Stochastic dynamics of genetic networks: modelling and parameter identification

Area of Science:

• Systems Biology
• Computational Biology
• Molecular Biology

Background:

• Gene regulatory network identification traditionally uses deterministic models.
• Emerging evidence highlights the inherent randomness (stochasticity) in gene regulation.
• Accurate data processing requires explicit accounting for stochasticity in modeling and algorithms.

Purpose of the Study:

• To develop a gene expression model that incorporates probabilistic transcription.
• To introduce a method for estimating gene network parameters from experimental data.
• To address challenges posed by sparse, noisy, and irregularly sampled time-course data.

Main Methods:

• A probabilistic model for prokaryotic gene expression, treating transcription as a random event.
• First-order deterministic kinetics for protein synthesis and degradation.
• A parameter estimation method for known interaction networks using time-course experimental data.

Main Results:

• The proposed method naturally handles sparse, irregularly sampled, and noisy data.
• The approach is applicable to gene networks of any size.
• Performance was validated using a model of nutrient stress response in Escherichia coli.

Conclusions:

• Stochasticity is crucial for accurate modeling of gene regulatory networks.
• The developed method provides a robust framework for parameter estimation in probabilistic gene expression models.
• This approach enhances the understanding of gene regulation dynamics, particularly under stress conditions.