Perspectives in the Modeling and Optimization of PHB Production by Pure and Mixed Cultures

Patnaik, Pratap R.

doi:10.1080/07388550500301438

Cited by 60 publications

(44 citation statements)

References 114 publications

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“…Thus, the solution of complex problems with several control variables satisfied the approximation error tolerance 141 . According to Patnaik 48 ,there is evidence that the results achieved by the model described above are "inferior" to those presented by Gadkar et al 140 . If so, the model complexity did not sufficiently point out the expected positive contributions.…”

mentioning

confidence: 76%

“…Simultaneously with the progress in kinetic modeling, the application of mathematical models in reactor designs, process optimization, substrate feeding strategies, copolymers production, one/two stage continuous cultivations and mixed cultures had increased. The reader can find all the situations described above, along with appropriate comments attached, in the review paper presented by Patnaik 48 who divided the described models into three groups:…”

Section: Modeling Approachesmentioning

confidence: 99%

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Mathematical Modelling as a Tool for Optimized PHA Production

Novák

Koller

Braunegg

et al. 2015

Chem.Biochem.Eng.Q.

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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.

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mentioning

confidence: 76%

Section: Modeling Approachesmentioning

confidence: 99%

Mathematical Modelling as a Tool for Optimized PHA Production

Novák

Koller

Braunegg

et al. 2015

Chem.Biochem.Eng.Q.

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“…The spatial variations also require sampling of data from a number of locations, thereby exacerbating and delaying on-line data analysis and control. These difficulties either invalidate laboratory-scale kinetic models or render them too inaccurate for nonideal conditions [19]. Large nonideal bioreactors are described more faithfully by intelligent models with minimum or no reliance on mechanistic kinetics.…”

Section: Phb Biosynthesis With Single and Mixed Culturesmentioning

confidence: 99%

“…These methods too have a learning ability, which is mathematical instead of the biological learning embodied in cybernetic methods. All three types of models for the fermentative production of PHB have been reviewed by Patnaik [19].…”

Section: Kinetic Modelsmentioning

confidence: 99%

Cognitive optimization of microbial PHB production in an optimally dispersed bioreactor by single and mixed cultures

Patnaik

2008

Bioprocess Biosyst Eng

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Cognitive (or intelligent) models are often superior to mechanistic models for nonideal bioreactors. Two kinds of cognitive models--cybernetic and neural--were applied recently to fed-batch fermentation by Ralstonia eutropha in a bioreactor with optimum finite dispersion. In the present work, these models have been applied in simulation studies of co-cultures of R. eutropha and Lactobacillus delbrueckii. The results for both cognitive and mechanistic models have been compared with single cultures. Neural models were the most effective for both types of cultures and mechanistic models the least effective. Simulations with co-culture fermentations predicted more PHB than single cultures with all three types of models. Significantly, the predicted enhancements in PHB concentration by cognitive methods for mixed cultures were four to five times larger than the corresponding increases in biomass concentration. Further improvements are possible through a hybrid combination of all three types of models.

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“…PHB being non-growth associated). PHB degradation was not considered here knowing that in nitrogen free media with excess of carbon, the rate of degradation of PHB by hydrolysis is very low and does not affect the biopolymer content in the time frame used for fermentation [35]. Neither was cell death considered at his stage.…”

Section: Poly-3-hydroxybutyrate Detectionmentioning

confidence: 99%

Building a predictive model for PHB production from glycerol

Rivero

Sun

Theodoropoulos

et al. 2016

Biochemical Engineering Journal

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Poly-3-hydroxybutyrate (PHB) is a biodegradable biopolyester with plastic like properties, which on its own or as part of a heteropolymer, finds application in everyday products, competing directly with fossil fuel based plastics in terms of physical and mechanical properties. In nature, PHB is produced as an energy reservoir for the host cell, when environmental conditions limit growth. It is this inherent condition for PHB synthesis (i.e. an environment unsuitable for growth) that challenges design of conventional batch production systems. Balance between growth (driven by nitrogen availability) and PHB production (enhanced by an excess of carbon) is the critical aspect for consideration in such designs. However, selecting the best operating conditions is not obvious for this system and so a systematic approach has been used in this paper, utilising simulations based on a purpose built model to supplement experimental studies.The interaction between the carbon and nitrogen sources (glycerol and ammonium sulphate respectively) was carefully evaluated and incorporated into a low-structured model able to describe the dynamics of substrate consumption and product accumulation during C.necator DSM 545 cultivation at small scale. The kinetic parameters thus determined have been assumed to be constant, fixed accordingly, and the model used to predict the fermentation profiles for different operating conditions. Results showed good agreement with experimental data, supporting the efficacy of this approach. The dual utilization of multiple substrates suggests there is a system capacity to which both growth and PHB production contribute and that sets the maximum total biomass concentration. A logistic type term added to both growth and product rate equations enabled the effective decoupling of cell proliferation and PHB accumulation for a wide range of scenarios. In this way, the combination of predictive modelling and experimental verification potentially reduces, by a significant amount, the number of experiments required to establish operational targets such as specific growth rate and productivity as well as identifying often sought criteria such as optimum C:N ratio. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 List of symbols:Half saturation constant for glycerol (g/L)Inhibition constant for PHB production (g/L)Half saturation constant for nitrogen (g/L)Half saturation constant for PHB (g/L)Carrying capacity (g/L)Hill coefficient (-)Nitrogen concentration (g/L)Yield of PHB based on glycerol (g/g)Yield of non-PHB biomass based on nitrogen (g/g)Yield of non-PHB biomass based on glycerol (g/g)

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Perspectives in the Modeling and Optimization of PHB Production by Pure and Mixed Cultures

Cited by 60 publications

References 114 publications

Mathematical Modelling as a Tool for Optimized PHA Production

Mathematical Modelling as a Tool for Optimized PHA Production

Cognitive optimization of microbial PHB production in an optimally dispersed bioreactor by single and mixed cultures

Building a predictive model for PHB production from glycerol

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