H Jonathan G Lindström1, Ran Friedman2. 1. Department of Chemistry and Biomedical Sciences, Linnaeus University, 391 82, Kalmar, Sweden. 2. Department of Chemistry and Biomedical Sciences, Linnaeus University, 391 82, Kalmar, Sweden. ran.friedman@lnu.se.
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.
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.
Authors: Brian J Skaggs; Mercedes E Gorre; Ann Ryvkin; Michael R Burgess; Yongming Xie; Yun Han; Evangelia Komisopoulou; Lauren M Brown; Joseph A Loo; Elliot M Landaw; Charles L Sawyers; Thomas G Graeber Journal: Proc Natl Acad Sci U S A Date: 2006-12-12 Impact factor: 11.205
Authors: Edwin F Juarez; Roy Lau; Samuel H Friedman; Ahmadreza Ghaffarizadeh; Edmond Jonckheere; David B Agus; Shannon M Mumenthaler; Paul Macklin Journal: BMC Syst Biol Date: 2016-09-21