Simulating aging to increase human healthspan: how in silico life sciences could tackle real societal challenges
Marian Breuer: Assistant Professor, Maastricht Centre for Systems Biology (MaCSBio), Maastricht, The Netherlands
Silvia Bolognin: Assistant Professor, Institute for Technology-Inspired Regenerative Medicine (MERLN), Maastricht University, Maastricht, The Netherlands
Shuyi Yang: PhD student, Maastricht Centre for Systems Biology (MaCSBio), Maastricht, The Netherlands
Renaud B. Jolivet: Professor of Neural Engineering & Computation, Maastricht Centre for Systems Biology (MaCSBio), Maastricht, The Netherlands
Realistic opportunities to extend the human healthspan and ease the societal burden of aging-related diseases are emerging, but require long-term funding for infrastructures that support large-scale data collection and systems-based aging research, advocate Prof. Renaud Jolivet and colleagues.
DOI: https://doi.org/10.25453/plabs.28658561
Published on March 25th, 2025
Major non-communicable diseases cause both unfathomable human suffering and bring about a vast economic burden: cancer and cardiovascular disease alone accounted together for almost half of all deaths globally in 2021 (1, 2), while the estimated global cost of dementia alone exceeded US$1 trillion in 2019 (3). While distinct in nature and affected organ systems, these diseases share one crucial characteristic: aging is the major risk factor. It has been estimated that finding a cure for either cancer or cardiovascular disease would raise overall life expectancy by around 3 years. Delaying aging itself, if existing approaches in animal models could be extrapolated to humans, could yield a 10-fold higher increase of 30 additional years (4).
While treating aging itself, rather than just the morbidities it promotes, used to be seen as a mirage, progress in recent decades has potentially brought this prospect within reach (5). This has led, for instance, to the approval of the Targeting Aging with METformin (TAME) trial by the United States Food and Drug Administration (FDA). This clinical trial intends to test the effect of the diabetes drug metformin not against a single disease, but in delaying the onset of a set of age-related diseases in general—as a proxy for aging itself (6). Given the huge impact of aging and associated diseases, and the recent feasibility of researching interventions against aging itself, following this path is both an ethical and economic imperative. This is reflected in the Dublin Longevity Declaration (2023), which calls for allocating substantial resources to research on aging and interventions against it. At the time of writing, the Declaration has been undersigned by 2672 researchers in aging, members of the broader academic community, and members of the public (7).
Studying aging and possible interventions is daunting, however. Aging affects the entire human body across vast spatial and temporal scales, i.e., with changes ranging from the molecular to the tissue organization level, and over timescales spanning decades. These multiple layers of complexity necessitate a “systems” approach.
The computational model presented by Shichkova and colleagues in Frontiers in Science (8) illustrates this paradigm. These authors combined models of individual biochemical processes—such as neuronal activity, the biochemical reaction network that metabolically sustains neurons, and blood flow to the brain—into a multi-process model (9) that can simulate neuronal activity and its support system as a cohesive whole. Then, a large dataset (from mice experiments) was used to approximate the effects of aging. Simulating the system’s behavior in both old and young states suggests molecular changes induced by aging that would be difficult to probe experimentally. The model also proposes specific anti-aging interventions that can now be explored further, either in silico or in vivo.
The model by Shichkova and colleagues is representative of a broader trend whereby computational models now allow systems-level biomedical research, including on age-related changes in the human body. These models can be mechanistic, i.e. based on knowledge-derived mathematical descriptions of components and their interactions, or built in a data-driven manner using artificial intelligence (AI), for instance.
What does this mean for policies?
The model by Shichkova and colleagues (8) reemphasizes what really should be “Step 0” in any effort to extend the healthspan (years spent free of chronic morbidity), i.e. the avoidance of factors that actively shorten it. For example, this includes perturbed blood glucose levels, which are already modifiable today. The scale of this need cannot be overstated. In Europe, non-communicable diseases (NCD) cause 90% of deaths and 85% of years lived with disability. Of these deaths, ~60% can be attributed directly to avoidable risk factors, such as an unhealthy diet. Globally, a third of all deaths can be attributed to tobacco, ultra-processed foods, fossil fuels, and alcohol (10). Yet, while the public sector interventions needed to reduce the burden of non-communicable diseases arising from unhealthy products and practices have been known for years, their implementation is consistently and repeatedly limited due to opposition from vested interests (10). Again, removing or reducing factors that shorten the healthspan would not only reduce human suffering, but also have great economic benefit. This Step 0 needs only political resolve to act on the science that is already available.
The work by Shichkova and colleagues also illustrates an important limitation of fragmentation in neuroscience research, which has for now often focused on neurons, glial cells, or the vasculature in isolation, and which often treats diseases as separate research questions. These authors’ approach exemplifies the path that the field must now take to move beyond a piecemeal description of the brain and its diseases. The increasing availability of very large datasets in neuroscience and the inherent complexity of the brain—and indeed all biological systems—call for in silico systems approaches (as outlined in the Dublin Longevity Declaration) and for the brain to be viewed more holistically as a network of diverse cells rather than merely a large network of neurons. This should encompass both data-driven AI methods and mechanistic models, such as the one by Shichkova et al., that leverage the vast preexisting knowledge of chemical and biological rules governing processes in the human body.
This has important implications for research funding and associated policies. The complexity of biological systems necessitates durable research infrastructures for biomedical research, and for neuroscience in particular. The right balance must be struck between funding the new, large-scale data collection initiatives that are necessary and the extensive in silico research efforts required to fully exploit these data. Additionally, policymakers need to understand that the promise of these approaches can only truly be unlocked through sustained research efforts requiring long-term funding. Unfortunately, most funding vehicles accessible today for biomedical research are utterly unsuited for such an ambition: they are too limited in amplitude and in duration. Again, the investments necessary to pursue these research efforts are not only a moral imperative—they will realize great economic value in the long term.
A broadening of the research scope, from its current focus on independent diseases to a more holistic view of aging and healthspan, must continue. Importantly, we need not curb our ambitions concerning healthspan extension. The work of Shichkova et al. investigates readily available anti-aging interventions (e.g., blood glucose control) and novel ones, such as nicotinamide adenine dinucleotide (NAD+) supplementation. More fundamental strategies currently being investigated in clinical trials, such as senolytic treatments to clear detrimental senescent cells from the body (11), have true disruptive potential in the extension of the human healthspan. Policymakers need to recognize this disruptive potential, and direct support and resources to fully reap the immense human and economic benefits.
Copyright statement
Copyright: © 2025 [author(s)]. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in Frontiers Policy Labs is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Generative AI statement
The authors declare that no generative AI was used in the creation of this article.
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