Gene-centric constraint of metabolic models

Fyson, Nick; Kim, Min Kyung; Lun, Desmond S.; Colijn, Caroline

doi:10.1101/116558

Cited by 2 publications

(3 citation statements)

References 57 publications

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“…We made use of GC-flux. 37 The GC-flux algorithm constrains the rate of the metabolic reaction in the model based on the expression levels of the genes coding for the corresponding enzymes. GC flux distributes the gene expression of a single gene over all reactions associated with that gene, such that the total sum of those reaction fluxes cannot exceed maximum flux associated with the gene expression value.…”

Section: Resultsmentioning

confidence: 99%

“…The gene expression data integration method used in this study is Gene-centric flux (GC-flux). 37 In this study, the linear programming problem is slightly altered from the original stoichiometric matrix-based linear programming problem. Using the GPRTransform package, 38 we split up each reaction into multiple versions of the same reaction, one for every possible isoenzyme.…”

Section: Methodsmentioning

confidence: 99%

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Predicting Metabolism from Gene Expression in an Improved Whole-Genome Metabolic Network Model ofDanio rerio

et al. 2019

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Zebrafish is a useful modeling organism for the study of vertebrate development, immune response, and metabolism. Metabolic studies can be aided by mathematical reconstructions of the metabolic network of zebrafish. These list the substrates and products of all biochemical reactions that occur in the zebrafish. Mathematical techniques such as flux-balance analysis then make it possible to predict the possible metabolic flux distributions that optimize, for example, the turnover of food into biomass. The only available genome-scale reconstruction of zebrafish metabolism is ZebraGEM. In this study, we present ZebraGEM 2.0, an updated and validated version of ZebraGEM. ZebraGEM 2.0 is extended with gene-protein-reaction associations (GPRs) that are required to integrate genetic data with the metabolic model. To demonstrate the use of these GPRs, we performed an in silico genetic screening for knockouts of metabolic genes and validated the results against published in vivo genetic knockout and knockdown screenings. Among the single knockout simulations, we identified 74 essential genes, whose knockout stopped growth completely. Among these, 11 genes are known have an abnormal knockout or knockdown phenotype in vivo (partial), and 41 have human homologs associated with metabolic diseases. We also added the oxidative phosphorylation pathway, which was unavailable in the published version of ZebraGEM. The updated model performs better than the original model on a predetermined list of metabolic functions. We also determined a minimal feed composition. The oxidative phosphorylation pathways were validated by comparing with published experiments in which key components of the oxidative phosphorylation pathway were pharmacologically inhibited. To test the utility of ZebraGEM2.0 for obtaining new results, we integrated gene expression data from control and Mycobacterium marinum-infected zebrafish larvae. The resulting model predicts impeded growth and altered histidine metabolism in the infected larvae.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Predicting Metabolism from Gene Expression in an Improved Whole-Genome Metabolic Network Model ofDanio rerio

et al. 2019

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“…Similar to E-flux2, the regularised context-specific model extraction method (RegrEx) is based on principles of regularised least squares optimisation by minimising the squared Euclidean distance between fluxes and experimental data [121] to calculate fluxes which are independent of user-defined parameters. Instead of assigning expression measurements to individual genes or reactions, the GC-Flux algorithm splits GPR strings for each reaction into functional gene complexes to overcome the assumption of proteins catalysing more than one reaction at a time [122]. Another recent development is the use of the Huber penalty convex optimisation function (HPCOF) combined with flux minimisation, to achieve a more accurate prediction of fluxes which are closer to experimentally measured values [123].…”

Section: Regulatory Methods To Generate Context-specific Metabolic Mo...mentioning

confidence: 99%

Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling

Vijayakumar

Conway

Lió

et al. 2017

Briefings in Bioinformatics

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Metabolic modelling has entered a mature phase with dozens of methods and software implementations available to the practitioner and the theoretician. It is not easy for a modeller to be able to see the wood (or the forest) for the trees. Driven by this analogy, we here present a 'forest' of principal methods used for constraint-based modelling in systems biology. This provides a tree-based view of methods available to prospective modellers, also available in interactive version at http://modellingmetabolism.net, where it will be kept updated with new methods after the publication of the present manuscript. Our updated classification of existing methods and tools highlights the most promising in the different branches, with the aim to develop a vision of how existing methods could hybridize and become more complex. We then provide the first hands-on tutorial for multi-objective optimization of metabolic models in R. We finally discuss the implementation of multi-view machine learning approaches in poly-omic integration. Throughout this work, we demonstrate the optimization of trade-offs between multiple metabolic objectives, with a focus on omic data integration through machine learning. We anticipate that the combination of a survey, a perspective on multi-view machine learning and a step-by-step R tutorial should be of interest for both the beginner and the advanced user.

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Gene-centric constraint of metabolic models

Abstract: Motivation: A number of approaches have been introduced

Cited by 2 publications

References 57 publications

Predicting Metabolism from Gene Expression in an Improved Whole-Genome Metabolic Network Model ofDanio rerio

Predicting Metabolism from Gene Expression in an Improved Whole-Genome Metabolic Network Model ofDanio rerio

Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling

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