What is it about?
Why do some animals live for decades while others survive only weeks? Why do some species reproduce early and abundantly, while others invest years before having their first offspring? These are among the oldest questions in biology, and answering them requires understanding how evolution shapes aging and lifespan across thousands of generations. The problem is that we cannot rewind evolutionary history to watch it unfold. AEGIS (Aging of Evolving Genomes In Silico) solves this by letting us run evolution on a computer. Virtual individuals are born, age, reproduce, and die according to simple rules encoded in heritable genomes. No aging pattern is pre-programmed: aging emerges on its own from the interaction between genetics and environmental pressures such as predation, infection, and competition for food. By changing these pressures and re-running the simulation, we can ask directly which intrinsic factors, like mutation rate or reproductive strategy, and which extrinsic ones, like predation or seasonal mortality, cause aging and lifespan to evolve the way they do. Critically, these experiments run at large scale on a standard laptop, making the approach broadly accessible. AEGIS comes with a graphical interface, built-in analysis tools, and a web server, so no programming experience is needed. It is freely available, fully documented, and designed for reproducibility. It can also serve as a teaching tool, recreating classical results in evolutionary biology from first principles.
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Why is it important?
Aging is universal, yet we still do not fully understand why it evolves, or why it evolves so differently across species. Answering this question has consequences well beyond basic biology: understanding the evolutionary logic of aging informs how we think about age-related disease, the limits of human lifespan, and the conditions under which longer, healthier lives can evolve. Until now, addressing these questions computationally required either highly simplified mathematical models, which gain tractability by sacrificing biological realism, or custom-built simulations that take months to develop and are rarely shared or reused. AEGIS fills this gap. Crucially, AEGIS is not just a tool for analysis after the fact. We use it before designing experimental evolution studies, to sharpen hypotheses and anticipate outcomes before committing to costly and time-consuming experiments. When empirical or observational data are available, they can be fed directly into AEGIS as ground truths, anchoring simulations in biological reality and letting us test whether our evolutionary models are consistent with what is actually observed in nature or the lab. This closes the loop between theory and experiment in a way that brings the practice of aging biology closer to how physics operates: iterating between models and measurements, with each informing and disciplining the other. AEGIS accelerates discovery by ensuring that the hypotheses we bring to the bench are already well-refined and well-tested in silico.
Perspectives
On a personal level, building and using AEGIS has been one of the most clarifying intellectual experiences of my career. Watching aging emerge, or fail to emerge, from simple rules in a virtual population forced me to confront assumptions I did not even know I was making. Several ideas I had held about how and why aging evolves turned out to be wrong, or at least far more contingent on specific conditions than I had assumed. No paper or lecture produced that correction as effectively as running the simulation myself. There is something deeper here too. More than once, patterns that emerged from AEGIS simulations sent me back to the literature, where I discovered that what I had stumbled upon was already a known result, a law I simply had not encountered yet. Rediscovering the Hill-Robertson effect this way, through my own simulations rather than through a textbook, was a reminder that simulation can be a genuine engine of learning, not just a tool for confirming what we already know. This is why I believe AEGIS has particular value as a training tool. For students and early-career scientists entering the biology of aging, it builds intuition about evolutionary processes in a way that is hands-on, immediate, and free of mathematical barriers. It makes the invisible visible. Beyond training, we see AEGIS growing into a collaborative platform, a shared language through which biologists, data scientists, and theoreticians can work on the same problems together. Aging biology has historically struggled to bridge the gap between empirical observation and formal modeling. AEGIS speaks both dialects: it is rooted in biological realism, yet it operates through the logic of simulation that data scientists and computational researchers immediately recognize. That common ground, we hope, will draw new minds into the field and push the questions we ask about aging in directions we have not yet imagined.
Dario Riccardo Valenzano
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This page is a summary of: AEGIS: Individual-based modeling of life history evolution, PLoS Computational Biology, March 2026, PLOS,
DOI: 10.1371/journal.pcbi.1014109.
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