Distinguishing different modes of growth using single-cell data

Abstract
Data availability
Article and author information
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Abstract

Collection of high-throughput data has become prevalent in biology. Large datasets allow the use of statistical constructs such as binning and linear regression to quantify relationships between variables and hypothesize underlying biological mechanisms based on it. We discuss several such examples in relation to single-cell data and cellular growth. In particular, we show instances where what appears to be ordinary use of these statistical methods leads to incorrect conclusions such as growth being non-exponential as opposed to exponential and vice versa. We propose that the data analysis and its interpretation should be done in the context of a generative model, if possible. In this way, the statistical methods can be validated either analytically or against synthetic data generated via the use of the model, leading to a consistent method for inferring biological mechanisms from data. On applying the validated methods of data analysis to infer cellular growth on our experimental data, we find the growth of length in E. coli to be non-exponential. Our analysis shows that in the later stages of the cell cycle the growth rate is faster than exponential.

Data availability

All data generated during this study are deposited in Dataverse-:Kar, Prathitha; Tiruvadi-Krishnan, Sriram; Männik, Jaana; Männik, Jaan; Amir, Ariel, 2021, "Distinguishing different modes of growth using single-cell data", https://doi.org/10.7910/DVN/BNQUDW, Harvard Dataverse, V1

The following data sets were generated

(2021) Distinguishing different modes of growth using single-cell data
Harvard Dataverse, V1.

https://doi.org/10.7910/DVN/BNQUDW

The following previously published data sets were used

(2017) Data from long-term growth data of Escherichia coli at a single-cell level
Figshare, doi: 10.6084/m9.figshare.c.3493548.

https://doi.org/10.6084/m9.figshare.c.3493548.v1

Article and author information

Author details

Prathitha Kar

Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, United States

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-4091-6860
Sriram Tiruvadi-Krishnan

Department of Chemistry and Chemical Biology, University of Tennessee, Knoxville, United States

Competing interests
The authors declare that no competing interests exist.
Jaana Männik

Department of Chemistry and Chemical Biology, University of Tennessee, Knoxville, United States

Competing interests
The authors declare that no competing interests exist.
Jaan Männik

Department of Chemistry and Chemical Biology, The University of Tennessee, Knoxville, United States

For correspondence
jmannik@utk.edu

Competing interests
The authors declare that no competing interests exist.
Ariel Amir

Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, United States

For correspondence
arielamir@seas.harvard.edu

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-2611-0139

Funding

US-Israel BSF Research Grant (2017004)

Jaan Männik

National Institutes of Health (R01GM127413)

Jaan Männik

National Science Foundation (NSF CAREER 1752024)

Ariel Amir

National Science Foundation (NSF award 1806818)

Prathitha Kar

National Institutes of Health (NIH grant 103346)

Prathitha Kar

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.