Predicted Metabolic Function of the Gut Microbiota of Drosophila melanogaster.

An important goal for many nutrition-based microbiome studies is to identify the metabolic function of microbes in complex microbial communities and their impact on host physiology. This research can be confounded by poorly understood effects of community composition and host diet on the metabolic traits of individual taxa. Here, we investigated these multiway interactions by constructing and analyzing metabolic models comprising every combination of five bacterial members of the gut microbiome (from single taxa to the five-member community of and species) under three nutrient regimes.

A robust and efficient method for estimating enzyme complex abundance and metabolic flux from expression data.

A major theme in constraint-based modeling is unifying experimental data, such as biochemical information about the reactions that can occur in a system or the composition and localization of enzyme complexes, with high-throughput data including expression data, metabolomics, or DNA sequencing. The desired result is to increase predictive capability and improve our understanding of metabolism. The approach typically employed when only gene (or protein) intensities are available is the creation of tissue-specific models, which reduces the available reactions in an organism model, and does not provide an objective function for the estimation of fluxes.

Dynamic epistasis under varying environmental perturbations.

Epistasis describes the phenomenon that mutations at different loci do not have independent effects with regard to certain phenotypes. Understanding the global epistatic landscape is vital for many genetic and evolutionary theories. Current knowledge for epistatic dynamics under multiple conditions is limited by the technological difficulties in experimentally screening epistatic relations among genes.

Computational approaches for understanding energy metabolism.

There has been a surge of interest in understanding the regulation of metabolic networks involved in disease in recent years. Quantitative models are increasingly being used to interrogate the metabolic pathways that are contained within this complex disease biology. At the core of this effort is the mathematical modeling of central carbon metabolism involving glycolysis and the citric acid cycle (referred to as energy metabolism).

Dynamic epistasis for different alleles of the same gene.

Epistasis refers to the phenomenon in which phenotypic consequences caused by mutation of one gene depend on one or more mutations at another gene. Epistasis is critical for understanding many genetic and evolutionary processes, including pathway organization, evolution of sexual reproduction, mutational load, ploidy, genomic complexity, speciation, and the origin of life. Nevertheless, current understandings for the genome-wide distribution of epistasis are mostly inferred from interactions among one mutant type per gene, whereas how epistatic interaction partners change dynamically for different mutant alleles of the same gene is largely unknown.

Yeast 5 - an expanded reconstruction of the Saccharomyces cerevisiae metabolic network.

Background: Efforts to improve the computational reconstruction of the Saccharomyces cerevisiae biochemical reaction network and to refine the stoichiometrically constrained metabolic models that can be derived from such a reconstruction have continued since the first stoichiometrically constrained yeast genome scale metabolic model was published in 2003. Continuing this ongoing process, we have constructed an update to the Yeast Consensus Reconstruction, Yeast 5. The Yeast Consensus Reconstruction is a product of efforts to forge a community-based reconstruction emphasizing standards compliance and biochemical accuracy via evidence-based selection of reactions.

Detection of heterozygous mutations in the genome of mismatch repair defective diploid yeast using a Bayesian approach.

DNA replication errors that escape polymerase proofreading and mismatch repair (MMR) can lead to base substitution and frameshift mutations. Such mutations can disrupt gene function, reduce fitness, and promote diseases such as cancer and are also the raw material of molecular evolution. To analyze with limited bias genomic features associated with DNA polymerase errors, we performed a genome-wide analysis of mutations that accumulate in MMR-deficient diploid lines of Saccharomyces cerevisiae.