Synthetic approaches to understanding biological constraints

Microbes can be readily cultured and their genomes can be easily manipulated. For these reasons, laboratory systems of unicellular organisms are increasingly used to develop and test theories about biological constraints, which manifest themselves at different levels of biological organization, from optimal gene-expression levels to complex individual and social behaviors. The quantitative description of biological constraints has recently advanced in several areas, such as the formulation of global laws governing the entire economy of a cell, the direct experimental measurement of the trade-offs leading to optimal gene expression, the description of naturally occurring fitness landscapes, and the appreciation of the requirements for a stable bacterial ecosystem.Alfred P. Sloan Foundation (Fellowship)Pew Charitable Trusts (Pew Scholars Program)National Science Foundation (U.S.) (NSF CAREER Award)National Institutes of Health (U.S.) (NIH R00 Pathway to Independence Award

Collective antibiotic resistance: mechanisms and implications

In collective resistance, microbial communities are able to survive antibiotic exposures that would be lethal to individual cells. In this review, we explore recent advances in understanding collective resistance in bacteria. The population dynamics of ‘cheating’ in a system with cooperative antibiotic inactivation have been described, providing insight into the demographic factors that determine resistance allele frequency in bacteria. Extensive work has elucidated mechanisms underlying collective resistance in biofilms and addressed questions about the role of cooperation in these structures. Additionally, recent investigations of ‘bet-hedging’ strategies in bacteria have explored the contributions of stochasticity and regulation to bacterial phenotypic heterogeneity and examined the effects of these strategies on community survival.United States. National Institutes of Health (6927557

Understanding microbial cooperation

The field of microbial cooperation has grown enormously over the last decade, leading to improved experimental techniques and a growing awareness of collective behavior in microbes. Unfortunately, many of our theoretical tools and concepts for understanding cooperation fail to take into account the peculiarities of the microbial world, namely strong selection strengths, unique population structure, and non-linear dynamics. Worse yet, common verbal arguments are often far removed from the math involved, leading to confusion and mistakes. Here, we review the general mathematical forms of Price's equation, Hamilton's rule, and multilevel selection as they are applied to microbes and provide some intuition on these otherwise abstract formulas. However, these sometimes overly general equations can lack specificity and predictive power, ultimately forcing us to advocate for more direct modeling techniques.National Institutes of Health (U.S.) (NIH K99 Pathways to Independence Award

A SLOWLY EVOLVING HOST MOVES FIRST IN SYMBIOTIC INTERACTIONS

Symbiotic relationships, both parasitic and mutualistic, are ubiquitous in nature. Understanding how these symbioses evolve, from bacteria and their phages to humans and our gut microflora, is crucial in understanding how life operates. Often, symbioses consist of a slowly evolving host species with each host only interacting with its own subpopulation of symbionts. The Red Queen hypothesis describes coevolutionary relationships as constant arms races with each species rushing to evolve an advantage over the other, suggesting that faster evolution is favored. Here, we use a simple game theoretic model of host–symbiont coevolution that includes population structure to show that if the symbionts evolve much faster than the host, the equilibrium distribution is the same as it would be if it were a sequential game where the host moves first against its symbionts. For the slowly evolving host, this will prove to be advantageous in mutualisms and a handicap in antagonisms. The result follows from rapid symbiont adaptation to its host and is robust to changes in the parameters, even generalizing to continuous and multiplayer games. Our findings provide insight into a wide range of symbiotic phenomena and help to unify the field of coevolutionary theory.National Institutes of Health (U.S.) (K99 Pathways to Independence Award

Relation between stability and resilience determines the performance of early warning signals under different environmental drivers

Shifting patterns of temporal fluctuations have been found to signal critical transitions in a variety of systems, from ecological communities to human physiology. However, failure of these early warning signals in some systems calls for a better understanding of their limitations. In particular, little is known about the generality of early warning signals in different deteriorating environments. In this study, we characterized how multiple environmental drivers influence the dynamics of laboratory yeast populations, which was previously shown to display alternative stable states [Dai et al., Science, 2012]. We observed that both the coefficient of variation and autocorrelation increased before population collapse in two slowly deteriorating environments, one with a rising death rate and the other one with decreasing nutrient availability. We compared the performance of early warning signals across multiple environments as “indicators for loss of resilience.” We find that the varying performance is determined by how a system responds to changes in a specific driver, which can be captured by a relation between stability (recovery rate) and resilience (size of the basin of attraction). Furthermore, we demonstrate that the positive correlation between stability and resilience, as the essential assumption of indicators based on critical slowing down, can break down in this system when multiple environmental drivers are changed simultaneously. Our results suggest that the stability–resilience relation needs to be better understood for the application of early warning signals in different scenarios.United States. National Institutes of Health (NIH DP2

Spatial dilemmas of diffusible public goods

The emergence of cooperation is a central question in evolutionary biology. Microorganisms often cooperate by producing a chemical resource (a public good) that benefits other cells. The sharing of public goods depends on their diffusion through space. Previous theory suggests that spatial structure can promote evolution of cooperation, but the diffusion of public goods introduces new phenomena that must be modeled explicitly. We develop an approach where colony geometry and public good diffusion are described by graphs. We find that the success of cooperation depends on a simple relation between the benefits and costs of the public good, the amount retained by a producer, and the average amount retained by each of the producer’s neighbors. These quantities are derived as analytic functions of the graph topology and diffusion rate. In general, cooperation is favored for small diffusion rates, low colony dimensionality, and small rates of decay of the public good. DOI: http://dx.doi.org/10.7554/eLife.01169.00

Dynamics of a producer-parasite ecosystem on the brink of collapse

Ecosystems can undergo sudden shifts to undesirable states, but recent studies with simple single species ecosystems have demonstrated that advance warning can be provided by the slowing down of population dynamics near a tipping point. However, it is not clear how this effect of critical slowing down will manifest in ecosystems with strong interactions between their components. Here we probe the dynamics of an experimental producer parasite ecosystem as it approaches a catastrophic collapse. Surprisingly, the producer population grows in size as the environment deteriorates, highlighting that population size can be a misleading measure of ecosystem stability. By analyzing the oscillatory producer parasite dynamics for over ~100 generations in multiple environmental conditions, we found that the collective ecosystem dynamics slows down as the tipping point is approached. Analysis of the coupled dynamics of interacting populations may therefore be necessary to provide advance warning of collapse in complex communities

Pairing off: a bottom‐up approach to the human gut microbiome

The human gut microbiome has been implicated in a variety of health outcomes, and extensive research has aimed to understand its composition and function, primarily via metagenomic analyses. An examination of how the microbiome develops and interacts through interspecies competition and cooperation has been lacking so far. In their recent work, Venturelli et al (2018) build a synthetic gut community and accurately predict its dynamics with a simple network of pairwise interactions

Migration alters oscillatory dynamics and promotes survival in connected bacterial populations

Migration influences population dynamics on networks, thereby playing a vital role in scenarios ranging from species extinction to epidemic propagation. While low migration rates prevent local populations from becoming extinct, high migration rates enhance the risk of global extinction by synchronizing the dynamics of connected populations. Here, we investigate this trade-off using two mutualistic strains of E. coli that exhibit population oscillations when co-cultured. In experiments, as well as in simulations using a mechanistic model, we observe that high migration rates lead to synchronization whereas intermediate migration rates perturb the oscillations and change their period. Further, our simulations predict, and experiments show, that connected populations subjected to more challenging antibiotic concentrations have the highest probability of survival at intermediate migration rates. Finally, we identify altered population dynamics, rather than recolonization, as the primary cause of extended survival.National Institutes of Health (U.S.) (Grant R01 GM102311-01)National Science Foundation (Award PHY-1055154)National Institutes of Health (U.S.) (Award GM085279-02)Alfred P. Sloan Foundation (Award BR2011-066)National Institutes of Health (U.S.) (Award DP2

Understanding microbial cooperation.

a b s t r a c t The field of microbial cooperation has grown enormously over the last decade, leading to improved experimental techniques and a growing awareness of collective behavior in microbes. Unfortunately, many of our theoretical tools and concepts for understanding cooperation fail to take into account the peculiarities of the microbial world, namely strong selection strengths, unique population structure, and non-linear dynamics. Worse yet, common verbal arguments are often far removed from the math involved, leading to confusion and mistakes. Here, we review the general mathematical forms of Price's equation, Hamilton's rule, and multilevel selection as they are applied to microbes and provide some intuition on these otherwise abstract formulas. However, these sometimes overly general equations can lack specificity and predictive power, ultimately forcing us to advocate for more direct modeling techniques