Mathematical Modelling as a Tool for Optimized PHA Production

Abstract

c ARENA -Association for resource efficient and sustainable technologies, Inffeldgasse 21b, 8010 Graz, AustriaThe 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.

Batch Cultivation Model for Biopolymer Production

“…Additionally to the improvements through genetic modifications, mathematical models have proved to be a relevant tool to enhance biopolymers production 8 . On the one hand, metabolic approaches are focused on metabolic pathways for PHA production, these can reflect the intracellular dynamics.…”

Building a predictive model for PHB production from glycerol

“…With essentially fixed yields (at least until more genetic information is released), and the substrate choice limited by the microorganism or by regional availability, reducing the cost gap between petroleum based plastics and bioplastics requires better fermentation process design, which brings the need for reliable kinetic and design models [18]. These can be of very different types, depending on the sort of information used for their development.…”

Composition of mixed culture PHA biopolymers and implications for downstream processing

“…Mathematical modelling and process/strain optimisation are tools to be exploited in order to enhance the productivity and properties of PHA producing microbial cultures. Existing mathematical models describing PHA storage can be divided into kinetic, low-structured, dynamic, metabolic (high-structured), cybernetic, and neural networks (Novak et al 2015).…”

“…Metabolic models vary in complexity, from the simple cases, where the metabolic pathway comprises two or three enzymatic reactions, to more intricate metabolic networks that represent the main catabolic and anabolic routes of the cellular metabolism (Novak et al 2015). (Beun et al 2000, Beun et al 2002 are based on the seven reactions described by van Aalst-van Leeuwen et al (1997) for Paracoccus pantotrophus.…”

“…The use of mathematical tools that exploit existing knowledge with respect to the cell metabolic networks have the potential to accurately describe and optimise PHA production. Stoichiometric models make use of fixed relationship between extracellular substrates, products and intracellular metabolites (Novak et al 2015). There are numerous methodologies which make use of these metabolic networks and the assumption of the pseudo-steady state to obtain deeper insights into cell metabolism.…”