Control-guided refinement of partially specified Boolean networks: applications to RTK signaling

Area of Science:

• Systems Biology
• Computational Biology
• Control Theory

Background:

• System control offers insights into biological dynamics.
• Executable models are crucial for identifying therapeutic targets in silico.
• Models are often underspecified due to incomplete mechanistic knowledge.

Purpose of the Study:

• To introduce a novel computational framework for control-guided model refinement.
• To predict informative perturbation experiments to reduce knowledge gaps in biological models.
• To extend the framework for handling oscillatory phenotypes as control targets.

Main Methods:

• Utilizes partially specified Boolean networks (PSBNs) for integrating uncertain information.
• Employs control-guided model refinement to predict experiments.
• Applies the framework to receptor-tyrosine kinase (RTK) signaling, specifically fibroblast growth factor signaling.

Main Results:

• Demonstrates the framework's applicability on RTK signaling pathways.
• Provides new insights into modeling the FGFR3-MAPK pathway.
• Successfully integrates uncertain or incomplete information into executable models.

Conclusions:

• The developed framework enhances the accuracy and completeness of biological system models.
• It facilitates the identification of therapeutic targets by systematically reducing knowledge gaps.
• The approach offers a powerful tool for systems biology research, particularly in cancer and developmental disorders.