Addressing biomedical complexity using the energy systems language.
5 affiliations
- 1DMSN-Department of Molecular Sciences and Nanosystems, Ca' Foscari University of Venice, Venice, Italy.
- 2SMAC-Centre for the Study of the Systemic Dynamics of Complex Diseases, Ca' Foscari University of Venice, Venice, Italy.
- 3NICHE-Centre for Environmental Humanities, Ca' Foscari University of Venice, Venice, Italy.
- 4DAIS-Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, Venice, Italy.
- 5Department of General Surgery and Medical-Surgical Specialties, University of Catania, Catania, Italy.
Abstract
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.
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BibTeX
@article{surra2026addressing,
title = {{Addressing biomedical complexity using the energy systems language.}},
author = {Federico Surra and Francesco Gonella and Luigi Conte and Alessandra Romano},
journal = {Frontiers in systems biology},
year = {2026},
month = oct,
doi = {10.3389/fsysb.2026.1903662},
url = {https://europepmc.org/article/MED/42908843},
note = {CC BY},
}
RIS
TY - JOUR
TI - Addressing biomedical complexity using the energy systems language.
AU - Federico Surra
AU - Francesco Gonella
AU - Luigi Conte
AU - Alessandra Romano
PY - 2026
DA - 2026/10/07
JO - Frontiers in systems biology
DO - 10.3389/fsysb.2026.1903662
UR - https://europepmc.org/article/MED/42908843
AB - 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.
SN - 2674-0702
ER -