Previous seminars: 2020

The evolution of multicellular organisms on earth is one of the most transformative events in the history of life. Despite its importance, we know little about the process by which nascent microscopic multicellular organisms overcome substantial mechanical constraints and dramatically increase their size. We experimentally study this process with the snowflake yeast model system: baker's yeast (S. cerevisiae) with a single mutation in ACE2 gene allows mother and daughter cells to remain attached via uncut chitin bonds. These yeast clusters are composed of a few hundred cells and grow to a maximum diameter of 200 microns. After a year of artificial selection for larger multicellular size, five populations of snowflake yeast surprisingly evolved to grow to a diameter of 1 mm. In this work we show how small changes at the cell level trait lead to emergent properties at the microscopic level and helped to overcome tremendous mechanical constraints on their size.

Mobile genetic elements (MGEs), such as plasmids, phages, and transposons, play a critical role in mediating the transfer and maintenance of diverse traits and functions in microbial communities. This role depends on the ability of MGEs to persist. For a community consisting of multiple populations transferring multiple MGEs, however, the conditions underlying the persistence of these MGEs are poorly understood. Computationally, this difficulty arises from the combinatorial explosion associated with describing the gene flow in a complex community using the conventional modeling framework. Here, we describe an MGE-centric framework that makes it computationally feasible to analyze such transfer dynamics. Using this framework, we derive the persistence potential: a general, heuristic metric that predicts the persistence and abundance of any MGEs. We validate the metric with engineered microbial consortia transferring mobilizable plasmids and quantitative data available in the literature. Our modeling framework and the resulting metric have implications for developing a quantitative understanding of natural microbial communities and guiding the engineering of microbial consortia.

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Living systems evolve one mutation at a time, but a single mutation can alter the effect of subsequent mutations. The underlying mechanistic determinants of such epistasis are unclear. Here, we demonstrate that the physical dynamics of a biological system can generically constrain epistasis. We analyze models and experimental data on proteins and regulatory networks. In each, we find that if the long-time physical dynamics is dominated by a slow, collective mode, then the dimensionality of mutational effects is reduced. Consequently, epistatic coefficients for different combinations of mutations are no longer independent, even if individually strong. Such epistasis can be summarized as resulting from a global non-linearity applied to an underlying linear trait, i.e., as global epistasis. This constraint, in turn, reduces the ruggedness of the sequence-to-function map. By providing a generic mechanistic origin for experimentally observed global epistasis, our work suggests that slow collective physical modes can make biological systems evolvable.

Many recent studies suggest that cellular heterogeneity and gene expression noise generally promote cancer chemo-resistance, evasion of apoptosis, and metastasis. However, prior demonstration that noise can weaken resistance to low stress cautions against the generality of these conclusions. In fact, examining the phenotypic effects of noise requires proper, mean-decoupled noise control, which had not been established for mammalian cells. Therefore, the role of mammalian gene expression noise in mammalian cell survival and evolution was unclear. To address this challenge, we developed synthetic gene circuits to decouple noise from the mean of Puromycin resistance gene expression in Chinese Hamster Ovary (Flp-In™-CHO) cells. In low Puromycin concentrations, the high-noise circuit delayed long-term adaptation vs. low-noise circuit, whereas it facilitated adaptation in high Puromycin, similar to short-term survival and evolution effects in yeast. These findings clarify the role of non-genetic variability in mammalian drug resistance evolution and may have profound implications for the molecular causes and remedies of chemotherapeutic inefficiency and cancer relapse.

Failure of modularity remains a significant challenge for synthetic gene circuits assembled with tested modules as they often do not function as expected. Competition over shared limited gene expression resources is a crucial underlying reason. Here, we first built a synthetic cascading bistable switches (Syn-CBS) circuit in a single strain with two coupled self-activation modules to achieve two successive cell fate transitions. Interestingly, we found that the in vivo transition path was redirected as the activation of one switch always prevailed against the other instead of the theoretically expected coactivation. This qualitatively different type of resource competition between the two modules follows a ‘winner-takes-all’ rule, where the winner is determined by the relative connection strength between the modules. To decouple the resource competition, we constructed a two-strain circuit, which achieved successive activation and stable coactivation of the two switches. We unveiled a nonlinear resource competition within synthetic gene circuits and provided a division of labor strategy to minimize unfavorable effects.

Link to the paper

Priority effects occur during ecological succession when an early arriving species changes the available habitat for a later arriving species, with the consequence that the order in which species arrive can significantly affect the final composition of an ecosystem. Ecological communities of bacteria in the animal gut can influence health and behavior, and priority effects could influence the bacterial species composition with its associated health effects. Using the tractable, low-complexity gut microbiome of Drosophila flies, I will present evidence that priority effects shape the gut microbiome composition by different mechanisms depending on the interacting bacterial species.

The study of experimental communities is fundamental to the development of ecology. Yet, for most ecological systems, the number of experiments required to build, model or analyse the community vastly exceeds what is feasible using current methods. I will discuss a new statistical approach to address this challenge that uses the results of a limited number of experiments to predict the outcomes (coexistence and species abundances) of all possible assemblages that can be formed from a given pool of species. Analysis of three well-studied experimental systems -- encompassing plants, protists, and algae with grazers -- shows that this method can predict the results of unobserved experiments with high accuracy, while making no assumptions about the dynamics of the systems. This approach also suggests interesting connections to the generalized Lotka-Volterra Model, and practical implications for the design on experiments. Finally, we will consider strategies to deal with possible complications, including relative abundance data, transient coexistence, and insufficient sampling.

Over the last two decades, an association between microbiome composition and some human diseases has been unambiguously established. The correlation between gut microbe composition and these diseases has prompted medical interest into bacteriotherapies, which seek to modify the gut microbiome composition in the hopes of treating the correlated disease. In this work we use generalized Lotka-Volterra (gLV) models to probe the ecological mechanisms through which these bacteriotherapies function. We first describe direct bacteriotherapies, which drive a microbiome to a target state via an instantaneous influx of foreign microbes (e.g. probiotics or fecal microbiota transplantation). Then, we present a novel control framework for indirect bacteriotherapies, which drive a microbiome to a target state by deliberately modifying its environment (e.g. diet, acidity, or nutrients). Throughout, we use the dimensionality-reduction technique steady-state reduction to guide these simulated therapies. These dual control methods for gLV systems, interpreted as bacteriotherapies, could eventually inform personalized medicine for the microbiome.

Microbial communities play key roles in determining the health of virtually all living organisms, as well as that of the entire planet. In order to engineer and control these communities, it is crucial to be able to predict their composition. It was recently demonstrated that the composition of bacterial communities is well predicted from pairwise interaction over short time scales. However, it is still unknown to what extent this predictability holds over hundreds of generations, where evolution might play a significant role in altering community composition. To address this question, we have conducted a high-throughput evolutionary experiment, propagating 118 unique microbial communities for ~400 generations while tracking the community composition over time. We found that significant compositional changes occur in most communities at evolutionary timescales. While these changes typically increased the dissimilarity between replicates communities, they have also presented a degree of repeatability; species with a higher tendency to increase in relative abundance had almost always increased their abundance when coevolved with a partner with a lower tendency. Finally, we found that changes that occurred when species evolved in pairs, were consistent with changes that occur in trios, thus enabling to accurately predict the trios community composition from pairs composition even after hundreds of generations of coevolution.

Gut microbiota undergo substantial changes in composition and function during aging. However, little is known about how gut microbiota in aging hosts respond to environmental perturbations, such as antibiotics treatment and inflammation in the intestine. Here we studied the dynamics of gut microbiome in aging mice and the effects of fecal microbiota transplantation (FMT) on microbiome restoration and intestinal homeostasis. We treated 20-month-old mice with a cocktail of four antibiotics and found that the perturbation greatly reduced gut microbiota diversity, which did not recover to the original state after 6 weeks. In contrast, FMT from donors of the same age (autologous) or 2-month-old mice (heterochronic) following antibiotics treatment helped restore the microbiota diversity, as well as the transcriptome of colon tissues. Furthermore, we found that the composition of restored gut microbiota after FMT is donor-dependent. After treatment of dextran sulfate sodium that induced acute inflammation in the intestine, gut microbiota of the recipients of autologous FMT was more resilient to the perturbation and had less severe disease outcomes in comparison to the recipients of heterochronic FMT. Our study demonstrates that autologous FMT may be the best strategy for restoring intestinal homeostasis in aging hosts.

The assembly and chemical transformation activities of microbial communities emerge from a complex and dynamic web of interactions linking constituent community members and abiotic factors. Prediction of community dynamics and functional activities is highly challenging due to unknown interactions and mapping between community composition and functional capabilities. In this talk, I will discuss how we used a model-guided experimental design framework for microbial communities to predict community assembly and the production of the health-relevant metabolite butyrate by a complex 25‑member synthetic community that mirrors the phylogenetic diversity of the human gut microbiome. Based on limited experimental measurements, our model accurately forecasts community assembly and butyrate production at every possible level of complexity. Our results provide key insights into ecological and molecular mechanisms impacting butyrate production. Our model-guided iterative approach provides a flexible framework for understanding and predicting community functions for a broad range of applications.

In this talk, we focus on a central question—how are network structures linked to the functional properties of complex clock circuitry. A typical molecular pathway of a biological clock may contain hundreds of regulators, making it hard to extract essential information. An appealing idea is that a finite set of simple structures, called network motifs, may underlie larger-scale networks to determine specific functions. Studies have shown evidence that a small set of molecular network structures are enriched in various networks for bacterial gene regulation, decision-making, cell polarization, perfect adaptation, and morphogen patterns. However, limited work has systematically characterized such topology-function relationships in clock networks, due to their diverse and complex molecular mechanisms, and incomplete network interaction database. To overcome the difficulty, we developed an alternative approach by theoretically enumerating all possible networks in the topology space. We computationally generated an atlas of oscillators and found that, while certain core topologies are essential for robust oscillations, local structures substantially modulate the degree of robustness. Strikingly, two key local structures, incoherent inputs and coherent inputs, can modify a core topology to promote and attenuate its robustness, additively. The findings underscore the importance of local modifications to the performance of the whole network. This may explain why auxiliary structures not required for oscillations are evolutionary conserved.

To experimentally test the topology-function relation, we developed a bottom-up synthetic-cell system to encapsulate the cell-free extracts in cell-sized water-in-oil microemulsion droplets. These artificial cells can mimic many dynamic processes just like in real cells but allow flexible manipulation that would be hard in live cells. We started with building a minimal, cytoplasmic-only mitotic cell. Unlike bulk extracts where oscillations last only a few cycles before damping, these microdroplets are stable for multiple days and perform 30-40 cycles of resilient self-sustained oscillations, significantly better than many existing synthetic oscillators. The minimally constructed artificial mitotic cell has no nuclei and performs cell-cycle oscillations in the absence of cell growth, division, or any other morphological changes. Thus, it provides a unique ‘clean’ system to analyze the design-function of the circuit itself. Next, we built a more complex cell that contains nuclei. Adding demembranated sperm DNA, the system undergoes chromosome condensation/de-condensation and nuclear envelope breakdown (NEB)/reformation cycles like in real cells, suggesting the oscillator preserves the function capable of driving the periodic progression of downstream mitotic events. The flexibility of decoupling and reassembling subcellular components into complicated spatial and temporal organizations grants the system the full power of interrogating step by step the basic mechanisms underlying complex cellular processes.

Interspecies interactions shape the structure and function of microbial communities. In particular, positive, growth-promoting interactions can significantly affect the diversity and productivity of natural and engineered communities. However, the prevalence of positive interactions and the conditions in which they occur are not well understood. To address this knowledge gap, we used kChip, an ultra-high throughput coculture platform, to measure 180,408 interactions among 20 soil bacteria across 40 carbon environments. We find that positive interactions, often described to be rare, occur commonly, primarily as parasitisms between strains that differ in their carbon consumption profiles. Notably, non-growing strains are almost always promoted by strongly growing strains (85%), suggesting a simple positive interaction-mediated approach for cultivation, microbiome engineering, and microbial consortium design.

The presence or absence of oxygen in the environment is a strong effector of cellular metabolism and physiology. Like many eukaryotes and some bacteria, Bacillus subtilis primarily utilizes oxygen during respiration to generate ATP. Despite the importance of oxygen for B. subtilis survival, we know little about how populations adapt to shifts in oxygen availability. To address this, we depleted oxygen from B. subtilis cultures and found that ~90% of cells died while the remaining cells maintained colony-forming ability. We show that the biosurfactant surfactin maintains viability during oxygen depletion by depolarizing the membrane. We further show that chemical and genetic perturbations that alter oxygen consumption or redox state support a model in which surfactin-mediated membrane depolarization maintains viability through slower oxygen consumption and/or a shift to a more reduced metabolic profile. These findings highlight the importance of membrane potential in regulating cell physiology and growth, and demonstrate that antimicrobials that depolarize cell membranes can benefit cells when the terminal electron acceptor in respiration is limiting. This foundational knowledge has deep implications for environmental microbiology, clinical anti-bacterial therapy, and industrial biotechnology.

In addition, I will briefly describe a second project where we utilize a high-throughput co-culture screen to identify B. subtilis strains that could be supported by wild-type cells in a biofilm and show a biofilm environment can support mutualism between nutrient-deficient strain and a matrix-deficient strain. This finding is an example of how multicellular biofilm communities can support genetic differentiation and resource sharing and may have implications in understanding the evolution of multicellular communities.

Nonequilibrium phase transitions from survival to extinction have recently been observed in computational models of evolutionary dynamics. Dynamical signatures predictive of population collapse have been observed in yeast populations under stress. We experimentally investigate the population response of the budding yeast Saccharomyces cerevisiae to biological stressors (temperature and salt concentration) in order to investigate the system’s behavior in the vicinity of population collapse. While both conditions lead to population decline, the dynamical characteristics of the population response differ significantly depending on the stressor. Under temperature stress, the population undergoes a sharp change with significant fluctuations within a critical temperature range, indicative of a continuous absorbing phase transition. In the case of salt stress, the response is more gradual. A similar range of response is observed with the application of various antibiotics to Escherichia coli, with a variety of patterns of decreased growth in response to antibiotic stress both within and across antibiotic classes and mechanisms of action. These findings have implications for the identification of critical tipping points for populations under environmental stress.

Migration is a common phenomenon across many taxonomical groups and can provide immense benefits of space and resources, but often at a high cost. One particularly successful case of migration is that of a surface-attached Proteus mirabilis colony. P. mirabilis is able to colonize much harder surfaces than other bacteria, making it a particular problem for humans as it can invade the urinary tract by migrating across catheters. Examination of P. mirabilis’ migration pattern reveals that the Rcs system, widely conserved in the Enterobacteriaceae family, suppress motility in the majority of the colony, leaving only 1% of the colony to participate in migration. Further analysis shows that this suppression of motility is nutrient-dependent, leading to a spatial pattern of motility in which cells on the nutrient-rich edge of the colony participate in migration, while cells in the nutrient-limited center of the colony remain in place. This system of partial migration helps P. mirabilis to optimize the balance of costs and benefits inherent to migration and may shed light on migration strategies of a wide variety of species.

Bacteria communicate by the release and detection of small diffusible molecules, a process termed quorum-sensing. The broad diffusivity of these molecules suggests that in spatially structured communities, quorum-sensing would report for the global composition of a specific strain. However, some quorum-sensing regulated traits, such as conjugative transfer, depend on micron scale changes in community composition. It is therefore unclear how quorum-sensing can effectively control such traits. Here, we compare the range of communication of multiple quorum-sensing systems and show that, besides systems supporting long-range communication, some quorum-sensing systems support a new form of highly localized communication. In these systems, signal molecules propagate no more than a few microns away from signaling cells, due to the irreversible uptake of the signal molecules from the environment. This enables cells to accurately detect micron scale changes in the community composition. We find that several mobile genetic elements, including conjugative elements and phages, employ short-range communication to assess the fraction of susceptible host cells in their vicinity and adaptively trigger horizontal gene transfer in response. Our results provide a fundamentally new perspective on the spatial biology of bacteria, where cells both communicate and interact at widely different spatial scales.

Microbes exist in complex, multi-species communities with diverse interactions that play an essential role in both human health as well as the health of the planet. Over the last decade tremendous progress has been made in characterizing these communities, but the lack of experimentally tractable model systems has made it difficult to discern the rules governing microbial community assembly and function. In this talk I will describe our recent experimental efforts to develop a bottom-up approach to understanding the dynamics of these communities. We find that simple, phenomenological descriptions of microbial interactions provide a surprising amount of insight into how communities assemble and change under stress.

Social interaction between microbes can be described at many levels of details: from the biochemistry of cell-cell interactions to the ecological dynamics of populations. Choosing an appropriate level to model microbial communities without losing generality remains a challenge. Here we show that modeling cross-feeding interactions at an intermediate level between genome-scale metabolic models of individual species and consumer-resource models of ecosystems is suitable to experimental data. We applied our modeling framework to three published examples of multi-strain Escherichia coli communities with increasing complexity: uni-, bi-, and multi-directional cross-feeding of either substitutable metabolic byproducts or essential nutrients. The intermediate-scale model accurately fit empirical data and quantified metabolic exchange rates that are hard to measure experimentally, even for a complex community of 14 amino acid auxotrophies. By studying the conditions of species coexistence, the ecological outcomes of cross-feeding interactions, and each community’s robustness to perturbations, we extracted new quantitative insights from these three published experimental datasets. Our analysis provides a foundation to quantify cross-feeding interactions from experimental data, and highlights the importance of metabolic exchanges in the dynamics and stability of microbial communities.

Complex systems such as microbial communities play key roles in global processes and human life, yet are often challenging to understand. Although mechanistic knowledge in biology is generally rooted in manipulative experiments, perturbing these systems can encounter practical and ethical barriers. Thus, extensive attempts have been made to infer causal knowledge by analyzing observations of taxon abundance over time. When, and to what extent, does this strategy yield genuine insight? Unfortunately, the literature of causal inference can be formidable and controversial, as it draws from divergent fields such as philosophy, statistics, econometrics, and chaos theory. Most benchmarking papers focus on performance details of causal inference approaches, rather than fundamental issues such as the underlying assumptions and their reasons, conceptual distinctions, and universal limitations. This presentation is a preview of a new synthesis article covering popular causal inference approaches including pairwise correlation and Reichenbach's common cause principle, Granger causality, and state space reconstruction. We find that each of these requires that certain properties of the data do not change with time (e.g. “IID”, “stationarity”, “reverting dynamics”). We provide new ways of visualizing key concepts, point out important issues that have been under-emphasized, and in some cases describe novel pathologies of causal inference methods. Although our synthesis is motivated by microbial communities, all arguments apply to other types of dynamic systems. We strive to balance precision with accessibility, and hope that our synthesis will motivate future development on causal inference approaches. To facilitate communication to a broad audience, we have made an accompanying video walkthrough (https://youtu.be/TZvEk3jXQfY).

Reaction-diffusion waves have long been used to describe the growth and spread of populations undergoing a range expansion. Such waves are generally classed as either pulled, where the dynamics are driven by the very tip of the front and stochastic fluctuations are high, or pushed, where cooperation results in a bulk driven wave in which fluctuations are suppressed. These concepts have been well studied experimentally in populations where the cooperation manifests in a density-dependent growth rate, but relatively little is known about experimental populations that instead exhibit a density-dependent dispersal rate. Using bacteriophage T7 as a test organism, we present novel experimental measurements that demonstrate that the diffusion of phage T7, in a lawn of host E. coli, is hindered by the physical presence of the host bacteria cells, resulting in a density-dependent diffusion rate. By designing a system of reaction-diffusion equations, we show that this effect, and an additional implicit density-dependence in the diffusion of the phage that emerges as a result of the viral incubation period, can result in the transition from a pulled to pushed expansion, and that both effects play a key role in determining the nature of the transition. Our results indicate both that bacteriophage can be used as a controllable laboratory population to investigate the impact of density-dependent dispersal on evolution, and that the genetic diversity and adaptability of expanding viral populations could be much greater than is currently assumed.

Naturally competent bacteria can acquire DNA from the environment that facilitates evolutionary adaptation via horizontal gene transfer (HGT). The quantitative roles of the DNA donor and recipient to natural transformation in microbial communities are not well understood. We investigated how molecular origin of the DNA impacts the dynamics of HGT in a synthetic two-member consortium. We found that donor strain can significantly increase plasmid-mediated gene transfer. By contrast, donor growth impairment is required for chromosomal-mediated transfer. We developed a dynamic computational model of HGT in the co-culture and inferred model parameter based on the time-series data. Consistent with our model, perturbations that reduce the plasmid or chromosomal donor strain growth rate have opposing effects on the efficiency of transfer. Plasmids can also facilitate chromosomal gene transfer by imposing a metabolic burden on the donor strain. We found that the donor and recipient were physically associated in the co-culture and separating the cells by filter reduced plasmid-mediated gene transfer. In sum, our results show that the donor strain can play an active role in mediating HGT via natural competence and the molecular origin of the transferred sequences is a critical determinant of the role of the donor strain in this process.

Demographic noise, the change in the composition of a population due to random births and deaths, is an important driving force in evolution along with selection, mutation, and recombination. Demographic noise is especially important in range expansions, such as biofilms and solid cancer tumors, because its strength is enhanced by the added positional advantage of individuals at an expansion front. Using microbial colonies as a toy model, it has been shown that distantly related organisms, i.e. different species or strains, can exhibit dramatic differences in the strength of demographic noise, but it is unclear if and how the strength of demographic noise changes through evolution. In particular, the local mutational landscape of demographic noise, i.e. to what extent a random single mutational step can have a substantial effect, is unknown. We present a new label-free method to measure demographic noise in microbial colonies and use it to characterize the distribution of demographic noise in ~200 randomly chosen single gene knockouts from the E. coli Keio collection. Colony- and cell-level phenotypes are able to predict the variance in the measured distribution of demographic noise by more than half. The establishment probability of beneficial mutations on different single gene knockout backgrounds varies by more than an order of magnitude, showing that the measured differences in demographic noise have an effect on evolutionary outcomes. Our results suggest that demographic noise should be considered an evolvable phenotype that can modify the rate of evolution.

Experimental studies of microbial communities routinely reveal that they have multiple stable states. While each of these states is generally resilient, certain perturbations such as antibiotics, probiotics, and diet shifts, result in transitions to other states. Can we reliably both predict such stable states as well as direct and control transitions between them? Here we present a new conceptual model—inspired by the stable marriage problem in game theory and economics—in which microbial communities naturally exhibit multiple stable states, each state with a different species’ abundance profile. Our model’s core ingredient is that microbes utilize nutrients one at a time while competing with each other. Using only two ranked tables, one with microbes’ nutrient preferences and one with their competitive abilities, we can determine all possible stable states as well as predict inter-state transitions, triggered by the removal or addition of a specific nutrient or microbe. Further, using an example of seven Bacteroides species common to the human gut utilizing nine polysaccharides, we predict that mutual complementarity in nutrient preferences enables these species to coexist at high abundances.

Accurate measurements of promoter activities are crucial for understanding the biophysics of transcription and enable the predictive construction of genetic systems. However, the strength of promoters in absolute units is rarely known and is more often measured indirectly with a reporter gene and provided in “arbitrary units”. To address this problem, we have developed a method to simultaneously count plasmid DNA, RNA transcripts, and protein expression in single living bacteria. From these data, the activity of a promoter in units of RNAP/s can be inferred. This work facilitates the reporting of promoters in absolute units, the variability in their activity across a population, and their quantitative toll on cellular resources, all of which provide critical insights for cellular engineering.

The resilience of ecological systems is their ability to resist and recover from perturbations on species abundances (i.e., state variables) and on the structure of the system (i.e., model parameters). However, it has been unclear how to measure recovery from arbitrary abundance perturbations (i.e., dynamical indicator) and resistance to parameter perturbations (i.e., structural indicator) and whether these indicators provide independent information. Here, we show that the faster the recovery from abundance perturbations, the higher the resistance to parameter perturbations. We propose to separate resilience into full and partial resilience. We show that the return rate along the slowest direction (i.e., full recovery) is negatively associated with the largest parameter perturbation that a system can withstand before losing any species (i.e., full resistance). We also show that the return rate along the second slowest direction (i.e., partial recovery) is negatively associated with the largest parameter perturbation that a system can withstand before at most one species survives (i.e., partial resistance). Then, we use a data set of microbial systems to confirm our theoretical results and to show that full and partial resilience are complimentary indicators.

Many facets of ecological and evolutionary theory rely on the analysis of invasion processes (of a species, a phenotype, a strategy). General approaches exist to understand the early stages of an invasion, but predicting the long-term impacts on resident communities remains a challenge. We show that short-term invasion success and long-term consequences are two distinct axes of variation controlled by different properties of both invader and resident community. Whether a species can invade is controlled by its invasion fitness, which depends on environmental conditions and direct interactions with resident species. But whether this invasion will cause significant transformations, such as extinctions or a regime shift, depends on a specific measure of indirect feedbacks that may involve the entire resident community. We show how these two factors control and can be inferred from the invader's dynamical trajectory. Our approach applies to arbitrarily complex communities, from few competing phenotypes in adaptive dynamics to large nonlinear food webs.

Microbial community diversity is pivotal for the functioning of our planet, but its drivers are still unclear, in particular the role of resource number and identity. To fill this gap, we studied the assembly of hundreds of soil-derived microbial communities on a wide range of well-defined resource environments, from single carbon sources to combinations of up to 16. We found a remarkable diversity in single resources but a linear one-by-one increase in the number of species with the number of additional resources. We show, both experimentally and theoretically, that both these observations could originate from generalist and specialist taxa interacting in a modular fashion within the community. Since generalists and specialists are ubiquitous in natural microbiomes, our results might apply to a variety of different ecological settings, providing a framework to predict how community diversity responds to changes in resource availability.

Many facets of ecological and evolutionary theory rely on the analysis of invasion processes (of a species, a phenotype, a strategy). General approaches exist to understand the early stages of an invasion, but predicting the long-term impacts on resident communities remains a challenge. We show that short-term invasion success and long-term consequences are two distinct axes of variation controlled by different properties of both invader and resident community. Whether a species can invade is controlled by its invasion fitness, which depends on environmental conditions and direct interactions with resident species. But whether this invasion will cause significant transformations, such as extinctions or a regime shift, depends on a specific measure of indirect feedbacks that may involve the entire resident community. We show how these two factors control and can be inferred from the invader's dynamical trajectory. Our approach applies to arbitrarily complex communities, from few competing phenotypes in adaptive dynamics to large nonlinear food webs.

Microbial communities often perform important functions that arise from interactions among member species. Community functions can be improved via artificial selection: Many communities are repeatedly grown, mutations arise, and communities with the highest desired function are chosen to reproduce where each is partitioned into multiple offspring communities for the next cycle. Since selection efficacy is often unimpressive in published experiments and since multiple experimental parameters need to be tuned, we sought to use computer simulations to learn how to design effective selection strategies. We simulated community selection to improve a community function that requires two species and imposes a fitness cost on one of the species. This simplified case allowed us to distill community function down to two fundamental and orthogonal components: a heritable determinant and a nonheritable determinant. We then visualize a “community function landscape” relating community function to these two determinants, and demonstrate that the evolutionary trajectory on the landscape is restricted along a trail designated by ecological interactions. This trail can prevent the attainment of maximal community function, and trap communities in landscape locations where community function has low heritability. Exploiting these observations, we devise a species spiking approach to shift the path to improve community function heritability and consequently selection efficacy. We show that our approach is applicable to communities with complex and unknown function landscapes.

Microbial communities are highly dimensional, with many species and many variable environmental factors. Macroecology, which studies communities as statistical ensembles, is a promising way to connect these complex data to mechanistic models. In this talk, I will discuss a minimal set of macroecological patterns that characterize the statistical properties of species abundance fluctuations across communities and over time. A mathematical model based on environmental stochasticity quantitatively predicts these three macroecological laws, as well as non-stationary properties of community dynamics. Building on these results, it is possible to disentangle the (statistical) properties that determine ecosystems' stability over time and reproducibility across communities.

In the ocean, organic particles harbor diverse bacterial communities, which collectively digest and recycle essential nutrients. So far, we lack principles that help us understand how these communities are organized, how bacteria interact to drive particle degradation, and how these factors are shaped by the environment.

Here, we approach this question from the bottom up by focusing on potential trophic interactions as a guiding principle for community assembly. We begin by performing high-throughput characterization of a large number of isolates and identify three metabolic archetypes -- specialists for sugars, organic acids, and generalists – which allow us to predict outcomes of pairwise competitions. We next assemble complex synthetic communities, in two ways: Firstly, using a soluble sugar as sole carbon source, we find evidence for a rewiring of community-scale metabolism as a function of nutrient concentration, from a focus on the primary resource at high concentrations to organic and amino acid metabolism at low nutrient concentrations, suggesting increased trophic interactions in carbon-limited environments. Secondly, using chitin as a model marine particulate carbon source, we find that cooperative trophic interactions are highly prevalent in polysaccharide-degrading communities, where a numerical minority of chitin degrading bacteria supports a high abundance of non-degraders. Taken together, our results show that synthetic communities assembled from environmental isolates are a powerful tool to identify principles of microbial community assembly, and that pervasive trophic interactions are a promising candidate for such a principle.

Our planet is experiencing an accelerated process of change associated to a variety of anthropogenic phenomena. The future of this transformation is uncertain, but there is general agreement about its negative unfolding that might threaten our own survival. Furthermore, the pace of the expected changes is likely to be abrupt: catastrophic shifts might be the most likely outcome of this ongoing, apparently slow process. Although different strategies for geo-engineering the planet have been advanced, none seem likely to safely revert the large-scale problems associated to carbon dioxide accumulation or ecosystem degradation. An alternative possibility considered here is inspired in the rapidly growing potential for engineering living systems. It would involve designing synthetic organisms capable of reproducing and expanding to large geographic scales with the goal of achieving a long-term or a transient restoration of ecosystem-level homeostasis. Such a local, regional or even planetary-scale engineering would have to deal with the complexity of our biosphere. It will require not only a proper design of organisms but also understanding their place within ecological networks and their evolvability. This is a likely future scenario that will require integration of ideas coming from currently weakly connected domains, including synthetic biology, ecological and genome engineering, evolutionary theory, climate science, biogeography and invasion ecology, among others.

A central goal of life science has been to understand the limits of species coexistence. However, we know surprisingly little about the structure of species coexistence below such limits, and how it affects the assembly and disassembly of ecological systems. Here we introduce a novel hypergraph-based formalism that fully captures the structure of coexistence in multispecies systems. Our formalism uncovers that, below its limits, coexistence in ecological systems has ubiquitous discontinuities that we call “coexistence holes." These coexistence holes do not occur arbitrarily but tend to obey patterns that make them predictable. We provide direct evidence showing that the biotic and abiotic constraints of empirical systems produce an over-representation of coexistence holes. By highlighting discontinuities in the form of coexistence holes, our work provides a new platform to uncover the order and structure of the assembly and disassembly of ecological systems.

Microbial communities reside in physical spatial architectures that sub-divide a global environment into complete or semi-isolated local environments. These physical architectures partition a microbial community into a collection of local communities that are separate from one another. Despite its ubiquity, how and to what extent spatial partitioning affects the structures of microbial communities are poorly understood. Using both modeling and quantitative experiments with synthetic and natural bacterial communities, here we find that spatial partitioning modulates the community dynamics by modulating the type of interactions in local communities and overall interaction strength. Specifically, partitioning promotes co-existence of populations with negative interactions but suppresses co-existence of those with positive interactions. For a community consisting of populations with both positive and negative interactions, an intermediate level of partitioning maximizes the overall diversity of the community. Our results reveal a general mechanism underlying maintenance of microbial diversity and have implications for stable assembly of natural or engineering microbial communities.

Metabolism and evolution are closely connected: if a mutation incurs extra energetic costs for an organism, there is a baseline selective disadvantage that may or may not be compensated for by other adaptive effects. A long-standing, but to date unproven, hypothesis is that this disadvantage is equal to the fractional cost relative to the total resting metabolic expenditure. I will present our recent work which validates this hypothesis from physical principles through a general growth model and show that it holds to excellent approximation for experimental parameters drawn from a wide range of species. I will briefly overview its applications to improve our understanding of the evolutionary narrative and discuss a potential setup for experimental verification.