Aging as a Loss of Goal-Directedness: An Evolutionary Simulation and Analysis Unifying Regeneration with Anatomical Rejuvenation

Although substantial advancements are made in manipulating lifespan in model organisms, the fundamental mechanisms driving aging remain elusive. No comprehensive computational platform is capable of making predictions on aging in multicellular systems. Focus is placed on the processes that build and maintain complex target morphologies, and develop an insilico model of multiscale homeostatic morphogenesis using Neural Cellular Automata (NCAs) trained by neuroevolution. In the context of this model: 1) Aging emerges after developmental goals are completed, even without noise or programmed degeneration; 2) Cellular misdifferentiation, reduced competency, communication failures, and genetic damage all accelerate aging but are not its primary cause; 3) Aging correlates with increased active information storage and transfer entropy, while spatial entropy distinguishes two dynamics, structural loss and morphological noise accumulation; 4) Despite organ loss, spatial information persists in tissue, implementing a memory of lost structures, which can be reactivated for organ restoration through targeted regenerative information; and 5) rejuvenation is found to be most efficient when regenerative information includes differential patterns of affected cells and their neighboring tissue, highlighting strategies for rejuvenation. This model suggests a novel perspective on aging as loss of goal‐directedness, with potentially significant implications for longevity research and regenerative medicine.

Keywords: aging, cognition, computational modeling, in silico, morphogenesis, neural cellular automata (NCA)

Aging is a system‐level, near‐ubiquitous biological process characterized by morphological and functional alterations in cellular and extracellular components, resulting in a systematic decline in biological functions.[ 1 ] This degenerative process is modulated by genetics, lifestyle, and environmental factors.[ 2 ] As cellular functionality diminishes with age, there is a corresponding decrease in physiological capacities and an enhanced vulnerability to a spectrum of diseases, including cardiovascular disorders, cancer, neurodegenerative diseases, type II diabetes, and various infections. These conditions collectively compromise the quality of human life.[ 2 , 3 ] Figure 1 A schematically illustrates the typical life cycle of a complex organism comprising various stages from embryogenesis through maturity, decline, aging, and eventual death.

Several theories have been proposed to explain the aging process, which we can classify into damage‐based and programmatic theories,[ 1 , 4 , 5 ] with the former being more extensively studied.[ 1 , 3 , 6 , 7 ] Damage‐based theories assert that aging results from the accumulation of molecular damage, impacting crucial cellular components like the genome, telomeres, mitochondria, and proteins, driven by imperfect repair mechanisms.[ 6 , 7 , 8 , 9 ] Conversely, programmatic theories suggest that aging is genetically predetermined, or quasi‐programmed and encoded within the genome, not merely due to stochastic accumulation of damage.[ 10 , 11 , 12 , 13 ] Here, we formulate and computationally simulate a fundamentally different hypothesis based on the nature of morphogenesis as a homeodynamic system working toward anatomical setpoints: that aging is the result of a proto‐cognitive dysfunction among the cellular collective, and does not require damage or evolved antagonistic mechanisms in order to occur. Instead, we propose that, as observed in animal behavior,[ 14 ] cellular living systems without goals to pursue tend to degrade. Our hypothesis is that aging is the result of intrinsic dynamics of complex cybernetic (goal‐directed) systems, and is not caused by external noise or damage. Nevertheless, it is consistent with other theories of aging[ 15 , 16 ] which propose additional dynamics that can lead to loss of setpoints of anatomical homeostasis, and can be accelerated by various kinds of damage.

Antagonistic pleiotropy posits that certain genes have beneficial effects in early life but detrimental effects in later life. These genes confer advantages such as increased fertility and resilience in youth, which are critical for survival and reproduction. However, as an individual ages, the same genes contribute to aging and decline because their negative effects accumulate. The theory suggests that natural selection favors these genes because of their early‐life benefits, despite their harmful subsequent impacts. This results in a trade‐off between early benefits and later costs.[ 17 ] Another related idea is the hyperfunction theory, which suggests that aging is not primarily a result of accumulating molecular damage or genetic programming, but rather is due to a continuous and excessive operation of cellular growth processes that were beneficial during the earlier stages of life.[ 15 , 16 ] Central to this theory is the role of the Target of Rapamycin (TOR) pathway, which is a major regulator of growth and metabolism in cells. During the growth phase of an organisms life, the TOR pathway promotes cellular and organismal growth by driving processes like protein synthesis, nutrient uptake, and cell division. These functions are vital for development and reproductive success. However, aging arises when these growth‐promoting activities are not appropriately down‐regulated after the organism has passed its reproductive prime. Instead of adjusting to a maintenance mode that would be more suitable for the non‐reproductive phase of life, the TOR pathway continues to stimulate cellular processes actively.

This ongoing activity can lead to “hyperfunction”, where cellular components and systems are overstimulated.[ 15 , 16 ] It is not clear, in that scenario, why the efficient processes of morphological homeostasis[ 18 , 19 ] do not orchestrate these active processes toward upkeep of functional anatomy (c.f., Figure 1B,C).

Our approach is consistent with the antagonistic pleiotropy and hyperfunction models in that we also propose that fundamental morphogenetic processes are involved in aging. However, we do not focus on specific genes with early versus late roles, but on the role that large‐scale morphogenetic plasticity plays in establishing and maintaining bodies and the possible disorders of such cybernetic (goal‐driven) processes. We and others have emphasized the indirect link[ 21 , 23 , 24 , 25 ] between genotype and phenotype: not just complexity and pleiotropy, but the fact that the morphogenetic layer has active homeodynamic capabilities that enable collections of cells to be aligned with respect to setpoints in the space of anatomical possibilities[ 18 , 26 , 27 ] (c.f., Figure 1B,C). Living materials must navigate the space of possible organ and tissue arrangements as needed, starting from diverse initial states single cells (egg‐based development), a different body (in the case of metamorphosis), or a damaged body (in the case of regeneration). In all of these cases, drastic growth and remodeling must occur while failing or no longer needed sub‐components must be replaced on‐the‐fly. This process has been described as a collective intelligence,[ 28 , 29 ] because of its problem‐solving capacities; like other advanced intelligences, it will be subject to specific failure modes whose origin is not organic disease or damage, but the informational dead end of completing ones mission and not having a new setpoint toward which to strive.

We formulate, and simulate the consequences of, the hypothesis that the aging phenotype results from morphogenetic processes ceasing their targeted navigation of anatomical morphospace, after development is completed. But adulthood, and the rigors of daily life, are precisely when these processes are needed, in the context of stress, degeneration, and cell replacement, to maintain the Ship of Theseus that is the body. What will bind the cellular components of a body toward common paths in anatomical space after their goals have been reached and the body built? Tissue renewal will continue locally, for a time, but the thing that bound individual cells into a coherent embryo a commitment to a specific navigation path in anatomical space disappears at the global scale. Damage‐based theories of aging are thus hypothesized to be secondary: they induce aging to the extent that they rob cells of clear and consistent anatomical and histological setpoints for which to aim. But the primary new proposal here is that complex goal‐driven systems can experience a kind of intrinsic disorganization after they have met their goal. In the absence of strong effort toward targets in morphospace, the cellular collective begins to disband, giving way to systemic disease states such as degeneration and cancer the inevitable outcome of a body that increasingly becomes a group of individual cells, not a unified higher‐order morphogenetic agent.[ 30 ]

Recent research suggests a shift in our understanding of aging, proposing that it results from changes in biological information processing rather than solely from cellular and molecular damage. This concept, linked to a malfunction in the “software of life”, focuses on how aging processes are governed by the epigenetic changes, either bioelectrical or chromatin modifications.[ 5 , 31 , 32 ] The differing dynamics, capabilities, and failure modes of the molecular layer and the physiological computational layers have long been studied in neuroscience, but are only now beginning to be related to areas such as developmental biology and cancer.[ 33 , 34 , 35 ] Novel computational frameworks are needed to begin to integrate these ideas in aging and longevity research. Although substantial advancements have been made in extending lifespan in simple model organisms through genetic, dietary, and pharmacological means,[ 2 , 36 , 37 ] the fundamental mechanisms driving aging in humans remain elusive and widely debated. Current life extension strategies show limited effectiveness.[ 38 ] Much of the research has adopted a bottom‐up approach, focusing on the biological hardware, the cellular components such as genes, molecular pathways, and, more recently, the epigenome. However, managing aging at such a micro‐level may not be practical for complex structures like human anatomy.[ 19 , 39 , 40 ] This suggests that a broader, possibly more integrated approach may be necessary in order to fully understand and effectively intervene in the aging process.

Computational modeling has proven useful in the past to help characterize dynamics guiding complex morphogenesis.[ 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ] However, to the best of our knowledge, there is currently no computational model in the literature explicating the aging process as a loss of goal‐directedness or directionality within the morphogenetic control system. The field is mostly dominated by experimental approaches and focused on the mathematical and computational modelling of a specific hallmark of aging,[ 6 ] network approaches to aging, or the impact of the environment on aging.[ 50 , 51 , 52 , 53 ] One prior study utilized (similar to our model) an NCA to perform morphogenesis (see next section): with gradient‐based deep learning, the self‐regulatory interaction dynamics of distributed cellular agents have been optimized to grow a target morphology in a fully self‐orchestrated way. However, without explicitly stabilizing morphogenesis by additionally training the cellular agents on regenerative tasks, anatomies kept growing uncontrollably after development. Thus, the study focused on an adversarial approach to stabilize self‐orchestrated morphogenesis rather than studying aging and its causes in such a multi‐cellular tissue per se.[ 54 , 55 ]

A key knowledge gap lies in the absence of models that explicitly simulate the evolutionary pressures shaping developmental processes while accounting for the lack of anatomical morphostasis post‐development. Existing frameworks often overlook how multi‐scale competencies – spanning molecular, cellular, and tissue levels – transition from goal‐directed morphogenesis to maintenance, leading to intrinsic disorganization and degradation without clear setpoints. This oversight limits our ability to predict aging trajectories in complex multicellular systems and to explore interventions that reactivate latent regenerative potentials.

Existing mathematical models have not yet captured the biological signaling dynamics in aging tissue or revealed the underlying information dynamics that drive aging and the progressive degradation of anatomy over time. Unlike previous approaches, we uniquely address this gap by developing an evolutionary framework that integrates both molecular and systemic aspects of aging within a multi‐competency architecture. Our model demonstrates how cells, trained solely through evolution to execute developmental tasks, inherently lack a regenerative goal post‐development. By simulating this dynamic, we reveal that aging arises from the absence of targeted navigation in anatomical morphospace after development, offering a novel perspective on the root cause of aging. Moreover, our approach uncovers the potential for dormant regenerative programs to be reactivated, enabling targeted restoration of anatomical structures (c.f., Figure 1).

Morphogenesis is the result of multicellular collectives acting together to achieve a specific endpoint within the space of anatomical possibilities[ 23 ] (see Figure 1). This process is driven by intercellular communication and information processing, where cells use different communication channels (biochemical, biomechanical or bioelectrical) to exchange information with their neighbors in a tissue[ 22 , 26 ] and implement a process of anatomical homeostasis where deviations from the species‐specific target morphology are progressively minimized by active changes in growth and form. This enables cells to adaptively respond in a context‐sensitive manner and regulate their behavior through local signaling protocols while collectively following a system‐level agenda to grow and maintain an organism's species‐specific anatomical form in a self‐orchestrated way. Thus, the development of embryonic tissues, the growth and remodeling of adult organs, and even the suppression of cancer and aging can be understood as morphological computation.[ 22 ] In this sense, morphogenesis can be seen as an intermediate layer of physiological computation between genetic variation and a mature anatomical phenotype (see Figure 1C, significantly effecting the process of evolution.[ 23 , 46 ]

Another important aspect of biology, so far neglected in the models of aging is multiscale competency[ 23 , 28 , 56 ] (see Figure 1B,C). Biological systems implement an architecture in which context‐sensitive problem‐solving operates across various levels, from molecular networks to individual cells, tissues, organs, organisms, and even collective groups such as swarms. This structure allows biological entities to solve problems within specific domains and problem spaces relevant to each scale (metabolic, physiological, anatomical, and behavioral state spaces), contributing to the overall adaptability and resilience of life.[ 22 , 26 , 57 ] At the molecular and cellular level, biological units navigate metabolic spaces to remain within adaptive regions despite changing circumstances. At higher organizational levels, these units form tissues and organs, which tackle more complex and larger‐scale physiological and morphological problems. For instance, cells can make autonomous decisions to maintain local balance while contributing to larger‐scale biological processes like tissue repair or development, and to how higher levels of organization constrain and facilitate the behavior of their parts.[ 23 , 58 , 59 , 60 , 61 , 62 , 63 ] This enables organisms to adapt to changing conditions and maintain functionality even when faced with disruptions, as observed in regulative development and regeneration where cellular activity is managed toward specific anatomical outcomes despite injury, changes of cell number and size, etc.[ 27 ] This is critical because resistance to both cancer and aging must involve both the upkeep of complex structures and a resistance to their progressive decline within the body.[ 64 ]

A key advantage of this multiscale architecture is that each level is capable of addressing its own problem‐spaces without needing constant guidance from higher organizational structures (local homeodynamic competency, see Figure 1C). This decentralized problem‐solving approach ensures that different biological processes, from cell division to behavioral adaptation, occur simultaneously and efficiently. Furthermore, this structure allows biological systems to be both robust and flexible, as failures at one level can often be compensated for by others. Additionally, the interplay between levels within this architecture enables the emergence of complex properties that cannot be explained by examining components in isolation. For example, the collective behavior of cells during processes like regeneration or morphogenesis illustrates how local interactions scale up to contribute to the organism's overall form and function.[ 28 ] Similarly, defects of collective behavior can lead to developmental defects or aging. These interactions are not merely a sum of their parts; rather, they represent emergent capabilities that arise from the coordinated effort of biological subunits. The relative autonomy of layers in biology must surely impact on evolutionary and physiological constraints and drivers of aging, making it essential to study their properties with respect to the lifespan of individuals and evolutionary lineages.

This hierarchical but integrated organization is essential for the evolution of complex life forms. It promotes adaptability, allowing organisms to respond to environmental changes, evolve new features, and develop intricate systems that support survival and reproduction. Moreover, this multiscale competency architecture provides a framework for innovation in fields such as synthetic biology and regenerative medicine,[ 29 , 39 , 65 ] where understanding and harnessing these principles could lead to breakthroughs in tissue engineering, organismal repair and aging.[ 29 , 31 ] This aspect of living forms – the critical layer of computational, problem‐solving competency that lies between the genotype and the phenotype – is an essential component of any theory of aging that integrates evolutionary and physiological dynamics to understand this complex phenomenon. Thus, we focused our computational model on the role of multiscale morphogenetic competency in the aging process.

Recently,[ 46 ] we quantitatively investigated in silico the influence of varying levels of cellular competency of a multi‐scale competency architecture[ 22 , 23 ] on the evolutionary process. We utilized tools from Artificial Life,[ 66 ] a field of research focused on developing computational and cybernetic models that exhibit life‐like behavior based on ideas derived from biology; one prominent example is Cellular Automata[ 67 , 68 , 69 , 70 ] (CAs) such as Conway's Game of Life.[ 71 ] Specifically, we deployed NCAs[ 72 ] as a model for evolving morphogenesis (see Figure 2 A–C): NCAs comprise a regular spatial grid of locally interacting cellular agents which maintain a numerical state vector that is regulated by an internal, cell‐specific Artificial Neural Network (ANN). In that way, the cells of an NCA can be trained to perform a collective self‐orchestrated system‐level task, such as morphogenesis[ 44 , 45 , 46 ] or even collective navigation behavior,[ 56 , 73 ] solely via successive perceptions of their local environment, i.e., by reading neighboring cell state information and computing corresponding actions to regulate their own states in accordance with a system‐level agenda.

Thus, NCAs exhibit striking similarities with the genome‐ and collective intelligence‐based multi‐scale competency architectures of biological life :[ 21 , 23 , 25 , 56 , 74 ] the instructions for how to grow an organism from the bottom up are encoded in its genome, corresponding to the NCA's (functional) ANN parameters (c.f., Figure 2A). Moreover, through subsequent localized perception‐action cycles of their ANN‐based unicellular agents (c.f., Figure 2B), NCAs model the intercellular communication pathways, intracellular memory, and integrated information processing of the adaptive and flexible internal decision‐making mechanisms of biological cells:[ 75 , 76 , 77 , 78 , 79 ] while biological cells regulate their gene expressions via gene regulatory networks, the cells of an NCA regulate their cell types via their internal ANN, and while biological cells utilize bioelectrical and molecular cues for intercellular signaling, cells in an NCA exchange numerical state information in a similar manner. In both cases, such communication pathways are localized between neighboring cells while the collective of cells may still follow a global agenda to self‐orchestrate the process of morphogenesis and thereby give rise to the anatomical form of a system‐level organism.

Building on our previous work,[ 46 ] we utilize evolutionary algorithms (EAs)[ 80 ] to optimize the parameters of an NCA so that its cellular agents can reliably self‐assemble a particular spatial target pattern of predefined cell states within a fixed number of developmental steps (c.f., Figure 2C). Inspired by the ability of biological organisms to sustain their physiological integrity for much longer than is necessary for reproduction, we study the long‐term behavior of NCAs by deploying the evolved virtual system for an extended period far beyond the developmental stage at which evolutionary selection occurred. In turn, this may lead to an accumulation of morphological changes that, while not necessarily lethal, can make a multiscale collective increasingly dysfunctional by altering its homeostatic setpoint (c.f., Figure 2D).

Based on such virtual organisms, we here investigate in silico the information‐dynamic implications – using Active Information Storage (AIS),[ 81 ] Transfer Entropy (TE),[ 82 ] and Spatial Entropy (SE) – of temporally affected competencies or information loss at the cellular level of a multi‐cellular collective whose parts pursue a system‐level agenda. To systematically interfere with the cellular competencies in our NCA model over time, we deploy four different strategies: We a) increase the misdifferentiation rate of cell‐state expressions; b) reduce the cellular agents' ability to self‐regulate their respective states by corrupting the reliability at which cellular actions are actually considered in the NCA's cell state updates; c) reduce the connectivity between neighboring cells, reminiscent of closing an increasing number of gap junctions (GJs) to neighbors; and d) accumulate genetic damage to the NCA's functional parameters by randomly modifying the corresponding ANN parameters with Gaussian noise successively throughout the lifetime of our virtual organisms.

Thus, we relate degrading competencies and temporal information loss at the cellular level to aging at the system level. Finally, based on the hypothesis of the information‐dynamical implications of aging caused by decaying cellular competencies, we demonstrate – in a minimal in silico model – a reliable method of rejuvenation of facial features, based on targeted interventions at the cellular or organ level.

We suggest that even in the absence of accumulated cellular defects that affect information processing, communication, or genetic material, a biological system that lacks any new goal in the morphospace will begin to degrade anatomically. This is permitted by evolution, as development is typically prioritized over long‐term morphostasis, potentially leaving organisms without a clear anatomical goal after development. Using an NCA‐based in silico model of morphogenesis, we find: 1) Aging arises naturally after development due to the absence of evolved regenerative goals, rather than being caused by specific detrimental properties of developmental programs (e.g., antagonistic pleiotropy or hyperfunction); 2) While cellular misdifferenciation, affected competency and communication pathways, and genetic damage all accelerate aging, they are not its primary causes; 3) Aging correlates with increased active information storage and transfer entropy, while spatial entropy reflects structural loss and accumulated morphological noise; 4) Despite organ loss, corresponding spatial information persists in our cybernetic tissue, indicating that a pattern‐memory of lost structures can be reactivated for organ restoration through targeted regenerative information; 5) Efficient restoration and rejuvenation protocols include regenerative information of differential patterns of affected cells and their neighboring tissue. These findings provide a novel perspective on aging dynamics with significant implications for longevity research and regenerative medicine.

CAs were initially introduced by von Neumann to study self‐replicating machines.[ 67 ] Since then, they had become widely used as simple models for Artificial Life.[ 66 ] The core concept behind CAs revolved around maintaining a discrete spatial grid of cells, with each individual cell i being assigned a binary, numerical, or even vector‐valued state c i (t k ) at every time step t k .

The evolution of these cell states over time follows local update rules. Specifically, it was considered that ci(tk+1)=u(Ni(tk)), where u(·) is a function of the current state of cell i, i.e., ci(tk)≡ci0(tk), and of the states ciν(tk) of its i ν = i 1, …, i N neighboring cells, which were collected in the matrix Ni(tk)=ci0(tk),…,ciN(tk).

Despite their typically simple and predefined update rules, CAs often exhibit complex dynamics (c.f., Conway's Game of Life[ 71 ]) and had even been employed for universal computation tasks, as in Wolfram's rule 110.[ 68 , 69 ]

NCAs[ 72 ] could be seen as extensions to “traditional” CAs. In this context, the local update rule was replaced with a more flexible ANN, u(·) → f θ(·), where θ denotes the set of trainable parameters of the ANN, f θ(·) (see, e.g., Appendix A in[ 46 ]). By employing Machine Learning (ML) techniques, NCAs had been utilized to perform tasks such as self‐orchestrated pattern formation[ 44 ] and the simultaneous co‐evolution of a rigid robot's morphology and controller;[ 56 ] and they have been proposed as a promising candidate for robust, decentralized controllers of autonomous drug delivery systems.[ 73 ]

An NCA was essentially a grid of interconnected cells, each equipped with an identical ANN that was capable of perceiving the numerical states of its immediate cellular neighbors, Ni(tk), and proposing actions, ai(tk)=fθ(Ni(tk)), to regulate its own cell state, following the equation

where possible imperfections in the cell‐specific state updates were also accounted for via a Gaussian noise term of amplitude ξ c .

Thus, the cellular agents of an NCA perceive the numerical states of their immediate respective neighborhoods, Ni(tk), at any given time step, t k , to update their own states (c.f., Figure 2A,B). Importantly, no explicit signature of aging was included in the model, i.e., there was no cell‐internal marker or explicit input to the cellular agents that contains temporal information, other than the self‐regulated cellular states of those agents.

However, being immersed in a dynamical multi‐cellular environment where every part had its own agenda, the cellular agents' state updates could also be utilized for active communicating with their neighbors, following a policy π(Ni(tk))≈fθ(Ni(tk)). From the perspective of Reinforcement Learning,[ 84 ] an NCA could thus be considered a trainable multi‐agent system that needs to utilize local communication rules to achieve a target system‐level outcome.