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      Inferring time-dependent population growth rates in cell cultures undergoing adaptation

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      BMC Bioinformatics
      BioMed Central
      Growth rate, Adaptation, Cell counting

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          Abstract

          Background

          The population growth rate is an important characteristic of any cell culture. During sustained experiments, the growth rate may vary due to competition or adaptation. For instance, in presence of a toxin or a drug, an increasing growth rate indicates that the cells adapt and become resistant. Consequently, time-dependent growth rates are fundamental to follow on the adaptation of cells to a changing evolutionary landscape. However, as there are no tools to calculate the time-dependent growth rate directly by cell counting, it is common to use only end point measurements of growth rather than tracking the growth rate continuously.

          Results

          We present a computer program for inferring the growth rate over time in suspension cells using nothing but cell counts, which can be measured non-destructively. The program was tested on simulated and experimental data. Changes were observed in the initial and absolute growth rates, betraying resistance and adaptation.

          Conclusions

          For experiments where adaptation is expected to occur over a longer time, our method provides a means of tracking growth rates using data that is normally collected anyhow for monitoring purposes. The program and its documentation are freely available at https://github.com/Sandalmoth/ratrack under the permissive zlib license.

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          Most cited references25

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          Snakemake--a scalable bioinformatics workflow engine.

          Snakemake is a workflow engine that provides a readable Python-based workflow definition language and a powerful execution environment that scales from single-core workstations to compute clusters without modifying the workflow. It is the first system to support the use of automatically inferred multiple named wildcards (or variables) in input and output filenames. http://snakemake.googlecode.com. johannes.koester@uni-due.de.
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            Is Open Access

            Raincloud plots: a multi-platform tool for robust data visualization

            Across scientific disciplines, there is a rapidly growing recognition of the need for more statistically robust, transparent approaches to data visualization. Complementary to this, many scientists have called for plotting tools that accurately and transparently convey key aspects of statistical effects and raw data with minimal distortion. Previously common approaches, such as plotting conditional mean or median barplots together with error-bars have been criticized for distorting effect size, hiding underlying patterns in the raw data, and obscuring the assumptions upon which the most commonly used statistical tests are based. Here we describe a data visualization approach which overcomes these issues, providing maximal statistical information while preserving the desired ‘inference at a glance’ nature of barplots and other similar visualization devices. These “raincloud plots” can visualize raw data, probability density, and key summary statistics such as median, mean, and relevant confidence intervals in an appealing and flexible format with minimal redundancy. In this tutorial paper, we provide basic demonstrations of the strength of raincloud plots and similar approaches, outline potential modifications for their optimal use, and provide open-source code for their streamlined implementation in R, Python and Matlab ( https://github.com/RainCloudPlots/RainCloudPlots). Readers can investigate the R and Python tutorials interactively in the browser using Binder by Project Jupyter.
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              Extreme slow growth as alternative strategy to survive deep starvation in bacteria

              Bacteria can become dormant or form spores when they are starved for nutrients. Here, we find that non-sporulating Bacillus subtilis cells can survive deep starvation conditions for many months. During this period, cells adopt an almost coccoid shape and become tolerant to antibiotics. Unexpectedly, these cells appear to be metabolically active and show a transcriptome profile very different from that of stationary phase cells. We show that these starved cells are not dormant but are growing and dividing, albeit with a doubling time close to 4 days. Very low nutrient levels, comparable to 10,000-fold diluted lysogeny broth (LB), are sufficient to sustain this growth. This extreme slow growth, which we propose to call ‘oligotrophic growth state’, provides an alternative strategy for B. subtilis to endure nutrient depletion and environmental stresses. Further work is warranted to test whether this state can be found in other bacterial species to survive deep starvation conditions.
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                Author and article information

                Contributors
                jonathan.lindstrom@lnu.se
                ran.friedman@lnu.se
                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                17 December 2020
                17 December 2020
                2020
                : 21
                : 583
                Affiliations
                GRID grid.8148.5, ISNI 0000 0001 2174 3522, Department of Chemistry and Biomedical Sciences, , Linnaeus University, ; 391 82 Kalmar, Sweden
                Author information
                http://orcid.org/0000-0001-8696-3104
                Article
                3887
                10.1186/s12859-020-03887-7
                7745411
                95895476-cf9b-4d4e-8017-9f3cf33582b3
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 1 July 2020
                : 18 November 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100002794, Cancerfonden;
                Award ID: CAN 2015/387
                Award ID: CAN 2018/362
                Award Recipient :
                Funded by: Linnaeus University
                Categories
                Software
                Custom metadata
                © The Author(s) 2020

                Bioinformatics & Computational biology
                growth rate,adaptation,cell counting
                Bioinformatics & Computational biology
                growth rate, adaptation, cell counting

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