Araştırma Makalesi
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Yıl 2024, Cilt: 42 Sayı: 3, 778 - 786, 12.06.2024

Öz

Kaynakça

  • [1] Blackman FF. Optima and limiting factors. Ann Bot 1905;19:281–295.
  • [2] Srinivasan B. A guide to the Michaelis-Menten equation: steady state and beyond. FEBS J 2022;289:6086–6098. [CrossRef]
  • [3] Liu Y. Overview of some theoretical approaches for derivation of the Monod equation. Appl Microbiol Biotechnol 2007;73:1241–1250. [CrossRef]
  • [4] Wang ZW, Li Y. A theoretical derivation of the contois equation for kinetic modeling of the microbial degradation of insoluble substrate. Biochem Eng J 2014;82:134–138. [CrossRef]
  • [5] Chae KJ, Jang A, Yim SK, Kim IS. The effects of digestion temperature and temperature shock on the biogas yields from the mesophilic anaerobic digestion of swine manure. Bioresour Technol 2008;99:1–6. [CrossRef]
  • [6] Halmi MIE, Shukor MS, Johari WLW, Shukor MY. Mathematical modeling of the growth kinetics of bacillus sp. on tannery effluent containing chromate. J Environ Bioremediat Toxicol 2014;2:6–10. [CrossRef]
  • [7] Kong JD. Modeling microbial dynamics: Effects on environmental and human health. Available at: https://era.library.ualberta.ca/items/0191844e-958e-49fb- bc42-feec802a29ea/view/b06920dd-a80e-427e-b7b9-4aa9433c579e/Kong_Jude_D_201708_PhD.pdf. Accessed on May 10, 2024.
  • [8] Mrwebi M. Testing Monod: Growth rate as a function of glucose concentration in saccharomyces cerevisiae. Available at: https://scholar.sun.ac.za/server/api/core/bitstreams/41fb1789-7052-4446-82e4-a96e843b9231/content. Accessed on May 10, 2024.
  • [9] Moser H. Structure and dynamics of bacterial populations maintained in the chemostat. Cold Spring Harb Symp Quant Biol 1957;22:121–137. [CrossRef]
  • [10] Mahanta DJ, Borah M, Saikia P. A study on kinetic models for analysing the bacterial growth rate. Am Intl J Res Sci Technol Eng Math 2014;8:68–72.
  • [11] Krishnan J, Kishore AA, Suresh A, Murali AK, Vasudevan J. Biodegradation, kinetics of Azo Dye mixture: Substrate inhibition modeling. Res J Pharm Biol Chem Sci 2017;8:365–375.
  • [12] Delhomenie MC, Nikiema J, Bibeau Heitz M. A new method to determine the microbial kinetic parameters in biological air filters. Chem Eng Sci 2008;63:4126– 4134. [CrossRef]
  • [13] Dutta K, Venkata DV, Mahanty B, Anand PA. Substrate inhibition growth kinetics for cutinase producing pseudomonas cepacia using tomato-peel extracted cutin. Chem Biochem Eng Q 2015;29:437–445. [CrossRef]
  • [14] Dey S, Mukherje S. Performance and kinetic evaluation of phenol biodegradation by mixed microbial culture in a batch reactor. Int J Water Resour Environ Eng 2010;3:40–49.
  • [15] Singh N, Balomajumd C. Batch growth kinetic studies for elimination of phenol and cyanide using mixed microbial culture. J Water Process Eng 2016;11:130– 137. [CrossRef]
  • [16] Wu H, Fleng YL, Li H, Wang HJ, Wang JJ. Co-metabolism kinetics and electrogenesis change during cyanide degradation in a microbial fuel cell. Royal Soc Chem Adv 2018;8:40407–40416. [CrossRef]
  • [17] Muloiwa M, Nyende-Byakika S, Dinka M. (2020) Comparison of unstructured kinetic bacterial growth models. South Afr J Chem Eng 2020;33:141–150. [CrossRef]
  • [18] Borah M, Mahanta DJ. Rapid parameter estimation of three parameter non-linear growth models. Int J Math Arch 2013;4:274–282.
  • [19] Kapur JN, Saxena HC. Mathematical Statistics.New Delhi: S. Chand; 2003.
  • [20] Saikia P, Mahanta DJ. An approach to estimate the parameters of schnute growth model for growth of Babul (Acacia Nilotica) trees in India. J Interdiscip Math 2020;23:403–412. [CrossRef]
  • [21] Mohanty MP, Brahmacharimayum B, Ghosh PK. Effects of phenol on sulfate reduction by mixed microbial culture: kinetics and bio-kinetics analysis. Water Sci Technol 2018;77:1079–1088. [CrossRef]
  • [22] Choi HJ, Lee SY. Advances in micro-algal biomass/bioenergy production with agricultural by-products: Analysis with various growth rate models. Environ Eng Res 2019;24:271–278. [CrossRef]
  • [23] Ardestani F, Shafiei S. Non-structured kinetic model for the cell growth of saccharomyces cerevisiae in a Batch Culture, Iranian (Iranica) J Energy Environ 2014;5:8–12. [CrossRef]

Rapid parameter estimation of four non-linear growth models for analyzing the growth of Escherichia Coli

Yıl 2024, Cilt: 42 Sayı: 3, 778 - 786, 12.06.2024

Öz

In this paper we develop five new methods of estimation to estimate the parameters of four widely used nonlinear models namely Haldane, Powell, Moser and Webb model. A standard growth data set of Escherichia Coli is considered for estimating the parameters. The estimated model parameters are analyzed by evaluating statistical parameters χ2, Root Mean Square Error, R2, R2 a and R2 pre. As a result, the Powell model gives the best fit with estimation of R2 as 99.7% with respect to method IV. Moreover, the other three models also provide remarkable fit along with the newly introduced methods. Method II gives R2 value as 99% in case of the Haldane model. The method IV estimates with R2 value as 99.6% in the Moser model and the method III estimates with R2 value as 99.4% in case of the Webb model.

Kaynakça

  • [1] Blackman FF. Optima and limiting factors. Ann Bot 1905;19:281–295.
  • [2] Srinivasan B. A guide to the Michaelis-Menten equation: steady state and beyond. FEBS J 2022;289:6086–6098. [CrossRef]
  • [3] Liu Y. Overview of some theoretical approaches for derivation of the Monod equation. Appl Microbiol Biotechnol 2007;73:1241–1250. [CrossRef]
  • [4] Wang ZW, Li Y. A theoretical derivation of the contois equation for kinetic modeling of the microbial degradation of insoluble substrate. Biochem Eng J 2014;82:134–138. [CrossRef]
  • [5] Chae KJ, Jang A, Yim SK, Kim IS. The effects of digestion temperature and temperature shock on the biogas yields from the mesophilic anaerobic digestion of swine manure. Bioresour Technol 2008;99:1–6. [CrossRef]
  • [6] Halmi MIE, Shukor MS, Johari WLW, Shukor MY. Mathematical modeling of the growth kinetics of bacillus sp. on tannery effluent containing chromate. J Environ Bioremediat Toxicol 2014;2:6–10. [CrossRef]
  • [7] Kong JD. Modeling microbial dynamics: Effects on environmental and human health. Available at: https://era.library.ualberta.ca/items/0191844e-958e-49fb- bc42-feec802a29ea/view/b06920dd-a80e-427e-b7b9-4aa9433c579e/Kong_Jude_D_201708_PhD.pdf. Accessed on May 10, 2024.
  • [8] Mrwebi M. Testing Monod: Growth rate as a function of glucose concentration in saccharomyces cerevisiae. Available at: https://scholar.sun.ac.za/server/api/core/bitstreams/41fb1789-7052-4446-82e4-a96e843b9231/content. Accessed on May 10, 2024.
  • [9] Moser H. Structure and dynamics of bacterial populations maintained in the chemostat. Cold Spring Harb Symp Quant Biol 1957;22:121–137. [CrossRef]
  • [10] Mahanta DJ, Borah M, Saikia P. A study on kinetic models for analysing the bacterial growth rate. Am Intl J Res Sci Technol Eng Math 2014;8:68–72.
  • [11] Krishnan J, Kishore AA, Suresh A, Murali AK, Vasudevan J. Biodegradation, kinetics of Azo Dye mixture: Substrate inhibition modeling. Res J Pharm Biol Chem Sci 2017;8:365–375.
  • [12] Delhomenie MC, Nikiema J, Bibeau Heitz M. A new method to determine the microbial kinetic parameters in biological air filters. Chem Eng Sci 2008;63:4126– 4134. [CrossRef]
  • [13] Dutta K, Venkata DV, Mahanty B, Anand PA. Substrate inhibition growth kinetics for cutinase producing pseudomonas cepacia using tomato-peel extracted cutin. Chem Biochem Eng Q 2015;29:437–445. [CrossRef]
  • [14] Dey S, Mukherje S. Performance and kinetic evaluation of phenol biodegradation by mixed microbial culture in a batch reactor. Int J Water Resour Environ Eng 2010;3:40–49.
  • [15] Singh N, Balomajumd C. Batch growth kinetic studies for elimination of phenol and cyanide using mixed microbial culture. J Water Process Eng 2016;11:130– 137. [CrossRef]
  • [16] Wu H, Fleng YL, Li H, Wang HJ, Wang JJ. Co-metabolism kinetics and electrogenesis change during cyanide degradation in a microbial fuel cell. Royal Soc Chem Adv 2018;8:40407–40416. [CrossRef]
  • [17] Muloiwa M, Nyende-Byakika S, Dinka M. (2020) Comparison of unstructured kinetic bacterial growth models. South Afr J Chem Eng 2020;33:141–150. [CrossRef]
  • [18] Borah M, Mahanta DJ. Rapid parameter estimation of three parameter non-linear growth models. Int J Math Arch 2013;4:274–282.
  • [19] Kapur JN, Saxena HC. Mathematical Statistics.New Delhi: S. Chand; 2003.
  • [20] Saikia P, Mahanta DJ. An approach to estimate the parameters of schnute growth model for growth of Babul (Acacia Nilotica) trees in India. J Interdiscip Math 2020;23:403–412. [CrossRef]
  • [21] Mohanty MP, Brahmacharimayum B, Ghosh PK. Effects of phenol on sulfate reduction by mixed microbial culture: kinetics and bio-kinetics analysis. Water Sci Technol 2018;77:1079–1088. [CrossRef]
  • [22] Choi HJ, Lee SY. Advances in micro-algal biomass/bioenergy production with agricultural by-products: Analysis with various growth rate models. Environ Eng Res 2019;24:271–278. [CrossRef]
  • [23] Ardestani F, Shafiei S. Non-structured kinetic model for the cell growth of saccharomyces cerevisiae in a Batch Culture, Iranian (Iranica) J Energy Environ 2014;5:8–12. [CrossRef]
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Biyokimya ve Hücre Biyolojisi (Diğer)
Bölüm Research Articles
Yazarlar

Udoy Narayan Gogoi Bu kişi benim 0000-0003-2297-2700

Pallabi Saikia Bu kişi benim 0000-0001-8297-878X

Dimpal Jyoti Mahanta Bu kişi benim 0000-0003-0344-7433

Yayımlanma Tarihi 12 Haziran 2024
Gönderilme Tarihi 27 Temmuz 2022
Yayımlandığı Sayı Yıl 2024 Cilt: 42 Sayı: 3

Kaynak Göster

Vancouver Narayan Gogoi U, Saikia P, Mahanta DJ. Rapid parameter estimation of four non-linear growth models for analyzing the growth of Escherichia Coli. SIGMA. 2024;42(3):778-86.

IMPORTANT NOTE: JOURNAL SUBMISSION LINK https://eds.yildiz.edu.tr/sigma/