Multi-level and hybrid modelling approaches for systems biology

Area of Science:

• Computational Systems Biology
• Bioinformatics
• Developmental Biology

Background:

• Biological systems exhibit complexity across scales, with dynamic regulations evident in processes like ontogenesis.
• High-throughput techniques generate vast biological data, highlighting the need for sophisticated analytical approaches.
• Biological information is often fragmented across scientific domains with distinct representational formalisms.

Purpose of the Study:

• To review key contributions in multi-level and hybrid modeling for computational systems biology.
• To emphasize the importance of integrating diverse formalisms for accurate biological modeling.
• To discuss the utility of hybrid models in understanding complex biological phenomena.

Main Methods:

• Review of existing literature on multi-level and hybrid modeling techniques.
• Analysis of how different formalisms are integrated within biological models.
• Examination of case studies in computational systems biology.

Main Results:

• Multi-level and hybrid models offer enhanced accuracy and knowledge integration capabilities.
• These models effectively handle the heterogeneity of biological data and formalisms.
• The reviewed approaches provide powerful tools for dissecting complex biological systems.

Conclusions:

• Multi-level and hybrid models are essential for advancing computational systems biology.
• Integrating diverse formalisms is key to building comprehensive and predictive biological models.
• This review highlights the current state and future directions in hybrid biological modeling.