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Network reconstructions are a common denominator in systems biology. Bottom–up metabolic network reconstructions have been developed over the last 10 years. These reconstructions represent structured knowledge bases that abstract pertinent information on the biochemical transformations taking place within specific target organisms. The conversion of a reconstruction into a mathematical format facilitates a myriad of computational biological studies, including evaluation of network content, hypothesis testing and generation, analysis of phenotypic characteristics and metabolic engineering. To date, genome-scale metabolic reconstructions for more than 30 organisms have been published and this number is expected to increase rapidly. However, these reconstructions differ in quality and coverage that may minimize their predictive potential and use as knowledge bases. Here we present a comprehensive protocol describing each step necessary to build a high-quality genome-scale metabolic reconstruction, as well as the common trials and tribulations. Therefore, this protocol provides a helpful manual for all stages of the reconstruction process.

Key Points Many 'omics' data sets are becoming available for various model organisms that can be used to describe many aspects of the cell for a given time and/or condition. They can be broadly classified as components data, which describe the specific molecular contents of the cell; interactions data, which detail the connectivity between cellular components; or functional-states data, which reveal the overall behaviour, or phenotype, of the cell or system in response to genetic and/or environmental perturbations. Even though each of these genome-scale data types can be powerful on their own, researchers are gaining valuable additional insights into cellular phenomena through the integration of 'omics' data sets. The computational tools that have been developed for integrating 'omics' data generally tackle three specific tasks: first, identifying the network scaffold by delineating the connections that exist between cellular components; second, decomposing the network scaffold into its constituent parts in an attempt to understand the overall network structure; and third, developing cellular or system models to simulate and predict the network behaviour that gives rise to particular cellular phenotypes. In addition to the development of methods, many researchers are using 'omics' integration to drive studies that are aimed at delineating systems-wide behaviour. For example, many efforts have been devoted to using genome-scale data integration to completely map the cellular pathways that are responsible for the observed cellular responses to environmental perturbations or developmental events. In some cases, these studies have also led to the development of biomarkers, or patterns of cellular-component expression that are associated with medical disorders, such as various cancers. Researchers are also using omics integration to address fundamental evolutionary questions that were previously beyond the scope and scale of standard techniques. Specifically, omics data-integration techniques have been used to examine cellular differences that are associated with speciation, and other studies have used them to study selective pressures that are likely to have arisen due to cellular-network structure. The integration of omics data has primarily affected basic research efforts so far. Increasingly, however, this strategy is taking on significant roles in clinically relevant areas, as shown by its stimulation of the fields of toxicogenomics and nutrigenomics, which are applying genome-scale technologies and integrative analyses to problems in toxicology and nutrition, respectively. Even though many challenges related to data quality and accessibility remain, researchers continue to work towards meeting the ultimate future goals of employing these strategies to drug-development applications and in personalized medicine. Many genome-scale, or 'omics', data sets are becoming available for various model organisms. Although each of these data types is valuable on its own, further insights into whole systems can be gained through the integration of omics data sets. Various technologies can be used to produce genome-scale, or 'omics', data sets that provide systems-level measurements for virtually all types of cellular components in a model organism. These data yield unprecedented views of the cellular inner workings. However, this abundance of information also presents many hurdles, the main one being the extraction of discernable biological meaning from multiple omics data sets. Nevertheless, researchers are rising to the challenge by using omics data integration to address fundamental biological questions that would increase our understanding of systems as a whole.

Metabologenomics integrates metabolomics with other omics data types to comprehensively study the genetic and environmental factors that influence metabolism. These multi-omics data can be incorporated into genome-scale metabolic models (GEMs), which are highly curated knowledge bases that explicitly account for genes, transcripts, proteins and metabolites. By including all known biochemical reactions catalysed by enzymes and transporters encoded in the human genome, GEMs analyse and predict the behaviour of complex metabolic networks. Continued advancements to the scale and scope of GEMs — from cells and tissues to microbiomes and the whole body — have helped to design effective treatments and develop better diagnostic tools for metabolic diseases. Furthermore, increasing amounts of multi-omics data are incorporated into GEMs to better identify the underlying mechanisms, biomarkers and potential drug targets of metabolic diseases. Metabologenomics integrates multi-omics data into genome-scale metabolic models (GEMs) to analyse complex metabolic networks. Mardinoglu and Palsson review advancements in GEMs at the global, cell- and tissue-specific, microbiome and whole-body levels, with insights into their applications towards improving health care.

Key Points Our ability to reconstruct genome-scale metabolic networks in microbial cells from genomic and high-throughput data has grown substantially in recent years. There are currently more than 25 genome-scale metabolic reconstructions of microbial cells, and 6–10 more are being produced each year. An increasing number of research groups around the world are working on genome-scale reconstructions of metabolism in their target organism. There is no single source that practitioners can access to learn about and understand the reconstruction process. This Review details the data flows and work flows that underlie the reconstruction process and thus provides a basis for newcomers in the field. Biological network reconstructions continue to grow in scope and are expected to include transcriptional regulation and protein synthesis over the next few years. Expansion in scope will probably also include small RNAs and two-component signalling networks. Genome-scale reconstructions are a common denominator in systems biology of microorganisms and are reaching an advanced stage of development, which indicates that systems analysis of microbial functions and phenotypes will progress in the years to come. In this Review, Bernhard Palsson and colleagues describe the steps that are necessary for reconstruction of genomic-scale biochemical reaction networks based on systems analysis of microorganisms. This article provides guidelines for the reconstruction of metabolic, transcription and translation and transcriptional regulatory networks. Systems analysis of metabolic and growth functions in microbial organisms is rapidly developing and maturing. Such studies are enabled by reconstruction, at the genomic scale, of the biochemical reaction networks that underlie cellular processes. The network reconstruction process is organism specific and is based on an annotated genome sequence, high-throughput network-wide data sets and bibliomic data on the detailed properties of individual network components. Here we describe the process that is currently used to achieve comprehensive network reconstructions and discuss how these reconstructions are curated and validated. This Review should aid the growing number of researchers who are carrying out reconstructions for particular target organisms.

Background Genome-scale metabolic reconstructions under the Constraint Based Reconstruction and Analysis (COBRA) framework are valuable tools for analyzing the metabolic capabilities of organisms and interpreting experimental data. As the number of such reconstructions and analysis methods increases, there is a greater need for data uniformity and ease of distribution and use. Description We describe BiGG, a knowledgebase of Biochemically, Genetically and Genomically structured genome-scale metabolic network reconstructions. BiGG integrates several published genome-scale metabolic networks into one resource with standard nomenclature which allows components to be compared across different organisms. BiGG can be used to browse model content, visualize metabolic pathway maps, and export SBML files of the models for further analysis by external software packages. Users may follow links from BiGG to several external databases to obtain additional information on genes, proteins, reactions, metabolites and citations of interest. Conclusions BiGG addresses a need in the systems biology community to have access to high quality curated metabolic models and reconstructions. It is freely available for academic use at http://bigg.ucsd.edu .

Growth is a fundamental process of life. Growth requirements are well‐characterized experimentally for many microbes; however, we lack a unified model for cellular growth. Such a model must be predictive of events at the molecular scale and capable of explaining the high‐level behavior of the cell as a whole. Here, we construct an ME‐Model for Escherichia coli —a genome‐scale model that seamlessly integrates metabolic and gene product expression pathways. The model computes ∼80% of the functional proteome (by mass), which is used by the cell to support growth under a given condition. Metabolism and gene expression are interdependent processes that affect and constrain each other. We formalize these constraints and apply the principle of growth optimization to enable the accurate prediction of multi‐scale phenotypes, ranging from coarse‐grained (growth rate, nutrient uptake, by‐product secretion) to fine‐grained (metabolic fluxes, gene expression levels). Our results unify many existing principles developed to describe bacterial growth. A constraint‐based approach for integrative modeling of metabolism and gene expression is developed. New constraints on molecular catalysis increase both the accuracy and scope of computable phenotypes corresponding to optimal microbial growth. Synopsis A constraint‐based approach for integrative modeling of metabolism and gene expression is developed. New constraints on molecular catalysis increase both the accuracy and scope of computable phenotypes corresponding to optimal microbial growth. An integrated network of metabolic and gene expression pathways is built for E. coli . A growth model is developed by adding demands and constraints on molecular catalysis. Model yields accurate predictions of growth phenotypes from molecules to whole cell. A few basic principles underlie growth rate optimization at the systems level.

The number and scope of methods developed to interrogate and use metabolic network reconstructions has significantly expanded over the past 15 years. In particular, Escherichia coli metabolic network reconstruction has reached the genome scale and been utilized to address a broad spectrum of basic and practical applications in five main categories: metabolic engineering, model-directed discovery, interpretations of phenotypic screens, analysis of network properties and studies of evolutionary processes. Spurred on by these accomplishments, the field is expected to move forward and further broaden the scope and content of network reconstructions, develop new and novel in silico analysis tools, and expand in adaptation to uses of proximal and distal causation in biology. Taken together, these efforts will solidify a mechanistic genotype-phenotype relationship for microbial metabolism.

Recent advances in structural bioinformatics have enabled the prediction of protein-drug off-targets based on their ligand binding sites. Concurrent developments in systems biology allow for prediction of the functional effects of system perturbations using large-scale network models. Integration of these two capabilities provides a framework for evaluating metabolic drug response phenotypes in silico. This combined approach was applied to investigate the hypertensive side effect of the cholesteryl ester transfer protein inhibitor torcetrapib in the context of human renal function. A metabolic kidney model was generated in which to simulate drug treatment. Causal drug off-targets were predicted that have previously been observed to impact renal function in gene-deficient patients and may play a role in the adverse side effects observed in clinical trials. Genetic risk factors for drug treatment were also predicted that correspond to both characterized and unknown renal metabolic disorders as well as cryptic genetic deficiencies that are not expected to exhibit a renal disorder phenotype except under drug treatment. This study represents a novel integration of structural and systems biology and a first step towards computational systems medicine. The methodology introduced herein has important implications for drug development and personalized medicine.

Staphylococcus aureus is a preeminent bacterial pathogen capable of colonizing diverse ecological niches within its human host. We describe here the pangenome of S. aureus based on analysis of genome sequences from 64 strains of S. aureus spanning a range of ecological niches, host types, and antibiotic resistance profiles. Based on this set, S. aureus is expected to have an open pangenome composed of 7,411 genes and a core genome composed of 1,441 genes. Metabolism was highly conserved in this core genome; however, differences were identified in amino acid and nucleotide biosynthesis pathways between the strains. Genome-scale models (GEMs) of metabolismwere constructed for the 64 strains of S. aureus. These GEMs enabled a systems approach to characterizing the core metabolic and panmetabolic capabilities of the S. aureus species. All models were predicted to be auxotrophic for the vitamins niacin (vitamin B3) and thiamin (vitamin B1), whereas strain-specific auxotrophies were predicted for riboflavin (vitamin B2), guanosine, leucine, methionine, and cysteine, among others. GEMs were used to systematically analyze growth capabilities in more than 300 different growth-supporting environments. The results identified metabolic capabilities linked to pathogenic traits and virulence acquisitions. Such traits can be used to differentiate strains responsible for mild vs. severe infections and preference for hosts (e.g., animals vs. humans). Genome-scale analysis of multiple strains of a species can thus be used to identify metabolic determinants of virulence and increase our understanding of why certain strains of this deadly pathogen have spread rapidly throughout the world.

Flux balance analysis is a mathematical approach for analyzing the flow of metabolites through a metabolic network. This primer covers the theoretical basis of the approach, several practical examples and a software toolbox for performing the calculations.