Aging as a Loss of Goal-Directedness: An Evolutionary Simulation and Analysis Unifying Regeneration with Anatomical Rejuvenation
We conclude that boundaries (and more generally, relations) between organs matter in our regeneration experiments: including surrounding tissue in detecting affected organs and in subsequent regeneration experiments that specifically target the wrongly expressed cells of the affected organs can be an efficient approach for regeneration and rejuvenation applications in our system.
A different but likewise promising approach is replacing the affected organs or cells directly with their target states (rather than their respective initial states as here). However, while “transplanting” entire organs can be effective, there is little quantitative difference in rejuvenation performance between replacing cells with their target states and reconfiguring cells into their initial states. From a biological perspective, the latter approach of resetting cells to their primordial states strikes us as conceptually and technologically more feasible, as the number of specialized target states might greatly exceed the number of necessary primordial initial states: resetting cells to their initial cell state, or at least to a particular primordial cell state suitable for starting a specific morphogenetic task, could serve as a general fallback mechanism to induce regeneration of the respective (here virtual) organism by restarting the developmental program locally.
At this point, we would like to stress that the developmental program of our NCA has not been optimized either for such long lifespans or for the here‐proposed intervention experiments. The latter can thus be seen as minimally invasive ad‐hoc cellular reconfiguration interventions, i.e. a dynamic and targeted “hacking” of the organism's developmental plan by an external agent,[ 23 ] with the external agent in this case being the researchers (us).
Overall, we conclude that such a procedure of targeted cellular interventions is a reliable approach for rejuvenation in our system.
In this paper, we aim to address a significant gap in the aging literature which, to the best of our knowledge, so far lacks computational models of aging considering cellular information processing and multi‐scale competency. We seek to address this knowledge gap by developing an evolutionary framework integrating the multi‐scale competency architecture of multicellular tissue.
Specifically, we here utilize numerical tools from Artificial Life,[ 66 ] so‐called Neural Cellular Automata (NCAs)[ 44 , 46 , 72 ] which we trained with an evolutionary algorithm to perform self‐orchestrated targeted pattern formation, i.e., morphogenesis of a 16 × 16 smiley‐face pattern. The localized unicellular decision‐making centers in our NCA implementation are modeled by Artificial Neural Networks (ANNs), which perceive the numerical states of their direct cellular neighbors on the grid of the NCA to regulate their own cell state individually. In that way, NCAs substitute homeostatic and morphogenetic pathways of “real” multicellular biological tissue, with the NCA‐specific ANNs and numerical states respectively corresponding to cell‐internal decision‐making machinery (such as gene regulatory networks) and cell‐type expressions.
Analogously to their biological counterparts, the NCA's cells form their decisions following a global agenda, allowing the collective of cells to assume a predefined target pattern over a predefined developmental time, thus simulating the process of embryogenesis. The evolutionary algorithm selects mature phenotypes directly after the predefined developmental stage (i.e., fully grown tissue), specifically quantifying how well the cells of the corresponding NCAs can grow the facial features and organs of the 16 × 16 smiley‐face pattern in a fully self‐orchestrated and decentralized way. Notably, the NCA's cells do not in any way explicitly perceive time or their own age (in terms of simulation time steps), but their dynamics are fully governed by regulating and exchanging noisy state information on the NCA's grid. Yet, when deploying a successfully trained NCA for much longer times than necessary for development (at which stage evolutionary selection would have occurred), the tissue of the NCA expresses signs of aging, such as successively blurring boundaries between organs, or entirely losing one or more organs. We emphasize that this behavior is emergent and happens without any further intervention.
We propose that, even in the absence of accumulated cellular or genetic damage, a biological system left without any goal in the morphospace will start degrading anatomically. Using an in silico model of homeostatic morphogenesis with a multi‐scale competency architecture and information dynamics analysis, which provide a novel perspective on aging dynamics with significant implications for longevity research and regenerative medicine, we found the following: 1) Absence of long‐term morphostasis goal: aging emerges naturally after development due to the lack of an evolved regenerative goal, rather than being caused by specific detrimental properties of developmental programs (e.g., antagonistic pleiotropy or hyperfunction); 2) Acceleration factors versus root cause: cellular misdifferentiation, reduced competency, communication failures, and genetic damage all accelerate aging but are not its primary cause; 3) Information dynamics in aging: aging correlates with increased active information storage and transfer entropy, while spatial entropy measures distinguish two dynamics, i) the loss of structure and ii) morphological noise accumulation; 4) Dormant regenerative potential: despite organ loss, spatial information persists in our cybernetic tissue, indicating a memory of lost structures, which can be reactivated for organ restoration through targeted regenerative information; and 5) Optimized regeneration strategies: restoration is most efficient when regenerative information includes differential patterns of affected cells and their neighboring tissue, highlighting strategies and protocols for rejuvenation.
While the cells in our system learn to perform morphogenesis, i.e., how to grow an adult morphology successfully, we still observe aging effects in long‐term simulations even without artificially perturbing the NCAs parameters. These results suggest that development and morphostasis, while close in terms of morphological trajectories and tasks, still significantly diverge. To counteract aging, it is therefore necessary to (re)activate a developmental/regenerative goal. We explicitly demonstrated this by providing our cybernetic tissue with the necessary information for regeneration, thereby inducing rejuvenation through self‐organization at the anatomical level. Our results suggest a roadmap for rejuvenation therapies, particularly that reversing aging may imply providing a new goal to the tissue, e.g., regeneration. In other words, this suggests that activating regeneration may stop or reverse aging. As currently debated, regenerative species such as salamanders or planaria might not age at all. While already known for planaria, this is the subject of current research on salamanders. The latter not only lack senescent phenotypes, but it is indeed impossible to define an epigenetic clock for a specimen older than four years.[ 102 ] One extension to the present work will therefore involve comparing an aging system that has only been trained on morphogenesis tasks to a system that has learned to regenerate and then actively maintain morphostasis after development. Understanding their differences in terms of information dynamics will allow us to form a thorough understanding of the homeostatic loops that allow differently expressed cells in a self‐assembling tissue to maintain their integrity against adversarial attacks or thermodynamic noise.
This work also emphasized how to optimize regenerative strategies. Our results show the importance of the information at the level of the borders of integrated tissue or organs for regeneration. This importance can be found in the field of bioelectricity. Indeed, research indicates that mutations in critical neurogenesis genes, such as Notch, can lead to developmental defects.[ 103 ] These organ defects can be cured through the overexpression or activation of native HCN2 (hyperpolarization‐activated cyclic nucleotide‐gated) channels.[ 104 , 105 , 106 ] As voltage‐sensitive ion channels, HCN2 channels respond to their cellular environment by enhancing hyperpolarization in cells that are slightly polarized, while remaining inactive in depolarized cells. This mechanism functions analogously to “contrast enhancers,” which sharpen weak differences in membrane potential (Vmem) across compartment boundaries, much as a sharpening filter brings clarity to a blurred image. In other words, HCN2 enhances and sharpens the borders in the bioelectrical pattern.
Moreover, our results suggest that a multicellular collective is capable of maintaining the memory about fully degraded features of an organism, as explicitly demonstrated here via organ regeneration experiment and information dynamics analysis. This suggests that this information is dormant and can be “retrieved” by injecting the appropriate signals that trigger regeneration in our system. This finding opens up exciting possibilities for future research, particularly in the realm of regenerative medicine and bioengineering. By understanding how cells store and retrieve hierarchical anatomical information, we could potentially develop new methods to promote regeneration in damaged tissues or organs. Additionally, exploring the mechanisms by which cells communicate and coordinate to maintain this memory could lead to breakthroughs in preventing age‐related degeneration. For example, targeting specific bioelectrical patterns or enhancing the function of key ion channels like HCN2 could help maintain tissue integrity and function over time. Furthermore, the here‐discussed principles targeting the software of life could be applied to synthetic biology and rejuvenation, where designing artificial systems with similar regenerative capabilities[ 107 , 108 ] as well as new aging treatments might become feasible.[ 31 , 39 ]
While our current framework is capable of self‐assembling target patterns of heterogeneously distributed cell types, the ANN‐based decision‐making centers of our NCA's cells are implemented homogeneously: all cells of an NCA typically share the same ANN structure and parameters, and thus the same basic functionality. Yet, the cells might be configured differently and thus effectively behave heterogeneously due to cell‐specific state expressions and input relations, in short, due to their morphogenetic history. In that way, these artificial cells' functionality mirrors gene regulatory networks. Thus, further in silico studies could investigate minimally complex high‐level interventions necessary to induce robust regenerative effects in affected tissue simply by reconfiguring the cellular collective locally. Furthermore, allowing cell‐specific functional mutations during the lifetime of such virtual organisms might be an intriguing way to study morphogenetic and aging‐related implications of heterogeneity.
Our results validate aspects of both damage‐based and programmatic theories of aging[ 1 , 4 , 5 ] but in very different ways. On the one hand, accelerated morphological decline (or an increased rate of aging) in our system mainly originates from: increasing cellular misdiffentiation, decreasing cellular competency, defective cell–cell communication, and accumulation of genetic damage. While these four characteristics align with damage‐based theories of aging,[ 6 ] they are not really causal, as our system also undergoes aging in their absence. Therefore, our model tends toward programmatic theories of aging, antagonistic pleiotropy or hyperfunction,[ 16 , 17 ] or the theories of the software design flaw,[ 5 , 31 ] in which the causes of aging have to be found in development. Similarly, but not analogously, we here relate aging to the absence of a developmental/regenerative or morphostatic goal, which nonetheless may imply an abnormal continuation of specific developmental pathways that are not beneficial for anatomical homeostasis. However, our results suggest that developmental programs (learned during evolution) can also qualify as regenerative pathways when “reactivated” in aging individuals, thus representing a potential protocol for a cure for aging (similarly to[ 109 ]). In a sense, regeneration is learned for free and is merely dormant after development. This is an important result and indicates that we should focus our research efforts for curing aging on the reactivation of the regenerative capabilities. Additionally, our results suggest an experimental research program for regenerative medicine wherein we create an embryo‐like environment allowing the tissue to regenerate.
In turn, when interpreting the NCAs' numerical states as bioelectric degrees of freedom, instead of transcriptomic cell‐state expressions, a promising approach toward innovative no‐aging or rejuvenation treatments might be to focus on high‐level control layers such as bioelectricity that encode collective anatomical goals.[ 31 , 107 ]
The observation that spatial information about lost organs persists in the cybernetic tissue suggests a form of morphological memory that could be harnessed for regeneration. This resonates with the findings of Deng and Terunuma (2024), who review the potential of adoptive natural killer (NK) cell therapy in clearing senescent cells (SNCs) and alleviating senescence‐associated secretory phenotypes (SASPs) to promote longevity.[ 110 ] In both frameworks, aging involves the obstruction of inherent restorative capabilities – morphological setpoints in our cybernetic tissue or latent regenerative programs in biological systems, which can be reactivated: our model does so by leveraging retained information for anatomical restoration, while NK therapy removes dysfunctional cells to allow the body's natural repair processes to re‐emerge, ultimately promoting tissue homeostasis and longevity.
Our study showed fundamental computational principles in multicellular aging systems, bridging developmental biology and aging research. We explicitly demonstrate that developmental pathways, initially used for morphogenesis, may be dormant and can be reactivated for regeneration and tissue maintenance later in life. Moreover, we observe that cells collectively retain and manipulate morphological information, indicating that targeted interventions could potentially rejuvenate or maintain tissue integrity as organisms age.
While our model offers conceptual insights into aging as a loss of goal‐directedness in morphospace, several limitations must be highlighted. The individual cells in our NCA framework are highly simplified, lacking the molecular, genetic, and signaling complexities of real biological cells, although the present NCA approach is flexible enough to provide a simulation component to a wide variety of known biological mechanisms that might be found to be involved in future work. The simulated multicellular systems are small‐scale, as are organoids currently used to screen various interventions, rather than capturing the vast complexity of full organs or entire organisms like humans. Additionally, we did not include genetic control after development and selection shadow. Although the simulations illustrate the plausibility of developmental systems evolving for maturation but not long‐term maintenance, this does not empirically prove that such mechanisms drive biological aging. These constraints highlight the need for future empirical validation and more biologically detailed models to bridge the gap between model predictions and real‐world applicability. This experimental work is however ongoing; indeed, senescent cells show a particular voltage membrane depolarization[ 111 ] and their accumulation may corrupt the bioloelectrical pattern in charge of the anatomical goal.[ 39 ] It also remains critical to understand and model the dynamics of pattern memory in organisms that do not age (e.g., asexual planaria), which is an on‐going research direction in our lab.
Our loss of goal‐directedness paradigm of aging may also lead to testable predictions with existing data and raises fundamental questions about the relationship between growth patterns, goal‐directedness, and aging across biological systems. Our model predicts that indeterminate growth patterns (or regeneration) should correlate with minimal aging phenotypes, as persistent morphogenetic goals would prevent the loss of anatomical homeostasis goal that characterizes aging. This aligns with observations of negligible senescence and extreme longevity in species like lobsters, certain sharks, planaria for continuous regeneration, and some trees and fungi that maintain continuous growth throughout their lifespans.[ 112 , 113 , 114 , 115 , 116 ] However, this raises the paradoxical question of why natural selection hasn't universally favored slow, continuous growth as an anti‐aging strategy.
The answer may lie in energetic trade‐offs and ecological constraints: maintaining active morphogenetic programs requires substantial metabolic investment that may compromise reproductive success or survival under resource limitation.[ 117 , 118 ] Our model also predicts that organisms with indeterminate growth should exhibit reduced cancer incidence, as continuous morphogenetic goal‐directedness would maintain cellular coordination toward system‐level objectives rather than allowing cells to pursue lower cellular goals like cancer,[ 93 ] something we can also observe in the species mentioned above showing continuous growth.[ 115 , 119 , 120 ] On the other hand, evolving ecosystems can be seen as learning systems.[ 121 ] Populations of ever‐expanding, undying individuals with limited variability would dominate the gene pool, limiting generational turnover.[ 122 ] In contrast, organisms with finite lifespans ensure continual renewal, effectively enhancing adaptability at the system level,[ 123 ] positioning aging as a potential mechanism for promoting evolvability at the population level.[ 124 , 125 , 126 ]
Overall, we focused on the loss of goal‐directedness in morphospace as the cause of the degradation within the system described in this article. However, it is also conceivable that loss of goal‐directedness could cause degradation not only within morphospace, but also at other levels within both biological and artificial systems. For example, it may be possible to counteract psychological decline during aging by providing new life goals to the organism, which is in line with resistance to cognitive decline that has been found in elderly having high engagement in social and/or physical activities.[ 127 , 128 , 129 ] At another level, developing new goals can also be accomplished by providing new patterns within a body (as occurs in metamorphosis[ 130 , 131 ]), or via communication of signals between bodies, as recently observed in cross‐embryo morphogenetic error correction effects.[ 132 ] In terms of learning theory, we could ask: what is the objective of a goal‐directed system once it reached its goal? Our results may point towards the idea that a goal‐directed system that has achieved its goal becomes an aging system in a broad sense: a system slowly deviating from its goal. For a morphogenetic system, this might be reflected by a slowly degrading anatomy, a slight shift in morphospace[ 26 ] ultimately leading to death: an anatomical configuration incompatible with life. For AI systems, a decline in performance, similarly to what has been seen with ChatGPT or other LLMs,[ 133 ] could be interpreted as aging or developmental defects after the target morphology has been reached.[ 44 ] Our results suggest that reaching a goal by generative means is completely different from maintaining it. Thus, to engineer robust and autonomous AI and intelligent robotic systems or synthetic morphology we should integrate the learning of these two different tasks.
Exploring these principles further could lead to significant advancements in delaying or reversing age‐related decline or diseases. Indeed, the loss of goal‐directedness at the morphogenetic level allows the cells to pursue their own goals; cancer (an aging‐related disease) may be seen as one such new cellular goal.[ 93 ] Integrating computational models with experimental biology might pave the way to uncover new strategies to enhance regeneration through manipulating cellular behavior, potentially extending the healthy lifespan of tissues.
Future research will focus on applying these computational insights to real‐world biological systems, aiming to develop therapies that modulate aging processes and improve recovery from injuries, thus optimizing the health span of multicellular organisms. These findings not only deepen our understanding of the aging process but also highlight the potential of using computational approaches to predict and control the dynamics of cellular systems in aging. The insights gained from this study open new avenues for research in aging and regenerative medicine, paving the way for novel interventions that could one day transform our approach to aging and chronic disease treatment.
The Levin lab has a sponsored research agreement with Astonishing Labs, a company seeking to impact the longevity and biomedicine of aging field.
L.‐P.‐L, and B.‐H., contributed equally to this work. The authors thank the members of the Levin lab for helpful discussions. and Axel de Baat for helpful comments on an earlier version of the paper. B.H. acknowledged an APART‐MINT stipend by the Austrian Academy of Sciences. M.L. gratefully acknowledged the support of Grant 62212 from the John Templeton Foundation (the opinions expressed in this publication are those of the author(s) and do not necessarily reflect the views of the John Templeton Foundation) and of Astonishing Labs via a sponsored research agreement. The authors thank Julia Poirier for assistance with the manuscript.
Pio‐Lopez L., Hartl B., and Levin M., “Aging as a Loss of Goal‐Directedness: An Evolutionary Simulation and Analysis Unifying Regeneration with Anatomical Rejuvenation.” Adv. Sci. 12, no. 46 (2025): e09872. 10.1002/advs.202509872
The data that support the findings of this study are available from the corresponding author upon reasonable request.
The data that support the findings of this study are available from the corresponding author upon reasonable request.