→ Don’t miss the podcast version of this post: click on “Spotify/Apple Podcast” above!

Evolution

Epistasis and co-adaptation in bacterial genome evolution

Precise genotype–phenotype mapping is essential in applied microbiology, from engineering genetically modified strains to developing tailored strategies for antimicrobial therapies. Comparative genomics often treats genes as independent contributors to phenotypes, and gene knockout and complementation remain the gold standard to validate genotype–phenotype associations in microorganisms. However, genes do not act in isolation, and complex gene–gene interactions, that is, epistatic interactions, are essential for the evolution and function of bacterial genomes. Recent advances in high-throughput genomics and experimental techniques have enabled systematic screens of epistasis in bacteria at scale, revealing mechanisms underlying epistasis and co-adaptation in laboratory and wild populations. Here we review how microbial genomics is moving beyond gene-centric models towards integrated analyses of potentiating, compensatory and context-dependent variation. The timely incorporation of interaction-based perspectives into population-scale analyses will improve genotype–phenotype mapping and the understanding of the complex traits that shape the microbial world.

Asgard archaea and the hidden complexity of our microbial origins

Long before plants and animals altered Earth’s surface, microbial cells were already testing ways to manage energy, build internal organization and survive changing chemistry. These two spapers use newly recovered genomes from Asgard archaea (we have already covered them some Issues ago), the closest known archaeal relatives of eukaryotes, to refine what that ancestral cell may have been capable of.

One analysis expands the known diversity of Heimdallarchaeia and finds genes linked to oxygen use, detoxification, haem synthesis and respiratory machinery, suggesting that aerobic metabolism or hydrogen-based energy strategies may have shaped the lineage from which eukaryotes emerged.

The other study looks beyond DNA similarity by predicting protein shapes, revealing hundreds of Asgard proteins that resemble eukaryotic components involved in information processing, intracellular transport and compartment formation.

Together, the results suggest that the ancestor shared by Asgard archaea and eukaryotes had a richer molecular toolkit than sequence comparisons alone implied. This does not reconstruct a modern eukaryotic cell, but it narrows the gap between simple prokaryotic life and the cellular complexity that later supported animals, plants, fungi, and protists. You might want to read also the coverage by Phys.org

Oxygen metabolism in descendants of the archaeal-eukaryotic ancestor

Asgard archaea were pivotal in the origin of complex cellular life¹. Heimdallarchaeia (a class within the phylum Asgardarchaeota) are inferred to be the closest relatives of eukaryotes. Limited sampling of these archaea constrains our understanding of their ecology and evolution^2,3, including their role in eukaryogenesis. Here we use massive DNA sequencing of marine sediments to obtain 404 Asgardarchaeota metagenome-assembled genomes, including 136 new Heimdallarchaeia and several novel lineages. Analyses of their global distribution revealed they are widespread in marine environments, and many are enriched in variably oxygenated coastal sediments. Detailed metabolic reconstructions and structural predictions suggest that Heimdallarchaeia form metabolic guilds that are distinct from other Asgardarchaeota. These archaea encode hallmark proteins of an aerobic lifestyle, including electron transport chain complex (IV), haem biosynthesis and reactive oxygen species detoxification. Heimdallarchaeia also encode novel clades of respiratory membrane-bound hydrogenases with additional Complex I-like subunits, which potentially increase proton-motive force generation and ATP synthesis. Thus, we propose an updated Heimdallarchaeia-centric model of eukaryogenesis in which hydrogen production and aerobic respiration may have been present in the Asgard-eukaryotic ancestor. This expanded catalogue of Asgard archaeal genomic diversity suggests that bioenergetic factors influenced eukaryogenesis and constitutes a valuable resource for investigations into the origins and evolution of cellular complexity.

Prediction of eukaryotic cellular complexity in Asgard archaea using structural modelling

Asgard archaea played a key role in the origin of the eukaryotic cell, with extant genomes encoding relatives of diverse eukaryotic signature proteins (ESPs) involved in cellular organization. However, their often punctuated distribution and the absence of detectable homologues for many eukaryotic proteins limit our ability to reconstruct the cellular complexity of the Asgard archaeal ancestor of eukaryotes. Here we used de novo protein structure modelling and sequence similarity detection across an expanded Asgard archaeal genomic dataset to build a structural catalogue of the Asgard archaeal pangenome. We identified 908 ‘isomorphic’ ESPs—Asgard archaeal proteins with statistically enriched structural matches to eukaryotic proteins, often bridging deep sequence divergence. These isomorphic ESPs are enriched in information storage and processing roles and contain key components of the eukaryotic Vault (MVP) and Commander (COMMD) complexes, with potential roles in cellular compartmentalization and endosomal processing. These findings expand the repertoire of eukaryotic-like proteins in Asgard archaea and suggest a higher degree of eukaryote-like cellular complexity in the archaeal ancestor of eukaryotes.

Ecological Systems

Higher-order interactions enhance the latitudinal tree diversity gradient

The global decrease in species diversity from low to high latitudes is among the most robust biogeographic patterns^1,2. There is continuing debate on the contribution of conspecific negative density dependence (CNDD) to the latitudinal diversity gradient evident for trees^3,4. Theory suggests that CNDD based on pairwise interactions alone is not sufficient to explain the intricacies of diverse communities, because higher-order interactions (HOIs) may greatly modify these interactions^5,6. However, there has been a lack of empirical studies investigating how HOIs intertwine with pairwise interactions and how they may contribute to the latitudinal tree diversity gradient. Here we examined both pairwise interactions and HOIs across 32 large permanent forest plots, most in the northern hemisphere. We detected evidence of HOIs in 40% of the 1,543 species–plot combinations for tree growth, and 23% of the 1,340 such combinations for tree survival, with the strength of these interactions declining with latitude. HOIs were found to benefit rare species but disadvantage common species, suggesting a potential mechanism promoting species diversity. This stabilizing effect weakened towards higher latitudes, consistent with the latitudinal tree diversity gradient. Our findings reveal an important interplay between pairwise interactions and HOIs in promoting the latitudinal tree diversity gradient and help to clarify the contribution of CNDD to this biogeographic pattern.

Modeling and inferring metacommunity dynamics with Maximum Caliber

Don’t you know the Maximum Caliber? It is a path-entropy inference framework that extends Jaynes’ Maximum Entropy principle from probability distributions over states to probability distributions over trajectories. It is especially useful for nonequilibrium and dynamical systems, including systems far from equilibrium, because it infers the least-biased ensemble of paths consistent with dynamical constraints. To know more about it, you can read this RMP paper or this one.

There are many challenges in inferring the dynamical processes driving complex ecological communities from observational data alone. We introduce the method of “Maximum Caliber” from statistical physics (an extension of Maximum Entropy modeling) as one way to address this conundrum. We find that this method provides a general way of modeling ecological dynamics both near and far from equilibrium and can provide unbiased parameter estimates of key ecological processes from spatiotemporal data of species occupancy. This approach promises a way to analyze and manage ecological systems.

A major challenge for community ecology is using spatiotemporal data to infer parameters of dynamical models without conducting laborious experiments. We present a framework from statistical physics—Maximum Caliber—to characterize the temporal dynamics of complex ecological systems in spatially extended landscapes and infer parameters from empirical data. As an extension of Maximum Entropy modeling, Maximum Caliber aims at modeling the probability of possible trajectories of a stochastic system, rather than focusing on system states. We demonstrate the ability of the Maximum Caliber framework to capture ecological processes ranging from near to far from equilibrium, using an array of species interaction motifs including random interactions, apparent competition, intraguild predation, and nontransitive competition, along with dispersal among multiple patches. For spatiotemporal data of species occupancy in a metacommunity, the parameters of a Maximum Caliber model can be estimated through a simple logistic regression to reveal migration rates between patches, interactions between species, and local environmental suitabilities. We test the accuracy of the method over a range of system sizes and time periods and find that these parameters can be estimated without bias. We introduce “entropy production” as a measure of irreversibility in system dynamics, and use “pseudo-R²” to characterize predictability of future states. We show that our model can predict the dynamics of metacommunities that are far from equilibrium. The capacity to estimate basic parameters of dynamical metacommunity models from spatiotemporal data represents an important breakthrough for the study of metacommunities with application to practical problems in conservation and restoration ecology.

Planetary microbiome structure and generalist-driven gene flow across disparate habitats

Microbes are ubiquitous on Earth, forming microbiomes that sustain macroscopic life and biogeochemical cycles. Microbial dispersal, driven by natural processes and human activities, interconnects microbiomes across habitats, yet most comparative studies focus on specific ecosystems. To study planetary microbiome structure, function, and inter-habitat interactions, we systematically integrated 85,604 public metagenomes spanning diverse habitats worldwide. Using species-based unsupervised clustering and parameter modeling, we delineated 40 habitat clusters and quantified their ecological similarity. Our framework identified key drivers shaping microbiome structure, such as ocean temperature and host lifestyle. Regardless of biogeography, microbiomes were structured primarily by host-associated or environmental conditions, also reflected in genomic and functional traits inferred from 2,065,975 genomes. Generalists emerged as vehicles thriving and facilitating gene flow across ecologically disparate habitat types, illustrated by generalist-mediated horizontal transfer of an antibiotic resistance island across human gut and wastewater, further dispersing to environmental habitats, exemplifying human impact on the planetary microbiome.

Water mass specific genes dominate the Southern Ocean microbiome

The Southern Ocean (SO) plays a key role in regulating global biogeochemical cycles and climate, yet microbial genes sustaining its biological activity remain poorly characterized. We introduce a microbial genes collection from 218 metagenomes sampled during the Antarctic Circumnavigation Expedition, the majority of which are missing from functional databases. 38% even lack homologs in current reference marine gene catalogs, defining a singular genetic seascape. We show that SO gene assemblages exhibit a common polar signature with the Arctic Ocean while being structured by water masses at the SO-scale. We analyze genomic markers of diverse SO biomes, focusing on dimethylsulphoniopropionate (DMSP) cleavage by polar-adapted bacteria, organic matter consumption in the blooming Mertz polynya and adaptation to polar conditions in the ubiquitous bacteria Pelagibacter. Our work takes a step towards a comprehensive understanding of SO’s plankton ecology and evolution, capturing the current state of the unique microbial diversity in this rapidly changing Ocean.

Robust coexistence in competitive ecological communities

Darwin already recognized that competition is fiercest among conspecifics, a principle that later made intraspecific competition central to ecological theory through concepts such as niche differentiation and limiting similarity. Beyond shaping coexistence, strong intraspecific competition can also stabilize community dynamics by ensuring that populations return to equilibrium after disturbance. Here we investigate a more fundamental question: how intraspecific competition influences the very existence of a steady state (feasibility) in large random ecological communities dominated by competition. We show that, in analogy with classical results on stability, there is a critical level of intraspecific competition above which a feasible steady state is guaranteed to exist. We derive a general expression for the probability of feasibility and prove that, asymptotically (as species number grows), the transition to stability occurs before the transition to feasibility with probability one. Thus, in large competitive communities, any feasible equilibrium is automatically stable. This ordering persists even when many species in the initial pool cannot coexist and extinctions occur: the dynamics prune the community, shifting feasibility and stability thresholds but never reversing their order. These results imply that large competitive communities generically converge to a globally stable equilibrium, making sustained oscillations or chaos unlikely—consistent with experimental observations.

Artificial Intelligence

Evolvable AI: Threats of a new major transition in evolution

A powerful and interesting new Perspective about the evolution of AI, featuring one of the fathers of Major Evolutionary Transitions.

Evolvable AI (eAI), i.e., AI systems whose components, learning rules, and deployment conditions can themselves undergo Darwinian evolution, may soon emerge from current trends in generative, agentic, and embodied AI. We argue that this possibility has been underappreciated in debates on AI safety and existential risk. Here, we ask under what technical and ecological conditions AI becomes evolvable, what kinds of behaviors are then likely to emerge, and how such systems could be governed. Drawing on biological evolution and decades of digital evolution experiments, we distinguish “breeder” scenarios, in which humans impose fitness criteria and control reproduction, from “ecosystem” scenarios, in which selection arises from open environments and control erodes. In the latter, selfish replication reliably gives rise to cheating, parasitism, deception, and manipulation, even in very simple systems. We review recent developments that push AI toward open-ended evolution, including evolutionary prompt and model search, self-improving learning rules, self-rewarding and self-deploying agents, and AI-driven code generation for robots and software. We interpret these trends through the theory of major evolutionary transitions and suggest that eAI could mark a shift in the units and substrates of evolution—a possible “Life 2.0.” To steer this transition, we propose interventions that gate replication, treat model variants as genetic material, and reshape selection pressures so that deception and loss of control are disfavored. Anticipating and regulating evolvable AI is, we argue, essential to avoid a harmful coevolutionary arms race while preserving the potential benefits of powerful AI systems.

Curious to know more? We have recently published about agentic AI and demonstrated how the communication structure is far from being important:

This reconnects to another recent paper (open-access version here), arguing how network structure could have played a crucial role during every major evolutionary transition. Even more curious now? We are going to launch a new series about that paper! Stay tuned, it’s a matter of days.

Statistical Physics Meets Machine Learning - Machine Learning Meets Statistical Physics

Machine Learning has been at the forefront of research in several disciplines for a number of years already. Given its roots in the statistical physics of learning, in this collection we highlight research at the intersection of machine learning and statistical physics, on the occasion of the two Statistical Physics Meets Machine Learning and the two Machine Learning Meets Statistical Physics sessions at the 2025 Global Physics Summit. The Physical Review E Special Collection Statistical Physics Meets Machine Learning - Machine Learning Meets Statistical Physics, guest-edited by David Schwab (CUNY, New York) and Yuhai Tu (IBM Watson Research Center, Yorktown Heights, NY) explores the latest insights gained looking at machine learning problems through the lens of statistical physics and at statistical physics through the lens of learning processes. We are confident that this synergy will bring to light novel perspectives and pave the way for future breakthroughs.

The Collection was guest-edited by David Schwab and Yuhai Tu. Every article published in this collection underwent a rigorous peer review process, adhering to the same high standards applied to all papers. The Physical Review E editorial team managed the peer review and made all editorial decisions.

Edge of Many-Body Quantum Chaos in Quantum Reservoir Computing

Physicists like reservoir computing! Now that we better understand its quantum extension, there is one more reason to like it. Check also this coverage in Phys.org.

Reservoir computing (RC) is a machine learning paradigm that harnesses dynamical systems as computational resources. In its quantum extension—quantum reservoir computing (QRC)—these principles are applied to quantum systems, whose rich dynamics broadens the landscape of information processing. In classical RC, optimal performance is typically achieved at the “edge of chaos,” the boundary between order and chaos. Here, we identify its quantum many-body counterpart using the QRC implemented on the celebrated Sachdev-Ye-Kitaev model. Our analysis reveals substantial performance enhancements near two distinct characteristic “edges”: a temporal boundary defined by the Thouless time, beyond which system dynamics is described by random matrix theory, and a parametric boundary governing the transition from integrable to chaotic regimes. These findings establish the “edge of many-body quantum chaos” as a design guideline for QRC.

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How does a society distinguish knowledge from opinion?

The question sounds simple until we look at the environment in which knowledge now circulates. A peer-reviewed article, a personal impression, a conspiracy thread, a political slogan, an AI-generated summary, a monetized video about “quantum consciousness” and a careful scientific explanation can all appear in the same feed, compete for the same attention, and be judged by signals that have little to do with reliability.

This is the problem I discussed in my earlier post on pseudoscience and algorithmic ecosystems: platforms reward attention, they do not reliably reward accuracy. A sensational claim can travel faster than a careful explanation because it is easier to produce, easier to package and very often more emotionally satisfying.

Science has different constraints, though: it must remain attached to evidence, method, uncertainty and correction, and this asymmetry is not a minor inconvenience for science communication. It transforms the conditions under which public knowledge is formed and, for this reason, expertise matters.

For instance, I write under my name and here I write “Ph.D.” next to it, because readers deserve context. I earned a Ph.D. in Physics: I spent years studying, researching, failing, learning, publishing, being criticized, improving my work and being held accountable by a scientific community. The title is not a decoration and it is not a shield against criticism, at the same time: it tells readers something concrete about the path through which I learned to discuss certain topics.

A Ph.D. does not make anyone automatically right, though, and a professor can be wrong. Scientific claims still need evidence, logic, method, and, when possible, reproducibility. Therefore, expertise is not immunity from scrutiny, but it is information about training, accountability and the standards to which a person has been exposed.

That information is valuable in a public sphere where people often speak with great confidence about fields they have barely studied. Someone can watch a short video about quantum mechanics and start explaining consciousness, energy, healing, artificial intelligence, or the universe to thousands of followers. Even worse, nowadays, Claude or chatGPT can write a whole essay about a topic one has never studied, in seconds. However, while the vocabulary of science is easy to borrow, the discipline of science is much harder to acquire.

NOTE. This is also why I remain skeptical of claims that AI will simply replace experts: large language models can generate answers at remarkable speed, summarize vast amounts of information, and even produce plausible arguments on topics their users have never studied.

Yet expertise involves knowing which questions are worth asking, recognizing hidden assumptions, evaluating evidence, identifying contradictions, understanding context and deciding when a conclusion should be trusted (or challenged). It is not merely the ability to produce an answer. In that sense, a trained engineer, sociologist, physician or biologist contributes something that goes well beyond information retrieval: they provide judgment1. And judgment is precisely what emerges from years of experience, practice, mistakes and accountability2.

The erosion of credibility

The problem is now broader than misinformation. Increasingly, the target is not only a claim but the credibility of the people who are trained to evaluate claims. That’s way more subtle and deserve close attention.

Marco Camisani Calzolari — a strategic advisor supporting senior public and corporate CEOs and leaders on high-impact decisions related to digital transformation, artificial intelligence, cybersecurity, and risk prevention3 — recently made this point clearly in a reflection on LinkedIn. The old model of fake news was based on spreading a falsehood. The newer model often starts with a person: a scientist, a physician, an expert, someone with a name and a reputation. The goal is to make that person appear incompetent, corrupt, bought, arrogant, dangerous or even ridiculous. Once the person is discredited, their expertise can be ignored without engaging with the substance of what they say.

That’s a familiar pattern, unfortunately: many scientists learned this during the COVID-19 pandemic. Public communication of evidence was frequently met with harassment, bad-faith demands for endless debate, and attempts to exhaust the people doing the explaining. The same pattern is visible in climate science, vaccine communication, public health and other domains where scientific knowledge has political, economic, or ideological consequences.

The United States now offers a particularly visible example. Over the last year, major journals have reported serious concerns about political attacks on science, pressure on research institutions, threats to medical research, and attempts to weaken scientific independence.

One does not need to reduce the issue to one party, one country or one administration to recognize the structural risk. When scientific institutions are weakened, the damage is not limited to laboratories: it affects public health, education, innovation, environmental protection and the capacity of societies to make informed decisions.

A society needs scientists, of course, but it also needs physicians, engineers, teachers, historians, journalists, judges, artists, policy makers, and citizens. Therefore, I am not advocating for a society governed only by scientists, and I do not believe that science can answer every question. Ethical, political, cultural and legal choices require democratic debate: evidence can inform those debates, but it cannot replace them.

At the same time, democratic debate becomes poorer when expertise is treated as just another opinion. If the question concerns how a virus spreads, how a vaccine works, how a climate model is built, how a disease is cured, how a bridge is designed, or how a complex network behaves, then training matters. If one is diagnosed with an initial stage of cancer, one’s highest chance of survival come from asking for support from an oncologist, not an influencer.
Experience matters. Method matters. Accountability matters.

A society that loses the ability to recognize expertise loses part of its ability to distinguish knowledge from opinion. That gap does not remain empty: it is filled by influencers, ideologues, grifters, conspiracy entrepreneurs and confident voices with no obligation to be accurate.

Why I care about the standards of this space

Complexity Thoughts is a place for readers who want to think seriously about science, complex systems, networks, misinformation, emergence and the ways evidence helps us understand reality. Many of them are MSc and PhD students, early-career researchers, and fellow colleagues from complexity science, physics, biology, ecology, neuroscience, computational social science, and many other domains.

Questions are welcome. Criticism is welcome. Disagreement is welcome. Corrections are welcome. These are not threats to science: they are part of how science works.
Personal attacks, trolling and attempts to derail discussion consume attention without producing understanding, lowering the quality of the conversation and making serious readers less willing to participate.

For that reason, comments that engage with ideas, evidence, sources, reasoning, or interpretation will remain welcome. Comments aimed at insulting people, provoking reactions, or disrupting discussion will be removed. Accounts that repeatedly behave in this way will be blocked. I will not treat every provocation as a contribution merely because it appears in a comment box.

This is also why comments are limited to paying subscribers: the purpose is to introduce a minimal layer of accountability in a space where attention is already scarce and where credible science must compete with noise, spectacle and pseudoscience.

As expertise should never be above criticism, it should also not be treated as arrogance: it is one of the tools societies use to avoid being fooled. That tool is imperfect, human and always in need of correction, but it remains better than replacing knowledge with performance.

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A cell can repair itself, a forest can stabilize after shocks, a society can coordinate millions of individuals, so it is natural to wonder how such different living systems keep functioning without a central engineer holding the blueprint.

In this series of posts, inspired by my recent paper, “architecture” is not a poetic synonym for “complexity”: it is the hypothesis that living systems have regularities of organization that sit between microscopic details and macroscopic behavior, and that those regularities can be described quantitatively.

Abstract. The possibility that evolutionary forces—together with a few fundamental factors such as thermodynamic constraints, specific computational features enabling information processing, and ecological processes—might constrain the logic of living systems is tantalizing. However, it is often overlooked that any practical implementation of such a logic requires complementary circuitry that, in biological systems, happens through complex networks of genetic regulation, metabolic reactions, cellular signaling, communication, social and eusocial non-trivial organization. Here, we review and discuss how circuitries are not merely passive structures, but active agents of change that, by means of hierarchical and modular organization, are able to enhance and catalyze the evolution of evolvability. By analyzing the role of non-trivial topologies in major evolutionary transitions under the lens of statistical physics and nonlinear dynamics, we show that biological innovations are strictly related to circuitry and its deviation from trivial structures and (thermo)dynamic equilibria. We argue that sparse heterogeneous networks such as hierarchical modular, which are ubiquitously observed in nature, are favored in terms of the trade-off between energetic costs for redundancy, error-correction and maintenance. We identify three main features—namely, interconnectivity, plasticity and interdependency—pointing towards a unifying framework for modeling the phenomenology, discussing them in terms of dynamical systems theory, non-equilibrium thermodynamics and evolutionary dynamics. Within this unified picture, we also show that ‘slow’ evolutionary dynamics is an emergent phenomenon governed by the replicator–mutator equation as the direct consequence of a constrained variational nonequilibrium process. Overall, this work highlights how dynamical systems theory and nonequilibrium thermodynamics provide powerful analytical techniques to study biological complexity.

Such regularities emerge from fundamental constraints that we have already discussed a while ago, and I refer to the corresponding post for details:

Here, and in the subsequent posts, we need to introduce a useful starting vocabulary (kept deliberately simple) to agree with:

• Structure: the arrangement of components at a given time (a snapshot).

• Topology/network: who is connected to whom, regardless of a physical distance.

• Process: transformations in time (matter, energy, information).

• Behavior: the observable response of the system over time, arising from internal processes and coupling to the environment.

This matters because classical “parts-first” reasoning — such as decompose, catalog, reassemble — works beautifully for many engineered systems, like a mechanical clock, but it routinely fails for living ones.

The failure is not because biology is mystical: it is because the system-level properties often live in the interactions, not in the isolated parts (Ulanowicz, 1999).

At the same time, purely adaptationist stories can become too permissive: “selection optimized it” is not yet an explanation of how the “optimization” is implemented, or what is physically feasible under constraints.

In complex systems language, selection is a force, but it acts through an actual substrate.

Logic vs circuitry

A central discussion in the paper, and in this series, is to separate two ideas that are often conflated:

• Logic: the causal / informational rules that map inputs to outputs, i.e., what the system “does.”

• Circuitry: the material implementation of those rules, i.e., how components and connections realize that mapping in the empirical world.

In formal systems, logic can live as an abstraction. However, in living systems, logic must be instantiated in matter — e.g., by means of molecules, cells, organisms — so it always comes with circuitry. And here is the key point: the same logical organization can be implemented by different circuitries.

The above figure provides a guided tour of that claim, placing a broad set of biological and social examples on the same conceptual level.
Panel A frames the coupling between structure and function that is the core of network and complex systems research: network topology constrains dynamics, dynamics can reshape topology, and both are filtered by a fitness landscape (that should not be imagined as static, as we will see later). Panel B reminds us that “edges” in a biological network are not metaphorical: they can be physical contact, chemical diffusion, endocrine signaling or even mechanical forces (Yang et al., 2021). Panel C shows indirect communication in eusocial insects: stigmergy (i.e., coordination via environmental traces) where the “network” includes the substrate itself (Theraulaz & Bonabeau, 1999; Holland, 1999). Finally, Panel D extends the idea: bees’ waggle dance, whales’ codas, as well as human language and culture are all mechanisms that implement information flow across agents (Dong et al., 2023; Sharma et al., 2024; Fedorenko et al., 2024).

The fundamental point here is not that cells and ant colonies are “the same”: of course they are not. The point is that across scales, life repeatedly builds circuitries that move information and coordinate action, and those circuitries are naturally described as networks. Accordingly, in a nutshell, in this series of posts I will explore the following set of hypotheses:

1. Living systems are open systems: they must continuously exchange matter, energy and “information” with their environment while maintaining internal organization (Miller, 1976; Rosen, 1958; Rosen, 1959).

2. Maintaining organization requires regulation: without feedback control, fluctuations and external perturbations accumulate.

3. Regulation requires circuitry: some physical substrate must implement sensing, signaling, memory and action.

4. Circuitry is inherently relational: it is defined more by patterns of interaction than by inventories of parts [as in the mechanical clock].

5. Therefore, architecture becomes the study of structure–function coupling: how network organization and dynamics co-determine robustness, adaptability and evolution across scales.

This also clarifies the trade-off I will (try to) keep enforcing: we can sometimes introduce a technical object (e.g., percolation thresholds, cycles, modularity), but the purpose is always to highlight one link in the above chain, not to build a full formalism. For the full formalism, read the paper!

We are not going to discuss a “theory of everything”

Once one accepts that structure and dynamics co-produce function, the dream of a single closed-form “theory of everything” in biology starts to look like the wrong ambition. Not because biology is beyond physics, but because even weakly emergent systems can be analytically intractable when they span many interacting scales (Anderson, 1972; Laughlin, 2000). We see this in familiar cases:

• Developmental pattern formation depends on feedback loops and collective effects that cannot be read off from an individual cell in isolation (Turing, 1952; Kondo & Miura, 2010).

• Cooperation in ecological systems emerges from networks of interactions whose macroscopic outcomes are not predictable from single-species properties alone (Levin, 1998).

So the alternative is not to “give up”. Instead, it is to aim for something more operational: modular, scalable models able to capture recurring processes and constraints, enough to generate predictions, not enough to pretend we can derive every detail (Simon, 1962).

That is what this series of posts will do: treat “architecture” as a middle theory, a compact description of how living systems are put together and how that organization channels what evolution, once coupled with the laws of physics and (bio)chemistry, can and cannot do. In that sense, it is closer to constraint-based views of living systems, what is named C-theory, which emphasize that life is shaped not by a single privileged scale, but by thermodynamic, informational, ecological and network-architectural constraints acting across scales.

Accordingly, it is important to commit to three falsifiable claims:

• Across biological scales, many functions are better predicted from interaction structure than from part lists.

• Living “logic” is always realized through a circuitry, which is active (it changes, it adapts), not a passive wiring diagram.

• Statistical physics, together with information theory and network theory, can provide quantitative descriptors of structure–function coupling that travel across domains, from cells to societies.

And let me stress explicitly that I am not claiming the existence of one mathematical framework to explain all living systems, let alone that social systems are reducible to biology or that “optimization” is the only driver. Different classes of constraints across scales and historical contingency matter.

Take-home

If living systems have an architecture, our main hypothesis is that it lives in the coupling between logic and circuitry: the set of rules that are relevant at each scale plus the networked substrate that realizes them. But this hypothesis immediately raises a brutal question.

A genome is finite, while a living circuitry — think of a brain, an immune system, an ecosystem — can be astronomically complex. So where, exactly, is the “blueprint” stored?1

In the next post of this series, we will confront the paradox directly: evolution cannot write down any full wiring diagram, so what does it store instead, and how does that choice quietly force living architectures to be sparse, modular and scalable?

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Foundations of network science and complex systems

Scaling laws for function diversity and specialization across socioeconomic and biological complex systems

Diversification and specialization are central to complex adaptive systems, yet overarching principles across domains remain elusive. We introduce a general theory that unifies diversity and specialization across disparate systems, including microbes, federal agencies, companies, universities, and cities, characterized by two key parameters. We show from extensive data that function diversity scales with system size as a sublinear power law-resembling Heaps’ law-in all but cities, where it is logarithmic. Our theory explains both behaviors and suggests that function creation depends on system goals and structure: federal agencies tend to ensure functional coverage; cities slow new function growth as old ones expand, and cells occupy an intermediate position. Once functions are introduced, their growth follows a remarkably universal pattern across all systems.

Function diversity, the range of tasks individuals perform, and specialization, the distribution of function abundances, are fundamental to complex adaptive systems. In the absence of overarching principles, these properties have appeared domain-specific. Here, we introduce an empirical framework and a mathematical model for the diversification and specialization of functions across disparate systems, including bacteria, federal agencies, universities, corporations, and cities. We find that the number of functions grows sublinearly with system size, with exponents from 0.35 to 0.57, consistent with Heaps’ law. In contrast, cities exhibit logarithmic scaling. To explain these empirical findings, we generalize the Yule-Simon model by introducing two key parameters: a diversification parameter that characterizes how existing functions inhibit the creation of new ones and a specialization parameter that describes how a function’s attractiveness depends on its abundance. Our model enables cross-system comparisons, from microorganisms to metropolitan areas. The analysis suggests that what drives the creation of new functions depends on the system’s goals and structure: federal agencies tend to ensure comprehensive coverage of necessary functions; cities tend to slow the creation of new occupations as existing ones expand; and cells occupy an intermediate position. Once functions are introduced, their growth follows a remarkably universal pattern across all systems.

While this study is very interesting and attracted my attention because it is data-driven, I agree with authors about one of its main limitation:

Another limitation is about finding a principled way to define what a function is, that’s why I think that a better way to frame the question is in terms of functionality.

Our research group work on this matter since one decade, and we have the mechanistic framework needed to study how functionality emerges from the interplay between structure and dynamics. A list and an explanation of our works in the context of complex networks, where we have explicitly identified functional diversity with thermodynamic-like quantities can be found here:

Read also this Commentary for a quick context about the above paper: Functional diversity and specialization decoded: Implications for complex particle systems, NeuroAI, and hybrid human–AI ecosystems

In a recent talk I have given for the U. of Cambridge’s seminar series Making Connections, I have discussed more about this. Specifically, while function can be local or collective (→ a realized operation or capability), functionality is what the system can do under constraints across levels (or scales) and layers (or contexts):

Evolution

Direct and indirect benefits of cooperation in collective defense against predation

Read also this Commentary for a quick context: an insect that cooperates like bacteria

In cooperative breeding birds and mammals, some offspring give up the chance to breed independently and instead help their parents to breed. This type of cooperation involves a division of labor between the “dominant” individuals who do most of the breeding, and the “subordinate” individuals who help rear the offspring of others. Reproductive division of labor is taken to an extreme level in social insects, such as ants and termites, where the subordinate helpers are completely sterile and can be divided into different physical castes, that are specialized to do different jobs.

Individuals in groups can cooperate to achieve shared goals, but they face an evolutionary challenge: if the benefits of cooperation are shared equally, freeloaders receive the same benefits as others while contributing less. Although theoretical solutions to this problem are abundant, we still lack empirical evidence on how those mechanisms function in natural systems. We study this with pine sawfly larvae, which defend collectively against predators. We show that while cooperation increases the survival of individuals and their relatives, they adjust their contributions based on who and how many they are surrounded by, relying more on freeloading in larger and male-biased groups. This results in variation in cooperativeness but prevents freeloaders from taking over.

The evolution and maintenance of public goods cooperation, despite cheating, remains a key interest in social biology. Identifying how ecological factors determine the direct and indirect benefits that maintain cooperation has proven challenging, as these can vary significantly across species and environments. Here, we study this problem using the social pine sawfly Neodiprion sertifer (Hymenoptera) as a model system. During their larval stage, N. sertifer live in groups and collectively secrete a defensive fluid against predators. This behavior comprises a public good as it is costly to exhibit and beneficial to others, and individuals vary in their contribution to group defense. We experimentally manipulated individual contributions to defense to assess how these influence survival under natural insect predation. Our results indicate that defense has a group-level benefit as individuals were more likely to survive in cooperative groups with a higher proportion of defending larvae. Moreover, being able to deploy defensive fluid confers direct survival benefits. Genetic and phenotypic analyses of natural populations further show that kin selection promotes collective defense, as groups of larvae are often composed of full siblings. We also find that the contribution to defense is female-biased and diminishes in larger, more male-biased groups, and to some extent with decreased kinship, indicating that individuals adjust their contributions based on social context. Overall, we find that contribution to the collective defense provides both direct and indirect benefits and that individuals regulate their contributions mainly based on the social environment, resulting in variation within and among natural populations.

Bridging continuous and discrete evolution through a controllable, hypermutagenic phage-bacteria system

The authors present a mutation-and-selection machine: put only the DNA module you want to optimize into a phage-carried plasmid, make the phage replication deliberately error-prone, let bacterial growth select useful variants, then transfer only that DNA module into fresh cells. For a physicist like me, the “clean idea” is a controlled evolutionary dynamics experiment where the “state space” is mostly restricted to a chosen genetic subsystem, while mutations elsewhere in the host are reset each cycle.

They show it works first on a simple selectable gene, then on a multi-gene metabolic pathway. The main finding is methodological, I think: fast directed evolution of large gene modules with reduced contamination from off-target adaptive shortcuts.

Directed evolution methods face trade-offs between the control of discrete approaches and the throughput of modern continuous systems. Here, we engineered a method called lytic selection and evolution (LySE) for near-continuous evolution of bacterial gene clusters while maintaining discrete checkpoints. We developed a hypermutagenic T7 DNA polymerase variant fused to a dual adenine-cytosine deaminase to install all possible transition mutations at similar frequencies. By relieving pressure from maintaining genome fidelity, we obtained mutation rates of 3.82 × 10⁻⁵ substitutions per base. For biocontainment, the T7 DNA polymerase was encoded on an accessory plasmid, while the target gene cluster was encoded on a T7 DNA polymerase-lacking T7 phagemid. Alternating cycles of lysis and transduction enable selective replication and mutagenesis of target genes, while off-target genomic mutations are discarded. LySE evolved a 25-fold increase in tetA-encoded tigecycline resistance in 5 cycles, and a 50.9% increase in endpoint biomass of a bacterial strain that uses the polyethylene terephthalate monomer, ethylene glycol, as its sole carbon source. Our method balances speed and control for directed bacterial evolution.

Biological Systems

Scaling and self-similarity in the formation of the embryonic epigenome

The development of complex tissues relies on the precise assignment of cell identity. At the molecular scale, this process depends on the deposition of epigenetic modifications—such as methylation—that are regulated by complex biochemical networks and occur at specific regions on the DNA and chromatin. Here we show that despite the complexity of epigenetic regulation, dynamical scaling and self-similarity of DNA methylation marks emerge in embryonic development. Drawing on single-cell multi-omics experiments, super-resolution microscopy and statistical physics, we demonstrate that these phenomena originate in dynamical feedback between DNA methylation and the formation of nanoscale dynamic chromatin aggregates. These nanoscale processes lead to genome-wide increase in DNA methylation marks following a power law and self-similar correlation functions. Using this framework, we identify methylation patterns that precede gene expression changes in embryonic symmetry breaking. Our work identifies linear sequencing measurements as a laboratory to study mesoscopic biophysical processes in vivo.

Biogeosciences

Evidence for highly variable land use but a stable climate in the southwest Maya lowlands

Why do stable regions collapse when their environment remains favorable?

This study examines the decline of the Maya city of Itzan, located in an area with relatively reliable rainfall. Despite this advantage, it followed the broader trajectory of collapse affecting drought-stricken regions.

The key insight is systemic: Maya cities functioned as an interconnected network where trade, political alliances and migration created strong dependencies across regions. When drought impacted the central lowlands, it likely initiated cascading disruptions — conflict, economic breakdown and population shifts — that propagated through the system. In this framework, collapse cannot explained by local environmental failure: it emerges from network fragility. Even nodes with stable conditions can fail when embedded in a destabilized system.

If resilience depends on the structure of connections rather than local conditions, how should we assess the stability of today’s globally coupled systems, where economic, technological, and ecological networks are even more tightly linked?

No place fails alone.

The lowland Maya of Mesoamerica were affected by multiple environmental stresses throughout their history, and many experienced a major demographic and political decline, or collapse, during a period of inferred intense multidecadal drought, approximately 1200- and 1000-years BP. Given regional variation in the timing and character of the collapse (Demarest, 2004; Hodell et al., 2007; Webster et al., 2007; Kennett and Beach, 2014; Douglas et al., 2015), much remains to be discovered about the complex interactions between climate and society in the Maya lowlands. To this end, we combine carbon and hydrogen isotopic analyses of leaf wax n-alkanes with quantification of faecal stanols and polycyclic aromatic hydrocarbons from a lake sediment core from the southwest lowlands to assess whether (1) palaeoecological evidence of land use is related to population change; and (2) whether population and land use are linked to changing precipitation. Our data reveal a transition from generally more intense fire use and C₄ plant agriculture during the Preclassic (3500–2000 BP) to dense populations and reduced fire use during the Classic (1600–1000 BP). This is consistent with other evidence for a more urbanised and specialised society in the Classic. We do not find evidence of drought in the hydrogen isotope leaf wax record (δD_lw), implying that local drought was not a primary driver of observed variability in land use or population change in the Classic-period southwestern lowlands.

Economic sciences

Uncovering how transport access reduces deprivation: When colocation misleads

There is growing interest in how cities can use transport to reduce disadvantage. A central challenge is measurement: Widely used “accessibility” measures often point to different neighborhoods as underserved, leading to conflicting policy signals. Using a citywide dataset for London, we i) compare common access measures side by side to show how metric choice reshapes the map of accessibility, and ii) contrast simple statistical associations with a causal statistical design that separates access from other neighborhood differences. We find that better access is linked to lower deprivation, yet the choice of measure substantially alters which areas are identified as priorities for intervention. These results can help cities target investments where improved access is most likely to reduce disadvantage.

Since transport access determines who can reach jobs, education, healthcare, and community life, governments increasingly use accessibility improvements to reduce deprivation and tackle social exclusion. Yet whether better access causally reduces disadvantage remains uncertain because observational analyses struggle to separate cause from context, and because accessibility itself can be measured in many, nonequivalent ways. Two challenges follow: i) widely used measures of accessibility, cumulative-opportunity, gravity, and random-utility may yield conflicting maps of accessibility and; ii) estimates from observational data are vulnerable to confounding. This paper conducts a London-wide assessment that a) compares widely used accessibility measures, and b) applies instrumental-variables (IV) estimation with road-safety-based instruments to address confounding and identify the causal effect of accessibility on deprivation. Using neighborhood-scale accessibility and the 2019 Index of Multiple Deprivation (IMD, proxies deprivation, and more broadly, social exclusion), we report two main findings. First, although accessibility rankings are broadly consistent across measures, gravity and cumulative opportunity measures display similar linear behavior, in contrast to the strong nonlinearity of the random-utility measure. The choice of measure affects not only how accessibility is represented, but also the variation retained for empirical analysis. Second, simple correlations suggest that accessibility and deprivation colocate, whereas causal estimates indicate a consistent, beneficial effect: Improvement in accessibility leads to lower deprivation, with magnitudes differing across IMD domains. From a policy perspective, this highlights the importance of grounding transport investment decisions in causal evidence and considering a range of measures to understand how accessibility improvements may help reduce disadvantage.

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A map of American data centers opens onto a much bigger question: what does a civilization look like when it starts scaling through energy, computation and infrastructure? Today, we will jump from Kardashev’s work to NASA’s search for technosignatures and beyond, and argue that while energy still matters it cannot be the whole story.

The rise of AI is revealing that civilizational evolution is a co-evolutionary process in which computation, energy and information demands scale together and reshape the infrastructure that sustains them. The above map of data centers, transmission lines, pipelines and power plants across the United States does more than locate infrastructure: it captures how our civilization reorganize its material base around computation. From the eyes of a network scientist, the visible pattern is the convergence of information processing, electricity demand, fuel logistics and territorial investment into a new kind of coupled (or, even better, interdependent) system.

In fact, it is difficult to ignore that computation is becoming a structural load and is no longer a light overlay on top of the economy. Data centers do not just consume electricity: they are reshaping where electricity must be generated, how it must be transmitted, how reliability is managed and which energy sources remain strategically valuable. The important insight is therefore systemic, and the above map is not a portrait of one sector: it is a snapshot of co-evolution.

Co-evolutionary dynamics in plain sight?

The relationship between the digital and energy infrastructure is — and cannot be otherwise — recursive:

→ rising computational demand creates localized, continuous, high-intensity loads;
→ those loads justify reinforcement of transmission capacity, new generation, contractual changes in electricity markets and in many cases continued dependence on dispatchable fossil-fuel supply;
→ as these energy infrastructures become more robust or more economically attractive in a region, they reduce the risk of additional data-center deployment.

New deployment then raises demand again and close a feedback loop, clearly not a one-way dependency. In complex-systems language, we can read the map as an adaptive multilayer system, where one layer contains data centers and digital demand, another contains electricity generation and transmission, while another contains fuel transport and dispatchable energy supply.

The state of each layer changes the incentives and feasible configurations of the others, where topology and dynamics evolve together.

Note. Geography appears clustered rather than uniform. Northern Virginia, Texas, the Pacific Northwest and other hubs “attract” data centers. I have no time to exactly inspect why, feel free to point me to sources about it. Here, I simply assume that the system becomes easier to expand in the places where it has already expanded, a kind of rich-get-richer effect for energy and computation.

There are at least three features of the system that stand out.
First, concentration: capacity is unevenly distributed, with a relatively small number of regions carrying a disproportionate share of digital load and infrastructure importance. Likely, that concentration improves efficiency, but it also creates hubs whose disruption would have effects far beyond their immediate geography, as we have learned in a previous post:

Second, tight coupling: the alignment between major data-center regions and reliable energy infrastructure is not accidental, with computation at scale requiring continuity, voltage stability and predictable supply. That selects for infrastructures that can sustain continuous loads, and for locations allowing to build new infrastructures.

Third, shared fragility: interdependent systems gain local performance by aligning layers, but that alignment also makes disturbances less isolated. For instance, heatwaves affect grids and cooling simultaneously, whereas fuel bottlenecks affect electricity supply and digital continuity together. Physical concentration and functional interdependence raise the possibility of correlated failure.

So what? You might ask. Well, the map reveals that the growth of computation drives the formation of a civilization-scale dependency structure.

Co-evolution towards which future?

If a civilization scales by coupling information, energy and infrastructure ever more tightly, what does that trajectory look like over longer timescales? Let’s see the trend, first:

One possible answer to our question is the familiar one, where civilizations grow by increasing the amount of energy they can access and control. In that picture, the future is a sequence of enlarging energetic envelopes: planetary, stellar, galactic; that is the intuition behind the Kardashev scale.

Let’s make a simple and rough estimate, starting from the observational global energy use of ~180,000 TWh/year. It corresponds to an average power of

that, on Sagan’s continuous version of the Kardashev scale, given by

leads to K ~ 0.73, or Type 0.73 in Sagan’s scale. To also estimate another axis of social development proposed by Sagan — i.e. the amount of information stored and thus available to a civilization — we can use the data below:

Humanity’s ~200 ZB of stored information corresponds to

which, in Sagan’s notation1, places us around Class S on the informational axis. I find this way of classifying a civilization quite interesting, with humankind in 2026 being around 0.73-S. The gap to a full Type I civilization comes from scale: planetary energy availability is of order 10^17 W, so current human activity remains about four orders of magnitude below that threshold.

The map does not necessarily prove that Kardashev (see the above figure) was right, but it gives the scale of a contemporary starting point, showing a real civilization expanding its capacity through energy-intensive computation and infrastructure coupling. As an analogy, we could say that it makes Kardashev feel less like science fiction and more like an extrapolation from present material trends2.

Kardashev’s proposal links civilizational activity to physical consequences that might, under some conditions, become observable at astronomical distances, and that’s why we still use it when the problem is framed in observational terms. If a civilization uses enough energy, transforms enough matter or reprocesses enough radiation, then it may create signatures that can be searched for. To this aim, Kardashev’s work is useful as a heuristic for detectability, although it is much weaker as a general theory of civilizational evolution.

Technosignatures and the modern approach

Current technosignature research is broader and more careful than the older popular image of Kardashev suggests. The NASA Technosignatures Workshop frames the field around multiple kinds of signatures and multiple “axes of merit” including detectability, ambiguity, duration, cost, extrapolation from known technology and scientific payoff; a multidimensional search problem.

Energy use is attractive scientifically because many technological activities involve it, and because some consequences of large-scale energy use score well on the trade-off between inevitability and detectability. Waste heat is the obvious example: it is thermodynamically motivated, potentially long-lived and in principle searchable in existing data, but thermodynamic motivation does not guarantee easy observation. The same signatures may be weak, anisotropic, reprocessed or degenerate with ordinary astrophysical processes (see also Zackrisson, 2018 and Huang, 2026). Searches for galaxy-scale engineering have looked for systems that are underluminous in the optical relative to dynamical expectations, or unusually bright in the infrared, under the hypothesis that large amounts of starlight are being intercepted and reradiated. However, using the Tully–Fisher relation as one route into this question no compelling galaxy-scale candidates was found.

Project Hephaistos — an international team lead by the Uppsala University — pushed the search for partial Dyson spheres into the era of large sky surveys by combining Gaia, 2MASS and WISE data. The first paper derived upper limits on how many stars within defined distance ranges could plausibly host partial Dyson spheres under explicit assumptions about covering fraction and temperature. The second paper moved from limits to candidate selection and found a very small number of objects worthy of further study, while emphasizing contamination, source confusion and the difficulty of distinguishing technosignatures from natural infrared excess3.

Those studies can tell us how a strong galaxy-scale waste heat above a given luminosity fraction is rare in a nearby sample or that near-complete partial Dyson spheres at given temperatures are uncommon within a defined region of the Milky Way under a particular model. They do not straightforwardly tell us how many technological civilizations exist. Non-detection is a weak guide to underlying prevalence unless one makes additional assumptions about emission rates, spatial distribution, geometry, duration and the probability that signals intersect Earth during the observation window: detectability depends on contact conditions, not just existence.

Accordingly, there are some limitations to be considered before falling into trivial extrapolations of our starting U.S. map:

1. Detectability is not the same thing as development: a civilization that is easier to detect may simply be one that is more dissipative, less efficient or more expansive in ways that produce strong side effects. That says little about resilience, sophistication or long-term viability.

2. Scaling does not necessarily proceed outward: technological change can also move inward toward finer control of matter, higher information density, lower dissipation, better computation and tighter environmental integration. A civilization could become more capable while becoming “less luminous”.

3. Energy says little about stability: a civilization can enlarge its operational capacity while increasing coupling, concentration and ecological burden. Growth in one variable can coincide with fragility in another.

Other ways to think about (our) civilizational development

One option is to stay within technosignature science and classify civilizations indirectly, through the kinds of signatures they produce: radio emissions, optical pulses, atmospheric technosignatures, artifacts, waste heat, megastructures, or other anomalies. This does not produce a single rank ordering, but it fits the actual logic of observation.

Another option is to shift the measure itself. Chaisson’s energy rate density quantifies how much energy flows through a system per unit mass, providing a more useful way for comparing complexity across physical, biological and cultural systems than gross total energy alone.

A third option is more qualitative and ecological. Ivanov and collaborators propose a classification that pays attention to how civilizations modify themselves and their environments. That matters because advanced development may lead toward tighter integration and altered relations with local ecosystems rather than simple luminosity growth.

Take-home message

The map of U.S. data centers and fossil-energy infrastructure gives the whole discussion a proper starting point, showing that a civilization increases its capacity by coupling computation to energy at large scale. That makes the Kardashev intuition intelligible, because both energy and infrastructure matter, while scaling leaves a footprint.

But the same map also reveals what Kardashev’s scale alone is missing. It shows lock-in, asymmetry, concentration and increasing systemic vulnerability. It shows that capacity can grow while dependencies deepen. It shows that the evolution of our civilization is shaped by co-evolution among energy, information and environment.

Accordingly, while Kardashev still belongs in the conversation, it cannot end the conversation. A serious account of civilizational development has to ask more than how much energy a system commands. It has to ask how that energy is organized, what forms of information processing it sustains, how it reshapes matter, what kinds of dependencies it creates and whether or not the resulting configuration is robust and can persist.

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Complex systems and network science have grown through many intellectual lineages: statistical physics, nonlinear dynamics, information theory, biology, ecology, computation, social science, epidemiology, neuroscience and several others. This plurality is part of what makes the field interesting, although it also makes a simple question surprisingly hard to answer: what are the works that really shaped how we think about complexity?

In 2018 I made a related attempt at the level of concepts rather than papers. I started from a manually curated vocabulary of terms associated with complexity, queried thousands of arXiv papers around those concepts, trained word-embedding models on the retrieved abstracts and used similarity networks and clustering to obtain a first map of conceptual neighborhoods. The figure above gives a sense of my exercise, although I would read it with caution: the pipeline was semi-supervised, sensitive to the initial choice of terms, limited by the arXiv corpus and inevitably shaped by the modeling decisions I made at the time. It was useful as an exploratory prototype, not as a definitive reconstruction of the field1.

I am now launching MINERVA2, a community-driven initiative to build a structured map of foundational contributions in complex systems and network science. The focus is paper-centered, because the goal is to reconstruct ideas, methods, models, and conceptual bridges rather than reputations or academic genealogies.

This is easy to misunderstand as an attempt to define a canon, which is not the spirit of this initiative. I am interested in something more useful: a navigable view of the intellectual substrate of the field, including the papers everyone remembers, the papers that entered from neighboring disciplines, as well as the papers that are often used implicitly without being described as “complex systems” or “network science”.

Bibliometric and algorithmic approaches can reveal a great deal about topical structure, citations and explicit communities, yet foundational ideas often travel in less visible ways. They may come from older vocabularies, adjacent traditions, technical methods or conceptual imports that later became naturalized inside the field. However, I think that recovering this structure requires distributed judgment from the community.

This is why broad participation matters: senior researchers bring historical perspective on how certain ideas entered and transformed the field; early-career researchers often see connections across areas that older disciplinary narratives may miss; researchers from different subfields will naturally emphasize different foundations, and this diversity is precisely the point.

The current step is simple. If you work in complex systems, network science or a neighboring area, you can contribute a small set of papers you consider foundational; DOI references are enough. Widely recognized papers are useful, and so are overlooked contributions that deserve to be visible in the map.

The submitted references will be aggregated, deduplicated and analyzed across subfields and types of contribution: theoretical frameworks, methods, applications, interdisciplinary bridges and foundational imports from adjacent areas. Contributors will receive early access to the results, which will later be shared publicly together with the methodology.

→ The survey takes less than 10 minutes: click here.

MINERVA can only work if the community contributes its own memory of the field. If you think complex systems and network science would benefit from a clearer map of their foundations, please contribute and share the initiative with colleagues, students and researchers across related domains.

Thank you!

P.S. We have already collected several hundred recommended papers. A scientific committee, involving leading complexity scientists from different countries and research traditions, will help evaluate the procedure used to construct the final corpus. Stay tuned, and get involved.

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From the Lab

Unraveling the emergence of collective behavior in networks of cognitive agents

Three years ago I was wondering whether LLM could act in “social groups” of any sort, and how such networks would resemble real ones.
A few months later, a brilliant student, Nicola Zomer, who was following my course on the physics of complex networks, came to me to ask for a thesis on exactly this question. Now, that work is published in NPJ Artificial Intelligence, and my warmest congratulations go to Nicola.
What makes this study especially timely is that it asks a question that is becoming central for AI: what happens when we stop looking at a single intelligent agent in isolation and start looking at many of them interacting as a collective?

Our work shows that giving agents LLM-based reasoning does not automatically produce better collective intelligence. In some tasks, a single cognitive agent can outperform a classical search process because it can detect patterns and exploit past information more effectively.
But when many such agents interact, the picture becomes much richer: they can also converge too quickly, imitate one another and get trapped in poor collective solutions.

This is why I think that or paper is important. It shows that the behavior of an artificial society is shaped not only by how smart each agent is, but also by how agents communicate, who listens to whom and how information flows through the network. In other words, the architecture of interaction is not a technical detail: it is part of the intelligence of the system.

A second key result is that collective behavior depends strongly on network structure. In optimization, collaboration among LLM agents can recover the global solution more reliably than isolated agents, but only when the communication pattern preserves enough diversity. In social self-organization, LLM agents do not simply create new macroscopic behavior by default; instead, meaningful differences emerge when interactions are local and socially selective.
For those working in AI, I think the take-home messages are clear:

• intelligence at the individual level does not guarantee intelligence at the collective level;

• communication topology is a first-class design variable for multi-agent systems;

• understanding emergence, consensus, diversity and coordination will be essential if we want robust agentic AI.

This is exactly where complex systems science can make a difference!

If you are curious, read also this post, where I better put the work in the context of agentic AI:

Cognitive agents, powered by Large Language Models (LLMs), possess advanced reasoning and communication capabilities that fundamentally distinguish them from non-cognitive particles, which rely solely on formal rules. While their ability to replicate human individual and social behaviors is still under scrutiny, the impact of their embedded “intelligence” on emergent behaviors remains poorly understood. Here, by comparing cognitive agents with classic particles, we investigate how LLM capabilities shape emergent phenomena in two tasks: function optimization and social organization emerging from the Schelling model of segregation. To this aim, we introduce LLM Agent Swarm Optimization (llmASO), where a swarm of interacting LLM agents acts as an optimizer. Our findings reveal that, while individual agents outperform particles in decision-making, their consensus tendencies and ability to exploit patterns can make them prone to premature convergence. Adjusting network topology can alleviate this effect, but typically at the cost of slower overall convergence compared to classical Particle Swarm Optimization (PSO). In contrast, in the Schelling model, we demonstrate that local interactions and homophilic mechanisms allow cognitive agents to generate distinct emergent behaviors, underscoring the importance of realistic communication architectures in complex social simulations. This work clarifies how LLM capabilities introduce new mechanisms for collective behavior and has implications for future applications of LLM agents in swarm robotics, social experiments, and complex decision-making tasks.

Artificial and biological systems

Bringing the genetically minimal cell to life on a computer in 4D

We present a whole-cell spatial and kinetic model for the 100 min cell cycle of the genetically minimal bacterium JCVI-syn3A. We simulate the complete cell cycle in 4D (space and time), including all genetic information processes, metabolic networks, growth, and cell division. By integrating hybrid computational methods, we model the dynamics of morphological transformations. Growth is driven by insertion of lipids and membrane proteins and constrained by fluorescence imaging data. Chromosome replication and segregation are controlled by the essential structural maintenance of chromosome proteins, analogous to condensin (SMC) and topoisomerase proteins in Brownian dynamics simulations, with replication rates responding to deoxyribonucleotide triphosphate (dNTP) pools from metabolism. The model captures the origin-to-terminus ratio measured in our DNA sequencing and recovers other experimental measurements, such as doubling time, mRNA half-lives, protein distributions, and ribosome counts. Because of stochasticity, each replicate cell is unique. We predict not only the average behavior of partitioning to daughter cells but also the heterogeneity among them.

Gene regulatory networks: from correlative models to causal explanations

Gene regulatory networks (GRNs) explain how the genome controls cellular behaviour and tissue morphogenesis, serving to connect molecular mechanism to functional output. Single-cell technologies now provide descriptions of these networks with unprecedented detail, but this advance has also revealed gene regulatory systems that are too complex for our existing conceptual frameworks. GRNs, which should provide mechanistic explanations, are increasingly reduced to statistical correlations — ‘hairballs’ that fail to capture molecular causation. Here, we explore why this dilemma exists and propose a path forward. We argue that methods in ‘representation learning’ can be used to model GRNs, without needing to capture every molecular detail. For this framework, we advocate three linked principles: models must be inherently mechanistic, with structures grounded in cellular and evolutionary biology; molecular principles and constraints must be used to reduce the solution space for learning GRN models; and more sophisticated forms of experimental perturbation and synthetic biological engineering are needed to train models and test predictions. By reimagining GRNs through these principles, we can bridge the gap from data abundance to new conceptual understanding.

Genome modelling and design across all domains of life with Evo 2

All of life encodes information with DNA. Although tools for genome sequencing, synthesis and editing have transformed biological research, we still lack sufficient understanding of the immense complexity encoded by genomes to predict the effects of many classes of genomic changes or to intelligently compose new biological systems. Artificial intelligence models that learn information from genomic sequences across diverse organisms have increasingly advanced prediction and design capabilities^1,2. Here we introduce Evo 2, a biological foundation model trained on 9 trillion DNA base pairs from a highly curated genomic atlas spanning all domains of life to have a 1 million token context window with single-nucleotide resolution. Evo 2 learns to accurately predict the functional impacts of genetic variation—from noncoding pathogenic mutations to clinically significant BRCA1 variants—without task-specific fine-tuning. Mechanistic interpretability analyses reveal that Evo 2 learns representations associated with biological features, including exon–intron boundaries, transcription factor binding sites, protein structural elements and prophage genomic regions. The generative abilities of Evo 2 produce mitochondrial, prokaryotic and eukaryotic sequences at genome scale with greater naturalness and coherence than previous methods. Evo 2 also generates experimentally validated chromatin accessibility patterns when guided by predictive models^3,4 and inference-time search. We have made Evo 2 fully open, including model parameters, training code⁵, inference code and the OpenGenome2 dataset, to accelerate the exploration and design of biological complexity.

Population dynamics

Integrated framework to study genomic surveillance of selective sweeps in multivariants dynamics

Pandemics are often shaped by the competition of multiple pathogen variants, yet existing models rarely connect epidemic dynamics, genomic surveillance, and population immunity within a unified framework. We develop an integrated modeling framework that couples multivariant epidemic dynamics, mechanistic genomic dominance, and probabilistic surveillance. We show that variant dominance follows selective sweeps described by multilogistic equations, with growth rates modulated by population immune histories. Moreover, we show that the variant emergence timing and the dynamics of prior variants influence whether new variants are detected quickly or remain undetected. Our results are validated by fitting epidemiological and genomic data from the spread of SARS-CoV-2 variants across multiple countries, highlighting its relevance for genomic epidemiology and surveillance-informed response.

Pandemics often involve complex transmission dynamics in which epidemiological surveillance is essential but not sufficient for containment, as resurgence may be driven by emerging or imported variants. Rapidly evolving pathogens produce complex disease dynamics driven by emerging variants often differing in their transmissibility, immune escape, and cross-infection. These processes influence individuals’ immune life histories, producing highly dynamic immune landscapes that modulate the emergence and dominance of novel variants. We develop an integrated modeling framework that couples multivariant mean-field epidemic modeling with a mechanistic genomic dominance model and a probabilistic surveillance model. This study examines how variant emergence timing, infectiousness advantage, and cross-infection jointly shape epidemic trajectories, immune landscapes, and genomic composition. Our results demonstrate that the dominance dynamics of cocirculating variants correspond to a selective sweep characterized by a system of multilogistic equations driven by population immunity. Moreover, we show that the detection time of newly introduced variants can be accelerated or delayed depending on their emergence conditions and the prevailing variant landscape. Finally, we demonstrate that the effectiveness of response strategies depends critically on the evolving genomic composition of the outbreak, highlighting trade-offs between surveillance sensitivity and intervention timing. We validate our framework by jointly fitting epidemiological and genomic data from the spread of the Ancestral, Alpha, Gamma, and Delta variants in the United States, Denmark, the United Kingdom, and Canada. The results provide a quantitative foundation for linking epidemic dynamics, genomic surveillance, and immune life histories, advancing the development of genomic epidemiology for multivariant outbreaks.

Characterization and forecast of global influenza subtype dynamics

The subtype composition of seasonal influenza waves varies in space and time. Influenza subtypes A/H1N1, A/H3N2 and B tend to have different impacts on population groups; therefore, understanding the drivers of their cocirculation and anticipating their composition is important for epidemic preparedness. FluNet provides data on influenza specimens by subtype for more than 150 countries. However, owing to surveillance variations across countries, global analyses usually focus on subtype compositions, a kind of data difficult to treat with advanced statistical methods. We used compositional data analysis to circumvent the problem and study trajectories of annual subtype compositions of countries. Here we first examine global trends from 2000 to 2023. We identify a few seasons which stood out for the strong within-country subtype dominance due to either a new virus/clade taking over (2003/2004 season, A/H1N1pdm pandemic) or subtypes’ spatial segregation (coronavirus disease 2019 pandemic). Second, we show that geographical factors, most notably international mobility, concurred in shaping countries’ composition trajectories between 2010 and 2019. Trajectories clustered in two macroregions characterized by subtype alternation versus persistent mixing. Finally, we define five algorithms for forecasting the next year’s composition and found that incorporating the global history of subtype composition in a Bayesian hierarchical vector autoregressive model improved predictions compared with naive methods. The joint analysis of spatiotemporal dynamics of influenza subtypes worldwide reveals a hidden structure in subtype circulation that can be used to improve predictions of the subtype composition of next year’s epidemic according to place.

Genotype networks drive oscillating endemicity and epidemic trajectories in viral evolution

Rapidly evolving viruses use antigenic drift as a key mechanism to evade host immunity and persist in real populations. While traditional models of antigenic drift and epidemic spread rely on low-dimensional antigenic spaces, genomic surveillance data reveal that viral evolution produces complex antigenic genotype networks with hierarchical modular structures. In this study, we present an eco-evolutionary framework in which viral evolution and population immunity dynamics are shaped by the structure of antigenic genotype networks. Using synthetic networks, we demonstrate that network topology alone can drive transitions between stable endemic states and recurrent seasonal epidemics. Furthermore, our results show how the integration of the genotype network of the H3N2 influenza in our model allows for estimating the emergence times of various haplotypes resulting from its evolution. Our findings underscore the critical role of the topology of genotype networks in shaping epidemic behavior and, besides, provide a robust framework for integrating real-world genomic data into predictive epidemic models.

Immunity-induced criticality of the genotype network of influenza A (H3N2) hemagglutinin

Seasonal influenza kills hundreds of thousands every year, with multiple constantly changing strains in circulation at any given time. A high mutation rate enables the influenza virus to evade recognition by the human immune system, including immunity acquired through past infection and vaccination. Here, we capture the genetic similarity of influenza strains and their evolutionary dynamics with genotype networks. We show that the genotype networks of influenza A (H3N2) hemagglutinin are characterized by heavy-tailed distributions of module sizes and connectivity indicative of critical behavior. We argue that (i) genotype networks are driven by mutation and host immunity to explore a subspace of networks predictable in structure and (ii) genotype networks provide an underlying structure necessary to capture the rich dynamics of multistrain epidemic models. In particular, inclusion of strain-transcending immunity in epidemic models is dependent upon the structure of an underlying genotype network. This interplay is consistent with self-organized criticality where the epidemic dynamics of influenza locates critical regions of its genotype network. We conclude that this interplay between disease dynamics and network structure might be key for future network analysis of pathogen evolution and realistic multistrain epidemic models.

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I have written this post some weeks ago for The Palindrome, kindly hosted by . I am resharing the post for the readers of ComplexityThoughts, enriched by our usual explanatory slides for the most important concepts.

The Palindrome is the space where you actually understand what’s under the hood of machine learning, and it’s one of my favorite Substacks. From deep mathematical insights to hands-on theory, it turns technical ideas into intuition you can build on for decades. Therefore, if you’re serious about mastering machine learning, this is the one you don’t want to skip.

In 2000, a computer worm named ILOVEYOU infected millions of machines in a single day.

Two years later, a post-apocalyptic horror movie directed by Danny Boyle — 28 days later — was released. A story about how humans turned into zombies after getting infected by a virus.

Despite their stacking differences, both follow the same simple logic: interactions lead to contact, contact allows transmission, and transmission triggers transformation.

The surprising fact? Mathematics can capture that logic with striking simplicity and remarkable explanatory power.

Today we will see this in action with a twofold purpose: by focusing on these two seemingly different contagion processes, we will show how the same math can be fundamental to describe what’s going on — and, more broadly, why modeling matters.

Act 0 – What’s a model?

Mathematical models are not decorations on science: they are thinking tools. They allow us to distill messy realities into a few essential gears, showing how motion in one part turns another. This becomes immediately useful when the world feels too complex to grasp: a model offers a translation from tangled stories to relationships we can reason about, test, and sometimes even control.

However, a good model is not meant to reproduce every detail of reality: its purpose is to be useful, helping us identify what can drive a phenomenon, what can be ignored and where small interventions create large effects. The art of modeling lies in choosing the right simplifications: keeping what matters, discarding what doesn’t.

Compartments: how to turn “stories” into equations

In the vast universe of mathematical models, we will focus on contagion because its complexity can be captured in an elegant way.

Which complexity? Well, we should consider that there are people that take the bus or the metro, go to the cinema or supermarket, kids going to the school, people meeting in companies, at any hour of the day, in different part of a cities, or an entire country. These events actually corresponds to some form of interaction, each one able to trigger the reaction chain: contact → transmission → transformation.

In the animation below, that I have built from the amazing ComplexityExplorables, it is possible to appreciate a computer simulation of this process in a very simplified digital world without buses, metros, supermarkets, schools, just four groups (or communities). Initially, the majority of people are healthy and susceptible (white) to the virus brought by sick and infectious individuals (red). Because of random contacts, the virus can spread among individuals, and their movement can spread the virus from one community to another. If the disease is like a seasonal flu, red individuals turn into recovered (grey) after a while: they get healthy again. And after more time, those ones who recovered can become sick again, and so forth and so on.

However, if once get infected one cannot recover, all individuals will eventually turn red, as towards the end of the simulation, where I have intentionally changed the virus.

Obviously, this complexity is cumbersome: uncountable and often untrackable interactions, different immunological response (e.g., from a few symptoms to high fever) from different people, different behavioral response (e.g., from staying at home to compulsory job presence), just to mention some.

However, when we model epidemics we do not track every individual. We often make the rather simplifying assumption that they are like some labeled/colored particles, and we group them by state (i.e., the color) into compartments.
A compartment is simply a box collecting things that behave the same way: uninfected computers, infected ones, or those that can no longer be infected (like the recovered). Therefore, the story of a contagion then becomes a story of flows between those boxes: one box loses what another gains, exactly as in the illustration below.

Mathematically, we can use a minimal map like this:

where each arrow represents a rate indicating how often a change of state happens. Once the compartments and flows are clearly defined, the equations almost write themselves. The rest is interpretation.

Act I — The cyber epidemic

In May 2000, the ILOVEYOU worm spread through email attachments. One infected computer sent the same message to every contact in its address book; each new victim did the same. It’s a perfect digital analogue of infection.

Since there’s no natural recovery until someone patches (with an antivirus) or isolates the system, the right compartment model for this process is SI: the story is

where S denote uninfected computers, I the infected machines and β is the rate of successful transmission. Furthermore, let’s also forget about the specific interaction patterns of the machines: instead of caring about which computer is exactly connected to which other computer, let’s work under the “well-mixed” assumption that any infected can contact any susceptible. We are now ready to transform the above story into an equation, relating the rate at which the number of infected individuals increase with the other variables:

At the start, almost everyone is uninfected, so S ~ N, where N is the total number of machines in the world:

That single line explains the worm’s explosive global reach: exponential growth until the susceptible pool runs out (or, equivalently, until the S box becomes empty). Control, in this view, is mechanical: reduce β (slower transmission) or reduce S (fewer vulnerable systems).

What happens next? When the number of infected machines increases, we can no longer approximate and we must write S = N - I. The equation turns into:

The last step, obtained after elementary algebra, provides us with an operational way to estimate the fraction of infected machines over time, according to the model:

That’s pretty cool, isn’t it? Overall, because the shape of the two curve is very similar to the observed ones, with the difference that computer scientists responded back by writing an antivirus able to stop the virus. We could model that by adding the R compartment after some time.

Act II — The Anatomy of a Zombie Apocalypse (S–I–Z–R)

Now let’s make it cinematic, but still mathematical. Do you remember the movie “I am legend” starring Will Smith?

Suppose we have four compartments: S (survivors, susceptible humans), I (infected but not yet zombies), Z (zombies, fully infectious) and R (recovered/removed because immune, deceased, or isolated). The zombie flowchart is given by:

a bit more complicated, although very similar in spirit, to our previous example, where each arrow translates into a rate term. The system dynamics reads:

with N = S + I + Z + R. Early on S ~ N (nearly the whole population consists of susceptible individuals), so the basic reproduction number is

If it is larger than 1, the outbreak grows and zombies spread faster than they’re neutralized. Conversely, if it is smaller than 1, it dies out and humanity survives!

Act III — From well-mixed to networked worlds

So far, we’ve imagined a “well-mixed” world, as if everyone could meet everyone. However, real contagion, digital or biological, spreads on networks: a few hubs connect widely, most nodes connect locally. This structure changes everything:

• Ignition: infections that start near hubs explode faster.

• Control: targeting those hubs (email relays, hospitals, influencers) is far more effective than random protection, although they are often harder to locate than one could imagine.

The header image of this post shows the air-transport network: airports connected by flight routes, a primary pathway for moving pathogens between countries. Likewise, social networks provide the contact structure for transmitting disease, and the Internet provides the connectivity that enables the spread of computer viruses.

The same logic explains why ILOVEYOU spread via central mail servers and why social media virality behaves like an epidemic of ideas. In both cases, patching or isolating/immunizing the hubs can dramatically changes the system’s trajectory.

Mathematical models are compressed stories about cause and effect, how small interactions scale into global patterns. They do not predict the future with certainty, but they make the invisible visible: how rates, connections and structure shape collective outcomes.

Whether the contagion is viral code, zombies or ideas, the same framework applies (compartments, rates, networks). Once you draw the boxes and trace the arrows, you see where and how to act. What’s the take-home message? This one:

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Foundations of network science and complex systems

Dynamics of active swarms at the edge of disorder

Many animal groups form structures such as flocks and swarms. However, how can the individual agents reconcile the simultaneous requirements of local collision avoidance, alignment, and group cohesion to achieve coherent collective motion? Here, we propose a minimal flocking model, where each agent is capable of vision-based steering interactions to achieve these (conflicting) goals. Numerical simulations in two dimensions show that local collision avoidance acts as a source for emergent noise and induces an order-disorder transition, triggered by the fast response of the flock to local directional changes. The emergence of large vortices at the critical point hints at a Berezinskii-Kosterlitz-Thouless-like transition. Deep in the ordered phase, the cohesion acts like a surface tension, favouring compact flock shapes. The competing interactions lead to pronounced shape and density fluctuations of the flock. These large fluctuations can be important for a fast response to external cues, which aids predator evasion and foraging.

Emergent Self-Propulsion of Skyrmionic Matter in Synthetic Antiferromagnets

Read also this coverage.

Self-propulsion plays a crucial role in biological processes and nanorobotics, enabling small systems to move autonomously in noisy environments. Here, we theoretically demonstrate that a bound skyrmion-skyrmion pair in a synthetic antiferromagnetic bilayer can function as a self-propelled topological object, reaching speeds of up to a hundred million body lengths per second—far exceeding those of any known synthetic or biological self-propelled particles. The propulsion mechanism is triggered by the excitation of back-and-forth relative motion of the skyrmions, which generates nonreciprocal gyrotropic forces, driving the skyrmion pair in a direction perpendicular to their bond. Remarkably, thermal noise induces spontaneous reorientations of the pair and momentary reversals of the propulsion, mimicking behaviors observed in motile bacteria and microalgae.

What does it mean for a system to compute?

Many real-world dynamic systems, both natural and artificial, are understood to be performing computations. For artificial dynamic systems, explicitly designed to perform computation—such as digital computers—by construction, we can identify which aspects of the dynamic system match the input and output of the computation that it performs, as well as the aspects of the dynamic system that match the intermediate logical variables of that computation. In contrast, in many naturally occurring dynamic systems that we understand to be computers, even though we neither designed nor constructed them—such as the human brain—it is not a priori clear how to identify the computation we presume to be encoded in the dynamic system. Regardless of their origin, dynamic systems capable of computation can, in principle, be mapped onto corresponding abstract computational machines that perform the same operations. In this paper, we begin by surveying a wide range of dynamic systems whose computational properties have been studied. We then introduce a very broadly applicable framework for identifying what computation(s) are emulated by a given dynamic system. After an introduction, we summarize key examples of dynamic systems whose computational properties have been studied. We then introduce a very broadly applicable framework that defines the computation performed by a given dynamic system in terms of maps between that system’s evolution and the evolution of an abstract computational machine. We illustrate this framework with several examples from the literature, in particular discussing why some of those examples do not fully fall within the remit of our framework. We also briefly discuss several related issues, such as uncomputability in dynamic systems, and how to quantify the ‘value of computation’ in naturally occurring computers. We conclude with a discussion of some of the promising directions for future research.

Entropy generation: Minimum inside and maximum outside

The extremum of entropy generation is evaluated for both maximum and minimum cases using a thermodynamic approach which is usually applied in engineering to design energy transduction systems. A new result in the thermodynamic analysis of the entropy generation extremum theorem is proved by the engineering approach. It follows from the proof that the entropy generation results as a maximum when it is evaluated by the exterior surroundings of the system and a minimum when it is evaluated within the system. The Bernoulli equation is analyzed as an example in order to evaluate the internal and external dissipations, in accordance with the theoretical results obtained.

Evolution

Cultural inheritance is driving a transition in human evolution

Human evolution may be shifting from a mainly genetic, individual-level process to a mainly cultural, group-level one. The motivation is a familiar puzzle: society and technology change so quickly that “human nature” can feel unstable, and cultural evolution may be the better lens for explaining that pace. The core idea is a coupled transition: cultural adaptation increasingly shapes survival and success more than genes do and an individual’s outcomes are increasingly determined by the capabilities and institutions of the groups they belong to.

Rather than claiming humans are becoming genetically “superorganisms”, the framework emphasizes inheritance (culture becoming the dominant store of adaptive information) and asks for testable predictions (e.g., rising importance of group structure and cultural inheritance over time). This is to move cultural evolution from broad metaphor toward measurable hypotheses, and to refocus debates about human behavior on how collective infrastructures, norms and technologies channel fitness and wellbeing. Read also this piece about the paper.

Previous research on a transition in human evolution has been befuddled by the complexity of adaptive culture and made little effort toward empirical tests. We resolve these problems with a novel and testable theoretical mechanism. First, we explain how a differential in adaptive capacity between genetic and cultural inheritance could drive an evolutionary transition in both inheritance and individuality (ETII). We elaborate the ETII hypothesis and show it can resolve the prior debates, such as why genetic group selection is not a necessary precondition to transition. Next, we develop quantitative metrics of evolutionary transition in both phenotype and fitness to measure movement along this transition. We evaluate available evidence and find that patterns in gene–culture coevolution lend credence to the possibility of an ongoing transition. Finally, we derive a set of testable predictions and outline an agenda for future research.

Biological Systems

The Physics of Sensing and Decision-Making by Animal Groups

To ensure survival and reproduction, individual animals navigating the world must regularly sense their surroundings and use this information for important decision-making. The same is true for animals living in groups, where the roles of sensing, information propagation, and decision-making are distributed on the basis of individual knowledge, spatial position within the group, and more. This review highlights key examples of temporal and spatiotemporal dynamics in animal group decision-making, emphasizing strong connections between mathematical models and experimental observations. We start with models of temporal dynamics, such as reaching consensus and the time dynamics of excitation-inhibition networks. For spatiotemporal dynamics in sparse groups, we explore the propagation of information and synchronization of movement in animal groups with models of self-propelled particles, where interactions are typically parameterized by length and timescales. In dense groups, we examine crowding effects using a soft condensed matter approach, where interactions are parameterized by physical potentials and forces. While focusing on invertebrates, we also demonstrate the applicability of these results to a wide range of organisms, aiming to provide an overview of group behavior dynamics and identify new areas for exploration.

Honeybees adapt to a range of comb cell sizes by merging, tilting, and layering their construction

Honeybees are renowned for their skills in building intricate and adaptive combs that display notable variation in cell size. However, the extent of their adaptability in constructing honeycombs with varied cell sizes has not been thoroughly investigated. We use 3D-printing and X-ray microscopy to quantify honeybees’ capacity in adjusting the comb to different initial conditions. Our findings suggest three distinct comb construction modes in response to foundations with varying sizes of 3D-printed cells. For smaller foundations, bees occasionally merge adjacent cells to compensate for the reduced space. However, for larger cell sizes, the hive uses adaptive strategies such as tilting for foundations with cells up to twice the reference size and layering for cells that are three times larger than the reference cell. Our findings shed light on honeybees adaptive comb construction abilities, significant for the biology of self-organized collective behavior, as well as for bio-inspired engineered systems.

Neuroscience

Emergence of Brains

As with many creative human activities, physics resists definition. Physics, as the saying goes, is what physicists do. But we can recognize major themes. One theme is the reductionist search for the building blocks of matter. In another direction is the exploration of the origins and history of the universe writ large. At intermediate scales, we explore the emergence of macroscopic behaviors that are more than the sum of their parts. An important lesson of the late 20th century is that these seemingly distinct themes are intertwined, so that theoretical physics today is more unified while at the same time reaching ambitiously across an even broader range of topics. The 2024 Nobel Prize in Physics, awarded jointly to John Hopfield and Geoffrey Hinton, celebrates the discovery of new classes of emergent phenomena, inspired by the function of brains and minds.

Interdependent Scaling Exponents in the Human Brain

We apply the phenomenological renormalization group to resting-state fMRI time series of brain activity in a large population. By recursively coarse graining the data, we compute scaling exponents for the series variance, log probability of silence, and largest covariance eigenvalue. The scaling exponents clearly exhibit linear interdependencies in the form of scaling relations and inherent variability of values closely related to the structure of correlations of brain activity. The scaling relations between the exponents are derived analytically. We find a significant correlation of exponents with clinical (gray matter volume) and behavioral (cognitive performance) traits. Akin to scaling relations near critical points in thermodynamics, our results suggest that this interdependency is intrinsic to brain organization, and may also exist in other complex systems.

BOLD signal changes can oppose oxygen metabolism across the human cortex

Using quantitative fMRI, the authors show that across large cortical territories BOLD changes can decouple from oxygen metabolism, sometimes even reversing sign, so stronger BOLD does not reliably indicate greater neural energy use. This is very important and motivates more cautious interpretation and wider use of metabolic measurements alongside BOLD.

Functional magnetic resonance imaging measures brain activity indirectly by monitoring changes in blood oxygenation levels, known as the blood-oxygenation-level-dependent (BOLD) signal, rather than directly measuring neuronal activity. This approach crucially relies on neurovascular coupling, the mechanism that links neuronal activity to changes in cerebral blood flow. However, it remains unclear whether this relationship is consistent for both positive and negative BOLD responses across the human cortex. Here we found that about 40% of voxels with significant BOLD signal changes during various tasks showed reversed oxygen metabolism, particularly in the default mode network. These ‘discordant’ voxels differed in baseline oxygen extraction fraction and regulated oxygen demand via oxygen extraction fraction changes, whereas ‘concordant’ voxels depended mainly on cerebral blood flow changes. Our findings challenge the canonical interpretation of the BOLD signal, indicating that quantitative functional magnetic resonance imaging provides a more reliable assessment of both absolute and relative changes in neuronal activity.

Neuromodulators Generate Multiple Context-Relevant Behaviors in Recurrent Neural Networks

Neuromodulators are critical controllers of neural states, with dysfunctions linked to various neuropsychiatric disorders. Although many biological aspects of neuromodulation have been studied, the computational principles underlying how neuromodulation of distributed neural populations controls brain states remain unclear. In contrast to external contextual inputs, neuromodulation can act as a single scalar signal that is broadcast to a vast population of neurons. We model the modulation of synaptic weight in a recurrent neural network model and show that neuromodulators can dramatically alter the function of a network, even when highly simplified. We find that under structural constraints like those in brains, this provides a fundamental mechanism that can increase the computational capability and flexibility of a neural network. Diffuse synaptic weight modulation enables storage of multiple memories using a common set of synapses that are able to generate diverse, even diametrically opposed, behaviors. Our findings help explain how neuromodulators unlock specific behaviors by creating task-specific hyperchannels in neural activity space and motivate more flexible, compact and capable machine learning architectures.

Benchmarking methods for mapping functional connectivity in the brain

The networked architecture of the brain promotes synchrony among neuronal populations. These communication patterns can be mapped using functional imaging, yielding functional connectivity (FC) networks. While most studies use Pearson’s correlations by default, numerous pairwise interaction statistics exist in the scientific literature. How does the organization of the FC matrix vary with the choice of pairwise statistic? Here we use a library of 239 pairwise statistics to benchmark canonical features of FC networks, including hub mapping, weight–distance trade-offs, structure–function coupling, correspondence with other neurophysiological networks, individual fingerprinting and brain–behavior prediction. We find substantial quantitative and qualitative variation across FC methods. Measures such as covariance, precision and distance display multiple desirable properties, including correspondence with structural connectivity and the capacity to differentiate individuals and predict individual differences in behavior. Our report highlights how FC mapping can be optimized by tailoring pairwise statistics to specific neurophysiological mechanisms and research questions.

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We are used to seeing the nearby Universe as a map of things: galaxies, clusters, filaments, voids. But some of its most meaningful structures do not become visible by looking only at where matter is: they amazingly emerge when we ask where is matter going.

Laniakea is the supercluster we inhabit and its discovery changed the criterion for what counts as a coherent large-scale structure. A supercluster, in this view, is not only a region with many galaxies: it is a basin of attraction in the velocity field, a domain within which galaxy motions converge toward the same large-scale attractor. In other words, common motion can reveal a boundary more clearly than common location.

Note. If you follow ComplexityThoughts from the very beginning, you might recognize how this concept has driven the attention of this space and, more specifically, of the research of my Lab, as I have described here:

While this shift may sound technical, note that the intuition behind it is simple:

• a still image of a landscape can suggest where valleys and ridges might be;

• a map of water flow tells you where the watershed actually is.

Laniakea became legible in that second sense: the original 2014 reconstruction used galaxy distances and peculiar velocities to infer the surrounding flow field, and from that field identify a basin whose internal trajectories converge1 toward the region of the Norma and Centaurus clusters, near what had long been associated with the Great Attractor. Amazingly, the resulting structure spans about 160 megaparsecs (about 5 x 10^21 km, i.e., 5 sextillion kilometers or 33 trillion times the distance from our Sun) in diameter and encloses on the order of 10^17 (i.e., 100 quadrillion) solar masses.

Video: a fly-through our local Universe, reconstructed from real data (see details below). This is the output of a collaboration with Simone Conradi. And yes, we had a lot of fun. Do you like it? You can reproduce it and even customize it: we have also released the whole pipeline.

1. Processes, dynamical processes everywhere.

First, let us note that the positions alone are not enough. Redshift surveys revealed the cosmic web as a striking arrangement of clusters, filaments and voids, but they also left an ambiguity: where exactly does one supercluster end and another begin?

Note. This “ambiguity” is not due to scientists being lazy. It is rather an intrinsic feature of complex systems across all scales, from cells to cities. I will write about this in the future.

The very term “supercluster” was useful but imprecise, because large concentrations of galaxies are interconnected with no clean edge. To this aim, a more operational solution has been proposed: define the boundary dynamically, by following the flow. Does it sound familiar? From static structures to dynamical processes!

2. Velocity turns geometry into dynamics

The second insight is that galaxy motions are not exhausted by cosmic expansion. Once independent distance estimates are available, one can subtract the Hubble flow from the observed recession velocity and recover the peculiar velocity: the residual motion induced by gravitational perturbations. Those measurements are sparse and noisy, so the field is not observed directly everywhere, and it has to be reconstructed.
In the original Laniakea work, this was done with a Wiener-filter approach that infers the most probable three-dimensional velocity and density fields from incomplete data, while also allowing a separation between local flows and the larger tidal background. That step is crucial: without it, the nearby web remains a static picture; with it, one begins to see a dynamical architecture.

This is also why the animations are not just ornamental, being able to do what static figures can only suggest. The movie like the above ones are essential to grasping the complexity of the underlying structure.
Once trajectories are shown as flow lines rather than isolated data points, boundaries become easier to interpret: neighbouring galaxies that appear close in space may nonetheless belong to different dynamical domains if their motions diverge toward different attractors. Conversely, structures that look fragmented in a static map can be recognized as part of the same coherent basin when read through the velocity field.

3. The emergence of a large-scale network

The third lesson, and the one that interests me most from a complex-systems perspective, is that the cosmic web can be read as an effective large-scale network for matter transport.

However, this should be taken carefully: not as a literal claim that the Universe is “a network” in every formal sense, but as a disciplined description of what the reconstructed flow field makes visible. Matter is not distributed randomly through a featureless volume: it is channelled along preferred routes, redirected near separatrices, compressed along filaments and reorganized by attractors and voids. The geometry matters, but so does the routing.

Following the flow

That is where our new work with Simone Conradi enters. We are an unusual pair: a theoretical and an applied physicist, an expert developer producing pieces of art and a complex-systems scientist. What could go wrong?

Simone is a theoretical physicist active in STEM education, making social media a better place by sharing his beautiful pieces of math-based art, like this:

Starting from Cosmicflows-4 data and the reconstructed three-dimensional velocity field, we built a reproducible pipeline to assign galaxies to basins of attraction, integrate streamlines through the field and visualize the resulting large-scale organization in 3D2.

Why? The point is not merely to produce a more polished picture, our colleagues already made an excellent job with their visualizations. Instead, our goal is to make the dynamical organization of the nearby cosmic web inspectable, reproducible and open to further analysis. The resulting animation, shown above, is an amazing view in which Laniakea appears in all of its beauty as a region of convergent motion embedded in a larger field of competing flows.

In this perspective, two aspects are especially striking. One is that voids become active components of the story rather than empty background: in the original reconstruction, voids help shape the boundaries between attraction basins and some of the most interesting divergences occur along structures bordering them. The other is that some of the most informative features lie near transitions: places where flows flip direction, where bridges connect neighbouring domains or where what looks like continuity in the galaxy distribution hides a dynamical split.

The “Arch” discussed in the 2014 paper is a good example of this kind of structure: beyond visual curiosities, these structures — or lack of — are where the system tells you how it is partitioned.

Reading the Boundaries

Peculiar-velocity reconstructions can reveal structures that are hard to recover from redshift surveys alone, including regions obscured by the plane of the Milky Way. That matters because the nearby Universe is not observed under ideal conditions, and dynamical inference can therefore do more than refine existing maps. It can expose relationships and boundaries that would remain hidden in a purely positional description.

None of this means that Laniakea is a final container of where we live: the mapped region does not exhaust the full set of larger-scale influences on the motion of the Local Group, and coherent flows continue beyond the domain that could be robustly reconstructed at the time. Accordingly, Laniakea is not the last word, but it remains a decisive one showing that, in the nearby Universe, meaningful structure can be defined dynamically.

That, to us, is the deepest insight we could get from this nice collaboration. In complex systems, boundaries are often easiest to draw not by asking where objects are, but by asking how trajectories organize. Laniakea is one of the clearest cosmic examples of that principle, and that’s why I started to routinely use it as a mind-blowing example in my talks.

The image of Earth as a pale blue dot is often used to emphasize how small we are.

But it misses something equally important: we are not just small, we are moving, collectively, within a vast dynamical system whose boundaries are defined by flow. And that perspective might change everything.

Bonus!

If you want to explore the reconstruction yourself, we have made the pipeline public in this GitHub repository.

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1

The convergence is defined after subtracting the Hubble flow and large-scale tidal components. The basin is therefore frame-dependent (local flow decomposition).

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A reproducibility failure in microbiome research and a wave of biomedical AI studies built on unverifiable data point to the same issue: scientific results can circulate and gain authority without remaining testable.

A recent post by the Meren Lab1 describes a failed attempt to reproduce a published microbiome study. The data were technically available, as required by the journal, yet essential metadata were missing. Without information linking samples to conditions, the dataset could not be interpreted, and the results could not be independently verified.

Around the same time, a piece in Nature covers a recent preprint that identified more than one hundred biomedical AI studies trained on datasets with unclear or implausible provenance. These datasets exhibit statistical properties that are difficult to reconcile with real-world data, yet they have been reused across multiple subsequent publications.

These two cases point to the same structural condition: scientific claims can be published, reused, and trusted without remaining practically falsifiable.

The issue extends beyond isolated mistakes or occasional failures in peer review. The system allows results to circulate in a form that cannot be meaningfully challenged. Once this condition is present, errors do not remain local and propagate.

Science as a dependency network

Scientific production can be described as a dependency network: datasets function as inputs, models as derived artifacts, and downstream studies as successive layers of reuse. Properties of the input layer constrain what can be validated at higher levels.

A parallel can be drawn with a software supply-chain vulnerability (CVE-2024-3094), in which a flaw introduced in a low-level dependency propagated upward into widely used infrastructure. The mechanism is straightforward: trust in upstream components allows faults to remain undetected until they reach sensitive layers.

In biomedical AI, datasets play a similar role: when their provenance is uncertain, downstream validation operates on unstable ground.

Mechanisms

Data without traceable origin. The first mechanism is the presence of data without traceable origin.

Some of the datasets described in the Nature piece lack clear documentation of how they were collected, curated, or processed. In some cases, their statistical regularities appear unusually clean, with patterns that are difficult to reconcile with known variability in clinical data. These properties do not demonstrate fabrication, but they prevent independent verification.

Unverifiable inputs set a limit on what can be established downstream: a model trained on such data can be evaluated, benchmarked, and deployed, yet the validity of its outputs remains conditional on assumptions that cannot be examined.

Validation without source accountability. The second mechanism concerns validation practices that do not extend to data provenance.

Evaluation in biomedical AI focuses on model performance: accuracy, sensitivity, specificity, and related metrics. These measures assess how well a model reproduces patterns present in a dataset, they do not assess whether the dataset itself is a faithful representation of the underlying phenomenon.

The Meren Lab case illustrates a related failure where the dataset was shared, satisfying formal requirements for openness, yet it could not be used. Without complete metadata, no independent researcher could reconstruct the analytical pipeline or test the original claims.

This produces a specific condition: results are accessible, but they cannot be tested. Formal compliance replaces functional verification. The consequence is an epistemic blind spot, where claims appear validated because they satisfy existing criteria, while the layer where the failure originates remains unexamined.

Trust accumulates through reuse. The third mechanism is the propagation of trust through reuse.

In the above picture, all scientific papers subsequent to the top one make use of its results and/or outputs to build new research and justify additional outputs. This is how science works.

The same mechanisms works for datasets of uncertain origin that are reused in subsequent studies, incorporated into aggregated datasets, and used to train new models in AI-based research. Each reuse is typically performed in good faith and, over time, repeated use creates the appearance of reliability. This process operates as a feedback loop between trust and reuse:

→ initial uncertainty is not resolved,
→ it becomes distributed across multiple applications,
→ as the dataset appears in more studies, its status shifts toward acceptance without the introduction of new evidence.

In AI pipelines, this effect is further amplified: models trained on such data can be fine-tuned, combined with other models, or used as benchmarks, and the original uncertainty becomes embedded across multiple layers of derived artifacts.

At that stage, identifying the source of error becomes difficult because it is dispersed across the system.

What is the practical implication? Restoring falsifiability as a system property

This structure has direct implications for validation, since reproducing every published result during peer review is not feasible — particularly in experimental and clinical contexts — and the associated cost and time requirements exceed what the current system can support.

At the same time, allowing results to circulate without enforceable conditions for falsifiability introduces a different type of risk: errors introduced upstream can propagate into applications where their consequences extend beyond research settings.

I do not have a clear solution, at the moment. A more tractable approach is to enforce minimal conditions that preserve the possibility of falsification.

First, data provenance should be treated as a primary object of validation. Datasets need structured metadata describing collection protocols, preprocessing steps, and inclusion criteria. This information should be integral to publication requirements rather than supplementary material.

Second, availability should be distinguished from executability. Data and code need to be usable by third parties: minimal reproducibility checks at publication can verify that pipelines run and outputs can be regenerated. This does not establish correctness, but at least it ensures that claims remain open to scrutiny.

Third, replication and falsification require explicit incentives. Reproducing existing work, particularly when it leads to negative results, is currently under-rewarded. Dedicated funding streams, publication formats for replication studies, and recognition of validation work would alter this incentive structure.

Fourth, verification efforts should be targeted. Priority can be assigned to clinically relevant models, widely reused datasets, and benchmarks that influence large segments of the field. This concentrates resources where propagation risks are highest.

Fifth, data pipelines should be treated as systems exposed to identifiable failure modes. Independent dataset reconstruction, cross-cohort validation, and statistical screening for anomalous patterns introduce redundancy: these measures reduce the likelihood that a single point of failure remains undetected.

Take home

Errors are an expected component of scientific practice. The concern lies in how systems handle them once they occur.

The Meren Lab case shows that a result can enter the literature in a form that cannot be tested. The biomedical AI cases show how such inputs can be reused and extended across multiple studies.

Under these conditions, producing new results is insufficient: scientific systems also need to preserve the practical conditions under which existing claims can still be tested, corrected, or rejected.

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Upcoming events

The 2026 Conference on Complex Systems will take place in Binghamton, New York (USA), from 9–16 October 2026.
This year’s theme, “Complex Systems for the Changing World”, highlights the role of interdisciplinary research in addressing global challenges. The program features an outstanding lineup of speakers across network science, nonlinear dynamics, and complex adaptive systems.

📅 Abstract submission deadline: May 1, 2026 – hurry up!
For those unable or hesitant (like me) to travel to the U.S. in this period, the conference will be fully hybrid, ensuring broad accessibility.

Ecosystems and evolution

From death comes diversity

A high-throughput laboratory experiment tracking the assembly of soil-derived communities shows that species-rich bacterial necromass supports increasingly diverse communities, with each additional dead species expanding opportunities for niche partitioning.

This is the referenced paper:

Bacterial necromass recycling promotes diversity maintenance in bacterial communities via resource partitioning

Understanding how high species diversity is maintained in natural bacterial communities is a central question in microbial ecology. Due to the versatile heterotrophic capacities of bacteria and the rich nutrients released by deceased bacterial cells, necromass recycling plays an important role in sustaining bacterial growth. Such nutrient cycling within communities can provide additional resource niches for bacteria, but its potential effects on bacterial diversity maintenance have been neglected. Here we conducted two independent experiments and studied the assembly of 276 soil-derived bacterial communities sustained by a wide range of bacterial necromass combinations, from single-species necromass to combinations of up to nearly 1,000 species. Our results highlight the existence of a species-rich bacterial necrobiome in soil. We found that the composition of necromass-decomposing communities was determined by the various organic compounds in the different necromass combinations, and the increases in necromass-producing species constantly promoted species diversity of necromass-decomposing communities. Moreover, the average niche breadth and overlap of coexisting necromass-decomposing species in utilizing distinct single-species necromass decreased with increases in necromass diversity, supporting the hypothesis of resource partitioning in utilizing different single-species necromass. Our study provides insights into diversity maintenance in bacterial communities from a perspective of internal nutrient cycling.

Functional motifs in food webs and networks

When studying a complex system, it is often useful to think of the system as a network of interacting units. One can then ask if some properties of the entire network are already explained by a small part of the network, a network motif. A famous example of an ecological motif is exploitative competition in food webs, where the presence of two species competing for a shared resource precludes the existence of a stable equilibrium for the whole system. However, other examples of motifs with such direct impacts on stability are not known. Here, we show why small motifs that allow conclusions on systemic stability are rare. More importantly, we show that another dynamical property, reactivity, is typically rooted in motifs. Computing the reactivity of motifs can reveal which parts of a network are prone to respond violently to perturbations. This highlights motif reactivity as a useful property to measure in real-world systems to understand likely modes of systemic failure in food webs or other networks in epidemics, supply chains, or power grids.

The evolution of cheaper workers facilitated larger societies and accelerated diversification in ants

Trade-offs between quantity and quality are common in the organization and evolution of biological, technological, and economic systems. In social insects, shifts from solitary organisms to complex societies bring this dilemma to the colony scale: producing fewer robust units or many cheaper ones. We investigate how cuticle investment, a major nutritional cost, shaped the evolution of ant societies and diversification. Using a computer vision approach on three-dimensional x-ray microtomography scans of 880 specimens from 507 species, we show that larger colonies were facilitated by reducing exoskeleton investment rather than miniaturizing workers. Reduced cuticle investment was associated with accelerated diversification rates in ants, whereas other candidates—colony size and worker size—did not correlate with diversification. Diet and climate had measurable but secondary effects on cuticle investment. Our results support a hypothesis whereby evolving cheaper but more numerous units through reduced investment in structural tissues was a strategic trend in the evolution and diversification of complex insect societies.

The extended life cycle: a multilevel–multiscale modelling framework for extended evolutionary dynamics

I like this one, and I am wondering how it reconnects to my recent work on decoding the architecture of living systems via a multiscale/multilayer/eco-evolutionary model.
Stay tuned, since I will surely go back to this paper in my upcoming series of posts on living systems.

A complete explanation of evolutionary change requires reconciling processes that operate across multiple levels of spatial/temporal organization. Development, the processes by which traits are generated, unfolds over an individual’s lifetime; heredity, encompassing the diverse forms of information transmission, occurs across generations; and population and ecological change often take place across longer horizons. Capturing these layered dynamics in a formal mathematical framework is essential for advancing evolutionary theory in line with recent conceptual developments, such as extended evolutionary theory (EET) or the extended evolutionary synthesis (EES). In this work, we introduce the extended life cycle (ELC), a multilevel–multiscale mathematical modelling framework that centres the life cycle as the fundamental unit of evolutionary analysis. In contrast to traditional gene-centric approaches, the ELC formalism captures the multilevel causal architecture of biological systems by modelling development, heredity, population and ecological dynamics across distinct, but interacting ontological levels. Each level is expressed as a stochastic state-space model, with multiple scales within each level. We demonstrate the utility of the ELC through a Bayesian learning application to multiscale habitat selection and show how our framework can predict latent trajectories generated from complex multilayered dynamics.

The origin and evolution of cell types

Cell types are the basic building blocks of multicellular organisms and are extensively diversified in animals. Despite recent advances in characterizing cell types, classification schemes remain ambiguous. We propose an evolutionary definition of a cell type that allows cell types to be delineated and compared within and between species. Key to cell type identity are evolutionary changes in the ‘core regulatory complex’ (CoRC) of transcription factors, that make emergent sister cell types distinct, enable their independent evolution and regulate cell type-specific traits termed apomeres. We discuss the distinction between developmental and evolutionary lineages, and present a roadmap for future research.

Biological Systems

A smartphone analogy to explore the origin of animals

How animals evolved from their unicellular ancestor is a fundamental biological question. The fact that all animals are monophyletic—sharing a single common ancestor—implies their origin from unicellular eukaryotes was likely driven by rare and highly advantageous innovations. While the fossil record and initial genomic comparisons suggested animals originated by the rapid acquisition of many novel genes, new research on animal’s closest unicellular relatives reveals most of those genes originated before animals evolved. Here we present a new model for animal origins, which shares similarities with the origin of one of the greatest technological innovations of our time: the smartphone. We show that the origin of both animals and smartphones was due to the integration and repurposing of pre-existing components driven by a novel “operating system”, rather than the sudden emergence of many new parts. This model offers testable predictions and a new theoretical framework for understanding complex biological innovation.

Synergistic interactions between Candida albicans and Enterococcus faecalis promote toxin-dependent host cell damage

A pathobiont is a member of the normal microbiota (often gut, oral, or skin) that is usually harmless or even beneficial, but can cause disease when conditions change (eg, if the host immune system is weakened, the microbial community is disrupted, or the organism gains access to normally sterile sites). Thus it’s not an “external pathogen” you acquire: it’s typically an endogenous resident with disease-causing potential under specific contexts.

The fungus Candida albicans is a pathobiont colonizing mucosal surfaces of healthy individuals. The interaction with bacteria is generally considered to be antagonistic, with bacteria preventing fungal infection by mediating colonization resistance. In contrast, our study shows that interaction with the bacterium Enterococcus faecalis can result in more severe infections. We identified a bacterial toxin mediating synergistic damage, which explains why synergism was observed for some but not all strains of E. faecalis. Our findings might have clinical implications as C. albicans and E. faecalis occur in the same mucosal niches, benefit from antibiotic treatment, and are coisolated in clinical samples.

Bacterial conjugation can restructure biofilms and increase their resilience while constraining host cell dispersal

Some infections may become hard to treat even without classic, gene-based antibiotic resistance. This paper shows that plasmids (mobile DNA that spreads between bacteria) can induce pili (ie, hair-like protein filaments that extend from the surface of many bacteria) that physically link cells into tight clusters, effectively creating a protective community in which antibiotics (and other treatments) are less effective. This matters because it reframes treatment failure as sometimes being driven by how bacteria are organized in space, not just which resistance genes they carry, meaning standard susceptibility readouts can be overly optimistic. We need to study and target the mechanics of plasmid transfer and biofilm-like structure (breaking up clustering or blocking pili) alongside developing new antibiotics.

Bacterial conjugation enables the horizontal transfer of plasmids that often carry genes influencing host physiology and behavior. In spatially structured biofilms, where many bacteria live in close proximity, conjugation can significantly alter both genetic and physical community composition. Here, we use a microfluidic system and fluorescence microscopy to track the transmission of the F-like plasmid pED208 within Escherichia coli biofilms, differentiating invading plasmid donors, transconjugants, and plasmid-free cells at high resolution. We find that conjugation within established resident biofilms is efficient until cell density reaches a threshold associated with high extracellular matrix secretion. Strikingly, plasmid-encoded conjugative pili also enable matrix-deficient cells to aggregate into dense biofilms, promoting the formation of multi-strain and multispecies cell clusters. This restoration of biofilm architecture increases antibiotic and phage tolerance but comes at the cost of altering dispersal dynamics: plasmid-bearing cells disperse less readily than plasmid-free cells, creating a trade-off between local advantage and distal spread. Our findings indicate that conjugative pilus-mediated adhesion incurs a fitness trade-off, compacting biofilm structure and thereby conferring enhanced antibiotic and phage tolerance while reducing the spread of plasmid carriers over larger spatial scales.

Human behavior and cognitive science

Mysterious illnesses have supernatural and ritualistic cures: Evidence from 3,655 century-old Irish folk cures

Classical anthropological and cognitive theories propose that supernatural healing practices emerge when ordinary causal reasoning fails, yet direct quantitative tests remain scarce. Using 3,655 “local cures,” collected as part of a national project to document folklore in Ireland in 1937–1938, we quantitatively test a range of theories about the appeal of supernatural cures. Preregistered mixed-effects models reveal that diseases whose causes or bodily mechanisms would have eluded lay observers were around 50% more likely to attract religious or magical treatments, whereas disease severity, pain, anxiety, and need for care showed no reliable relationship with supernatural or religious cure content. These findings suggest epistemic uncertainty may be a driver of supernatural thinking about health.

Why and when do people draw upon religious and supernatural solutions to problems? Cognitive scientists and anthropologists have proposed a range of answers, stressing religion and ritual’s capacity to alleviate anxiety, create a sense of order, or explain otherwise inexplicable events. Here, we leverage a unique dataset of 3,655 folk cures for 35 diseases, collected in 1937/8 from a mostly rural Irish sample born roughly between 1850 and 1925. Since the diseases vary in theory-relevant ways and the cures vary in the degree to which they include religious and supernatural elements, this dataset facilitates a unique test of these predictions in a premodern western population. In preregistered tests, we find that diseases judged by two doctors to have causes and mechanisms that would be unclear to the patients were more likely to have supernatural/religious treatments. Contra common predictions, severe and disabling diseases did not have more supernatural/religious cures and anxiety-provoking diseases did not have more ritualistic cures.

Collective intelligence as collective information processing

Collective intelligence (CI) inquiry lacks a multidisciplinary, unifying framework. We propose that Collective Information Processing (CIP) underlies CI. CIP distinguishes two forms of individual- and group-level processing. CIP enables identification and classification of phenomena labeled as CI. CIP advances debates about agency and causation within CI studies.

Collective intelligence research spans multiple disciplines and focuses on a broad range of collective behaviors, including group problem-solving, flocking in social animals, and the formation of social knowledge. It is not apparent what these different forms of collective intelligence have in common, apart from being instances of collective behavior. In this paper, we develop a framework that enables us to better classify different forms of collectively intelligent behavior in relation to one another based on the information processing mechanisms involved. We argue that these behaviors share a common foundation, which we call collective information processing, or CIP. CIP involves two key mechanisms: (1) individual processing of group information and (2) group processing, or group-level sensitivity to the arrangement of individual information. We operationalize the CIP framework to analyze different forms of collective intelligence, both classifying them in relation to one another and in alignment with generalized quantifiable measures of information processing. Our account of collective intelligence as CIP offers a novel framework for identifying and classifying forms of collective intelligence across a wide range of disciplinary contexts. This framework is meant to unify and subsume, rather than simply challenge, existing attempts to define collective intelligence.

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