XomicsToModel: Omics data integration and generation of thermodynamically consistent metabolic models

Abstract

Constraint-based modelling can mechanistically simulate the behaviour of a biochemical system, permitting hypotheses generation, experimental design and interpretation of experimental data, with numerous applications, including modelling of metabolism. Given a generic model, several methods have been developed to extract a context-specific, genome-scale metabolic model by incorporating information used to identify metabolic processes and gene activities in a given context. However, existing model extraction algorithms are unable to ensure that the context-specific model is thermodynamically feasible. This protocol introduces XomicsToModel, a semi-automated pipeline that integrates bibliomic, transcriptomic, proteomic, and metabolomic data with a generic genome-scale metabolic reconstruction, or model, to extract a context-specific, genome-scale metabolic model that is stoichiometrically, thermodynamically and flux consistent. The XomicsToModel pipeline is exemplified for extraction of a specific metabolic model from a generic metabolic model, but it enables multi-omic data integration and extraction of physicochemically consistent mechanistic models from any generic biochemical network. With all input data fully prepared, algorithmic completion of the pipeline takes ~10 min, however manual review of intermediate results may also be required, e.g., when inconsistent input data lead to an infeasible model.

Identification of metabolites reproducibly associated with Parkinson’s Disease via meta-analysis and computational modelling

“…An established context-specific metabolic model extraction pipeline 'XomicsToModel' was used to generate a thermodynamically flux-consistent model [156]. This pipeline has been used to generate the model of dopaminergic neuronal metabolism [16]; it enables the integration of omics data, including genomic, transcriptomic, proteomic, metabolomic, and bibliomic data, and the extraction of a physicochemically consistent mechanistic model from a global metabolic network [156].…”

“…An established context-specific metabolic model extraction pipeline 'XomicsToModel' was used to generate a thermodynamically flux-consistent model [156]. This pipeline has been used to generate the model of dopaminergic neuronal metabolism [16]; it enables the integration of omics data, including genomic, transcriptomic, proteomic, metabolomic, and bibliomic data, and the extraction of a physicochemically consistent mechanistic model from a global metabolic network [156]. Due to the limited input data, we mainly used the 'thermoKernel' algorithm of the pipeline to extract the thermodynamically flux-consistent model, where the solver 'gurobi' was chosen to solve linear optimisation problems, the 'fastcc' algorithm [157] was chosen to check the flux consistency.…”

“…The weight of each core metabolite was calculated using the maximum study number if the metabolite changed in different trends, and then converted the weight to a negative value. The negative value promotes a metabolite or reaction to be present in the model, the positive value promotes a metabolite or reaction to be absent, and zero means ambivalence with respect to presence or absence [156].…”