Fault detection and therapeutic intervention in gene regulatory networks using SAT solvers

Area of Science:

• Systems biology
• Computational biology
• Genomics

Background:

• Somatic mutations disrupt cellular homeostasis, leading to diseases like cancer.
• Understanding these mutations is crucial for designing effective therapeutic interventions.
• Boolean network models represent biological regulatory systems, and mutations can be modeled as stuck-at faults.

Purpose of the Study:

• To address the dual problem of identifying multiple stuck-at faults in Boolean networks.
• To develop a method for designing therapeutic interventions based on identified mutations.
• To leverage systems theory for understanding and intervening in disease mechanisms.

Main Methods:

• Modeling mutations as stuck-at faults in Boolean networks.
• Mapping fault identification and therapeutic intervention design to the Boolean satisfiability (SAT) problem.
• Utilizing a fast SAT solver to efficiently solve these problems.

Main Results:

• Successfully mapped the identification of multiple stuck-at faults to the SAT problem.
• Developed a SAT-based approach for designing therapeutic interventions.
• Demonstrated the effectiveness of the SAT mapping using synthetic and biological examples.

Conclusions:

• The Boolean satisfiability (SAT) problem provides an effective framework for identifying mutations and designing therapies.
• This systems-theoretic approach offers a computationally efficient solution for complex biological problems.
• The methodology holds promise for advancing systems medicine research and cancer therapy.