Over the past decade, a growing community of researchers has emerged around the use of COnstraint-Based Reconstruction and Analysis (COBRA) methods to simulate, analyze and predict a variety of metabolic phenotypes using genome-scale models. The COBRA Toolbox, a MATLAB package for implementing COBRA methods, was presented earlier. Here we present a significant update of this in silico ToolBox. Version 2.0 of the COBRA Toolbox expands the scope of computations by including in silico analysis methods developed since its original release. New functions include: (1) network gap filling, (2) 13C analysis, (3) metabolic engineering, (4) omics-guided analysis, and (5) visualization. As with the first version, the COBRA Toolbox reads and writes Systems Biology Markup Language formatted models. In version 2.0, we improved performance, usability, and the level of documentation. A suite of test scripts can now be used to learn the core functionality of the Toolbox and validate results. This Toolbox lowers the barrier of entry to use powerful COBRA methods.

BackgroundCOnstraint-Based Reconstruction and Analysis (COBRA) methods are widely used for genome-scale modeling of metabolic networks in both prokaryotes and eukaryotes. Due to the successes with metabolism, there is an increasing effort to apply COBRA methods to reconstruct and analyze integrated models of cellular processes. The COBRA Toolbox for MATLAB is a leading software package for genome-scale analysis of metabolism; however, it was not designed to elegantly capture the complexity inherent in integrated biological networks and lacks an integration framework for the multiomics data used in systems biology. The openCOBRA Project is a community effort to promote constraints-based research through the distribution of freely available software.ResultsHere, we describe COBRA for Python (COBRApy), a Python package that provides support for basic COBRA methods. COBRApy is designed in an object-oriented fashion that facilitates the representation of the complex biological processes of metabolism and gene expression. COBRApy does not require MATLAB to function; however, it includes an interface to the COBRA Toolbox for MATLAB to facilitate use of legacy codes. For improved performance, COBRApy includes parallel processing support for computationally intensive processes.ConclusionCOBRApy is an object-oriented framework designed to meet the computational challenges associated with the next generation of stoichiometric constraint-based models and high-density omics data sets.Availabilityhttp://opencobra.sourceforge.net/

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.

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Nitric oxide (NO) is used by mammalian immune systems to counter microbial invasions and is produced by bacteria during denitrification. As a defense, microorganisms possess a complex network to cope with NO. Here we report a combined transcriptomic, chemical, and phenotypic approach to identify direct NO targets and construct the biochemical response network. In particular, network component analysis was used to identify transcription factors that are perturbed by NO. Such information was screened with potential NO reaction mechanisms and phenotypic data from genetic knockouts to identify active chemistry and direct NO targets in Escherichia coli. This approach identified the comprehensive E. coli NO response network and evinced that NO halts bacterial growth via inhibition of the branched-chain amino acid biosynthesis enzyme dihydroxyacid dehydratase. Because mammals do not synthesize branched-chain amino acids, inhibition of dihydroxyacid dehydratase may have served to foster the role of NO in the immune arsenal.systems biology ͉ chemoinformatics M ammals possess complex immune systems that have evolved to prevent microbial invasion. Nitric oxide (NO) is one of the key chemicals used by mammalian cells to combat infections (1). Although NO is known to act in a bacteriostatic fashion (2), the mechanism underlying this action is not completely understood. NO interferes with biological processes either directly, by reacting with metal centers and free radicals, or indirectly, by promoting the formation of reactive nitrogen oxide species (RNOS), such as peroxynitrite and N 2 O 3 (3). NO has been reported to react with various protein Fe-S clusters (4-7), and this reactivity has been implicated in the inhibition of tumor proliferation (8)(9)(10). NO also binds to the metal centers of respiratory enzymes, inhibiting bacterial respiration (11-13). Because NO is used to combat Escherichia coli in low oxygen environments, it is likely that respiration is not the only system targeted by NO.E. coli contains NO-consuming proteins NO reductase NorV (14), NO oxidase HmpA (15, 16), and cytochrome c nitrite reductase NrfA (17). Expression of norV and hmpA is increased in response to NO by NO-specific transcription factors (TFs) NorR (18) and NsrR, respectively (19,20). Our goal here was to identify the comprehensive NO response network, consisting of the direct NO targets leading to bacteriostasis, the response network resulting from bacteriostasis, bacterial NO sensors for self-defense, and the response network for self-defense.Previous genome-scale studies have used DNA microarrays to identify the global genetic response to [21][22][23][24]. Although transcriptome analysis provides substantial information regarding changes in gene expression, examination of individual genes cannot distinguish primary from secondary effects and does not directly identify the TFs and pathways leading to the transcriptional changes. Because many promoters are controlled by multiple TFs, the identity of the TF responsible for mediating transcr...

The molecular mechanisms underlying the development and progression of prostate cancer are poorly understood. AMP-activated protein kinase (AMPK) is a serine-threonine kinase that is activated in response to the hypoxic conditions found in human prostate cancers. In response to energy depletion, AMPK activation promotes metabolic changes to maintain cell proliferation and survival. Here, we report prevalent activation of AMPK in human prostate cancers and provide evidence that inhibition or depletion of AMPK leads to decreased cell proliferation and increased cell death. AMPK was highly activated in 40% of human prostate cancer specimens examined. Endogenous AMPK was active in both the androgensensitive LNCaP cells and the androgen-independent CWR22Rv1 human prostate cancer cells. Depletion of AMPK catalytic subunits by small interfering RNA or inhibition of AMPK activity with a small-molecule AMPK inhibitor (compound C) suppresses human prostate cancer cell proliferation. Apoptotic cell death was induced in LNCaP and CWR22Rv1 cells at compound C concentrations that inhibited AMPK activity. The evidence provided here is the first report that the activated AMPK pathway is involved in the growth and survival of human prostate cancer and offers novel potential targets for chemoprevention of human prostate cancer. [Mol Cancer Ther 2009;8(4):733-41]

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction‐based models and packages that extend the core with features suited to other model types including constraint‐based models, reaction‐diffusion models, logical network models, and rule‐based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single‐cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.

BackgroundMetabolic 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.ResultsHere, we describe a community-driven effort, in which more than 20 experts in S. Typhimurium biology and systems biology collaborated to reconcile and expand the S. Typhimurium BiGG knowledge-base. The consensus MR was obtained starting from two independently developed MRs for S. Typhimurium. Key results of this reconstruction jamboree include i) development and implementation of a community-based workflow for MR annotation and reconciliation; ii) incorporation of thermodynamic information; and iii) use of the consensus MR to identify potential multi-target drug therapy approaches.ConclusionTaken together, with the growing number of parallel MRs a structured, community-driven approach will be necessary to maximize quality while increasing adoption of MRs in experimental design and interpretation.