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Stigmergic self-organisation of bacterial communities. (a) Pseudomonas aeruginosa interstitial biofilm imaged using phase contrast microscopy depicting the emergent pattern formation. At the advancing edge are rafts of cells that initiate biofilm expansion, behind which there is an interconnected lattice-like network of cellular trails. Scale bar indicates 50 μm. (b) 3D rendered image of the interconnected furrow network underlying the P. aeruginosa interstitial biofilms imaged using atomic force microscopy (AFM) within the lattice-like network. Height scale is relative. (c) P. aeruginosa expressing cyan fluorescent protein (CFP; blue) interstitial biofilms were grown on media supplemented with the cell impermeant nucleic acid dye TOTO-1 to visualize eDNA (yellow) and imaged using OMX BLAZE wide-field microscopy. Scale bar indicates 5 μm. Swarming communities of (d) Pr. vulgaris and (e) M. xanthus grown on semisolid nutrient media and imaged using phase contrast microscopy revealing the phase bright trails routinely observed at the leading edge. Scale bar is 100 μm.
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The self-organisation of collective behaviours often manifests as dramatic patterns of emergent large-scale order. This is true for relatively "simple" entities such as microbial communities and robot "swarms," through to more complex self-organised systems such as those displayed by social insects, migrating herds, and many human activities. The p...
Citations
... The spatial architecture of living systems emerges through self-organization without a central coordination [1][2][3], often, with information propagating via selfgenerated chemical signals [4][5][6][7]. These systems display large-scale patterns, from shell and neural structures [8][9][10][11] to ecological distributions [12,13]. ...
Complex spatial patterns in biological systems often arise through self-organization without a central coordination, guided by local interactions and chemical signaling. In this study, we explore how motility-dependent chemical deposition and concentration-sensitive feedback can give rise to fractal-like networks, using a minimal agent-based model. Agents deposit chemicals only while moving, and their future motion is biased by local chemical gradients. This interaction generates a rich variety of self-organized structures resembling those seen in processes like early vasculogenesis and epithelial cell dispersal. We identify a diverse phase diagram governed by the rates of chemical deposition and decay, revealing transitions from uniform distributions to sparse and dense networks, and ultimately to full phase separation. At low chemical decay rates, agents form stable, system-spanning networks; further reduction leads to re-entry into a uniform state. A continuum model capturing the co-evolution of agent density and chemical fields confirms these transitions and reveals how linear stability criteria determine the observed phases. At low chemical concentrations, diffusion dominates and promotes fractal growth, while higher concentrations favor nucleation and compact clustering. These findings unify a range of biological phenomena - such as chemotaxis, tissue remodeling, and self-generated gradient navigation - within a simple, physically grounded framework. Our results also offer insights into designing artificial systems with emergent collective behavior, including robotic swarms or synthetic active matter.
... 3 This further suggests that an embodied world model, extending the system in space and time by its interactions with an environment, can be leveraged to maintain coherence. We hypothesise this explains why stigmergy [58][59][60][61] and other forms of extracellular signalling arise in biological systems. Throughout we have assumed that the free energy is minimised. ...
All intelligence is collective intelligence, in the sense that it is made of parts which must align with respect to system-level goals. Understanding the dynamics which facilitate or limit navigation of problem spaces by aligned parts thus impacts many fields ranging across life sciences and engineering. To that end, consider a system on the vertices of a planar graph, with pairwise interactions prescribed by the edges of the graph. Such systems can sometimes exhibit long-range order, distinguishing one phase of macroscopic behaviour from another. In networks of interacting systems we may view spontaneous ordering as a form of self-organisation, modelling neural and basal forms of cognition. Here, we discuss necessary conditions on the topology of the graph for an ordered phase to exist, with an eye towards finding constraints on the ability of a system with local interactions to maintain an ordered target state. By studying the scaling of free energy under the formation of domain walls in three model systems -- the Potts model, autoregressive models, and hierarchical networks -- we show how the combinatorics of interactions on a graph prevent or allow spontaneous ordering. As an application we are able to analyse why multiscale systems like those prevalent in biology are capable of organising into complex patterns, whereas rudimentary language models are challenged by long sequences of outputs.
... A similar principle is also used by ants to follow trails [12], by paper wasps to construct nests [13], and by honeybees to locate their queens [14][15][16]. Recently, stigmergy has been extended beyond social insects to bacterial colonies [17,18], spatiotemporal patterns of animal territories [19], cognition [20,21] and swarm robots [22][23][24]. ...
Collective behaviour defines the lives of many animal species on the Earth. Underwater swarms span several orders of magnitude in size, from coral larvae and krill to tunas and dolphins. Agent-based algorithms have modelled collective movements of animal groups by use of social forces , which approximate the behaviour of individual animals. But details of how swarming individuals interact with the fluid environment are often under-examined. How do fluid forces shape aquatic swarms? How do fish use their flow-sensing capabilities to coordinate with their schooling mates? We propose viewing underwater collective behaviour from the framework of fluid stigmergy , which considers both physical interactions and information transfer in fluid environments. Understanding the role of hydrodynamics in aquatic collectives requires multi-disciplinary efforts across fluid mechanics, biology and biomimetic robotics. To facilitate future collaborations, we synthesize key studies in these fields.
... The ability to exploit the traces left in the environment by the action of organisms is one of the simplest and oldest mechanisms used to coordinate collective behaviors in biological systems (34)(35)(36). In humans, over the past thirty years, the massive development of the Internet, together with applications that extensively use digital traces left voluntarily or not by their users, has reinforced the need to understand how these traces influence individual and collective behaviors (25,(37)(38)(39). ...
Stigmergy is a generic coordination mechanism widely used by animal societies, in which traces left by individuals in a medium guide and stimulate their subsequent actions. In humans, new forms of stigmergic processes have emerged through the development of online services that extensively use the digital traces left by their users. Here, we combine interactive experiments with faithful data-based modeling to investigate how groups of individuals exploit a simple rating system and the resulting traces in an information search task in competitive or noncompetitive conditions. We find that stigmergic interactions can help groups to collectively find the cells with the highest values in a table of hidden numbers. We show that individuals can be classified into three behavioral profiles that differ in their degree of cooperation. Moreover, the competitive situation prompts individuals to give deceptive ratings and reinforces the weight of private information versus social information in their decisions.
... Collective behavior refers to complex macroscopic dynamics of microbial communities exhibiting emergence and self-organization properties without a global controller (Balaban et al. 2018). The self-organization of collective behaviors often manifests as dramatic patterns of emergent large-scale order (Gloag et al. 2015). Bacterial stigmergy is a selforganization principle that explained how random and independent movements of individual cell (or trichomes) could result, by the transfer of local information (chemical, for example), coordinated behavior at a global level (Gloag et al. 2015). ...
... The self-organization of collective behaviors often manifests as dramatic patterns of emergent large-scale order (Gloag et al. 2015). Bacterial stigmergy is a selforganization principle that explained how random and independent movements of individual cell (or trichomes) could result, by the transfer of local information (chemical, for example), coordinated behavior at a global level (Gloag et al. 2015). Many other researchers of bacterial aggregations and collective rearrangement of the cells in colonies and metabolic dynamics also considered the coordinated social behavior in bacterial communities as self-organization (Caratozzolo et al. 2008;Brodsky 2009;Hengge and Sourjik 2013;Ebrahimi et al. 2019;de Astacio et al. 2020;You et al. 2021). ...
... Social behavior is found at every level of biological complexity, ranging from quorum sensing in bacteria to human altruism (Parrish et al. 2002;De Monte et al. 2007;Ballerini et al. 2008;Balázsi et al. 2011;Leu et al. 2013;Attanasi et al. 2014;Gloag et al. 2015). Sociobiology is an attractive interdisciplinary field. ...
The chapter presents an analytic description of evolutionary and developmental morphogenetic events in Metazoa using concepts of self-organization, morphological and molecular–genetic data, and the topological approach to the analysis. Biological objects are complex systems capable of dynamic self-organization at all levels of biological complexity. Some examples of self-organization in cyanobacteria, metazoan cells in vitro (chick embryo myogenic cells, molluscan hemocytes, sea urchin embryo cells), and animal communities of some vertebrates are shown. Following René Thom, a topological interpretation of some evolutionary and developmental transformations is presented using well-known mathematical concepts. Toroidal forms are considered as examples of functionally optimized biological design and attractors in metazoan morphogenesis. Molecular–genetic evidence of genomic–phenomic correlations determining the body plan and evolutionary trajectories in Metazoa is discussed. Gene regulatory networks and whole metazoan genomes are interpreted as self-organizing network systems dynamically transforming in development and evolution. Symmetry breaking, topological discontinuities and catastrophes, and body plan transformations are fundamental phenomena in metazoan development and evolution.
... Herein, all tested strains produced gliding motility-dependent furrows, indicating that the presence or absence of EPS or BPS does not qualitatively impact the formation of these substratum depressions. While it was not possible to distinguish between single and grouped cells via stereoscopy, additional cells were detected following the path of the various furrows ( Figure 1a); this supports sematectonic stigmergic coordination for the phenomenon of trail following on agar by M. xanthus cells (Gloag et al., 2015). ...
Exopolysaccharide (EPS) layers on the bacterial cell surface are key determinants of biofilm establishment and maintenance, leading to the formation of higher‐order 3D structures that confer numerous survival benefits to a cell community. In addition to a specific cell‐associated EPS glycocalyx, we recently revealed that the social δ‐proteobacterium Myxococcus xanthus secretes a novel biosurfactant polysaccharide (BPS) to the extracellular milieu. Together, secretion of the two polymers (EPS and BPS) is required for type IV pilus (T4P)‐dependent swarm expansion via spatio‐specific biofilm expression profiles. Thus the synergy between EPS and BPS secretion somehow modulates the multicellular lifecycle of M. xanthus. Herein, we demonstrate that BPS secretion functionally alters the EPS glycocalyx via destabilization of the latter, fundamentally changing the characteristics of the cell surface. This impacts motility behaviours at the single‐cell level and the aggregative capacity of cells in groups via cell‐surface EPS fibril formation as well as T4P production, stability, and positioning. These changes modulate the structure of swarm biofilms via cell layering, likely contributing to the formation of internal swarm polysaccharide architecture. Together, these data reveal the manner by which the combined secretion of two distinct polymers induces single‐cell changes that modulate swarm biofilm communities. Production of a recently‐identified biosurfactant polysaccharide (BPS) by Myxococcus xanthus results in destabilization of the surface exopolysaccharide (EPS) layer at the single‐cell level. This destabilization impacts all aspects of M. xanthus multicellular physiology including single‐cell and group modes of motility, fruiting body formation during development, and biofilm formation and structuration.
... Prediction 5: All retrievable biological memories are stigmergic. Beginning with bacteria (Gloag et al. 2015), biological systems ubiquitously employ stigmergic memories (Heylighen 2016). This is not a surprising observation to be explained, but rather an empirical confirmation in MP. ...
Theories of consciousness and cognition that assume a neural substrate automatically regard phylogenetically basal, nonneural systems as nonconscious and noncognitive. Here, we advance a scale-free characterization of consciousness and cognition that regards basal systems, including synthetic constructs, as not only informative about the structure and function of experience in more complex systems but also as offering distinct advantages for experimental manipulation. Our "minimal physicalist" approach makes no assumptions beyond those of quantum information theory, and hence is applicable from the molecular scale upwards. We show that standard concepts including integrated information, state broadcasting via small-world networks, and hierarchical Bayesian inference emerge naturally in this setting, and that common phenomena including stigmergic memory, perceptual coarse-graining, and attention switching follow directly from the thermodynamic requirements of classical computation. We show that the self-representation that lies at the heart of human autonoetic awareness can be traced as far back as, and serves the same basic functions as, the stress response in bacteria and other basal systems.
... While our model is simplified compared to the biological complexity of P. aeruginosa and other bacteria that employ twitching as a motility strategy, microscopic details of swimming motility have previously been shown to result in qualitative changes to collective dynamics 26,[91][92][93] and swarming-mode motility of P. aeruginosa 94 . Our well defined microscopic model of the twitching mode motility cycle captures the essential microscopic details that differentiate biologically relevant twitching motility from a purely idealized toy model of self-propelled rods and demonstrates that twitching motility is sufficient to exhibit physically mediated collectivity, without requiring additional long-range complications, such as photosensing and quorum sensing 95 or secretions 56 or other forms of bacterial stigmergy 96 . Although lacking a clear signal in the first order statistics of mean squared displacement, the collectivity of twitchers above a critical coverage fraction can be directly quantified by higher order statistics, including the non-Gaussian parameter, decorrelation lengths and the scaling of the fluctuations with local coverage. ...
Pseudomonas aeruginosa, like many bacilliforms, are not limited only to swimming motility but rather possess many motility strategies. In particular, twitching-mode motility employs hair-like pili to transverse moist surfaces with a jittery irregular crawl. Twitching motility plays a critical role in redistributing cells on surfaces prior to and during colony formation. We combine molecular dynamics and rule-based simulations to study twitching-mode motility of model bacilliforms and show that there is a critical surface coverage fraction at which collective effects arise. Our simulations demonstrate dynamic clustering of twitcher-type bacteria with polydomains of local alignment that exhibit spontaneous correlated motions, similar to rafts in many bacterial communities.
... Much of the above is evidence of the sustained and expanding influence of economic theory underpinning microbiology with calls for more explicit comparison between capitalist social systems and the behaviour of bacteria. Baranyi et al. (2015, p. 162) lament that 'microbiology has not yet explored this idea sufficiently' and argue for the compelling similarities of biology, 'politics and industry' (see also Gloag et al. 2015). Taking a more cognitivistic position on biotic markets, another contribution to the debate calls for greater consideration of bacteria as 'intensely social organisms' exhibiting '… information pooling, control skew, speed vs. accuracy trade-offs, local feed backs, quorum thresholds, conflicts of interest… collective decision-making in microbes shares many features with collective decision-making in higher taxa…' (Ross-Gillespie et al., 2015, p. 2; see also Cordero et al., 2012). ...
Abstract This paper seeks to report on the way economic principles, formulae and discourse infiltrate biological research on antimicrobial resistance (AMR) in the life sciences. AMR, it can be argued, has become the basis for performing certain forms of ‘economic imaginary’. Economic imaginaries are ways of projecting and materially restructuring economic and political orders through motifs, metaphors, images and practices. The paper contributes to critical social science and humanities research on the socio-economic underpinning of biological discourse. The performance of economy in this context can be seen to follow two key trajectories. The first trajectory, discussed at length in this paper, might be described as ‘economies of resistance’. Here the language of market economics structures and frames microbiological explanations of bacterial resistance. This can be illustrated through, for example, biological theories of ‘genetic capitalism’ where capitalism itself is seen to furnish microbial life with modes of economic behaviour and conduct. ‘Economies of resistance’ are evidence of the naturalisation of socio-economic structures in expert understandings of AMR. The methodological basis of this paper lies in a historical genealogical investigation into the use of economic and market principles in contemporary microbiology. The paper reports on a corpus of published academic sources identified through the use of keywords, terms, expressions and metaphors linked to market economics. Search terms included, but were not limited to: ‘trade-off’, ‘investment’, ‘market/s’, ‘competition’, ‘cooperation’, ‘economy’, ‘capital/ism’ and ‘socialist/ism’, etc. ‘Economies of resistance’ complements a second distinct trajectory that can be seen to flow in the opposite direction from biology to economic politics (the ‘resistance of economies’). Here, economic imaginaries of microbial life are redeployed in large-scale debates about the nature of economic life, about the future of the welfare state, industrial strategy, and about the politics of migration and race. ‘Economies of resistance’ and the ‘resistance of economies’ are not unrelated but, instead, they are mutually constituting dynamics in the co-production of AMR. In attempting to better understand this co-production, the paper draws upon literatures on the biopolitics of immunity in political philosophy and Science and Technology Studies (STS).
... Moreover, while unicellular organisms have initially been thought of as individualistic and disorganized, high levels of self-organization have also been evidenced through inter and intra specie interactions. Indeed, multiple observations shows that a microbe collective can self-organize, thus making large scale patterns to emerge to the point their behavior have been compared to this of a multicellular organism (Velicer et al., 1998;Velicer, 2003;Jacob et al., 2004;Gloag et al., 2015). To summarise, those observations suggest that microbes can assemble into 85 complex communities harboring interactions already described in macrobial community ecology. ...
Microbial communities play a key role in geochemical cycles and environmental bioprocesses. Despite their importance, the mechanisms involved in their structuration remain elusive and are poorly captured in current models. The modelling approach developed during this thesis stands as an alternative to the current empirical approaches. It relies on a novel theory of microbial growth (the MTS theory), which introduce a flux/force relationship between the microbial growth rate and the free energy gradients available in the biotope. The purpose of this thesis is to characterize the dynamic properties of the MTS model and to determine, through simulations, the part of the microbial communities’ spatio-temporal structuration that is intrinsically captured by the MTS theory and which does not pertain to parameters adjustment.Simulations firstly reveal that a characteristic of the MTS model is its ability to account for the simultaneous growth limitation by many resources of different kinds (electron acceptor/donor, but also nutrients), and to integrate them as stoichiometric limitations, giving rise to coherent populations dynamics.In a second stage, the MTS model has been used to predict the dynamics of microbial communities. Those studies revealed that the thermodynamics constraints on which the MTS kinetic theory is built intrinsically give rise to consistent ecological successions without the need to adjust specifically the parameters of each population. In the case of a simplified activated sludge ecosystem, after calibration using respirometric data, the model was able to reproduce ecosystem dynamics quantitatively with a reduced number of parameters compared to current Activated Sludge Models (ASM).In a third stage, a large database of experimental growth yield observations has been compiled from literature. The relationship between multiple physicochemical parameters characterizing the metabolisms (reduction degrees, catabolic energy...) and the growth yield has been investigated using statistical methods. This work confirms that microbial growth yields can be accurately predicted solely on thermodynamic properties of metabolic reactions. The growth yields predictor could be included in future developments of the MTS models.More generally, the work undertaken during this thesis evidenced that the MTS model proposes a formalization of the coupling between thermodynamic and dynamic variables of a microbial ecosystem. The simulated microbial populations and ecosystems display coherent dynamic behaviors. The model is able to account, by construction, for well-known ecological successions, without specific parameter adjustment. This model is peculiarly adapted to the prediction of the functional structure of communities in ecosystems dominated by selection by competition, rather than on species dispersion, diversification or genetic drift.Those results encourage the development of microbial ecosystems based on firmer theoretical grounds. Such models are necessary to the development of bioprocesses able to answer to the new technological and environmental challenges.
