The MetaFlux software supports creating, executing, and solving quantitative metabolic flux models using flux balance analysis (FBA). MetaFlux offers four modes of operation: (1) solving mode executes an FBA model for an individual organism or for an organism community, (2) gene knockout mode executes an FBA model with one or many gene knockouts, (3) development mode assists the user in creating and improving FBA models, and (4) flux variability analysis mode generates a report of the robustness of an FBA model. MetaFlux also solves dynamic FBA (dFBA) for both individual organisms and communities of organisms. MetaFlux can be used in two different environments: on your local computer, which requires the installation of the Pathway Tools software, or through the web, which does not require installation of Pathway Tools. On your local computer, MetaFlux offers all four modes of operation, whereas the web environment provides only the solving mode.Several visualization tools are available to analyze model solutions. The Cellular Overview tool graphically shows the reaction fluxes on an organism's metabolic map once a model is solved. The Omics Dashboard provides a hierarchical approach to visualizing reaction fluxes, organized by metabolic subsystems. For a community of organisms, plotting of accumulated biomasses and metabolites can be performed using the Gnuplot tool.In this chapter, we present eight methods using MetaFlux. Five solving mode methods illustrate execution of models for individual organisms and for organism communities. One method illustrates the gene knockout mode. Two methods for the development mode illustrate steps for developing new metabolic models.

We present new developments in the bioinformatics algorithms that reconstruct the metabolic network of an organism from its sequenced genome, and that convert that metabolic network into a quantitative metabolic model. These algorithms are implemented in the Pathway Tools software. The Pathway Tools metabolic reconstruction process begins with an annotated genome; its first step infers the subset of metabolic reactions in the MetaCyc database that are catalyzed by enzymes within the genome. Reactome inference is based on the enzyme name and/or EC numbers within the genome annotation. The second step uses the inferred reactome to infer the subset of MetaCyc metabolic pathways present in the organism using a pathway scoring algorithm. Recent improvements to these algorithms are based on a large increase in the number of EC numbers in recent years, growth in the reaction (+30%) and pathway (+22%) content of the MetaCyc database, and development of a new pathway scoring algorithm that makes use of key reactions that must be either present or absent, and pathway taxonomic range information. The resulting metabolic reconstruction is available in the form of a Pathway/Genome Database (PGDB) that can be queried and visualized via an extensive set of online tools. The BioCyc.org website contains such PGDBs for 14,700 organisms.The MetaFlux component of Pathway Tools enables creation and execution of quantitative steady‐state metabolic flux models. Several steps are used to refine a PGDB to a quantitative steady‐state metabolic flux model. First, we must identify a set of nutrients that support growth of the organism, and we must identify a set of biomass metabolites that form the molecular building blocks of the organism. Next we ensure that the reactome within the PGDB can synthesize each of the biomass metabolites from the provided nutrients. Often the reactome contains gaps due to incompleteness in the genome annotation. We present a new taxonomic gap‐filling algorithm with significantly improved accuracy: when evaluated on randomly degraded variants of the Escherichia coli metabolic model, it shows an average F1 score of 99.0, compared to 91.0 for the previous Pathway Tools gap filler, and compared to 80.3 for a basic gap filler. Once gaps have been filled in the reaction network, MetaFlux can execute the model, using flux‐balance analysis to compute flux rates for all reactions in the model. Those fluxes can be interpreted by visualizing them on a whole‐organism metabolic network diagram. MetaFlux can also simulate the effects of single and double‐gene knock‐out mutants on the model. And MetaFlux can execute groups of models that simulate the growth of organism communities.Support or Funding InformationThis work was supported by the National Institutes of Health under grants GM080746 and GM075742. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Central metabolism is a topic that has been studied for decades, and yet, this process is still not fully understood in Escherichia coli, perhaps the most amenable and well-studied model organism in biology. To further our understanding, we used a high-throughput method to measure the growth kinetics of each of 3,796 E. coli single-gene deletion mutants in 30 different carbon sources. In total, there were 342 genes (9.01%) encompassing a breadth of biological functions that showed a growth phenotype on at least 1 carbon source, demonstrating that carbon metabolism is closely linked to a large number of processes in the cell. We identified 74 genes that showed low growth in 90% of conditions, defining a set of genes which are essential in nutrient-limited media, regardless of the carbon source. The data are compiled into a Web application, Carbon Phenotype Explorer (CarPE), to facilitate easy visualization of growth curves for each mutant strain in each carbon source. Our experimental data matched closely with the predictions from the EcoCyc metabolic model which uses flux balance analysis to predict growth phenotypes. From our comparisons to the model, we found that, unexpectedly, phosphoenolpyruvate carboxylase (ppc) was required for robust growth in most carbon sources other than most trichloroacetic acid (TCA) cycle intermediates. We also identified 51 poorly annotated genes that showed a low growth phenotype in at least 1 carbon source, which allowed us to form hypotheses about the functions of these genes. From this list, we further characterized the ydhC gene and demonstrated its role in adenosine efflux.IMPORTANCE While there has been much study of bacterial gene dispensability, there is a lack of comprehensive genome-scale examinations of the impact of gene deletion on growth in different carbon sources. In this context, a lot can be learned from such experiments in the model microbe Escherichia coli where much is already understood and there are existing tools for the investigation of carbon metabolism and physiology (1). Gene deletion studies have practical potential in the field of antibiotic drug discovery where there is emerging interest in bacterial central metabolism as a target for new antibiotics (2). Furthermore, some carbon utilization pathways have been shown to be critical for initiating and maintaining infection for certain pathogens and sites of infection (3-5). Here, with the use of high-throughput solid medium phenotyping methods, we have generated kinetic growth measurements for 3,796 genes under 30 different carbon source conditions. This data set provides a foundation for research that will improve our understanding of genes with unknown function, aid in predicting potential antibiotic targets, validate and advance metabolic models, and help to develop our understanding of E. coli metabolism.

Genome-scale metabolic models have been utilized extensively in the study and engineering of the organisms they describe. Here we present the analysis of a published dataset from pooled transposon mutant fitness experiments as an approach for improving the accuracy and gene-reaction associations of a metabolic model for Zymomonas mobilis ZM4, an industrially relevant ethanologenic organism with extremely high glycolytic flux and low biomass yield. Gene essentiality predictions made by the draft model were compared to data from individual pooled mutant experiments to identify areas of the model requiring deeper validation. Subsequent experiments showed that some of the discrepancies between the model and dataset were caused by polar effects, mis-mapped barcodes, or mutants carrying both wild-type and transposon disrupted gene copies-highlighting potential limitations inherent to data from individual mutants in these high-throughput datasets. Therefore, we analyzed correlations in fitness scores across all 492 experiments in the dataset in the context of functionally related metabolic reaction modules identified within the model via flux coupling analysis. These correlations were used to identify candidate genes for a reaction in histidine biosynthesis lacking an annotated gene and highlight metabolic modules with poorly correlated gene fitness scores. Additional genes for reactions involved in biotin, ubiquinone, and pyridoxine biosynthesis in Z. mobilis were identified and confirmed using mutant complementation experiments. These discovered genes, were incorporated into the final model, iZM4_478, which contains 747 metabolic and transport reactions (of which 612 have gene-protein-reaction associations), 478 genes, and 616 unique metabolites, making it one of the most complete models of Z. mobilis ZM4 to date. The methods of analysis that we applied here with the Z. mobilis transposon mutant dataset, could easily be utilized to improve future genome-scale metabolic reconstructions for organisms where these, or similar, high-throughput datasets are available.

Zymomonas mobilis is a bacterium that produces ethanol from glucose at up to 97% of theoretical efficiency on a carbon basis. One factor contributing to the high efficiency of ethanol production is that Z. mobilis has a low biomass yield. The low biomass yield may be caused partly by the low ATP yield of the Entner-Doudoroff (ED) glycolytic pathway used by Z. mobilis, which produces only one ATP per glucose consumed. To test the hypothesis that ATP yield limits biomass yield in Z. mobilis, we attempted to introduce the Embden-Meyerhof-Parnas (EMP) glycolytic pathway (with double the ATP yield) by expressing phosphofructokinase (Pfk I) from Escherichia coli. Expression of Pfk I caused growth inhibition and resulted in accumulation of mutations in the pfkA gene. Co-expression of additional EMP enzymes, fructose bisphosphate aldolase (Fba) and triose phosphate isomerase (Tpi), with Pfk I did not enable EMP flux, and resulted in production of glycerol as a side product. Further analysis indicated that heterologous reactions may have operated in the reverse direction because of native metabolite concentrations. This study reveals how the metabolomic context of a chassis organism influences the range of pathways that can be added by heterologous expression.

MOTIVATION:Biological systems function through dynamic interactions among genes and their products, regulatory circuits and metabolic networks. Our development of the Pathway Tools software was motivated by the need to construct biological knowledge resources that combine these many types of data, and that enable users to find and comprehend data of interest as quickly as possible through query and visualization tools. Further, we sought to support the development of metabolic flux models from pathway databases, and to use pathway information to leverage the interpretation of high-throughput data sets.RESULTS:In the past 4 years we have enhanced the already extensive Pathway Tools software in several respects. It can now support metabolic-model execution through the Web, it provides a more accurate gap filler for metabolic models; it supports development of models for organism communities distributed across a spatial grid; and model results may be visualized graphically. Pathway Tools supports several new omics-data analysis tools including the Omics Dashboard, multi-pathway diagrams called pathway collages, a pathway-covering algorithm for metabolomics data analysis and an algorithm for generating mechanistic explanations of multi-omics data. We have also improved the core pathway/genome databases management capabilities of the software, providing new multi-organism search tools for organism communities, improved graphics rendering, faster performance and re-designed gene and metabolite pages.AVAILABILITY:The software is free for academic use; a fee is required for commercial use. See http://pathwaytools.com.CONTACT:pkarp@ai.sri.com.SUPPLEMENTARY INFORMATION:Supplementary data are available at Briefings in Bioinformatics online.

Microbial genome web portals have a broad range of capabilities that address a number of information-finding and analysis needs for scientists. This article compares the capabilities of the major microbial genome web portals to aid researchers in determining which portal(s) are best suited to their needs. We assessed both the bioinformatics tools and the data content of BioCyc, KEGG, Ensembl Bacteria, KBase, IMG, and PATRIC. For each portal, our assessment compared and tallied the available capabilities. The strengths of BioCyc include its genomic and metabolic tools, multi-search capabilities, table-based analysis tools, regulatory network tools and data, omics data analysis tools, breadth of data content, and large amount of curated data. The strengths of KEGG include its genomic and metabolic tools. The strengths of Ensembl Bacteria include its genomic tools and large number of genomes. The strengths of KBase include its genomic tools and metabolic models. The strengths of IMG include its genomic tools, multi-search capabilities, large number of genomes, table-based analysis tools, and breadth of data content. The strengths of PATRIC include its large number of genomes, table-based analysis tools, metabolic models, and breadth of data content.

MetaCyc (https://MetaCyc.org) is a comprehensive reference database of metabolic pathways and enzymes from all domains of life. It contains more than 2570 pathways derived from >54 000 publications, making it the largest curated collection of metabolic pathways. The data in MetaCyc is strictly evidence-based and richly curated, resulting in an encyclopedic reference tool for metabolism. MetaCyc is also used as a knowledge base for generating thousands of organism-specific Pathway/Genome Databases (PGDBs), which are available in the BioCyc (https://BioCyc.org) and other PGDB collections. This article provides an update on the developments in MetaCyc during the past two years, including the expansion of data and addition of new features.

BioCyc.org is a web portal that contains Pathway/Genome Databases for over 14,500 organisms, including 2400 human microbiome members. The BioCyc databases are unique in integrating a diverse range of data and providing a high level of curation for important microbes as well as model eukaryotic organisms and Homo sapiens. Each Pathway/Genome Database describes the genome and proteome of an organism, as well as its biochemical pathways and (for a small number of organisms) its regulatory network. BioCyc bioinformatics tools combine unparalleled breadth and user friendliness and include a unique set of visualization tools to speed comprehension of its extensive and complex data. BioCyc also includes MetaCyc, the largest collection of metabolic pathways and enzymes currently available, with over 2600 pathways. It contains the equivalent of about 9,400 textbook-pages of mini-review summaries for enzymes and pathways. The data in MetaCyc has been manually curated from over 58,000 published papers. Analysis tools in BioCyc include comparisons of pathways and genomes among multiple organisms, alignment of orthologous genes in a multi-genome browser, and searching for minimal-cost metabolic network routes that connect two substrates. Tools are also provided for visualizing omics data – such as metabolomics and gene expression data – on individual pathway diagrams, on personalized groups of multiple pathway diagrams called pathway collages, and on diagrams depicting the full metabolic network of an organism. A new visualization tool in BioCyc is the Omics Dashboard, an interactive tool for exploration and analysis of gene-expression and metabolomics datasets through a hierarchy of cellular systems and subsystems. This highly visual tool enables the user to survey their data at a very high level of abstraction, and to drill down to examine details at the pathway, subsystem, and gene levels. Patterns in the data can often be perceived quickly and easily. Another new tool is the MultiOmics Explainer, which helps researchers understand and interpret the results of their omics experiments in the context of what is known about an organism's metabolic and regulatory network. At this time this tool is not available online and requires installation of the software on a local computer. Another recent addition to BioCyc involves manual curation of databases for important pathogens. So far, the project includes Mycobacterium tuberculosis (H37Rv), Staphylococcus aureus (NCTC 8325) and Salmonella enterica (14028S) The BioCyc databases can be accessed through the BioCyc.org web site and can be downloaded. In addition, the Pathway Tools software can be downloaded and installed locally to create BioCyc-like databases for more genomes, curate existing databases, or perform additional analyses that are not available online. Access to MetaCyc and EcoCyc is unrestricted. Unlimited access to other databases requires a subscription. Support or Funding Information National Institute of General Medical Sciences of the National Institutes of Health (NIH) [GM080746, GM077678, GM75742] A portion of the initial display of the Omics Dashboard, showing the average gene expression levels of genes, at different time points, in pathways from different subsystems. Another example output of the Omics Dashboard, showing gene-expression profiles at the regulon level. A typical pathway diagram from a BioCyc database. The Multi-Genome Browser enables comparison of orthologous regions from different organisms. Genes in the Genome Browser are color-coded to indicate transcription units. Reaction diagrams provide color-coded atom mapping between reactants and products. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

MOTIVATION:The increasing availability of annotated genome sequences enables construction of genome-scale metabolic networks, which are useful tools for studying organisms of interest. However, due to incomplete genome annotations, draft metabolic models contain gaps that must be filled in a time-consuming process before they are usable. Optimization-based algorithms that fill these gaps have been developed, however, gap-filling algorithms show significant error rates and often introduce incorrect reactions.RESULTS:Here, we present a new gap-filling method that computes the costs of candidate gap-filling reactions from a universal reaction database (MetaCyc) based on taxonomic information. When gap-filling a metabolic model for an organism M (such as Escherichia coli), the cost for reaction R is based on the frequency with which R occurs in other organisms within the phylum of M (in this case, Proteobacteria). The assumption behind this method is that different taxonomic groups are biased toward using different metabolic reactions. Evaluation of the new gap-filler on randomly degraded variants of the EcoCyc metabolic model for E.coli showed an increase in the average F1-score to 99.0 (when using the variable weights by frequency method at the phylum level), compared to 91.0 using the previous MetaFlux gap-filler and 80.3 using a basic gap-filler. Evaluation on two other microbial metabolic models showed similar improvements.AVAILABILITY AND IMPLEMENTATION:The Pathway Tools software (including MetaFlux) is free for academic use and is available at http://pathwaytools.com. Additional code for reproducing the results presented here is available at www.ai.sri.com/pkarp/pubs/taxgap/supplementary.zip.SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

BioCyc.org is a microbial genome Web portal that combines thousands of genomes with additional information inferred by computer programs, imported from other databases and curated from the biomedical literature by biologist curators. BioCyc also provides an extensive range of query tools, visualization services and analysis software. Recent advances in BioCyc include an expansion in the content of BioCyc in terms of both the number of genomes and the types of information available for each genome; an expansion in the amount of curated content within BioCyc; and new developments in the BioCyc software tools including redesigned gene/protein pages and metabolite pages; new search tools; a new sequence-alignment tool; a new tool for visualizing groups of related metabolic pathways; and a facility called SmartTables, which enables biologists to perform analyses that previously would have required a programmer's assistance.

The EcoCyc model-organism database collects and summarizes experimental data for Escherichia coli K-12. EcoCyc is regularly updated by the manual curation of individual database entries, such as genes, proteins, and metabolic pathways, and by the programmatic addition of results from select high-throughput analyses. Updates to the Pathway Tools software that supports EcoCyc and to the web interface that enables user access have continuously improved its usability and expanded its functionality. This article highlights recent improvements to the curated data in the areas of metabolism, transport, DNA repair, and regulation of gene expression. New and revised data analysis and visualization tools include an interactive metabolic network explorer, a circular genome viewer, and various improvements to the speed and usability of existing tools.

Metabolic engineering uses genetic approaches to control microbial metabolism to produce desired compounds. Computational tools can identify new biological routes to chemicals and the changes needed in host metabolism to improve chemical production. Recent computational efforts have focused on exploring what compounds can be made biologically using native, heterologous, and/or enzymes with broad specificity. Additionally, computational methods have been developed to suggest different types of genetic modifications (e.g. gene deletion/addition or up/down regulation), as well as suggest strategies meeting different criteria (e.g. high yield, high productivity, or substrate co-utilization). Strategies to improve the runtime performances have also been developed, which allow for more complex metabolic engineering strategies to be identified. Future incorporation of kinetic considerations will further improve strain design algorithms.

BACKGROUND:Shewanella is a genus of facultatively anaerobic, Gram-negative bacteria that have highly adaptable metabolism which allows them to thrive in diverse environments. This quality makes them an attractive bacterial target for research in bioremediation and microbial fuel cell applications. Constraint-based modeling is a useful tool for helping researchers gain insights into the metabolic capabilities of these bacteria. However, Shewanella oneidensis MR-1 is the only strain with a genome-scale metabolic model constructed out of 21 sequenced Shewanella strains.RESULTS:In this work, we updated the model for Shewanella oneidensis MR-1 and constructed metabolic models for three other strains, namely Shewanella sp. MR-4, Shewanella sp. W3-18-1, and Shewanella denitrificans OS217 which span the genus based on the number of genes lost in comparison to MR-1. We also constructed a Shewanella core model that contains the genes shared by all 21 sequenced strains and a few non-conserved genes associated with essential reactions. Model comparisons between the five constructed models were done at two levels - for wildtype strains under different growth conditions and for knockout mutants under the same growth condition. In the first level, growth/no-growth phenotypes were predicted by the models on various carbon sources and electron acceptors. Cluster analysis of these results revealed that the MR-1 model is most similar to the W3-18-1 model, followed by the MR-4 and OS217 models when considering predicted growth phenotypes. However, a cluster analysis done based on metabolic gene content revealed that the MR-4 and W3-18-1 models are the most similar, with the MR-1 and OS217 models being more distinct from these latter two strains. As a second level of comparison, we identified differences in reaction and gene content which give rise to different functional predictions of single and double gene knockout mutants using Comparison of Networks by Gene Alignment (CONGA). Here, we showed how CONGA can be used to find biomass, metabolic, and genetic differences between models.CONCLUSIONS:We developed four strain-specific models and a general core model that can be used to do various in silico studies of Shewanella metabolism. The developed models provide a platform for a systematic investigation of Shewanella metabolism to aid researchers using Shewanella in various biotechnology applications.

This paper presents a review of two classical business approaches to inventory control. This is followed by a discussion of an engineering approach using Linear Quadratic Gaussian (LQG) control. The main result is to show that under certain conditions all three methods generate the same policy. Using the state-space model of the LQG formulation, we explore the multi-echelon system and model uncertainty cases.