Phenomenological Model of the Clavulanic Acid production, inhibitor of the nzymes β-lactamases using a method of multiple regression analysis nonlinear

被引:0
|
作者
Gomez, N. A. [1 ]
Sanchez, C. P. [1 ]
Quintero, J. C. [1 ]
机构
[1] Univ Antioquia, Dept Chem Engn, Medellin, Colombia
关键词
Clavulanic Acid; Modeling; Numerical methods; STREPTOMYCES-CLAVULIGERUS;
D O I
暂无
中图分类号
R-058 [];
学科分类号
摘要
The filamentous bacterium Streptomyces clavuligerus (Sc), is of great commercial interest and a metabolite of interest is Clavulanic Acid (CA), a potent inhibitor of the enzyme beta-lactamase that confers resistance to beta-lactam antibiotics to microorganisms both Gram positive and Gram negative. The fermentation was performed in a bioreactor 3L with a working volume of 1 L, 28 degrees C, 1 vvm, 500 rpm and a pH of 6.8 in batch. Samples were taken every 24 hours for the determinations analytical, from which obtains the kinetic behavior of the fermentation. The model shows the formation of biomass (X), the consumption of substrate (S) and the formation of product (P). Kinetic parameters were estimated from the Levengerg-Marquardt method. The experimental data used in the simulation, modeling and parametric adjustment were obtained using a chemically defined medium. It performed a sensitivity analysis of the parameters, varying each parameter within a range of +/- 10% of the optimal value of the parameter. Model obtained represent the kinetics and experimental behavior of the production process of secondary metabolite which helps make the process more economical.
引用
收藏
页数:4
相关论文
共 50 条
  • [1] Phenomenological model of the clavulanic acid production process utilizing Streptomyces clavuligerus
    Baptista-Neto, A
    Gouveia, ER
    Badino, AC
    Hokka, CO
    BRAZILIAN JOURNAL OF CHEMICAL ENGINEERING, 2000, 17 (4-7) : 809 - 818
  • [2] Phenomenological model of the clavulanic acid production process utilizing Streptomyces clavuligerus
    Baptista-Neto, A.
    Gouveia, E.R.
    Badino, Jr., A.C.
    Hokka, C.O.
    Brazilian Journal of Chemical Engineering, 2000, 17 (04) : 809 - 818
  • [3] Using nonlinear regression method in oil production prediction
    Sun, ZS
    Dong, F
    PROCEEDINGS OF THE 3RD WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-5, 2000, : 2092 - 2094
  • [4] Predicting the production rate of diamond wire saws using multiple nonlinear regression analysis
    Sadegheslam, Golsa
    Mikaeil, Reza
    Rooki, Reza
    Ghadernejad, Saleh
    Ataei, Mohammad
    GEOSYSTEM ENGINEERING, 2013, 16 (04) : 275 - 285
  • [5] Model evaluation and selection in multiple nonlinear regression analysis
    Jekabsons, G.
    Lavendels, J.
    Sitikovs, V.
    MATHEMATICAL MODELLING AND ANALYSIS, 2007, 12 (01) : 81 - 90
  • [6] Prediction Model of Multiple Linear Regression Analysis in Grain Production
    Li, Zhuoshi
    Cao, Xuejun
    Ding, Xiaoqi
    Chen, Hang
    PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON INFORMATION ENGINEERING FOR MECHANICS AND MATERIALS, 2015, 21 : 1290 - 1293
  • [7] A PHENOMENOLOGICAL STUDY OF RADIATION-EFFECTS DATA USING MULTIPLE REGRESSION ANALYSIS TECHNIQUES
    MARKWORTH, AJ
    EGGERS, PE
    TRANSACTIONS OF THE AMERICAN NUCLEAR SOCIETY, 1968, 11 (01): : 154 - +
  • [8] Motion estimation method using multiple linear regression model
    Kim, HS
    Lee, JC
    Park, KT
    VISUAL COMMUNICATIONS AND IMAGE PROCESSING '97, PTS 1-2, 1997, 3024 : 600 - 607
  • [9] Monitoring of nonlinear respiratory elastance using a multiple linear regression analysis
    Muramatsu, K
    Yukitake, K
    Nakamura, M
    Matsumoto, I
    Motohiro, Y
    EUROPEAN RESPIRATORY JOURNAL, 2001, 17 (06) : 1158 - 1166
  • [10] A Robust Estimation Method for Nonlinear Model Coefficients Using Ridge Regression
    Xu, Qiang
    Zhang, Wei
    Wang, Guizhen
    Xia, Xiangjie
    Liu, Ying
    Tang, Youxi
    2020 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY (ISSPIT 2020), 2020,