Practical Guidelines for Incorporating Knowledge-Based and Data-Driven Strategies into the Inference of Gene...

Area of Science:

• Systems Biology
• Computational Biology
• Genomics

Background:

• Modeling gene regulatory networks (GRNs) is crucial for understanding gene expression and interactions.
• Reverse engineering is a common approach for GRN modeling, but faces challenges in optimizing network behavior accuracy and biological topology validity simultaneously.

Purpose of the Study:

• To propose an integrative modeling framework that addresses the dual challenges of inferring accurate network behaviors and valid biological topologies for GRNs.
• To develop a method that combines knowledge-based and data-driven approaches for comprehensive GRN construction.

Main Methods:

• An integrative modeling framework was developed, combining knowledge-based and data-driven inputs.
• The framework was validated using a real dataset from the yeast S. cerevisiae cell cycle.

Main Results:

• The proposed framework successfully inferred solutions that satisfy both network behavior accuracy and biological structure validity.
• The approach demonstrated effectiveness in constructing biological topologies alongside their corresponding network behaviors.

Conclusions:

• The integrative modeling framework offers a robust solution for GRN modeling, overcoming limitations of existing methods.
• The validated approach provides exploitable outcomes for guiding future in vivo experimental designs in systems biology.