Level 5: Quantitative Models
INTEGRATE 43
INTEGRATE is a computational pipeline that integrates metabolomics and transcriptomics data to characterize multi-level metabolic regulation. The pipeline first computes differential reaction expression from transcriptomic data and uses constraint-based modeling to predict if the differential expression of metabolic enzymes directly originates differences in metabolic fluxes. In parallel, the pipeline uses metabolomics to predict how differences in substrate availability translate into differences in metabolic fluxes. It is an upscaling/inference algorithm.
This algorithm uses level 4 stoichiometries as constraints for flux balance analysis, RNA levels as enzyme abundance proxies to predict metabolomic fluxes then compare it with the observed data. The prior information is a further curated subset of RECON3D metabolomic construction called ERGO2. Once the metabolomic and transcriptomic data is mapped to the network intermediary scores are calculated for Feasible Flux Distributions (based on static analysis), Reaction Activity Scores (based on RNA levels) and Reaction Propensity Score (based on substrate levels). Agreement between these metrics, calculated by Variation Concordance Analysis is the final output and can be used for both explanation/extraction and upscaling.
The pipeline was applied to a set of immortalized normal and cancer breast cell lines. The results showed that the pipeline was able to identify metabolic reactions that are regulated at both the metabolic and gene expression levels. The pipeline was also able to identify metabolic reactions that are differentially regulated in cancer cells compared to normal cells.
SUMMER 44
SUMMER (Shiny Utility for Metabolomics and Multiomics Exploratory Research) uses reaction rate potentials to perform pathway enrichment analysis on metabolomic data. SUMMER uses level 4 metabolomic networks from the KEGG database7. This is a network upscaling method from level 4 to level 5 as a first step of quantitative modeling.
SUMMER uses reaction rate potentials to model the feedback effects between an enzyme, its substrate(s), and its product(s). It also infers the catalytic activity of each enzyme using integrated transcriptomics or proteomics data. The method then uses this integrated model to understand the change in reaction rate potentials between a perturbed condition and a reference condition. The resulting ratio of the resulting reaction rate potentials between a perturbed condition and a reference condition is then bootstrapped to calculate a ranking score between each reaction. Using the rank scores, SUMMER identifies the “hotspot” reactions in the network.
The authors applied SUMMER to re-analyze a metabolomic and transcriptomic dataset generated from a mouse model of accelerated aging and dementia. They wanted to understand the pathways that were altered by a neuroprotective compound. They found that treatment with this compound was associated with an increase in acetyl-CoA activity and an enrichment of TCA cycle activity.