Innovative computational strategies in modeling complex biological systems

Precision modeling in complex biological systems represents a burgeoning field aimed at understanding and predicting intricate system behaviors using advanced computational and mathematical approaches. This domain leverages techniques such as chaos theory and agent-based modeling to unravel the nonlinear, dynamic interactions within biological systems. These innovative approaches address longstanding challenges in capturing the inherent complexity and unpredictable nature of biological processes, driving advancements in fields such as systems biology, ecology, and biomedical sciences.
Recent developments have demonstrated the efficacy of chaos theory in explicating the seemingly erratic dynamics of biological systems, offering new perspectives on stability, oscillations, and bifurcations within these systems. Agent-based modeling has been instrumental in simulating the behavior of individual entities and their interactions, yielding insights into emergent phenomena and system-level outcomes. Nevertheless, ongoing challenges include enhancing the accuracy of these models and ensuring their applicability across diverse biological contexts.
This Research Topic targets the exploration and development of stochastic modeling techniques designed to dissect and predict the behavior of complex biological systems. By refining these mathematical and computational tools, we aim to foster a deeper understanding of complex system dynamics and enhance predictive capabilities.
To gather further insights into stochastic modeling in complex systems, we welcome articles addressing, but not limited to, the following themes:
• Application of chaos theory in biological systems modeling
• Advances in agent-based modeling for complex system analysis
• Dynamics of nonlinear interactions and emergent behavior in biological systems
• Real-world case studies demonstrating stochastic modeling applications
• Challenges and innovations in computational approaches to complex system predictions
Article types accepted include original research, reviews, and perspectives focusing on refining and applying mathematical and computational models to better understand and predict complex biological system behaviors.

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
• 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
• Case Report
• Clinical Trial
• Community Case Study
• 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
• Policy Brief
• Review
• Systematic Review
• Technology and Code

Keywords: precision modeling, complex biological systems, chaos theory, agent-based modeling, nonlinear dynamics, emergent behavior, systems biology, computational modeling, predictive modeling, biomedical applications

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.