Directories
1-Proteome data processing (directory)
	This directory contains all source data and one processing R script.
	The source data files are list as follows:
		1-IS-S288C.csv, this file contains abundance information (iBAQ) of all identified 15N-labelled IS proteins, iBAQ values of each identified proteins are used for absolute quantification of sample proteins. Detailed description of the method can be found in the Material and Method part of the main text.   
		1-ProteinGroupsS288C.csv, this file contains quantification information of all identified proteins in the samples as Ratio H/L normalized SXXX, which represents the heavy labelled and low labelled peptide ratios of identified proteins. this information combined with IS iBAQ value can be used to get abundance value of each identified sample protein.  
		1-TotalProteinMeasured.csv, this file contains total proein weight ratio in the dry cell weight for each sample, this information can be used to get absolute quantification of each protein in individual samples.
	The R script "1-processRawToGenerateProteomeData" is designed to using the above three files contained information to generate absolute quantification of individual proteins identified under all studied samples. Details of the absolute quantification method can be found in the Material and Method part of the main text.
2-Protein_vs_flux and mRNA_vs_flux correlation analysis (directory)
	This directory contains all source data and one processing python script.
	The source data file: "2-FluxTranscriptomeAndProtomeCorrelationData.xlsx" is an Excel workbook, which contains 5 sheets: 1)Yeast_7_6: the Yeast genome scale metabolic model Yeast7.6; 2) EnzymeNameConversion: contains mapping of gene name to its row number in the next sheet; 3) Enzyme: contains absolute concentrations of all proteins identified in each samples; 4) mRNA: contains absolute concentrations of all mRNAs identified in each specific growth rate; 5) Fluxes: contains fluxes of each reactions in the Yeast7.6 model.
	The python script "2-doCorrelationAnalysisFlux_mRNA_Protein.py" is an python script, which did the correlation analysis of mRNA abundance vs reaction flux and protein abundance vs reaction flux. This script is the main code for generating Fig. 3A.

Another self designed code GECKO has been published in 2017 and corresponding codes are submitted to the public open source repository in the following url：
https://github.com/SysBioChalmers/GECKO/releases/tag/v1.1.1
