A Survey of Statistical Models for Reverse Engineering Gene Regulatory Networks

Area of Science:

• Computational Biology
• Systems Biology
• Bioinformatics

Background:

• Gene regulatory networks (GRNs) control cellular functions.
• Reverse engineering GRNs is crucial for understanding biological systems.
• Existing statistical models for GRN inference vary widely.

Purpose of the Study:

• To survey statistical models for reverse engineering gene regulatory networks (GRNs).
• To propose a graphical modeling framework for a systematic review.
• To elucidate the development and future of GRN research.

Main Methods:

• A graphical modeling framework is proposed to structure the review.
• Existing GRN models are reviewed based on the framework, discussing pros and cons.
• Network inference algorithms are surveyed, categorized as point and probabilistic solutions.

Main Results:

• A comprehensive overview of statistical models for GRN inference is provided.
• The graphical framework facilitates systematic comparison of different modeling approaches.
• Connections and differences among network inference algorithms are highlighted.

Conclusions:

• The graphical modeling framework aids in understanding GRN modeling issues.
• This survey clarifies the landscape of GRN reverse engineering.
• Statistical signal processing is positioned as central to future GRN research.