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Current Biotechnology

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ISSN (Print): 2211-5501
ISSN (Online): 2211-551X

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Research Article

Modelling Cell Growth and Polyhydroxyalkanoate (PHA) Polymer Synthesis by Pseudomonas Putida LS46 under Oxygen-Limiting Conditions

Author(s): Shabnam Sharifyazd, Masoud Asadzadeh and David B. Levin*

Volume 11, Issue 1, 2022

Published on: 24 January, 2022

Page: [39 - 50] Pages: 12

DOI: 10.2174/2211550111666211216111054

Price: $65

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Abstract

Background: Polyhydroxyalkanoates (PHAs) are biodegradable, biocompatible, and non-toxic polymers synthesized by bacteria that may be used to displace some petroleum-based plastic materials. One of the major barriers to the commercialization of PHA biosynthesis is the high cost of production.

Objective: Oxygen-limitation is known to greatly influence bacterial cell growth and PHA production. In this study, the growth and synthesis of medium chain length PHAs (mcl-PHAs) by Pseudomonas putida LS46, cultured in batch-mode with octanoic acid, under oxygen-limited conditions, was modeled.

Methods: Four models, including the Monod model, incorporated Leudeking-Piret (MLP), the Moser model incorporated Leudeking-Piret (Moser-LP), the Logistic model incorporated Leudeking- Piret (LLP), and the Modified Logistic model incorporated Leudeking-Piret (MLLP) were investigated. Kinetic parameters of each model were calibrated using the multi-objective optimization algorithm, Pareto Archived Dynamically Dimensioned Search (PA-DDS), by minimizing the sum of absolute error (SAE) for PHA production and growth simultaneously.

Results and Conclusion: Among the four models, MLP and Moser-LP models adequately represented the experimental data for oxygen-limited conditions. However, the MLP and Moser-LP models could not adequately simulate PHA production under oxygen-excess conditions. Modeling cell growth and PHA will assist in the development of a strategy for industrial-scale production.

Keywords: Bioprocess engineering, Fermentation, Pseudomonas putida, Polyhydroxyalkanoates, Batch kinetic modeling, Dissolved oxygen, Multi-objective optimization.

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