as were peptides of fewer than six residues. Spectral libraries for each
each condition were built and refined using Spectrast (ISB), only keep-
ing those peptides that were identified in three or more individual
samples, and collapsing individual spectra into a consensus spectra
for each peptide.
Relative protein quantification. The raw mass spectrometry data files
were converted to the mzml format using conversion tools provided
by AB Sciex, and the consensus libraries from Spectrast were used to
quantify each of the (non-centroided) mzml files using our in-house
quantification software^8 (Massacre). In brief, the intensity for each
peptide is integrated over a patch in RT, m/z space that encloses the
envelope for the light and heavy peaks. After collapsing data in the RT
dimension, the light and heavy peaks are fit to a multinomial distribu-
tion (a function of the chemical formula of each peptide) using a least
squares Fourier transform convolution routine^9 , which yields the rela-
tive intensity of the light and heavy species. The ratio of the unlabelled
to labelled peak intensity is obtained for each peptide in each sample.
A confidence measure for each fit is calculated from a support vector
machine (SVM) trained on a large set of user scoring events.
The relative protein level for each protein in each sample is obtained
as a ratio by taking the weighted median (using the SVM score) of the
ratios of all its corresponding peptides.
Uncertainty of individual measurements
Biological replicates show the following typical uncertainties in meas-
ured quantities: growth rate, roughly 5%; lag times, roughly 15% for long
lag times (longer than 1 h). Short lag times (less than 1 h) show higher
relative variabilities.
Reporting summary
Further information on research design is available in the Nature
Research Reporting Summary linked to this paper.
Data availability
Lag times are provided in Supplementary Tables 2, 3. All other data are
found in downloadable Excel files for each figure. Data for Fig. 3a were
taken from ref.^3 and are deposited with the paper on the Molecular
Systems Biology website. Source data are provided with this paper.
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Acknowledgements We thank A. Murray for helpful comments and suggestions, and V.
Patsalo for technical support and development of the proteomics method. M.B.
acknowledges a SystemsX.ch fellowship. T. Honda acknowledges a Japan Student Services
Organization (JASSO) long-term graduate fellowship award. Work in the Hwa laboratory is
supported by the National Institutes of Health (NIH) through grant R01GM109069 and by the
Simons Foundation through grant 330378. J.R.W. acknowledges NIH support through grant
R01GM118850.Author contributions M.B., T. Hwa and U.S. designed the study. Experiments were performed
by M.B., T. Honda, M.H., Y.-F.C., E.L., A.M., H.O., B.R.T., J.M.S. and C.S., and all authors
contributed to the analysis of experimental data. Specifically, lag times for E. coli were
measured by M.B. and T. Honda. Metabolite measurements and analysis were performed by
M.B. and M.H. Proteomics measurements were performed by T. Honda and H.O. Proteomics
data analysis was performed by T. Honda, D.C., J.M.S and J.R.W. Genetic constructs were made
by M.B., Y.-F.C and H.O. Lag times for S. cerevisiae and B. subtilis were measured by Y.-F.C.
Growth rates for B. thetaiotaomicron were measured by B.R.T. Experiments for single-cell lag
phases from microfluidics and plates were performed and analysed by E.L., A.M. and C.S. M.B.,
D.C., T. Hwa and U.S. developed the model. M.B., J.P., T. Hwa and U.S. wrote the paper and the
Supplementary Information.Competing interests The authors declare no competing interests.Additional information
Supplementary information is available for this paper at https://doi.org/10.1038/s41586-020-
2505-4.
Correspondence and requests for materials should be addressed to M.B., T.H. or U.S.
Peer review information Nature thanks Jeff Gore, Christopher Marx, Arjan de Visser and the
other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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