Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0.

in Nature protocols (2019), 14(3), 639-702

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of ... [more ▼]

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods. [less ▲]

Do Genome-scale Models Need Exact Solvers or Clearer Standards?

in Molecular Systems Biology (2015), 11(10), 1

Inferring the metabolism of human orphan metabolites from their metabolic network context affirms human gluconokinase activity.

in Biochemical Journal (2013), 449(2), 427-435

Metabolic network reconstructions define metabolic information within a target organism and can therefore be used to address incomplete metabolic information. In the present study we used a computational ... [more ▼]

Metabolic network reconstructions define metabolic information within a target organism and can therefore be used to address incomplete metabolic information. In the present study we used a computational approach to identify human metabolites whose metabolism is incomplete on the basis of their detection in humans but exclusion from the human metabolic network reconstruction RECON 1. Candidate solutions, composed of metabolic reactions capable of explaining the metabolism of these compounds, were then identified computationally from a global biochemical reaction database. Solutions were characterized with respect to how metabolites were incorporated into RECON 1 and their biological relevance. Through detailed case studies we show that biologically plausible non-intuitive hypotheses regarding the metabolism of these compounds can be proposed in a semi-automated manner, in an approach that is similar to de novo network reconstruction. We subsequently experimentally validated one of the proposed hypotheses and report that C9orf103, previously identified as a candidate tumour suppressor gene, encodes a functional human gluconokinase. The results of the present study demonstrate how semi-automatic gap filling can be used to refine and extend metabolic reconstructions, thereby increasing their biological scope. Furthermore, we illustrate how incomplete human metabolic knowledge can be coupled with gene annotation in order to prioritize and confirm gene functions. [less ▲]

Multiscale modeling of metabolism and macromolecular synthesis in E. coli and its application to the evolution of codon usage.

in PLoS ONE (2012), 7(9), 45635

Biological systems are inherently hierarchal and multiscale in time and space. A major challenge of systems biology is to describe biological systems as a computational model, which can be used to derive ... [more ▼]

Biological systems are inherently hierarchal and multiscale in time and space. A major challenge of systems biology is to describe biological systems as a computational model, which can be used to derive novel hypothesis and drive experiments leading to new knowledge. The constraint-based reconstruction and analysis approach has been successfully applied to metabolism and to the macromolecular synthesis machinery assembly. Here, we present the first integrated stoichiometric multiscale model of metabolism and macromolecular synthesis for Escherichia coli K12 MG1655, which describes the sequence-specific synthesis and function of almost 2000 gene products at molecular detail. We added linear constraints, which couple enzyme synthesis and catalysis reactions. Comparison with experimental data showed improvement of growth phenotype prediction with the multiscale model over E. coli's metabolic model alone. Many of the genes covered by this integrated model are well conserved across enterobacters and other, less related bacteria. We addressed the question of whether the bias in synonymous codon usage could affect the growth phenotype and environmental niches that an organism can occupy. We created two classes of in silico strains, one with more biased codon usage and one with more equilibrated codon usage than the wildtype. The reduced growth phenotype in biased strains was caused by tRNA supply shortage, indicating that expansion of tRNA gene content or tRNA codon recognition allow E. coli to respond to changes in codon usage bias. Our analysis suggests that in order to maximize growth and to adapt to new environmental niches, codon usage and tRNA content must co-evolve. These results provide further evidence for the mutation-selection-drift balance theory of codon usage bias. This integrated multiscale reconstruction successfully demonstrates that the constraint-based modeling approach is well suited to whole-cell modeling endeavors. [less ▲]

Detailing the optimality of photosynthesis in cyanobacteria through systems biology analysis.

in Proceedings of the National Academy of Sciences of the United States of America (2012), 109(7), 2678-2683

Photosynthesis has recently gained considerable attention for its potential role in the development of renewable energy sources. Optimizing photosynthetic organisms for biomass or biofuel production will ... [more ▼]

Photosynthesis has recently gained considerable attention for its potential role in the development of renewable energy sources. Optimizing photosynthetic organisms for biomass or biofuel production will therefore require a systems understanding of photosynthetic processes. We reconstructed a high-quality genome-scale metabolic network for Synechocystis sp. PCC6803 that describes key photosynthetic processes in mechanistic detail. We performed an exhaustive in silico analysis of the reconstructed photosynthetic process under different light and inorganic carbon (Ci) conditions as well as under genetic perturbations. Our key results include the following. (i) We identified two main states of the photosynthetic apparatus: a Ci-limited state and a light-limited state. (ii) We discovered nine alternative electron flow pathways that assist the photosynthetic linear electron flow in optimizing the photosynthesis performance. (iii) A high degree of cooperativity between alternative pathways was found to be critical for optimal autotrophic metabolism. Although pathways with high photosynthetic yield exist for optimizing growth under suboptimal light conditions, pathways with low photosynthetic yield guarantee optimal growth under excessive light or Ci limitation. (iv) Photorespiration was found to be essential for the optimal photosynthetic process, clarifying its role in high-light acclimation. Finally, (v) an extremely high photosynthetic robustness drives the optimal autotrophic metabolism at the expense of metabolic versatility and robustness. The results and modeling approach presented here may promote a better understanding of the photosynthetic process. They can also guide bioengineering projects toward optimal biofuel production in photosynthetic organisms. [less ▲]

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

in BMC Systems Biology (2011), 5

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 ... [more ▼]

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. 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. CONCLUSION: Taken 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. [less ▲]

Reconstruction annotation jamborees: a community approach to systems biology.

in Molecular Systems Biology (2010), 6

A detailed genome-wide reconstruction of mouse metabolism based on human Recon 1

in BMC Systems Biology (2010), 4

BACKGROUND: Well-curated and validated network reconstructions are extremely valuable tools in systems biology. Detailed metabolic reconstructions of mammals have recently emerged, including human ... [more ▼]

BACKGROUND: Well-curated and validated network reconstructions are extremely valuable tools in systems biology. Detailed metabolic reconstructions of mammals have recently emerged, including human reconstructions. They raise the question if the various successful applications of microbial reconstructions can be replicated in complex organisms. RESULTS: We mapped the published, detailed reconstruction of human metabolism (Recon 1) to other mammals. By searching for genes homologous to Recon 1 genes within mammalian genomes, we were able to create draft metabolic reconstructions of five mammals, including the mouse. Each draft reconstruction was created in compartmentalized and non-compartmentalized version via two different approaches. Using gap-filling algorithms, we were able to produce all cellular components with three out of four versions of the mouse metabolic reconstruction. We finalized a functional model by iterative testing until it passed a predefined set of 260 validation tests. The reconstruction is the largest, most comprehensive mouse reconstruction to-date, accounting for 1,415 genes coding for 2,212 gene-associated reactions and 1,514 non-gene-associated reactions.We tested the mouse model for phenotype prediction capabilities. The majority of predicted essential genes were also essential in vivo. However, our non-tissue specific model was unable to predict gene essentiality for many of the metabolic genes shown to be essential in vivo. Our knockout simulation of the lipoprotein lipase gene correlated well with experimental results, suggesting that softer phenotypes can also be simulated. CONCLUSIONS: We have created a high-quality mouse genome-scale metabolic reconstruction, iMM1415 (Mus Musculus, 1415 genes). We demonstrate that the mouse model can be used to perform phenotype simulations, similar to models of microbe metabolism. Since the mouse is an important experimental organism, this model should become an essential tool for studying metabolic phenotypes in mice, including outcomes from drug screening. [less ▲]

Functional characterization of alternate optimal solutions of Escherichia coli's transcriptional and translational machinery.

in Biophysical Journal (2010), 98(10), 2072-81

The constraint-based reconstruction and analysis approach has recently been extended to describe Escherichia coli's transcriptional and translational machinery. Here, we introduce the concept of reaction ... [more ▼]

The constraint-based reconstruction and analysis approach has recently been extended to describe Escherichia coli's transcriptional and translational machinery. Here, we introduce the concept of reaction coupling to represent the dependency between protein synthesis and utilization. These coupling constraints lead to a significant contraction of the feasible set of steady-state fluxes. The subset of alternate optimal solutions (AOS) consistent with maximal ribosome production was calculated. The majority of transcriptional and translational reactions were active for all of these AOS, showing that the network has a low degree of redundancy. Furthermore, all calculated AOS contained the qualitative expression of at least 92% of the known essential genes. Principal component analysis of AOS demonstrated that energy currencies (ATP, GTP, and phosphate) dominate the network's capability to produce ribosomes. Additionally, we identified regulatory control points of the network, which include the transcription reactions of sigma70 (RpoD) as well as that of a degradosome component (Rne) and of tRNA charging (ValS). These reactions contribute significant variance among AOS. These results show that constraint-based modeling can be applied to gain insight into the systemic properties of E. coli's transcriptional and translational machinery. [less ▲]

Identification of potential pathway mediation targets in Toll-like receptor signaling.

in PLoS Computational Biology (2009), 5(2), 1000292

Recent advances in reconstruction and analytical methods for signaling networks have spurred the development of large-scale models that incorporate fully functional and biologically relevant features. An ... [more ▼]

Recent advances in reconstruction and analytical methods for signaling networks have spurred the development of large-scale models that incorporate fully functional and biologically relevant features. An extended reconstruction of the human Toll-like receptor signaling network is presented herein. This reconstruction contains an extensive complement of kinases, phosphatases, and other associated proteins that mediate the signaling cascade along with a delineation of their associated chemical reactions. A computational framework based on the methods of large-scale convex analysis was developed and applied to this network to characterize input-output relationships. The input-output relationships enabled significant modularization of the network into ten pathways. The analysis identified potential candidates for inhibitory mediation of TLR signaling with respect to their specificity and potency. Subsequently, we were able to identify eight novel inhibition targets through constraint-based modeling methods. The results of this study are expected to yield meaningful avenues for further research in the task of mediating the Toll-like receptor signaling network and its effects. [less ▲]