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Modeling Microbial Growth Curves with GCAT

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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/.

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Notes

  1. Users are advised to take care choosing appropriate blank values and OD transform options. For example, in many experiments, OD ranges approximately from 0 to 1. A relatively small δ, e.g., 0.1, can be more appropriate in such cases. Using a large δ affects growth curve shape in a way that parameters such as specific growth rate do not have their customary meanings. The standard log(x) transform is advised if blank OD is known and inoculate OD is sufficiently high.

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Acknowledgments

We gratefully acknowledge Drs. David Benton, Richard LeDuc, Peris Navarro, and Steven Slater for encouragement and stimulating discussions. James McCurdy and Michael H. Whitney contributed to GCAT software development. Branden Timm was instrumental in the deployment of GCAT software and gave valuable advice on security. This work was funded by the DOE Great Lakes Bioenergy Research Center (DOE BER Office of Science DE-FC02-07ER64494).

Conflict of Interest

The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, University of Wisconsin – Madison, 1552 University Ave, Madison, WI, 53726, USA

    Yury V. Bukhman, Nathan W. DiPiazza, Jeff Piotrowski, Minh Duc Bui, Enhai Xie & Trey K. Sato

  2. Department of Biostatistics, University of Washington, F-600, Health Sciences Building, Box 357232, Seattle, WA, 98195-7232, USA

    Jason Shao

  3. Department of Medicine, University of Wisconsin – Madison, Room 304, 310 N Midvale Blvd., Madison, WI, 53705, USA

    Adam G. W. Halstead

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  1. Yury V. Bukhman
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  2. Nathan W. DiPiazza
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  3. Jeff Piotrowski
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  7. Enhai Xie
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  8. Trey K. Sato
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Correspondence to Yury V. Bukhman.

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Bukhman, Y.V., DiPiazza, N.W., Piotrowski, J. et al. Modeling Microbial Growth Curves with GCAT. Bioenerg. Res. 8, 1022–1030 (2015). https://doi.org/10.1007/s12155-015-9584-3

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