A dynamic regression analysis tool for quantitative assessment of bacterial growth written in Python

Jennifer L. Hoeflinger; Daniel E. Hoeflinger; Michael J. Miller

doi:10.1016/j.mimet.2016.11.015

A dynamic regression analysis tool for quantitative assessment of bacterial growth written in Python

Jennifer L. Hoeflinger, Daniel E. Hoeflinger, Michael J. Miller

Research output: Contribution to journal › Article › peer-review

Abstract

Herein, an open-source method to generate quantitative bacterial growth data from high-throughput microplate assays is described. The bacterial lag time, maximum specific growth rate, doubling time and delta OD are reported. Our method was validated by carbohydrate utilization of lactobacilli, and visual inspection revealed 94% of regressions were deemed excellent.

Original language	English (US)
Pages (from-to)	83-85
Number of pages	3
Journal	Journal of Microbiological Methods
Volume	132
DOIs	https://doi.org/10.1016/j.mimet.2016.11.015
State	Published - Jan 1 2017

Keywords

Bacterial growth
Growth kinetics
Logistic growth
Python
Regression analysis

ASJC Scopus subject areas

Microbiology
Molecular Biology
Microbiology (medical)

Online availability

10.1016/j.mimet.2016.11.015

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title = "A dynamic regression analysis tool for quantitative assessment of bacterial growth written in Python",

abstract = "Herein, an open-source method to generate quantitative bacterial growth data from high-throughput microplate assays is described. The bacterial lag time, maximum specific growth rate, doubling time and delta OD are reported. Our method was validated by carbohydrate utilization of lactobacilli, and visual inspection revealed 94% of regressions were deemed excellent.",

keywords = "Bacterial growth, Growth kinetics, Logistic growth, Python, Regression analysis",

author = "Hoeflinger, {Jennifer L.} and Hoeflinger, {Daniel E.} and Miller, {Michael J.}",

note = "Publisher Copyright: {\textcopyright} 2016 Elsevier B.V.",

year = "2017",

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doi = "10.1016/j.mimet.2016.11.015",

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AU - Hoeflinger, Daniel E.

AU - Miller, Michael J.

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AB - Herein, an open-source method to generate quantitative bacterial growth data from high-throughput microplate assays is described. The bacterial lag time, maximum specific growth rate, doubling time and delta OD are reported. Our method was validated by carbohydrate utilization of lactobacilli, and visual inspection revealed 94% of regressions were deemed excellent.

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KW - Growth kinetics

KW - Logistic growth

KW - Python

KW - Regression analysis

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A dynamic regression analysis tool for quantitative assessment of bacterial growth written in Python

Abstract

Keywords

ASJC Scopus subject areas

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