Synergistic Applications of Mechanistic Models and AI in Understanding Biology and Medicine

The integration of Mechanistic Modeling (MM) and Artificial Intelligence (AI) is transforming how we study biological systems and medical phenomena. While traditional mechanistic models, such as stochastic, ordinary, delay, and partial differential equations have long provided deep mechanistic insights, AI and machine learning offer powerful new tools for discovery, parameter estimation, and data-driven refinement of these models.

This Research Topic focuses on the interdisciplinary integration of MM and AI across biological and medical sciences. By critically evaluating emerging approaches that unify mechanistic frameworks with AI methodologies, we aim to push the boundaries of biomedical research. Key primary objectives include exploring how AI enhances parameter estimation, model selection, and uncertainty quantification in classical MM; advancing data-driven methodologies for discovering biological laws directly from data; and developing hybrid frameworks that increase both model interpretability and the predictive strength of modern AI.

Submissions should demonstrate the integration of MM approaches and AI or machine learning methodologies. Methodological or theoretical studies without biological relevance will be considered out of scope. We welcome contributions including, but not limited to, the following themes:

• AI-enhanced parameter inference, sensitivity analysis, and uncertainty quantification for mechanistic models
• Data-driven model discovery and learning of governing equations from empirical data
• Hybrid modeling methods unifying physics-based, statistical, and deep learning approaches
• AI-informed prediction, real-time control, and decision-making in health and disease management
• Deep learning solutions for extracting actionable features from complex omics, imaging, or clinical datasets
• Applications in infectious disease, systems and synthetic biology, personalized medicine, pharmacokinetics, ecological and population modeling, and multi-omics integration

We welcome original research articles, methods papers, reviews, perspectives, and opinions.

Article types and fees

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

• Brief Research Report
• Community Case Study
• Conceptual Analysis
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• ... 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
• Community Case Study
• Conceptual Analysis
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Mathematical modeling, Artificial intelligence, Mechanistic models, Machine learning, Computational biology

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.