The acetylation of lysine residues in histones are an important regulatory mechanism. But still relatively little is known about the dynamics of histone acetylation and their dependency on metabolic processes. We therefore developed a stable isotope labelling approach based on ^13^C-glucose that allows the quantitative analysis of histone acetylation dynamics in human cell lines. The substrate for histone acetylation is acetyl-CoA which first needs to be synthesized from ^13^C-glucose causing a delay in the label incorporation of histone-acetyl-lysins. To correctly determine histone-acetylation dynamics we therefore simultaneously extracted proteins and metabolites and measured the time-dependent incorporation of ^13^C into both acetyl-CoA and acetylated histone peptides. We than used an ODE based modelling approach to calculate the correct histone acetylation and deacetylation rates. We show that without correction alterations in metabolic fluxes would erroneously be interpreted as changes in histone acetylation dynamics while our approach allows to discriminate between both processes.

Metabolic alterations are associated with many especially age related diseases and they are caused by changes in the expression of the corresponding enzymes. Although analysis of gene expression data has become a standard approach, predicting large scale metabolic alterations is still challenging. Here, we present a mathematical framework GEMCAT, Gene Expression-based Metabolite Centrality Analysis Tool, that is based on the centrality of nodes in a directed graph. Through integration of differential expression data from either transcriptomics or proteomics we can predict metabolic alterations for a large set of metabolites but avoid artificial biomass or energy constraints required for other genome-scale modelling approaches. We demonstrate the predictive efficacy of GEMCAT using two distinct datasets: one involving RNA sequencing data and metabolomics from a human cell line featuring a deleted mitochondrial NAD-transporter, and another comprising proteomics and metabolomics data from patients afflicted with inflammatory bowel disease. We furthermore extended our approach to trace the experimentally confirmed metabolic alterations back to the expression changes enabling network based multi-omics integration.