## 2 Experimental Section
### Data Analysis  
GC-MS results were analysed using AMDIS software [20]. Metabolites were identified using an in-house MS library [17]. The GC-peak intensities corresponding to each identified compound were normalized by both the GC-peak intensity of the internal standard (2,3,3,3-d4-alanine) and the biomass concentration (Table S1). The normalized peak intensities were then transformed into Z-scores, _i.e.,_ standard scores that reflect how many standard deviations above or below the population mean a raw score is. Z-scores were calculated by subtracting the average peak intensity corresponding to a metabolite \(K\) among all the \(n\) samples (including replicates) in the set of experiments, from the peak intensity value (\(I_{K,i}\)) for that metabolite in sample \(i\), and dividing that result by the standard deviation of all measured peak intensities corresponding to that metabolite \(K\), according to:  
\[[\,Z-score_{\varepsilon_{k,i}}=\frac{\left(I_{k,i}-mean\,I_{k,1\ldots k,n} \right)}{SD_{k,1\ldots k,n}}] \tag{1}\]  
Further data processing and statistical analyses were performed with MATLAB (version 2009b, The Mathworks, Inc). The nonparametric two-way method, the Mack-Skillings test, was used to test the null hypothesis (\(H_{0}\)) of no differences among experiments and to look for significant alterations between metabolic profiles that might be related to either factor: bacterial strain (Factor A) or dilution rate (Factor B). The design matrix for the Mack-Skillings test is provided in Table S2.  
Metabolite profiles with p-values less than 0.01 were considered to have statistically significant differences between experimental conditions (for a 99% confidence level). We should be aware that there are slight differences in the media composition due to auxotrophic requirements (kanamycin and valine were added to the medium used to cultivate the mutant strain). Because we were concerned that the metabolic state of the two cultures would not be comparable if valine was also added to the wild-type culture (_i.e.,_ while the mutant strain would use valine essentially to balance the inefficiency to naturally synthesize valine for biosynthetic purposes, in the wild-type, it would be used as a carbon source, which would interfere with the intracellular carbon distribution and, consequently, with the metabolic state of cells), we considered a stringent confidence level (99%) to reduce the influence of these environmental variations and metabolites in the biosynthetic pathway of valine which were not considered to be significant biomarkers.  
Metabolite profiles that presented statistically significant changes regarding factor A (_i.e._, bacterial strains) were further used to determine the degree of association between the metabolite profiles produced by the W3110 and _xrel_A _E. coli_ cultures. We have applied a correlation analysis based on Pearson's correlation coefficients (_r_) that measure the strength of the association between two conditions (e.g. bacterial strains), which can vary between 1 and \(-\)1 (_r_ \(>\) 0 indicates a positive relationship, \(r\) = 0 indicates the absence of a relationship and \(r\) \(<\) 0 indicates a negative relationship). In this work, metabolite profiles with \(r\) \(<\) 0.6 were considered to correspond to a weak relationship between the metabolic behavior of W3110 and _xrel_A _E. coli_ cultures.  
Enrichment pathways analyses were performed using two bioinformatics tools: the Metabolite Biological Role (MBRole) a web-server tool for carrying out over-representation analysis of biological and chemical annotations in metabolomics data [21]; and the Pathway Activity Profiling (PAPi), an algorithm that measures metabolic pathway activities from metabolite profiles at different experimental conditions [22].