionary computation can guide complex systems from chaotic to ordered states.
- This approach offers a pathway to designing more robust and predictable biological models.
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