Automated Synthesis and Analysis of Switching Gene Regulatory Networks.

Area of Science:

• Developmental Biology
• Systems Biology
• Computational Biology

Background:

• Gene regulatory networks (GRNs) control cell differentiation.
• Understanding dynamic network changes is crucial for developmental processes.

Purpose of the Study:

• Introduce Switching Gene Regulatory Networks (SGRNs) to model dynamic GRN topology changes.
• Develop a method to synthesize SGRNs satisfying biological constraints.
• Analyze network reconfiguration during cell fate specification.

Main Methods:

• Utilized Satisfiability Modulo Theories (SMT) solvers for SGRN synthesis.
• Developed a constraint-based approach for network construction.
• Evaluated feasibility and scalability using synthetic benchmarks.

Main Results:

• Demonstrated the feasibility of synthesizing SGRNs.
• Showcased the approach's scalability on synthetic data.
• Applied the method to a simplified mammalian cortical neuron network.

Conclusions:

• SGRNs provide a framework for modeling dynamic gene regulatory processes.
• SMT-based synthesis is a viable method for constructing biologically relevant SGRNs.
• This approach aids in understanding cell fate specification and development.