Fast growth can counteract antibiotic susceptibility in shaping microbial community resilience to antibiotics.

Microbial communities often face external perturbations that can induce lasting changes in their composition and functions. Our understanding of how multispecies communities respond to perturbations such as antibiotics is limited, with susceptibility assays performed on individual, isolated species our primary guide in predicting community transitions. Here, we studied how bacterial growth dynamics can overcome differences in antibiotic susceptibility in determining community resilience: the recovery of the original community state following antibiotic exposure. We used an experimental community containing Corynebacterium ammoniagenes and Lactobacillus plantarum that displays two alternative stable states as a result of mutual inhibition. Although C. ammoniagenes was more susceptible to chloramphenicol in monocultures, we found that chloramphenicol exposure nonetheless led to a transition from the L. plantarum-dominated to the C. ammoniagenes-dominated community state. Combining theory and experiments, we demonstrated that growth rate differences between the two species made the L. plantarum-dominated community less resilient to several antibiotics with different mechanisms of action. Taking advantage of an observed cooperativity­a dependence on population abundance­in the growth of C. ammoniagenes, we next analyzed in silico scenarios that could compromise the high resilience of the C. ammoniagenes-dominated state. The model predicted that lowering the dispersal rate, through interacting with the growth at low population densities, could make the C. ammoniagenes state fragile against virtually any kind of antibiotic, a prediction that we confirmed experimentally. Our results highlight that species susceptibility to antibiotics is often uninformative of community resilience, as growth dynamics in the wake of antibiotic exposure can play a dominant role.

Spatial dynamics of synthetic microbial mutualists and their parasites.

A major force contributing to the emergence of novelty in nature is the presence of cooperative interactions, where two or more components of a system act in synergy, sometimes leading to higher-order, emergent phenomena. Within molecular evolution, the so called hypercycle defines the simplest model of an autocatalytic cycle, providing major theoretical insights on the evolution of cooperation in the early biosphere. These closed cooperative loops have also inspired our understanding of how catalytic loops appear in ecological systems. In both cases, hypercycle and ecological cooperative loops, the role played by space seems to be crucial for their stability and resilience against parasites. However, it is difficult to test these ideas in natural ecosystems, where time and spatial scales introduce considerable limitations. Here, we use engineered bacteria as a model system to a variety of environmental scenarios identifying trends that transcend the specific model system, such an enhanced genetic diversity in environments requiring mutualistic interactions. Interestingly, we show that improved environments can slow down mutualistic range expansions as a result of genetic drift effects preceding local resource depletion. Moreover, we show that a parasitic strain is excluded from the population during range expansions (which acknowledges a classical prediction). Nevertheless, environmental deterioration can reshape population interactions, this same strain becoming part of a three-species mutualistic web in scenarios in which the two-strain mutualism becomes non functional. The evolutionary and ecological implications for the design of synthetic ecosystems are outlined.

Cooperative growth in microbial communities is a driver of multistability.

Microbial communities often exhibit more than one possible stable composition for the same set of external conditions. In the human microbiome, these persistent changes in species composition and abundance are associated with health and disease states, but the drivers of these alternative stable states remain unclear. Here we experimentally demonstrate that a cross-kingdom community, composed of six species relevant to the respiratory tract, displays four alternative stable states each dominated by a different species. In pairwise coculture, we observe widespread bistability among species pairs, providing a natural origin for the multistability of the full community. In contrast with the common association between bistability and antagonism, experiments reveal many positive interactions within and between community members. We find that multiple species display cooperative growth, and modeling predicts that this could drive the observed multistability within the community as well as non-canonical pairwise outcomes. A biochemical screening reveals that glutamate either reduces or eliminates cooperativity in the growth of several species, and we confirm that such supplementation reduces the extent of bistability across pairs and reduces multistability in the full community. Our findings provide a mechanistic explanation of how cooperative growth rather than competitive interactions can underlie multistability in microbial communities.

Emergent phases of ecological diversity and dynamics mapped in microcosms.

From tropical forests to gut microbiomes, ecological communities host notably high numbers of coexisting species. Beyond high biodiversity, communities exhibit a range of complex dynamics that are difficult to explain under a unified framework. Using bacterial microcosms, we performed a direct test of theory predicting that simple community-level features dictate emergent behaviors of communities. As either the number of species or the strength of interactions increases, we show that microbial ecosystems transition between three distinct dynamical phases, from a stable equilibrium in which all species coexist to partial coexistence to emergence of persistent fluctuations in species abundances, in the order predicted by theory. Under fixed conditions, high biodiversity and fluctuations reinforce each other. Our results demonstrate predictable emergent patterns of diversity and dynamics in ecological communities.

Transient invaders can induce shifts between alternative stable states of microbial communities.

Microbial dispersal often leads to the arrival of outsider organisms into ecosystems. When their arrival gives rise to successful invasions, outsider species establish within the resident community, which can markedly alter the ecosystem. Seemingly less influential, the potential impact of unsuccessful invaders that interact only transiently with the community has remained largely ignored. Here, we experimentally demonstrate that these transient invasions can induce a lasting transition to an alternative stable state, even when the invader species itself does not survive the transition. First, we develop a mechanistic understanding of how environmental changes caused by these transient invaders can drive a community shift in a simple, bistable model system. Beyond this, we show that transient invaders can also induce switches between stable states in more complex communities isolated from natural soil samples. Our results demonstrate that short-term interactions with an invader species can induce lasting shifts in community composition and function.

On Singularities and Black Holes in Combination-Driven Models of Technological Innovation Networks.

It has been suggested that innovations occur mainly by combination: the more inventions accumulate, the higher the probability that new inventions are obtained from previous designs. Additionally, it has been conjectured that the combinatorial nature of innovations naturally leads to a singularity: at some finite time, the number of innovations should diverge. Although these ideas are certainly appealing, no general models have been yet developed to test the conditions under which combinatorial technology should become explosive. Here we present a generalised model of technological evolution that takes into account two major properties: the number of previous technologies needed to create a novel one and how rapidly technology ages. Two different models of combinatorial growth are considered, involving different forms of ageing. When long-range memory is used and thus old inventions are available for novel innovations, singularities can emerge under some conditions with two phases separated by a critical boundary. If the ageing has a characteristic time scale, it is shown that no singularities will be observed. Instead, a "black hole" of old innovations appears and expands in time, making the rate of invention creation slow down into a linear regime.

Synthetic collective intelligence.

Intelligent systems have emerged in our biosphere in different contexts and achieving different levels of complexity. The requirement of communication in a social context has been in all cases a determinant. The human brain, probably co-evolving with language, is an exceedingly successful example. Similarly, social insects complex collective decisions emerge from information exchanges between many agents. The difference is that such processing is obtained out of a limited individual cognitive power. Computational models and embodied versions using non-living systems, particularly involving robot swarms, have been used to explore the potentiality of collective intelligence. Here we suggest a novel approach to the problem grounded in the genetic engineering of unicellular systems, which can be modified in order to interact, store memories or adapt to external stimuli in collective ways. What we label as Synthetic Swarm Intelligence defines a parallel approach to the evolution of computation and swarm intelligence and allows to explore potential embodied scenarios for decision making at the microscale. Here, we consider several relevant examples of collective intelligence and their synthetic organism counterparts.

Catastrophic shifts and lethal thresholds in a propagating front model of unstable tumor progression.

Unstable dynamics characterizes the evolution of most solid tumors. Because of an increased failure of maintaining genome integrity, a cumulative increase in the levels of gene mutation and loss is observed. Previous work suggests that instability thresholds to cancer progression exist, defining phase transition phenomena separating tumor-winning scenarios from tumor extinction or coexistence phases. Here we present an integral equation approach to the quasispecies dynamics of unstable cancer. The model exhibits two main phases, characterized by either the success or failure of cancer tissue. Moreover, the model predicts that tumor failure can be due to either a reduced selective advantage over healthy cells or excessive instability. We also derive an approximate, analytical solution that predicts the front speed of aggressive tumor populations on the instability space.

Effects of punishment in a mobile population playing the prisoner's dilemma game.

We deal with a system of prisoner's dilemma players undergoing continuous motion in a two-dimensional plane. In contrast to previous work, we introduce altruistic punishment after the game. We find punishing only a few of the cooperator-defector interactions is enough to lead the system to a cooperative state in environments where otherwise defection would take over the population. This happens even with soft nonsocial punishment (where both cooperators and defectors punish other players, a behavior observed in many human populations). For high enough mobilities or temptations to defect, low rates of social punishment can no longer avoid the breakdown of cooperation.

Virus infection speeds: theory versus experiment.

In order to explain the speed of Vesicular Stomatitis Virus (VSV) infections, we develop a simple model that improves previous approaches to the propagation of virus infections. For VSV infections, we find that the delay time elapsed between the adsorption of a viral particle into a cell and the release of its progeny has a very important effect. Moreover, this delay time makes the adsorption rate essentially irrelevant in order to predict VSV infection speeds. Numerical simulations are in agreement with the analytical results. Our model satisfactorily explains the experimentally measured speeds of VSV infections.

Fronts from two-dimensional dispersal kernels: Beyond the nonoverlapping-generations model.

Most integrodifference models of biological invasions are based on the nonoverlapping-generations approximation. However, the effect of multiple reproduction events (overlapping generations) on the front speed can be very important (especially for species with a long life spam). Only in one-dimensional space has this approximation been relaxed previously, although almost all biological invasions take place in two dimensions. Here we present a model that takes into account the overlapping generations effect (or, more generally, the stage structure of the population), and we analyze the main differences with the corresponding nonoverlapping-generations results.