Defining and Evaluating Microbial Contributions to Metabolite Variation in Microbiome-Metabolome Association Studies.

Correlation-based analysis of paired microbiome-metabolome data sets is becoming a widespread research approach, aiming to comprehensively identify microbial drivers of metabolic variation. To date, however, the limitations of this approach and other microbiome-metabolome analysis methods have not been comprehensively evaluated. To address this challenge, we have introduced a mathematical framework to quantify the contribution of each taxon to metabolite variation based on uptake and secretion fluxes.

Emergent biosynthetic capacity in simple microbial communities.

Microbes have an astonishing capacity to transform their environments. Yet, the metabolic capacity of a single species is limited and the vast majority of microorganisms form complex communities and join forces to exhibit capabilities far exceeding those achieved by any single species. Such enhanced metabolic capacities represent a promising route to many medical, environmental, and industrial applications and call for the development of a predictive, systems-level understanding of synergistic microbial capacity.

Towards a predictive systems-level model of the human microbiome: progress, challenges, and opportunities.

The human microbiome represents a vastly complex ecosystem that is tightly linked to our development, physiology, and health. Our increased capacity to generate multiple channels of omic data from this system, brought about by recent advances in high throughput molecular technologies, calls for the development of systems-level methods and models that take into account not only the composition of genes and species in a microbiome but also the interactions between these components. Such models should aim to study the microbiome as a community of species whose metabolisms are tightly intertwined with each other and with that of the host, and should be developed with a view towards an integrated, comprehensive, and predictive modeling framework.

Epistasis from functional dependence of fitness on underlying traits.

Epistasis between mutations in two genes is thought to reflect an interdependence of their functions. While sometimes epistasis is predictable using mechanistic models, its roots seem, in general, hidden in the complex architecture of biological networks. Here, we ask how epistasis can be quantified based on the mathematical dependence of a system-level trait (e.

Comparative determination of biomass composition in differentially active metabolic States.

Flux Balance Analysis (FBA) has been successfully applied to facilitate the understanding of cellular metabolism in model organisms. Standard formulations of FBA can be applied to large systems, but the accuracy of predictions may vary significantly depending on environmental conditions, genetic perturbations, or complex unknown regulatory constraints. Here we present an FBA-based approach to infer the biomass compositions that best describe multiple physiological states of a cell.

Prediction of orthologous relationship by functionally important sites.

Making accurate functional predictions plays an important role in the era of proteomics. Reliable functional information can be extracted from orthologs in other species when annotating an unknown gene. Here a site-based approach called PORFIS is proposed to predict orthologous relationship.