The biosphere is undergoing an unprecedented transformation driven by global warming, habitat loss, and resource depletion, threatening biodiversity through widespread species extinctions and population declines. Although conservation and restoration remain essential, the risk of irreversible tipping points demands new strategies. Synthetic biology offers one such approach: engineering existing ecosystems by modifying functional traits of resident communities to enhance resilience and prevent abrupt shifts. Despite and because of public concern, advances in biosafety and control have been achieved, mainly on a cellular scale. However, after decades of bioremediation efforts, a central question emerges: not only can interventions be perfectly controlled, but also whether they can persist and sustain ecological function. Meeting this challenge requires a paradigm shift in design philosophy, from classical to emergent engineering, embracing adaptation, feedback, and multiscale complexity as the foundation of ecosystem design.

Microbiome recovery after antibiotic-induced dysbiosis is often unpredictable, and the ecological mechanisms governing successful gut microbiome restoration remain unclear. Here we introduce a novel dynamical model of bacteria-phage-pathogen interactions to examine how gut virome reshapes gut microbiome states. The model identifies pathogen eradication thresholds and shows that recovery is strongly history-dependent, with bistability allowing either clearance or persistence depending on past conditions. Viral adsorption rates strongly regulate invasion probability and recurrence susceptibility, while microbial diversity enhances stability. Furthermore, we identify an ecological “firewall” mechanism in which the resident microbiome maintains a standing virome community that suppresses pathogen invasion and stabilizes healthy microbiome. These results reveal general ecological principles governing virus-mediated microbiome recovery and inform the design of virome-based therapeutic strategies.

Understanding how social networks form and change is key to explaining the adaptability of human and nonhuman societies. Collective social niche construction describes how individuals actively shape their social environment through interactions, generating network structures that influence cooperation, pathogen transmission, and information flow. Recent advances reveal that network adaptability emerges through distinct mechanisms: self-organisation and phase transitions enable rapid topological changes in response to environmental pressures, while behavioural flexibility—central to the Cumulative Cultural Brain Hypothesis—supports enhanced social learning and cultural accumulation in high-intelligence species. Both pathways exemplify how feedback loops between individual strategies and emergent network properties generate adaptive, resilient social structures. This perspective positions social networks as dynamic biological structures shaped by plasticity at multiple hierarchical levels.

Genomic instability is a major driver of tumor evolution, promoting diversification and adaptation while simultaneously increasing the accumulation of deleterious alterations. How tumor populations balance these opposing effects remains poorly understood. Here, we introduce a computational framework that explicitly represents diploid genomes, functional gene classes, point mutations, and chromosome-segregation errors in spatially constrained and well-mixed tumor populations. We identify a viability boundary separating sustained tumor expansion from instability-induced population collapse. Within the viable regime, mutation and selection generate a stable distribution of genomic-instability classes that is accurately captured by an analytical replicator–mutator description. Near the viability boundary, tumor dynamics exhibit prolonged extinction transients and strong sensitivity to stochastic fluctuations, with important differences between solid and liquid architectures. Chromosomal alterations further modify growth by creating transient benefits through increased gene dosage and genetic redundancy, while ultimately increasing genomic fragility. Finally, simulated interventions show that eliminating low-instability subpopulations or increasing the global mutational burden can displace tumors beyond their viability boundary and trigger irreversible collapse. These results identify genome instability as both an evolutionary advantage and an intrinsic vulnerability, providing a quantitative framework for developing therapies that exploit the limits of tumor evolution. AUTHOR SUMMARY Cancer cells can accumulate genetic changes that promote growth and adaptation, but excessive genomic instability can damage essential functions and threaten survival. We developed a computational model to examine how tumors balance these effects. The model represents diploid genomes and genes controlling proliferation, survival, mutation, and chromosome segregation, comparing solid tumors with freely mixing liquid tumors. We identify a viability boundary separating sustained growth from collapse caused by excessive genomic damage. Within the viable region, mutation and selection generate a stable mixture of cells with different instability levels. Near the boundary, tumor evolution becomes sensitive to random fluctuations, and extinction may follow prolonged transients. Simulated interventions show that increasing genomic damage or eliminating the stable cells sustaining tumor growth can push the population beyond this boundary, suggesting that genomic instability is both a driver of cancer evolution and an intrinsic vulnerability that could be exploited therapeutically.

Our biosphere exhibits remarkable diversity yet is constrained by universal organizational principles, including molecular homochirality. Advances in synthetic biology have raised the possibility of engineering alternative life forms based on mirror-image biomolecules, prompting both technological interest and biosecurity concerns. While current discussions of mirror life largely emphasize molecular feasibility and cellular function, its potential establishment in natural environments remains poorly understood. Here, we develop a theoretical framework to assess the invasion potential of mirror organisms within existing ecosystems. Using population-level models that incorporate resource competition, metabolic constraints, and ecological network interactions, we show that mirror life might face severe limitations arising from both nutrient incompatibility and competitive exclusion by established biota. In particular, the reliance on rare or achiral substrates and the asymmetry of interactions with natural organisms constrain growth and persistence across a broad range of ecological conditions. These results highlight the importance of ecological constraints in evaluating the risks and feasibility of synthetic life.

Large-language models (LLMs) are rapidly becoming part of human culture, reshaping how information is produced, transmitted, and used. Here we propose that their diffusion can be understood through a viral analogy, with LLM use spreading through populations, becoming embedded in cognitive and cultural practices. We model transitions among uncoupled, coupled, and persistently dependent users, and show that the interplay between social transmission, recovery, and collective reinforcement can generate tipping points and technological lock-in. A central consequence is the possibility of runaway dynamics: once a critical threshold is crossed, small increases in adoption can trigger rapid population-level shifts toward persistent dependence, with abrupt losses in cognitive competence. The same framework, however, identifies conditions for cognitive immunization, based on reducing transmission and facilitating reversibility. Our results highlight how LLM adoption may involve nonlinear collective transitions with important consequences for cognitive autonomy.

Restoring endangered ecosystems has become a pressing issue as the effects of global warming continue to harm existing communities. During the last decade, several studies have suggested the use of synthetic biology as a tool to protect these biodiverse communities by increasing their functionality. A critical example concerns soil microbiome communities in drylands, where increasing water retention by some of the constituent species could effectively protect the ecosystem from abrupt degradation. However, how to effectively deploy a functional synthetic construct that can scale its impact to the community level remains an open question. Recent experimental research has designed recombinant gene plasmids with the capacity to horizontally transfer across soil microbial communities. Here, we explore the impacts of synthetic horizontal gene transfer in models of ecological consortia. We define a consumer-resource model in which species can share an engineered plasmid that reduces resource loss, effectively coupling multispecies dynamics and gene spreading. By doing so, we identify a wide range of parameters and the optimal gene transfer conditions for which the intervention promotes biodiversity and biomass gains, while regulating gene propagation and the spread of engineered organisms. Our work provides a first step toward understanding the mechanisms and opportunities of engineered plasmid transfer in multispecies ecological communities.

Cognitive processes are realized across an extraordinary range of natural, artificial, and hybrid systems, yet there is no unified framework for comparing their forms, limits, and unrealized possibilities. Here, we propose a cognition space approach that replaces narrow, substrate-dependent definitions with a comparative representation based on organizational and informational dimensions. Within this framework, cognition is treated as a graded capacity to sense, process, and act upon information, allowing systems as diverse as cells, brains, artificial agents, and human-AI collectives to be analyzed within a common conceptual landscape. We introduce and examine three cognition spaces -- basal aneural, neural, and human-AI hybrid -- and show that their occupation is highly uneven, with clusters of realized systems separated by large unoccupied regions. We argue that these voids are not accidental but reflect evolutionary contingencies, physical constraints, and design limitations. By focusing on the structure of cognition spaces rather than on categorical definitions, this approach clarifies the diversity of existing cognitive systems and highlights hybrid cognition as a promising frontier for exploring novel forms of complexity beyond those produced by biological evolution.

The relationship between energy supply and biodiversity is a longstanding question in ecology. Although a monotonic increase in diversity with energy availability is often assumed, unimodal species-energy relationships have been widely documented across ecosystems, and their origin from first principles remains unclear. Here, we develop a geometric framework that recasts ecological feasibility in explicitly energetic terms. By treating total energy supply as a system-level constraint on an energy-based network model, we define nested feasibility domains in the space of energy capture rates and quantify feasibility probabilities as their volume ratios. We show that the probability of initializing a feasible network increases monotonically and saturates with energy supply, whereas the probability of sustaining steady-state biomass follows a unimodal relationship-revealing a bounded energetic window within which network maturation is most likely. Extending this analysis to all candidate subcommunities via feasibility partitions, we find that different community sizes are most feasible at different energy levels, and that average diversity itself peaks at intermediate supply. Together, these results suggest that energetic constraints determine the diversity of ecological networks not through energy scarcity alone, but through the geometric interplay between external energy supply and internal energy exchange.

The origin of agriculture represents a major evolutionary transition and a paradigmatic example of how complex collective behaviors emerge from simple interactions. Here we introduce an artificial society of reinforcement learning agents embedded in a dynamic ecological environment to identify general principles underlying this transition. Within this system, agricultural practices emerge spontaneously - without explicit instruction - through the coupled dynamics of learning and environmental modification. We show that this transition is governed by four key ingredients: individual planning through the valuation of delayed rewards, social vulnerability to cheaters, stabilization via social learning, and an emergent lock-in effect that renders agriculture effectively irreversible once established. In particular, we demonstrate that social learning acts as a "firewall" that suppresses cheater invasion and enables the propagation of successful strategies, leading to sustained population growth and nonlinear amplification of domesticated resources. Together, these results reveal universal mechanisms linking individual decision-making, social interactions, and ecological feedbacks. More broadly, they highlight the potential of artificial societies as experimental platforms to study the emergence of cultural innovations and major evolutionary transitions.

Cognition is often associated with complex brains, yet many forms of learning─such as habituation, sensitization, and even spacing effects─have been observed in single cells and aneural organisms. These simple cognitive abilities, despite their cost, offer evolutionary advantages by allowing organisms to reduce environmental uncertainty and improve survival. Recent studies have confirmed early claims of learning-like behavior in protists and slime molds, pointing to the presence of basal cognitive functions long before the emergence of nervous systems. In this work, we adopt a synthetic biology approach to explore how minimal genetic circuits can implement nonassociative learning in unicellular systems. Building on theoretical models and using well-characterized regulatory elements, we design and simulate synthetic circuits capable of reproducing habituation, sensitization, and the massed-spaced learning effect. Our designs incorporate activators, repressors, fluorescent reporters, and quorum-sensing molecules, offering a platform for experimental validation. By examining the structural and dynamical constraints of these circuits, we highlight the distinct temporal dynamics of gene-based learning systems compared to neural counterparts and provide insights into the evolutionary and engineering challenges of building synthetic cognitive behavior at the cellular level.

Mutualistic interactions are widespread in nature, from plant communities and microbiomes to human organizations. Along with competition for resources, cooperative interactions shape biodiversity and contribute to the robustness of complex ecosystems. We present a stochastic neutral theory of cooperator species. Our model shares with the classic neutral theory of biodiversity the assumption that all species are equivalent, but crucially differs in requiring cooperation between species for replication. With low migration, our model displays a bimodal species-abundance distribution, with a high-abundance mode associated with a core of cooperating species. This core is responsible for maintaining a diverse pool of long-lived species, which are present even at very small migration rates. We derive analytical expressions of the steady-state species abundance distribution, as well as scaling laws for diversity, number of species, and residence times. With high migration, our model recovers the results of classic neutral theory. We briefly discuss implications of our analysis for research on the microbiome, synthetic biology, and the origin of life.

Neurons and neural circuits are key evolutionary innovations underlying complex behaviour and cognition. From basic sensorimotor cells to intricate interneuronal networks, their emergence marks a major evolutionary transition in biological complexity. However, the origins of neural circuits and the relative role of development and ecology in their evolution remain poorly understood. Two main theoretical frameworks have been proposed to explain this process. One is based on changes in developmental programs while the second favours environmental factors. However, their interaction may better explain neural evolution. We present Eco-Evo-Devox (EEDx), an artificial life platform designed to study these combined factors. EEDx enables expressive, code-free ecological modelling and supports developmental neural networks, leveraging hardware acceleration to simulate large-scale environments and neural ontogeny.

Living systems have evolved cognitive complexity to reduce environmental uncertainty, enabling them to predict and prepare for future conditions. Anticipation, distinct from simple prediction, involves active adaptation before an event occurs and is a key feature of both neural and aneural biological agents. Building on the moving average convergence-divergence principle from financial trend analysis, we propose an implementation of anticipation through synthetic biology by designing and evaluating experimentally testable minimal genetic circuits capable of anticipating environmental trends. Through deterministic and stochastic analyses, we demonstrate that these motifs achieve robust anticipatory responses under a wide range of conditions. Our findings suggest that simple genetic circuits could be naturally exploited by cells to prepare for future events, providing a foundation for engineering predictive biological systems.

Understanding the selecting and generating forces operating across the self-organization of ecological systems is synonymous with understanding principles of life itself. Three prominent hypotheses in ecological research—Lotka’s Maximum Power Principle , Odum and Pinkerton’s Intermediate Efficiency Principle , and Morowitz’s Biological Cycle Principle —have independently attempted to explain these forces. However, how these hypotheses interconnect at the ecosystem scale remains unclear. Using a classical consumer-resource model, we reveal a formal property of ecological systems that leads them to maximize power exchange with intermediate efficiency while undergoing bifurcations from attracting points to attracting cycles. Our work reiterates the importance of ecosystem science rooted in energy flux, while illuminating a potential link between optimal thermodynamic properties and biological self-organization. ### Competing Interest Statement The authors have declared no competing interest. NSF, DEB-2436069 UNAM-PAPIIT, IA102225 Generalitat de Catalunya, AGAUR 2021 SGR 00751

When taking place under fluctuating environments, some classical results of evolutionary dynamics in fitness landscapes need to be reconsidered. Under such nonequilibrium conditions, the properties of adaptive evolution might escape from the expectations grounded in equilibrium systems. Here, an important contribution to this nonequilibrium dynamics results from the presence of a geometry (Berry) phase in the Crow-Kimura model of molecular evolution with asymmetric mutations. By considering changes in fitness alone as well as changes in both fitness and mutation, analytical expressions for the Berry phase are derived, showing strong singularities at bulk transition points. Periodically varying parameters are also analyed for the two-dimensional case. The potential implications for evolutionary and prebiotic scenarios are discussed.

The path towards the emergence of life in our biosphere involved several key events allowing for the persistence, reproduction and evolution of molecular systems. All these processes took place in a given environmental context and required both molecular diversity and the right non-equilibrium conditions to sustain and favour complex self-sustaining molecular networks capable of evolving by natural selection. Life is a process that departs from non-life in several ways and cannot be reduced to standard chemical reactions. Moreover, achieving higher levels of complexity required the emergence of novelties. How did that happen? Here, we review different case studies associated with the early origins of life in terms of phase transitions and bifurcations, using symmetry breaking and percolation as two central components. We discuss simple models that allow for understanding key steps regarding life origins, such as molecular chirality, the transition to the first replicators and cooperators, the problem of error thresholds and information loss and the potential for 'order for free' as the basis for the emergence of life.This article is part of the theme issue 'Origins of life: the possible and the actual'.

Self-organization of individual organisms at a very small scale may result in recognizable functional ecosystem structures at a larger spatial scale. Drylands, which cover almost half of emerged lands, host some of the most remarkable vegetation patterns on Earth, including "disordered hyperuniformity," a recently defined class of such emergent self-organization structures. Yet, the extent, causes, and consequences of disordered hyperuniform vegetation patterns in drylands remain virtually unknown. Here, we analyzed high-resolution remote sensing images of 425 spot-like drylands across the globe and found that disordered hyperuniformity shapes vegetation patterns in about one out of ten drylands, with the distribution of plants appearing to be "disordered" to the naked eye, but supporting highly recognizable (uniform) patterns at larger scales (ca. 50 to 500 m). Using mathematical models, we identify three potential mechanisms that can generate disordered hyperuniform vegetation patterns. These mechanisms are not limited to the well-studied Turing patterns and represent key general processes with respect to plant-plant or plant-sediment interactions. Further modeling indicates that disordered hyperuniformity enhances ecosystem functioning in terms of water retention use, and expands the range of aridity conditions under which the system can maintain itself, but may slow recovery of vegetation structure from disturbances. In a wider context, we also show that disordered hyperuniformity is likely to pertain to diverse dryland systems, such as termite-mound or fairy-circle landscapes. Our findings highlight that exploring disordered hyperuniformity of vegetation pattern of drylands (and potentially other large-scale systems) offers insights into the organization and resilience of ecosystems globally.

The questions of how life forms, whether life is an inevitable outcome and how diverse its presentation could be remain some of the most profound in science. Investigations into the origin of life confront key issues such as uncovering key constraints and universal features of life, the plausibility of alternative biochemistries and the transition from purely chemical systems to information-bearing, evolvable entities. Many of these issues can be associated with early cell formation and evolution. Thus, protocellular systems have emerged as a key focus of study. Here, the community can ask questions about physical constraints and the co-evolution of energy, matter and information. The pursuit of these answers spans a wide range of disciplines, including geochemistry, statistical physics, systems and evolutionary biology, artificial life, synthetic biology and information theory, and reflects the inherently interdisciplinary nature of origin-of-life research. This article surveys key theoretical frameworks and experimental approaches that have shaped our current understanding, while outlining the major unresolved challenges that continue to drive the field forward. It also summarizes and contextualizes the articles in this special issue that address these questions.This article is part of the theme issue 'Origins of life: the possible and the actual'.