Simulation of microbial growth based on Euler’s method
DOI:
https://doi.org/10.24191/mjcet.v7i1.1329Keywords:
Microbial growth, Euler's method, Exponential model, Logistic equation, Monod equationAbstract
Microorganisms such as bacteria, fungi and yeast produce valuable metabolites when they are grown in suitable culture conditions. The cultivation condition affects the cell growth, metabolism, and product production in a sophisticated and nonlinear way. Therefore, in this research, the growth of Lactococcus lactis NZ9000 in response to the growth conditions was simulated using different growth models. The objective was to simulate the effect of temperature, agitation speed, carbon and nitrogen sources, on the cell growth using the exponential model, logistic and Monod equations. All equations were solved according to the Euler’s method using MATLAB R2021a for simulation. The experimental data used for the simulation were from literature. The accuracy of the model was expressed as percentage relative error between the maximum value of experimental and simulated data. Simulation results shows that the optimum conditions for cell growth was achieved at temperature 27°C, agitation speed of 100 rpm with glucose and peptone as the carbon and nitrogen sources respectively equation. The maximum cell concentration by logistic equation gives the lowest percentage error of 6.40% and 0.33% for the effect of temperature and agitation respectively. While Monod equation give the closest accuracy of 1.84% and 7.11% for carbon and nitrogen sources respectively. Thus, it was shown that the complexity of the microorganism growth was able to be simulated using suitable model such as logistic equation with the lowest relative error.
References
Charlebois, D. A., & Balázsi, G. (2019). Modeling cell population dynamics. In silico Biology, 13(1-2), 21–39. https://doi.org/10.3233%2FISB-180470
Desta, S. S., & Mekashew, A. M. (2022). Euler’s Method for Solving Logistic Growth Model Using MATLAB. International Journal of Systems Science and Applied Mathematics, 7(3), 60–65. https://doi.org/10.11648/j.ijssam.20220703.13
Greenspan, D. (2008). Numerical Solution of Ordinary Differential Equations: For Classical, Relativistic and Nano Systems. Wiley-VCH.
Gülnur, C. K., & Kemal, A. (2012). Step size strategies for the numerical integration of systems of differential equations. Journal of Computational and Applied Mathematics, 236(15), 3805–3816. https://doi.org/10.1016/j.cam.2011.06.032
Hagen, S. J. (2010). Exponential growth of bacteria: Constant multiplication through division. American Journal of Physics, 78(12), 1290–1296. https://doi.org/10.1119/1.3483278
Ibrahim, S. B., Mohamad, R., & Rahim, R. A. (2010). Effects of agitation speed, temperature, carbon and nitrogen sources on the growth of recombinant Lactococcus lactis NZ9000 carrying domain 1 of aerolysin gene. African Journal of Biotechnology, 9(33), 5392-5398. https://doi.org/10.5897/AJB10.149
Kargi, F. (2009). Re-interpretation of the logistic equation for batch microbial growth in relation to Monod kinetics. Letters in Applied Microbiology, 48(4), 398–401. https://doi.org/10.1111/j.1472-765X.2008.02537.x
Lin, J., Gao, L., Lin, H., Ren, Y., Lin, Y., & Lin, J. (2017). Computer Simulation of Bioprocess. InTech. https://doi.org/10.5772/67732
Macedo, J. V. C, de Barros Ranke, F. F, Escaramboni, B., Campioni, T. S., Núñez, E.G.F., & de Oliva Neto, P. (2020). Cost-effective lactic acid production by fermentation of agro-industrial residues. Biocatalysis and Agricultural Biotechnology, 27, 101706. https://doi.org/10.1016/j.bcab.2020.101706
Mühlegger, M. (2023, Dec 21). Automated Bioprocessing – 7 Advantages of Automation. Single Use Support. https://www.susupport.com/knowledge/single-use-technology/automated-bioprocessing-advantages-automation
Monod, J. (1949). The Growth of Bacterial Cultures. Annual Review of Microbiology. 3, 371–394. https://doi.org/10.1146/annurev.mi.03.100149.002103
Wachenheim, D. E., Patterson, J. A., & Ladisch, M. R. (2003). Analysis of the logistic function model: derivation and applications specific to batch cultured microorganisms. Bioresource Technology, 86(2), 157–164. https://doi: 10.1016/s0960-8524(02)00149-9
Xu, P. (2020). Analytical solution for a hybrid Logistic-Monod cell growth model in batch and continuous stirred tank reactor culture. Biotechnology and Bioengineering, 117(3), 873–878. https://doi: 10.1002/bit.27230
Zentou, H., Zainal Abidin, Z., Yunus, R., Awang Biak, D. R., Zouanti, M., & Hassani, A. (2019). Modelling of molasses fermentation for bioethanol production: A comparative investigation of Monod and Andrews models accuracy assessment. Biomolecules, 9(8), 308. https://doi.org/10.3390%2Fbiom9080308
Ziadi, M., Rezouga, F., Bouallagui, H., Baâti, L., Othman, N. B., Thonart, P., & Hamdi, M. (2010). Kinetic study of Lactococcus lactis strains (SLT6 and SLT10) growth on papain-hydrolysed whey. World Journal of Microbiology and Biotechnology, 26(12), 2223–2230. https://doi.org/10.1007/s11274-010-0407-6
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