A survey on methods for modeling and analyzing integrated biological networks.

Area of Science:

• Systems Biology
• Computational Biology
• Molecular Systems Biology

Background:

• Cellular systems exhibit complex, integrated responses to environmental changes.
• Traditionally, signaling networks, gene regulation, and metabolism are modeled independently, limiting holistic understanding.
• Advancements in 'omics' technologies provide more data, driving the need for integrated modeling approaches.

Purpose of the Study:

• To review and discuss methodological frameworks for modeling and analyzing integrated biological networks.
• To compare various modeling approaches based on data requirements, scalability, and computational demands.
• To highlight the importance of integrated modeling for a deeper understanding of cellular behavior.

Main Methods:

• Network-based methods and Chemical Organization Theory
• Flux-Balance Analysis and its extensions
• Logical discrete modeling, Petri Nets, traditional kinetic modeling, Hybrid Systems, and stochastic models

Main Results:

• A comprehensive overview of diverse methodologies for integrated biological network modeling is presented.
• Comparisons of methods regarding data needs, scalability, and computational complexity are established.
• Successful case studies in large-scale genome models and specific subsystems are illustrated.

Conclusions:

• Integrated modeling of metabolic, gene regulatory, and signaling networks is crucial for understanding cellular systems.
• The reviewed methods offer various strengths for analyzing complex biological interactions.
• This work provides a guide for selecting appropriate modeling frameworks for systems biology research.