Data and model integration is a rapidly expanding field within computational and systems biology, playing a central role in our understanding of complex biological processes. The proliferation of high-throughput omics technologies and the increasing complexity of biological data have driven the need for comprehensive strategies that integrate heterogeneous data sources and modeling approaches. Currently, major challenges persist regarding effective data fusion across diverse scales and modalities, reliable pattern mining, and the seamless mapping and harmonization of different application models. Ongoing debates focus on best practices for dynamic modeling under uncertainty, as well as the semantic integration and FAIR (Findable, Accessible, Interoperable, and Reusable) management of data and models.

Recent studies have demonstrated the power of multi-omics integration for uncovering biological mechanisms, especially through sophisticated approaches in data fusion, network inference, and spatio-temporal modeling. Breakthroughs in dynamic and hybrid modeling frameworks, coupled with advanced parameter estimation and sensitivity analysis methods, have enabled more robust predictions and a deeper understanding of biological regulation and signaling. Nevertheless, the field still faces persistent gaps, such as the need for unified frameworks linking experimental data with mechanistic models, standards for representing model uncertainty, and systematic evaluation of model calibration techniques across various biological contexts.

This Research Topic aims to advance knowledge in data and model integration, with an emphasis on reviewing the latest methods for fusing diverse datasets, enhancing integration models, and robustly identifying and calibrating parameters within complex biological systems. We seek to highlight both methodological advances and practical applications, addressing challenges related to model identifiability, uncertainty quantification, and semantic interoperability. Key objectives include evaluating current strategies, identifying limitations, and proposing future directions for integrated data-model workflows.

To gather further insights into the broad yet focused landscape of data and model integration, we welcome articles that provide critical evaluations and syntheses of existing methods, conceptual frameworks, and emerging trends. We encourage contributions on, but not limited to, the following themes:
· Data fusion and integration across multi-omics datasets and biological scales
· Data mining and pattern discovery techniques for integrated biological data
· Dynamic, spatio-temporal, and stochastic modeling approaches with uncertainty quantification
· Model identifiability, calibration, parameter estimation, sensitivity analysis, and validation
· Network inference and analysis in regulatory, signaling, and metabolic systems, incorporating semantic and FAIR principles, as well as optimization methods

This Research Topic welcomes Review, Systematic Review, and Mini Review articles.

Are you interested in contributing a manuscript to this Research Topic?

Reviews in Data and Model Integration