Pharmacogenomic and clinical data link non-pharmacokinetic metabolic dysregulation to drug side effect pathogenesis.

Drug side effects cause a significant clinical and economic burden. However, mechanisms of drug action underlying side effect pathogenesis remain largely unknown. Here, we integrate pharmacogenomic and clinical data with a human metabolic network and find that non-pharmacokinetic metabolic pathways dysregulated by drugs are linked to the development of side effects.

A community-driven global reconstruction of human metabolism.

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.

Model-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation.

Macrophages are central players in immune response, manifesting divergent phenotypes to control inflammation and innate immunity through release of cytokines and other signaling factors. Recently, the focus on metabolism has been reemphasized as critical signaling and regulatory pathways of human pathophysiology, ranging from cancer to aging, often converge on metabolic responses. Here, we used genome-scale modeling and multi-omics (transcriptomics, proteomics, and metabolomics) analysis to assess metabolic features that are critical for macrophage activation.

Linkage of organic anion transporter-1 to metabolic pathways through integrated "omics"-driven network and functional analysis.

The main kidney transporter of many commonly prescribed drugs (e.g. penicillins, diuretics, antivirals, methotrexate, and non-steroidal anti-inflammatory drugs) is organic anion transporter-1 (OAT1), originally identified as NKT (Lopez-Nieto, C.

A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonella Typhimurium LT2.

Background: Metabolic reconstructions (MRs) are common denominators in systems biology and represent biochemical, genetic, and genomic (BiGG) knowledge-bases for target organisms by capturing currently available information in a consistent, structured manner. Salmonella enterica subspecies I serovar Typhimurium is a human pathogen, causes various diseases and its increasing antibiotic resistance poses a public health problem.

Results: Here, we describe a community-driven effort, in which more than 20 experts in S.

Connecting extracellular metabolomic measurements to intracellular flux states in yeast.

Background: Metabolomics has emerged as a powerful tool in the quantitative identification of physiological and disease-induced biological states. Extracellular metabolome or metabolic profiling data, in particular, can provide an insightful view of intracellular physiological states in a noninvasive manner.

Results: We used an updated genome-scale metabolic network model of Saccharomyces cerevisiae, iMM904, to investigate how changes in the extracellular metabolome can be used to study systemic changes in intracellular metabolic states.

Understanding human metabolic physiology: a genome-to-systems approach.

The intricate nature of human physiology renders its study a difficult undertaking, and a systems biology approach is necessary to understand the complex interactions involved. Network reconstruction is a key step in systems biology and represents a common denominator because all systems biology research on a target organism relies on such a representation. With the recent development of genome-scale human metabolic networks, metabolic systems analysis is now possible and has initiated a shift towards human systems biology.

A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology.

Genomic data allow the large-scale manual or semi-automated assembly of metabolic network reconstructions, which provide highly curated organism-specific knowledge bases. Although several genome-scale network reconstructions describe Saccharomyces cerevisiae metabolism, they differ in scope and content, and use different terminologies to describe the same chemical entities. This makes comparisons between them difficult and underscores the desirability of a consolidated metabolic network that collects and formalizes the 'community knowledge' of yeast metabolism.

A genome-scale, constraint-based approach to systems biology of human metabolism.

The recent sequencing and annotation of the human genome enables a new era in biomedicine that will be based on an interdisciplinary, systemic approach to the elucidation and treatment of human disease. Reconstruction of genome-scale metabolic networks is an important part of this approach since networks represent the integration of diverse biological data such as genome annotations, high-throughput data, and legacy biochemical knowledge. This article will describe Homo sapiens Recon 1, a functionally tested, genome-scale reconstruction of human cellular metabolism, and its capabilities for facilitating the understanding of physiological and disease metabolic states.

Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox.

The manner in which microorganisms utilize their metabolic processes can be predicted using constraint-based analysis of genome-scale metabolic networks. Herein, we present the constraint-based reconstruction and analysis toolbox, a software package running in the Matlab environment, which allows for quantitative prediction of cellular behavior using a constraint-based approach. Specifically, this software allows predictive computations of both steady-state and dynamic optimal growth behavior, the effects of gene deletions, comprehensive robustness analyses, sampling the range of possible cellular metabolic states and the determination of network modules.

Global reconstruction of the human metabolic network based on genomic and bibliomic data.

Metabolism is a vital cellular process, and its malfunction is a major contributor to human disease. Metabolic networks are complex and highly interconnected, and thus systems-level computational approaches are required to elucidate and understand metabolic genotype-phenotype relationships. We have manually reconstructed the global human metabolic network based on Build 35 of the genome annotation and a comprehensive evaluation of >50 years of legacy data (i.