Mathematical Modelling of the Growth of Caulobacter crescentus on Caffeine

Authors

  • Salihu Ibrahim Department of Microbiology, Gombe State University, P.M.B 127, Gombe, Nigeria.
  • Abdulrasheed Mansur Centre for Biotechnology Research, Bayero University PMB 3011 Kano, Nigeria.
  • Siti Aqlima Ahmad Department of Biochemistry, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia.

DOI:

https://doi.org/10.54987/jemat.v6i2.438

Keywords:

caffeine, Caulobacter crescentus, mathematical modelling, growth, Huang

Abstract

Caffeine is a purine alkaloid naturally found in many species of plant and can be degraded by bacteria. Prolong caffeine consumption is well-known to have serious adverse effects. The used of linearization technique using natural logarithm transformation, though standard, is erroneous and can just give an estimated value for the sole parameter measured; the specific growth rate. In this paper, for the first time we present different kinetics models such as Von Bertalanffy, Baranyi-Roberts, modified Schnute, modified Richards, modified Gompertz, modified Logistics and most recent Huang were used to get values for the above constants or parameters from Caulobacter crescentus bacterium growth on caffeine. Huang model was found to be the best model with the highest adjusted R2 value with the lowest RMSE value. The Accuracy and Bias Factors values were close to unity (1.0). The Huang parameters such as Ymax (bacterial growth upper asymptote), λ (lag time),  µmax (maximum specific bacterial growth rate) and A or Y0 (bacterial growth lower asymptote) were found to be 1.367 (95% confidence interval of 1.322 - 1.412), 2.683 (95% confidence interval of 2.030 - 3.337), 0.322 (95% confidence interval of 0.252 - 0.392) and 0.324 (95% confidence interval of 0.278 - 0.370).

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Published

31.12.2018

How to Cite

Ibrahim, S., Mansur, A., & Ahmad, S. A. (2018). Mathematical Modelling of the Growth of Caulobacter crescentus on Caffeine. Journal of Environmental Microbiology and Toxicology, 6(2), 13–17. https://doi.org/10.54987/jemat.v6i2.438

Issue

Vol. 6 No. 2 (2018)

Section

Articles

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