Addressing biomedical complexity using the energy systems language

Biological complex systems self-organize and evolve through feedback-driven interactions not adequately captured by linear cause-effect models. To address this gap, we propose a novel approach grounded in Energy Systems Language (ESL). It provides a description of how resource availability, carrying capacity and feedback networks shape the system trajectories. ESL is rooted in open systems Thermodynamics, shifting the focus from the biology of events to their systemic structure. Models are built on biomedical knowledge, with parameters determined by empirical observation and biophysical constraints, producing simulators that address "what if" exploratory questions. We applied this approach to the antigen-specific adaptive immune response as a proof-of-concept of the framework designed to investigate system-level dynamic regime stability and qualitative response to external driving forces. We developed a computational simulator reproducing hallmark features of humoral immunity at daily, monthly and decadal time-scales, including germinal-center dynamics, establishment of long-lived plasma cells, antibody secretion, and long-term decline in the absence of antigen re-exposure. Using this approach, future investigations of complex pathologies, e.g., lymphoid malignancies and autoimmune diseases, can be developed, providing computational tools for testing hypotheses on patient-specific therapeutic strategies.