Methods, Tools, and Models in Systems Biology

Systems biology is a rapidly evolving discipline that examines the complex interactions underlying biological function through integrative computational and experimental approaches. Over the past two decades, advances in high-throughput omics, quantitative imaging, and computational modelling have allowed researchers to capture system-level behaviours with unprecedented resolution.

However, this technological expansion has also revealed significant challenges: reproducibility issues, fragmented tool ecosystems, inconsistent benchmarking practices, and barriers to interoperability continue to limit the reproducibility and comparability of results. Recent studies have demonstrated promising progress, such as the development of standardized metabolic reconstructions, the integration of single-cell multi-omics data, and the use of AI-driven inference for network modelling. Nevertheless, the lack of unified frameworks and open standards indicates an urgent need for methodological synthesis to ensure reliability and translation across biological contexts.

This Research Topic aims to advance the methodological and computational foundations of systems biology by promoting transparent, reproducible, and scalable approaches to modelling and analysis. It seeks contributions that not only propose new techniques and models but also rigorously test, benchmark, and validate them in diverse biological systems. Key goals include the assessment of interoperability across software platforms, the implementation of FAIR data principles, and the exploration of cross-domain applications where methodological robustness can enhance interpretability and innovation. By encouraging dialogue between computational and experimental communities, this Research Topic aims to accelerate the adoption of best practices that strengthen both theory and application in systems-level biology.

To gather further insights into the methodological, conceptual, and translational boundaries of systems biology, we welcome articles addressing, but not limited to, the following themes:
• Development and benchmarking of dynamic modelling frameworks (ODEs, PDEs, Boolean networks, agent-based and hybrid models)
• Multi-omics data integration spanning genomics, transcriptomics, proteomics, metabolomics, and epigenomics
• Machine learning, AI, and deep learning approaches for model inference and network reconstruction
• Reproducibility, standardization, and FAIR data and model sharing principles
• Computational tools and databases facilitating systems-level analyses and visualisation
• Single-cell and spatial systems biology applications
• Translational systems approaches to cancer, metabolic disorders, infectious disease, and ageing

Article types include Original Research, Methods, technology and code, hypothesis and theory. Review or Mini Review (in the context of model, tool or method use), Perspectives, and specific use cases as reports.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Community Case Study
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Community Case Study
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Specialty Grand Challenge
• Systematic Review
• Technology and Code

Keywords: Systems biology, Computational modelling, Multi-omics integration, Reproducibility, Network reconstruction, Machine learning, Single-cell analysis, Dynamic modelling, FAIR principles, Methodological benchmarking

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.