Abstract

The potential of poly(hydroxyalkanoates) (PHAs) to replace conventional plastic materials justifies the increasing attention they have drawn both at lab-scale and in industrial biotechnology. The improvement of large-scale productivity and biochemical/genetic properties of producing strains requires mathematical modeling and process/strain optimization procedures. Current models dealing with structurally diversified PHAs, both structured and unstructured, can be divided into formal kinetic, low-structured, dynamic, metabolic (high-structured), cybernetic, neural networks and hybrid models; these attempts are summarized in this review. Characteristic properties of specific groups of models are stressed in light of their benefit to the better understanding of PHA biosynthesis, and their applicability for enhanced productivity. Unfortunately, there is no single type of mathematical model that expresses exactly all the characteristics of producing strains and/or features of industrial-scale plants; in addition, the different requirements for modelling of PHA production by pure cultures or mixed microbial consortia have to be addressed. Therefore, it is crucial to sophisticatedly adapt and fine-tune the modelling approach accordingly to actual processes, as the case arises. For “standard microbial cultivations and everyday practices”, formal kinetic models (for simple cases) and “low-structured” models will be appropriate and of great benefit. They are relatively simple and of low computational demand. To overcome the specific weaknesses of different established model types, some authors use hybrid models. Here, satisfying compromises can be achieved by combining mechanistic, cybernetic, and neural and computational fluid dynamics (CFD) models. Therefore, this hybrid modelling approach appears to constitute the most promising solution to generate a holistic picture of the entire PHA production process, encompassing all the benefits of the original modelling strategies. Complex growth media require a higher degree of model structuring. For scientific purposes and advanced development of industrial equipment in the future, real systems will be modelled by highly organized hybrid models. All solutions related to modelling PHA production are discussed in this review.

Highlights

  • Mathematical Modelling as a Tool for Optimized PHA ProductionThere is no single type of mathematical model that expresses exactly all the characteristics of producing strains and/or features of industrial-scale plants; in addition, the different requirements for modelling of PHA production by pure cultures or mixed microbial consortia have to be addressed

  • Poly(hydroxyalkanoates) (PHAs) are biodegradable intracellular polyesters synthesized by various eubacterial genera and some archaea[1,2,3,4,5]; further, the biosynthesis of PHAs in genetically modified yeasts[6] and in modified plants was reported[7]

  • After validation of the model by the experimental, laboratory scale, production process, the results indicated that the modular network, if trained with the EM algorithm, was able to organize itself in modules related to the basic biological pathways

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Summary

Mathematical Modelling as a Tool for Optimized PHA Production

There is no single type of mathematical model that expresses exactly all the characteristics of producing strains and/or features of industrial-scale plants; in addition, the different requirements for modelling of PHA production by pure cultures or mixed microbial consortia have to be addressed. For “standard microbial cultivations and everyday practices”, formal kinetic models (for simple cases) and “low-structured” models will be appropriate and of great benefit They are relatively simple and of low computational demand. Satisfying compromises can be achieved by combining mechanistic, cybernetic, and neural and computational fluid dynamics (CFD) models This hybrid modelling approach appears to constitute the most promising solution to generate a holistic picture of the entire PHA production process, encompassing all the benefits of the original modelling strategies.

Introduction
Modeling approaches
Dynamic models in PHA biosynthesis by pure and mixed cultures
Metabolic models in PHA biosynthesis
Metabolic models targeted for industrial PHA biosynthesis
Volatile fatty acids
Glucose Glycerol Pyruvate
Ralstonia eutropha
Cybernetic models in PHA biosynthesis
Neural networks and hybrid models in modelling of PHAs biosynthesis
Findings
Conclusion
Full Text
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