Optimal constrained stationary intervention in gene regulatory networks

Area of Science:

• Systems Biology
• Computational Biology
• Genetics

Background:

• Gene network modeling aims to control cellular behavior and prevent disease phenotypes.
• Probabilistic Boolean networks (PBNs) are used for gene regulation modeling.
• Current intervention strategies may have detrimental side effects, necessitating bounded treatment numbers.

Purpose of the Study:

• To develop a constrained approach for optimal therapeutic interventions in gene regulatory networks.
• To adapt reward functions for limiting the expected number of treatments.
• To apply the constrained approach to a mutated mammalian cell cycle model.

Main Methods:

• Formulation of a general constraint approach for optimal intervention.
• Adaptation of reward functions to incorporate treatment number bounds.
• Case study analysis using a mutated mammalian cell cycle.

Main Results:

• A novel framework for optimal therapeutic intervention with bounded treatments was established.
• The method successfully applied constraints to limit the expected number of interventions.
• The mutated mammalian cell cycle served as a validated test case.

Conclusions:

• The developed constrained approach offers a safer alternative for gene network interventions.
• This method can mitigate side effects by limiting treatment frequency.
• The approach has potential applications in precision medicine, particularly for complex diseases like cancer.