Dataset - Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models

Simulation data needed to reproduce the results from the manuscript “Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models” by V. Pandey, N. Hadadi and V. Hatzimanikatis "REMI manuscript - simData" folder contains all simulation data which can be used to generate results of the paper: • Expression_data: This folder contains Transcriptomics data from both studies: Ishii et al (see test_expr.mat) and Holm et al. • Fluxdata: Fluxomics data can be found form the studies Ishii et al and Holm et al. • Metabolomics: This contains metabolomics data of aforementioned both studies. • ModelsSolutions: We generated different models using with thermodynamics (TGex, TGexM, TM) and without thermodynamics models (Gex, GexM, M). Gex indicates integration with only gene expression, GexM indicates gene expression and metabolite, and M indicates only metabolites. ‘T’ is used for thermodynamic models. Models for different mutants and conditions (e.g. pgm, pgi) can be found in the corresponding folders (TGex, TGexM, TM, Gex, GexM, and M). Variables with the ‘store’ tag comprises flux solutions, correlation values and percentage error between simulation and experiment fluxes. • AlternativeMCS: We generated alternative states for MCS and saved results. • FVAMM: This is the result flux variability analysis can be found in this folder. • Scatter_plot: Scatter plots indicates correlation between measured and model predicted fluxes.VP and VH are supported by the RTD grant MalarX, no. 2013/155, within SystemsX.ch, the Swiss Initiative for Systems Biology evaluated by the Swiss National Science Foundation and by the École Polytechnique fédérale de Lausanne. NH is supported through the RTD grant MicroScapesX, no. 2013/158, within SystemX, the Swiss Initiative for System Biology evaluated by the Swiss National Science Foundation