Metapopulation theory has a long history in ecology, where it is used to describe the dynamics of organisms that frequent patchy and ephemeral habitats. Recently, metapopulation theory has been used to model host-associated microbiomes, with hosts acting as patches, and microbes as dispersing organisms. Extending this framework to scenarios where the hosts themselves exist as metapopulations gives what we term 'nested metapopulations'. Although similar models have been studied in epidemiology, the focus is typically the effect of limited dispersal among patches on disease spread. What is not considered, however, is the other key premise of ecological metapopulations - frequent extinction events at the patch scale. We explore models of nested metapopulations, focusing on how host-scale metapopulation structure and microbe-scale metapopulation structure interact to govern the distribution of microbes across both hosts and the habitat patches in which they live.
University of Idaho
"Eco-evolutionary Dynamics of Microbial Communities"
Microbes form complex communities which have profound effects on host health status. Understanding the evolutionary dynamics of microbial communities is a key step towards the goal of manipulating microbiomes to promote beneficial states. While interactions within a microbial community and between microbes and their environment collectively determine the community composition and population dynamics, we are often concerned with traits or functions of a microbiome that link more directly to host health. To study how traits of a microbiome are impacted by eco-evolutionary dynamics, we recast a classic resource-mediated population dynamic model into a population genetic framework which incorporates traits. Using simple communities as example, we illustrate how natural selection, mutation, and shifts in the environment work together to produce changes in trait values over time.
Instituto Gulbenkian de Ciência
"A critical transition in N-strain co-colonization dynamics"
Interacting systems with multiple strains generate epidemiological, ecological and evolutionary dynam- ics. These dynamics are typically hard to analyze, especially for high number of strains and population structure. Diversity in interaction traits enables the strains to create dynamically their niches for growth and persistence, ‘engineer’ and respond to their common environment. How such a network of interactions with others mediates collective coexistence remains hard to understand, and integrate with intervention effects such as drugs and vaccines. Furthermore, the gradients shaping stability and complexity in such systems remain poorly understood. In a mathematical study, we present a new analytic framework for an N-strain SIS epidemiological system with altered susceptibilities to co-colonization/co-infection between strains. We map the multi- strain SIS dynamics to a replicator equation for N frequencies using separation of timescales. This framework enables explicit examination of the key drivers of competition and coexistence regimes in such a system. We find the ratio of single to co-colonization μ critically determines the type of equilibrium and number of coexisting strains. This key quantity in the model encodes a trade-off between overall transmission intensity R0 and mean interaction coefficient in strain space k, and links our model with the stress-gradient hypothesis (SGH) in ecology. I will show how this co-colonization model provides fresh insights for understanding critical transitions in community dynamics potentiated by mean-field and environmental gradients.
"Kinetic fingerprint of bacterial depletion indicates phage synergy"
Phages are viruses that infect bacteria which leads to complex dynamics depending on their specific, potentially changing life styles: Lysogenic phages integrate into bacterial genomes and propagate through bacterial replication. Lytic phages replicate within their host cells and destroy them during phage release making them highly specific anti-microbial agents. Bacterial and phage population dynamics do not only depend on life styles but also on the details of the infection process of a specific bacterium and corresponding phage: Some phages can enter a bacterium at multiple sites whereas others are restricted to a single or few entry points. Bacteria further defend against phage induced lysis by various mechanisms including adaptive CRISPR-Cas immunity. Generally, the interaction between lytic phages with their corresponding susceptible bacteria is followed by rapid emergence of bacterial resistance against these phages. The time scales for the emergence (and sustainment) of resistance as well as the specific temporal evolution of bacterial and phage population sizes depend on the characteristics both of the specific phage and corresponding bacterial strain. This pattern can be seen as a kinetic fingerprint which gives insights into the underlying dynamics including binding kinetics and mechanisms of bacterial resistance and phage evasion. In studying the interaction between Klebsiella pneumoniae and its corresponding phage we see enhanced inter-phage synergy leading to faster depletion of bacterial populations than expected from simple mass action kinetics. Within our modelling framework we discuss potential underlying mechanisms of phage binding and synergy as well as the relevance of the kinetic fingerprint in characterizing the interaction between bacteria and phages.
"Multiscale Modelling of the Colonic Microbiota in Infants"
Nearly immediately after birth, a complex and dynamic ecosystem forms in the human gastrointestinal tract. The characteristics of this system influence the infants health in both the short- and long-term. It differs in striking ways from that found in adults,both in composition and in dynamics. The first few days generally feature an initial dominance of facultative anaerobic species such as Enterobacteriaceae, most often followed by a dominance of anaerobic species, particularly Bifidobacteriaceae. While the influence of oxygen in this succession is often hypothesised, there is no clear view of the impact or mechanism of this influence. We use a multi-scale spatiotemporal model of the infant colon to simulate the effects of variations in initial oxygen concentration on the composition and metabolic activity of the microbiota from birth to three weeks of age. Using flux balance analysis with molecular crowding on a consortium of genome-scale metabolic models from the AGORA project, we calculate species-specific bacterial fluxes for different locations and time points at a high resolution. The resulting fluxes are integrated together into a model of the ecosystem that feeds back into the flux calculations . The model takes into account the nutrition and development of the infant, and can so give insight and produce predictions for the composition and metabolite formation of the infant microbiota over time and under different conditions. We find that the initial presence of oxygen can explain the specific early dominance of Enterobacteriaceae and the succession by Bifidobacteriaceae out of a broad consortium of infant microbiota species. This is derived solely from the genome-derived differences in oxygen metabolism between species, without having to take into account oxygen toxicity. We also show a complex network of spatial separation and metabolic interactions emerging within the infant gut. Our general aim is to reach a deeper understanding of the major metabolic influences, such as prebiotics and nutrition as a whole, on the development of the infant microbiota. This in turn is the first step towards a more comprehensive understanding of the formation of a steady state adult GI-tract microbiota. This research was financially supported by Friesland Campina.