Structured kinetic model for Xanthomonas campestris growth

• Published in: Enzyme and microbial technology Vol. 34; no. 6; pp. 583 - 594
• Main Authors: Garcia-Ochoa, F., Santos, V.E., Alcon, A.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01-05-2004; Elsevier Science
• Subjects: Biological and medical sciences; Biotechnology; Cell model; Flow cytometry; Fundamental and applied biological sciences. Psychology; Intracellular compounds; Kinetic modeling; Xanthomonas campestris; Xanthomonas campestris; Flow cytometry; Kinetic modeling; Cell model; Intracellular compounds; Pseudomonadales; Kinetic model; Growth; Bacteria; Pseudomonadaceae; Intracellular
• ISSN: 0141-0229; 1879-0909
• DOI: 10.1016/j.enzmictec.2004.01.005

A structured kinetic model for
Xanthomonas campestris growth is proposed. The formulation of the model is made according to those steps employed for kinetic modeling of reaction networks. First, simplified reaction schemes are proposed, which are formed by four or five reactions using lumping and pseudosteady-state assumption for different compounds. Several expressions for the kinetic equations have been considered, checking their validity with the evolution of experimental data taking into account the different reactions in the scheme of reactions assumed. Afterwards, parameter values are calculated by regression to experimental data, using simple and multiple non-linear techniques. Then, model discrimination is carried out using both statistical and physical restrictions. The model finally proposed has four key compounds: ammonium, RNA, DNA and intracellular proteins. The analysis of the intracellular compounds (RNA, DNA and proteins) has been carried out using an experimental technique based in flow cytometry. Three experiments (under the same operational conditions but with different initial nitrogen concentrations in the medium) have been fitted to the model to obtain the values of the parameters, showing a prediction very close to the experimental data. The model is able to predict different system evolutions for different nitrogen initial concentrations.