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Applied and Environmental Microbiology, May 2005, p. 2355-2364, Vol. 71, No. 5
0099-2240/05/$08.00+0     doi:10.1128/AEM.71.5.2355-2364.2005
Copyright © 2005, American Society for Microbiology. All Rights Reserved.

Use of Stochastic Models To Assess the Effect of Environmental Factors on Microbial Growth

José Miguel Ponciano,¹ Frederik P. J. Vandecasteele,^2,5 Thomas F. Hess,² Larry J. Forney,³ Ronald L. Crawford,^4,5 and Paul Joyce^1,6*

Departments of Mathematics,¹ Statistics,⁶ Biological and Agricultural Engineering,² Biological Sciences,³ Microbiology, Molecular Biology and Biochemistry,⁴ Environmental Biotechnology Institute, University of Idaho, Moscow, Idaho 83844⁵

Received 21 September 2004/ Accepted 16 November 2004

We present a novel application of a stochastic ecological model to the study and analysis of microbial growth dynamics as influenced by environmental conditions in an extensive experimental data set. The model proved to be useful in bridging the gap between theoretical ideas in ecology and an applied problem in microbiology. The data consisted of recorded growth curves of Escherichia coli grown in triplicate in a base medium with all 32 possible combinations of five supplements: glucose, NH₄Cl, HCl, EDTA, and NaCl. The potential complexity of 2⁵ experimental treatments and their effects was reduced to 2² as just the metal chelator EDTA, the presumed osmotic pressure imposed by NaCl, and the interaction between these two factors were enough to explain the variability seen in the data. The statistical analysis showed that the positive and negative effects of the five chemical supplements and their combinations were directly translated into an increase or decrease in time required to attain stationary phase and the population size at which the stationary phase started. The stochastic ecological model proved to be useful, as it effectively explained and summarized the uncertainty seen in the recorded growth curves. Our findings have broad implications for both basic and applied research and illustrate how stochastic mathematical modeling coupled with rigorous statistical methods can be of great assistance in understanding basic processes in microbial ecology.

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* Corresponding author. Mailing address: Department of Mathematics and Department of Statistics, University of Idaho, Moscow, ID 83844-1103. Phone: (208) 885-6338. Fax: (208) 885-5843. E-mail: joyce{at}uidaho.edu.

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Applied and Environmental Microbiology, May 2005, p. 2355-2364, Vol. 71, No. 5
0099-2240/05/$08.00+0     doi:10.1128/AEM.71.5.2355-2364.2005
Copyright © 2005, American Society for Microbiology. All Rights Reserved.

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