Modeling Microbial Growth Curves with GCAT
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Modeling Microbial Growth Curves with GCAT Yury V. Bukhman & Nathan W. DiPiazza & Jeff Piotrowski & Jason Shao & Adam G. W. Halstead & Minh Duc Bui & Enhai Xie & Trey K. Sato
# Springer Science+Business Media New York 2015
Abstract In this work, we introduce the Growth Curve Analysis Tool (GCAT). GCAT is designed to enable efficient analysis of high-throughput microbial growth curve data collected from cultures grown in microtiter plates. GCAT is accessible through a web browser, making it easy to use and operating system independent. GCAT implements fitting of global sigmoid curve models and local regression (LOESS) model. We assess the relative merits of these approaches using experimental data. Additionally, GCAT implements heuristics to deal with some peculiarities of growth curve data commonly encountered in bioenergy research. GCAT server is publicly available at http://gcat-pub.glbrc.org/. The source code is available at http://code.google.com/p/gcat-hts/.
Keywords Growth curves . Cell-based assays . HTS . Software
Electronic supplementary material The online version of this article (doi:10.1007/s12155-015-9584-3) contains supplementary material, which is available to authorized users. Y. V. Bukhman (*) : N. W. DiPiazza : J. Piotrowski : M. D. Bui : E. Xie : T. K. Sato Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, University of Wisconsin – Madison, 1552 University Ave, Madison, WI 53726, USA e-mail: [email protected] J. Shao Department of Biostatistics, University of Washington, F-600, Health Sciences Building, Box 357232, Seattle, WA 98195-7232, USA A. G. W. Halstead Department of Medicine, University of Wisconsin – Madison, Room 304, 310 N Midvale Blvd., Madison, WI 53705, USA
Introduction Measurement of microbial growth curves is of great utility in microbial research as a tool for characterizing strain phenotypes. Typically, a growth curve is generated by monitoring the optical density of a liquid culture. Growth curves are usually sigmoid in shape. However, more complex patterns often arise as the result of various phenomena such as diauxic shifts, flocculation, cell death, etc. Growth curves are modeled by fitting specialized sigmoid equations or using local regression methods. Once a growth curve has been modeled, it is possible to estimate its essential properties, such as lag time, specific growth rate, and maximum growth plateau value. Modern instrumentation enables simultaneous measurement of dozens or even hundreds of growth curves in microtiter plates, with dozens or hundreds of data points in each curve [1]. The results can be used to screen microbes for desirable characteristics, such as the ability to divide rapidly in certain media, attempt to link phenotypes to genotypes in large strain collections, or characterize effects of various growth conditions [2–10]. A number of growth curve analysis software options are available commercially and in the public domain. Public domain offerings include R packages, such as grofit [11], cellGrowth [12], and opm [13, 14].
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