Special Session on Multi-Scale Modeling of Microbial Communities
With the increase in computational power, it has been increasingly feasible to perform large multi-scale simulations of processes in microbial communities. Microbial populations have wide ranging influence that extends well beyond the length-scale of a single microbe. Alternations to the microbial populations have ramifications for larger-scale macro-ecological populations affecting agriculture, human health, and global nutrient cycling. Traditional approaches to modeling communities and ecosystems that focus on a single scale have important limitations. They make it hard to incorporate realistic assumptions about the biology of microorganisms, cannot handle the extensive genetic and phenotypic heterogeneity of microbial population or the spatial and temporal complexity of their habitats. Multi-scale modeling enables us to investigate the emergent ecological properties stemming from interactions among microbes and their interactions with the environment.
The goal of this session is to make computer scientists and computational biologists with interest in tool development or theoretical modeling aware of the diverse and rapidly expanding opportunities in this area. An accompanying workshop will introduce already existing tools that can provide a foundation for a next generation of microbial community analyses. Invited and contributed talks will present different examples of how multi-scale modeling of microbial communities has been successfully used to provide new insight into the functioning and dynamics of communities.
Prof. Murat Eren
Department of Medicine
Title: Metapangenomics: Linking the genomic traits and niche partitioning of microbial populations
Abstract: Rapidly growing number of microbial isolates, metagenome-assembled genomes, single-cell genomes, and the increasing availability of environmental metagenomes provide new opportunities to investigate the functioning and the ecology of microbial populations in an unprecedented scale. By characterizing shared and accessory genes across multiple genomes, pangenomics elucidates functional relationships between closely related microorganisms. Although linking pangenomes and metagenomes could offer new insights into the functional basis of microbial niche partitioning and fitness, these two essential endeavors of microbiology have never been combined from a genomic perspective. The aim of this talk is to offer an analysis and visualization strategy that provides a framework to interpret pangenomes in conjunction with metagenomes.
Prof. Jason Kwan
School of Pharmacy
University of Wisconsin-Madison
Title: Integrating metagenomics and metatranscriptomics to understand microbial communities
Abstract: Microbes are prevalent most environments and are associated with all higher organisms, where they are thought to influence health and disease. Most natural microbial ecosystems contain a complex mixture of species, including many that have never been cultured in the laboratory. The inability to culture most environmental microbes complicates the study of how the individual species in microbial consortia interact, and how communities assemble and are stably maintained. Such knowledge would be a fundamental building block to help us influence microbiome function in the environment and in human health. Motivated by our interest in the expression of small bioactive molecules in complex bacterial communities, we devised a new method to automatically separate microbial genomes from culture-independent shotgun metagenome sequencing data (“binning”). We have already used this method to assemble between 80 and 130 microbial genome bins from single marine sponge metagenomes. I will present how we are combining this method with metatranscriptomics to study the chemical ecology of small molecules in marine sponges, which often harbor complex microbiomes rich in biosynthetic potential. In particular, I will outline our search for bacterial biofilm inhibitors from marine sponges, made by the sponge’s symbiotic microbiome.
Prof. Zan Luthey-Schulten
William and Janet Lycan Professor of Chemistry
School of Chemical Sciences
University of Illinois
Title: Stochastic Simulations of Cellular Processes: From Single Cells to Colonies
Abstract: Cryo-electron tomography (cryo-ET) has rapidly emerged as a powerful tool to investigate the three-dimensional spatial organization within cells. In parallel, the GPU-based technology to perform spatially resolved stochastic simulations of whole cells has advanced, allowing the simulation of complex biochemical networks over cell cycle timescales using data taken from -omics, single molecule experiments, and in vitro kinetics. By using real cell geometry derived from cryo-ET data, we have the opportunity to imbue these highly detailed structural data—frozen in time—with realistic biochemical dynamics and investigate how cell structure affects the behavior of the embedded chemical reaction network. Here we present several examples that illustrate the challenges and techniques involved in integrating structural and other experimental data into stochastic simulations: ribosome biogenesis in replicating E.coli cells, the emergence of metabolic cooperativity in a dense bacterial colony, and lastly a simple stochastic model of an inducible genetic switch in yeast.
Prof. William Harcombe
Department of Ecology, Evolution, and Behavior
University of Minnesota
Title: Linking intracellular metabolism to microbial ecosystem dynamics in structured environments.
Abstract: The inter-species exchange of metabolites plays a key role in the spatio-temporal dynamics of microbial communities. This raises the question whether ecosystem-level behavior of communities can be predicted using genome-scale models of metabolism for multiple organisms. We developed a modeling framework that integrates dynamic flux balance analysis with diffusion in a structured environment. We quantitatively predicted, and experimentally confirmed, the spatio-temporal dynamics in 2 and 3-species communities. We further used this modeling approach to elucidate how location mediates interactions between colonies. Finally we investigated the connection between ecological and genetic robustness. Our work highlights the complex nature of metabolic interactions in microbial communities, while at the same time demonstrating their predictability
Prof. Kalin Vetsigian
Department of Bacteriology
University of Wisconsin-Madison
Title: Multi-scale modeling of microbial eco-evolutionary dynamics
Abstract: A central challenge to understanding microbial community dynamics is that microbes exhibit complex, context-dependent interactions that can evolve fast. It will be argued that multi-scale modeling is an effective way of handling both context dependence and evolution and can lead to new insights about the types of dynamics that can arise through an interplay between ecology and evolution. This approach will be illustrated through an investigation of the dynamics that results when bacteria can freely evolve their investment into growth, antibiotic production and degradation with respect to multiple antibiotics. The study revealed that the dynamics can readily arrive at long-persistent diverse communities belonging to several different eco-evolutionary classes.
Prof. Nicholas Chia
Department of Surgery/Biomedical Engineering & Physiology
Title: Modeling the Microbiome in Colorectal Cancer: emerging approaches to an old problem
Abstract: The necessity of in silico modeling and hypothesis testing in the microbiome sciences is a product of the diversity of species, functions, and genes that potentially interact with the host to cause or ameliorate disease. Indeed, the gastrointestinal (GI) tract is a hotbed of metabolic cross-species interactions, a fact that has lead to the emergence of multiple hypotheses potentially linking the microbes and metabolites to GI diseases such as colorectal cancer (CRC). Here, we present our work on the role of microbially-produced hydrogen sulfide on the etiology of CRC. In our approach, we show how high-throughput sequencing and metabolomics technologies enable in silico modeling of metabolite production within the GI tract and how this can be linked with the carcinogenesis of CRC.