“ The goal is to build predictive models that bridge changes across biological scales: from subcellular to organism level, and timescales from cellular turnover to lifespan .”
BUFFALO, NY — June 11, 2026 — A new meeting report was published in Volume 18 of Aging on May 14, 2026, titled “ Foundations of Gerophysics .”
The report was led by first author Maximilian Unfried and corresponding authors Maximilian Unfried and Brian K. Kennedy from the National University of Singapore .
Aging is often studied through biology, genetics, and medicine. Yet despite tremendous advances, many fundamental questions remain unanswered: Why do organisms age at different rates? Why does resilience decline over time? And can the trajectory of aging be predicted before disease develops? Researchers participating in the inaugural Global Conference on Gerophysics explored whether answering these questions may require integrating biology with the quantitative principles of physics.
Held in Singapore on March 5–6, 2025, the conference brought together 160 researchers from physics, biology, computation, and medicine and featured 31 speakers from institutions around the world. The meeting focused on developing a predictive and testable science of aging by applying concepts from dynamical systems, thermodynamics, network theory, stochastic processes, and artificial intelligence to biological aging.
One major theme was the search for simple mathematical principles capable of explaining complex aging phenomena. Uri Alon of the Weizmann Institute of Science presented the “saturated removal” model, a framework that explains several hallmark aging patterns—including rising mortality rates and declining physiological function—through the balance between damage production and damage removal. Building on this work, Yifan Yang demonstrated how the same model may help distinguish interventions that extend lifespan from those that specifically compress sickspan, potentially improving quality of life in later years.
Researchers also explored whether aging itself may resemble a physical phase transition. Peter Fedichev and Jan Gruber described a phenomenological theory in which aging emerges from instability within gene regulatory networks. Their framework links resilience, entropy, and mortality patterns across species and suggests that age-related decline may follow predictable physical laws. Related presentations examined how network instability, loss of robustness, and critical transitions could help explain the progression from healthy aging to frailty and disease.
Artificial intelligence emerged as another major topic. Matt Kaeberlein discussed the “Million Molecule Challenge,” an ambitious effort that combines automated lifespan experiments and machine learning to screen more than one million compounds for longevity-promoting effects. Andrei Tarkhov presented work showing how AI-guided protein design can enhance cellular reprogramming factors used in age-reversal research, improving reprogramming efficiency by more than two orders of magnitude in human cells.
Several talks focused on biological age measurement and aging clocks. Andrew Teschendorff described advances in epigenetic clocks and showed how single-cell analyses are helping researchers distinguish stochastic age-related changes from biologically meaningful aging signals. Steffen Rulands presented evidence that age-related changes in DNA methylation may reflect collective behaviors emerging across genomic regions, suggesting that aging can be studied as a multi-scale physical process extending from molecular interactions to organism-level decline.
The conference also highlighted the growing importance of systems-level approaches. Researchers discussed how network science can model cascading failures in biological systems, how entropy-based measures may provide new biomarkers of aging, and how computational analyses of large clinical and molecular datasets are identifying potential geroprotective interventions. Presentations ranged from reproductive aging and skeletal muscle aging to comparative studies examining why some species live dramatically longer than others.
Metabolism and longevity were another key focus. Peter James Mullen presented multi-organ metabolomic analyses across several species, revealing tissue-specific metabolic signatures associated with aging. Maximilian Unfried described comparative lipidomics studies showing that longer-lived species exhibit more robust lipid interaction networks, while Brian K. Kennedy discussed the challenges of translating aging biomarkers into clinical tools capable of guiding interventions and assessing biological aging in humans.
A recurring message throughout the meeting was that future progress will depend on close collaboration between theory and experimentation. Rather than relying solely on increasingly complex datasets, participants emphasized iterative cycles in which mathematical models generate predictions that can be tested experimentally, with new data then refining those models.
“ The panellists noted that interdisciplinary communication remains a hurdle, urging joint training initiatives to align the languages of biology, physics, and computational science .”
The conference concluded with broad consensus around four priorities for advancing Gerophysics: the development of shared multi-modal datasets, physics-based definitions of aging and rejuvenation, predictive models capable of forecasting intervention outcomes, and stronger translational links between animal studies and human aging research.
As aging research increasingly incorporates tools from physics, artificial intelligence, and computational science, Gerophysics aims to transform aging biology from a largely descriptive discipline into a predictive science. By uncovering the quantitative principles that govern resilience, decline, and longevity, researchers hope to accelerate the development of interventions that promote healthier aging across the lifespan.
Paper DOI : https://doi.org/10.18632/aging.206378
Corresponding authors: Maximilian Unfried – unfried@nus.edu.sg , Brian K. Kennedy – bkennedy@nus.edu.sg
Keywords: aging, gerophysics, geroscience, aging biology, longevity, complex systems, theoretical physics
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Foundations of Gerophysics
14-May-2026
The authors declare no conflicts of interest.