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.
authors.affiliation: Department of Food Science and Human Nutrition, University of Illinois-Urbana Champaign, 905 South Goodwin Ave, Urbana, IL 61801, United States.
authors.forename: Jennifer L
authors.initials: JL
authors.lastname: Hoeflinger
authors.affiliation: Department of Food Science and Human Nutrition, University of Illinois-Urbana Champaign, 905 South Goodwin Ave, Urbana, IL 61801, United States.
authors.forename: Daniel E
authors.initials: DE
authors.lastname: Hoeflinger
authors.affiliation: Department of Food Science and Human Nutrition, University of Illinois-Urbana Champaign, 905 South Goodwin Ave, Urbana, IL 61801, United States. Electronic address: mille216@illinois.edu.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Miller
mesh_terms: D002241:Carbohydrates / Q000737:chemistry; D003470:Culture Media / Q000737:chemistry; D005069:Evaluation Studies as Topic; D007778:Lactobacillus / Q000254:growth & development* / Q000302:isolation & purification*; D011381:Programming Languages; D012044:Regression Analysis; D015203:Reproducibility of Results
chemical_list: D002241:Carbohydrates; D003470:Culture Media
keywords: Bacterial growth; Growth kinetics; Logistic growth; Python; Regression analysis