Forms Dynamics in Human Pathology: A Gestalt-Inspired Perspective on In Silico Ecophysical Modeling

Understanding pathological processes remains challenging, because clinical descriptions still rely predominantly on phenotypic observations, while the underlying dynamical mechanisms that generate, maintain, and transform pathological conditions often remain implicit. This Perspective article introduces forms dynamics as a physically grounded framework for interpreting pathology as the dynamical evolution of structured configurations sustained by continuous exchanges of energy, mass, and regulatory information with the environment. The proposed perspective integrates concepts from non-equilibrium thermodynamics, complex systems modeling, systems ecology, and Gestalt-inspired structural reasoning. Within this framework, pathological systems are represented through physically meaningful state variables and fluxes whose interactions can be expressed through coupled balance equations or equivalent graphical schematizations. Empirical observations, including clinical data, diagnostic measurements, and network-based representations of biological interactions, guide the identification of relevant variables, pathways, and couplings. Calibration and validation are discussed as procedures through which admissible dynamical regimes are constrained using physiological ranges, characteristic timescales, observed trajectories, and responses to perturbations. In this perspective, physiological and pathological conditions are interpreted as dynamically maintained regimes emerging from the coupling of variables and fluxes rather than as purely static structural states. As a foundational contribution, this article does not present a disease-specific case study but establishes the conceptual basis, illustrative mathematical structure, and operational workflow through which future disease-specific implementations may be developed. In this sense, forms dynamics is proposed as a unifying modeling perspective for complex diseases and as a possible foundation for future translational applications, including physics-informed digital twins and more interpretable computational tools for biomedical research and clinical support.