Insights in Data and Model Integration

Systems biology relies on the integration of diverse, high-throughput datasets with computational models capable of representing biological processes across multiple scales. Advances in genomics, transcriptomics, proteomics, metabolomics, imaging, clinical research, and scientific text mining have generated unprecedented opportunities to investigate complex biological systems. However, differences in data quality, resolution, dimensionality, provenance, and representation continue to limit the effective combination of these resources. Key questions concern how heterogeneous evidence can be reconciled, how biological networks and mechanistic models can be updated with new observations, and how integrated analyses can remain interpretable, reproducible, and biologically meaningful. Recent studies demonstrate the potential of multi-omics integration, network-based data fusion, machine learning, deep learning, and knowledge-graph approaches to improve disease modelling, biomarker discovery, pathway analysis, and biotechnology development. At the same time, challenges remain in model interoperability, uncertainty quantification, bias management, data sparsity, computational scalability, and the validation of predictions across experimental and clinical settings. Addressing these gaps requires closer collaboration across bioinformatics, computer science, statistics, and biology, together with robust standards, open tools, and methods that connect data-driven and mechanistic perspectives.This Insights in collection aims to provide an overview of recent advances in the integration of biological data and computational models, while identifying priorities for future research. It will examine how disparate molecular, network, literature-derived, and clinical data can be combined to improve the representation and prediction of biological systems. Contributions may address the computational and statistical foundations of integration, methods for assessing model reliability and uncertainty, and strategies for translating integrated analyses into practical applications in disease research, biomarker development, and biotechnology. The Insights in collection also seeks to clarify how emerging methods can overcome challenges associated with data heterogeneity, interpretability, reproducibility, interoperability, and scale.To gather further insights into data and model integration for systems biology, this Research Topic will focus on approaches that combine heterogeneous biological data with computational, statistical, or mechanistic models, from methodological development through to validation and application. We welcome articles addressing, but not limited to, the following themes:• Advanced frameworks for multi-omics data integration• Network-based data fusion and biological pathway analysis• Machine-learning and deep-learning methods for biological data synthesis• Integration of literature-derived knowledge, knowledge graphs, and biological databases• Interoperability of computational models and standardized database schemas• Scalable algorithms for high-throughput and real-time biological data processing• Integration of clinical data with mechanistic and predictive models• Model validation, uncertainty quantification, and interpretability• Open-access software tools and pipelines for reproducible integration• Applications in disease modelling, biomarker discovery, and biotechnologyThis collection welcomes contributions reflecting on current developments and plotting pathways for upcoming research, from Editorial Board Members of the section, from authors they refer, and, on invitation, from a small number of external researchers whose work is shaping the direction of the field. Authors are encouraged to engage critically with their fields, identify the current challenges, and propose novel solutions that advance our understanding of complex biological systems. Topic editors: Karamarie Fecho, Yang Zhang, Leonardo Medrano Sandonas, Gabriel Gil Source: https://www.frontiersin.org/research-topics/84857/insights-in-data-and-model-integration