Modelling gene expression control using P systems: The Lac Operon, a case study.

Area of Science:

• Computational Biology
• Systems Biology
• Theoretical Computer Science

Background:

• Biological systems exhibit complex gene regulation involving protein interactions.
• Modeling these systems requires capturing discrete, concurrent, and stochastic behaviors.
• Cellular compartmentalization and membrane functions are crucial for biological processes.

Purpose of the Study:

• To present P systems as a formal framework for specifying and simulating biological gene regulation systems.
• To model protein-protein and protein-DNA interactions within cellular compartments or colonies.
• To incorporate discrete, stochastic, and membrane-related aspects of cellular processes.

Main Methods:

• Utilizing P systems with rewriting rules on multisets of objects and strings.
• Explicitly modeling transcription and translation as concurrent, discrete processes.
• Employing an extension of Gillespie's algorithm, the Multicompartmental Gillespie's Algorithm, for system evolution.

Main Results:

• A formal framework for simulating gene regulation, including compartmentalization and membrane dynamics.
• Successful modeling of transcription and translation as concurrent, discrete processes.
• Demonstration of the approach's efficacy using the Lac Operon system as a case study.

Conclusions:

• P systems provide a robust framework for simulating complex biological systems, including gene regulation.
• The Multicompartmental Gillespie's Algorithm effectively captures the discrete and stochastic nature of cellular processes.
• The proposed method offers a valuable tool for understanding cellular mechanisms and genetic regulation.