Trend and fluctuations: analysis and design of population dynamics measurements in replicate ecosystems

Higher-Order Interaction between Species Inhibits Bacterial Invasion of a Phototroph-Predator Microbial Community

The composition of an ecosystem is thought to be important for determining its resistance to invasion. Studies of natural ecosystems, from plant to microbial communities, have found that more diverse communities are more resistant to invasion. In some cases, more diverse communities resist invasion by more completely consuming the resources necessary for the invader. We show that Escherichia coli can successfully invade cultures of the alga Chlamydomonas reinhardtii (phototroph) or the ciliate Tetrahymena thermophila (predator) but cannot invade a community where both are present. The invasion resistance of the algae-ciliate community arises from a higher-order interaction between species (interaction modification) that is unrelated to resource consumption. We show that the mode of this interaction is the algal inhibition of bacterial aggregation, which leaves bacteria vulnerable to predation. This mode requires both the algae and the ciliate to be present and provides an example of invasion resistance through an interaction modification.

Single microcolony diffusion analysis in Pseudomonas aeruginosa biofilms

The influence of the biofilm matrix on molecular diffusion is commonly hypothesized to be responsible for emergent characteristics of biofilms such as nutrient trapping, signal accumulation and antibiotic tolerance. Hence quantifying the molecular diffusion coefficient is important to determine whether there is an influence of biofilm microenvironment on the mobility of molecules. Here, we use single plane illumination microscopy fluorescence correlation spectroscopy (SPIM-FCS) to obtain 3D diffusion coefficient maps with micrometre spatial and millisecond temporal resolution of entire Pseudomonas aeruginosa microcolonies. We probed how molecular properties such as size and charge as well as biofilm properties such as microcolony size and depth influence diffusion of fluorescently labelled dextrans inside biofilms. The 2 MDa dextran showed uneven penetration and a reduction in diffusion coefficient suggesting that the biofilm acts as a molecular sieve. Its diffusion coefficient was negatively correlated with the size of the microcolony. Positively charged dextran molecules and positively charged antibiotic tobramycin preferentially partitioned into the biofilm and remained mobile inside the microcolony, albeit with a reduced diffusion coefficient. Lastly, we measured changes of diffusion upon induction of dispersal and detected an increase in diffusion coefficient inside the biofilm before any loss of biomass. Thus, the change in diffusion is a proxy to detect early stages of dispersal. Our work shows that 3D diffusion maps are very sensitive to physiological changes in biofilms, viz. dispersal. However, this study also shows that diffusion, as mediated by the biofilm matrix, does not account for the high level of antibiotic tolerance associated with biofilms.

Microbial biospherics: The experimental study of ecosystem function and evolution

Awareness that our planet is a self-supporting biosphere with sunlight as its major source of energy for life has resulted in a long-term historical fascination with the workings of self-supporting ecological systems. However, the studies of such systems have never entered the canon of ecological or evolutionary tools and instead, have led a fringe existence connected to life support system engineering and space travel. We here introduce a framework for a renaissance in biospherics based on the study of matter-closed, energy-open ecosystems at a microbial level (microbial biospherics). Recent progress in genomics, robotics, and sensor technology makes the study of closed systems now much more tractable than in the past, and we argue that the time has come to emancipate the study of closed systems from this fringe context and bring them into a mainstream approach for studying ecosystem processes. By permitting highly replicated long-term studies, especially on predetermined and simplified systems, microbial biospheres offer the opportunity to test and develop strong hypotheses about ecosystem function and the ecological and evolutionary determinants of long-term system failure or persistence. Unlike many sciences, ecosystem ecology has never fully embraced a reductionist approach and has remained focused on the natural world in all its complexity. We argue that a reductionist approach to ecosystem ecology, using microbial biospheres, based on a combination of theory and the replicated study of much simpler self-enclosed microsystems could pay huge dividends.

Monitoring microbial communities using light sheet fluorescence microscopy

Microbes often live in dense, dynamic, multi-species communities whose architecture and function are intimately intertwined. Imaging these complex, three-dimensional ensembles presents considerable technical challenges, however. In this review, I describe light sheet fluorescence microscopy, a technique that enables rapid acquisition of three-dimensional images over large fields of view and over long durations, and I highlight recent applications of this method to microbial systems that include artificial closed ecosystems, bacterial biofilms, and gut microbiota. I comment also on the history of light sheet imaging and the many variants of the method. Light sheet techniques have tremendous potential for illuminating the workings of microbial communities, a potential that is just beginning to be realized.

Large pseudocounts and L2-norm penalties are necessary for the mean-field inference of Ising and Potts models

The mean-field (MF) approximation offers a simple, fast way to infer direct interactions between elements in a network of correlated variables, a common, computationally challenging problem with practical applications in fields ranging from physics and biology to the social sciences. However, MF methods achieve their best performance with strong regularization, well beyond Bayesian expectations, an empirical fact that is poorly understood. In this work, we study the influence of pseudocount and L(2)-norm regularization schemes on the quality of inferred Ising or Potts interaction networks from correlation data within the MF approximation. We argue, based on the analysis of small systems, that the optimal value of the regularization strength remains finite even if the sampling noise tends to zero, in order to correct for systematic biases introduced by the MF approximation. Our claim is corroborated by extensive numerical studies of diverse model systems and by the analytical study of the m-component spin model for large but finite m. Additionally, we find that pseudocount regularization is robust against sampling noise and often outperforms L(2)-norm regularization, particularly when the underlying network of interactions is strongly heterogeneous. Much better performances are generally obtained for the Ising model than for the Potts model, for which only couplings incoming onto medium-frequency symbols are reliably inferred.