Probing the Kinetic Anabolism of Poly-Beta-Hydroxybutyrate in Cupriavidus necator H16 Using Single-Cell Raman Spectroscopy

Poly-beta-hydroxybutyrate (PHB) can be formed in large amounts in Cupriavidus necator and is important for the industrial production of biodegradable plastics. In this investigation, laser tweezers Raman spectroscopy (LTRS) was used to characterize dynamic changes in PHB content—as well as in the contents of other common biomolecule—in C. necator during batch growth at both the population and single-cell levels. PHB accumulation began in the early stages of bacterial growth, and the maximum PHB production rate occurred in the early and middle exponential phases. The active biosynthesis of DNA, RNA, and proteins occurred in the lag and early exponential phases, whereas the levels of these molecules decreased continuously during the remaining fermentation process until the minimum values were reached. The PHB content inside single cells was relatively homogenous in the middle stage of fermentation; during the late growth stage, the variation in PHB levels between cells increased. In addition, bacterial cells in various growth phases could be clearly discriminated when principle component analysis was performed on the spectral data. These results suggest that LTRS is a valuable single-cell analysis tool that can provide more comprehensive information about the physiological state of a growing microbial population.

Optimal control analysis of the dynamic growth behavior of microorganisms

Understanding the growth behavior of microorganisms using modeling and optimization techniques is an active area of research in the fields of biochemical engineering and systems biology. In this paper, we propose a general modeling framework, based on Monod model, to model the growth of microorganisms. Utilizing the general framework, we formulate an optimal control problem with the objective of maximizing a long-term cellular goal and solve it analytically under various constraints for the growth of microorganisms in a two substrate batch environment. We investigate the relation between long term and short term cellular goals and show that the objective of maximizing cellular concentration at a fixed final time is equivalent to maximization of instantaneous growth rate. We then establish the mathematical connection between the generalized framework and optimal and cybernetic modeling frameworks and derive generalized governing dynamic equations for optimal and cybernetic models. We finally illustrate the influence of various constraints in the cybernetic modeling framework on the optimal growth behavior of microorganisms by solving several dynamic optimization problems using genetic algorithms.

Microbial production of poly(hydroxybutyrate) from C₁ carbon sources

Polyhydroxybutyrate (PHB) is an attractive substitute for petrochemical plastic due to its similar properties, biocompatibility, and biodegradability. The cost of scaled-up PHB production inhibits its widespread usage. Intensive researches are growing to reduce costs and improve thermomechanical, physical, and processing properties of this green biopolymer. Among cheap substrates which are used for reducing total cost of PHB production, some C₁ carbon sources, e.g., methane, methanol, and CO₂ have received a great deal of attention due to their serious role in greenhouse problem. This article reviews the fundamentals of strategies for reducing PHA production and moves on to the applications of several cheap substrates with a special emphasis on methane, methanol, and CO₂. Also, some explanation for involved microorganisms including the hydrogen-oxidizing bacteria and methanotrophs, their history, culture condition, and nutritional requirements are given. After description of some important strains among the hydrogen-oxidizing and methanotrophic producers of PHB, the article is focused on limitations, threats, and opportunities for application and their future trends.

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

Poly(beta-hydroxybutyrate) or PHB is an important member of the family of polyhydroxyalkanoates with properties that make it potentially competitive with synthetic polymers. In addition, PHB is biodegradable. While the biochemistry of PHB synthesis by microorganisms is well known, improvement of large-scale productivity requires good fermentation modeling and optimization. The latter aspect is reviewed here. Current models are of two types: (i) mechanistic and (ii) cybernetic. The models may be unstructured or structured, and they have been applied to single cultures and co-cultures. However, neither class of models expresses adequately all the important features of large-scale non-ideal fermentations. Model-independent neural networks provide faithful representations of observations, but they can be difficult to design. So hybrid models, combining mechanistic, cybernetic and neural models, offer a useful compromise. All three kinds of basic models are discussed with applications and directions toward hybrid model development.

A Simple Structured Mathematical Model for Biopolymer (PHB) Production

Economic production technology for a biodegradable polymer (poly-beta-hydroxybutyrate, PHB) is urgently required to replace conventional polymers, which have an inherent disadvantage of staying in the environment forever. Various approaches have been applied for improving the productivity and reducing the production cost, which are considered to be the two major problems associated with industrial production of PHB. One of the engineering approaches to improve PHB productivity could be to design and implement model-based fed-batch cultivations to provide desirable nutrient availability. In the present study, growth and intracellular biopolymer storage kinetics of Ralstonia eutropha was studied in a batch cultivation process. It featured 19.7 g/L biomass and 10.89 g/L PHB with a productivity of 0.18 g/L.h. The effect of carbon, nitrogen, and phosphate limitations and inhibitions on growth was studied in detail. A structured model featuring typical growth limitations and/or possible inhibitions was then proposed. The value of the model parameters was found by minimizing the difference between experimental value and model simulation at all data points and for all process variables. The optimal batch model parameter values obtained above were used to solve the differential equations numerically. The simulated data obtained in this way was then compared with the experimental data to establish the validity of the batch model. The proposed model was then compared with literature reported mathematical models to reconfirm its accuracy. Statistical validity of the developed model and historical models to describe the observed experimental kinetics was then investigated to reinforce the accuracy of the developed simple model.

"Intelligent" descriptions of microbial kinetics in finitely dispersed bioreactors: neural and cybernetic models for PHB biosynthesis by Ralstonia eutropha

Background

For many microbial processes, the complexity of the metabolisms and the responses to transient and realistic conditions are difficult to capture in mechanistic models. The cells seem to have an innate intelligence that enables them to respond optimally to environmental changes. Some "intelligent" models have therefore been proposed and compared with a mechanistic model for fed-batch cultures of Ralstonia eutropha.

Results

Two kinds of models have been proposed to describe such cellular behavior. Cybernetic models are derived through postulates of cellular intelligence and memory, and neural models use artificial intelligence through neural networks. Some competing models of both kinds have been compared for their ability to portray and optimize the synthesis of poly-β-hydroxybutyrate by Ralstonia eutropha in fed-batch cultures with finite dispersion. Neural models enabled the formation of more of the polymer than cybernetic models, with lesser utilization of the carbon and nitrogen substrates. Both types of models were decidedly superior to a mechanistic model used as a reference, thus supporting the value of intelligent descriptions of microbial kinetics in incompletely dispersed bioreactors.

Conclusion

Neural and cybernetic models describe and optimize unsteady state fed-batch microbial reactors with finite dispersion more effectively than mechanistic models. However, these "intelligent" models too have weaknesses, and hence a hybrid approach combining such models with some mechanistic features is suggested.

Genetic network driven control of PHBV copolymer composition

We developed a detailed mathematical model describing the coupling between the molecular weight distribution dynamics of poly(3-hydroxybutyrate-co-3hydroxyvalerate) (PHBV) copolymer chains with those of hydroxybutyrate (HB) and hydroxyvalerate (HV) monomer formation. Sensitivity analysis of the model revealed that both the monomer composition and the molecular weight distribution of the copolymer chains are strongly affected by the ratio between the rates at which the two-monomer units are incorporated into the chains. This ratio depends on the relative HB and HV availability, which in turn is a function of the expression levels of genes encoding enzymes that catalyze monomer formation. Regulation of gene expression was accomplished through the aid of an artificial genetic network, the patterns of expression of which can be controlled by appropriately tuning the concentration of an extracellular inducer. Extensive simulations were used to study the effects of operating conditions and parameter uncertainties on the range of achievable copolymer compositions. Since the predicted conditions fell in the range of feasible bioprocessing manipulations, it is expected that such strategy could be successfully employed. Thus, the presented model constitutes a powerful tool for designing genetic networks that can drive the formation of PHBV copolymer structures with desirable characteristics.

Empirical modeling of batch fermentation kinetics for poly(glutamic acid) production and other microbial biopolymers

An empirical kinetic model is proposed for the batch production of poly(glutamic acid) from Bacillus subtilis IFO 3335. In addition, the proposed model was used to fit the kinetic data of poly(glutamic acid) production from other bacterial strains using different media, as well as kinetic data from different strains for the production of the exocellular biopolymers dextran, hyaluronic acid, xanthan, alginate, and the endocellular biopolymer polyhydroxybutyrate. The empirical model treats the biopolymer as a component of the biomass and fits the experimental biomass data using a sigmoidal relationship that includes the maximum specific growth rate, mu(max), and the substrate saturation parameter, K(S). An empirical parameter, the relative coefficient (r), quantifies, in relative terms, the degree of nongrowth-associated biopolymer formation.

Microbial metabolism as an evolutionary response: the cybernetic approach to modeling

The growth and metabolic capabilities of microorganisms depend on their interactions with the culture medium. Many media contain two or more key substrates, and an organism may have different preferences for the components. Microorganisms adjust their preferences according to the prevailing conditions so as to favor their own survival. Cybernetic modeling describes this evolutionary strategy by defining a goal that an organism tries to attain optimally at all times. The goal is often, but not always, maximization of growth, and it may require the cells to manipulate their metabolic processes in response to changing environmental conditions. The cybernetic approach overcomes some of the limitations of metabolic control analysis (MCA), but it does not substitute MCA. Here we review the development of the cybernetic modeling of microbial metabolism, how it may be combined with MCA, and what improvements are needed to make it a viable technique for industrial fermentation processes.

Are microbes intelligent beings?:

Microorganisms growing in a multi-substrate medium have different and varying preferences for the various components of the medium. The preferences depend on the operating conditions and the substrates may be utilized sequentially or simultaneously. Sometimes an organism may change its preferences among substrates and/or switch between sequential and simultaneous utilization. These aspects are difficult to describe through models based on chemical and physical laws alone. Cybernetic modeling ascribes to microorganisms the ability to perceive their environment (i.e. the growth medium) and make 'intelligent' choices regarding substrate utilization to maximize an objective, which is usually the growth rate. This article reviews the development of cybernetic modeling since it began in 1982. Different workers have suggested different perspectives of how microbes make optimal use of their resources. These are discussed and future directions for improvement are indicated.

Dynamics and modeling on fermentative production of poly (beta-hydroxybutyric acid) from sugars via lactate by a mixed culture of Lactobacillus delbrueckii and Alcaligenes eutrophus

The mixed culture system was considered in the present research where sugars such as glucose were converted to lactate by Lactobacillus delbrueckii and the lactate was converted to poly beta-hydroxybutyrate (PHB) by Alcaligenes eutrophus in one fermentor. For the modeling of the effect of NH3 concentration on the cell growth of A. eutrophus and PHB production rates, metabolic flux distributions were computed at two culture phases of cell growth and PHB production periods. It was found that the NADPH, generated through isocitrate dehydrogenate in TCA cycle, was predominantly utilized for the reaction from alpha-ketoglutalate to glutamate when NH3 was abundant, while it tended to be utilized for the PHB production through acetoacetyl CoA reductase as NH3 concentration decreased. This phenomenon was reflected in the development of mathematical model. In the mixed culture experiments, the two phases were observed, namely the lactate production phase due to L. delbrueckii and the lactate consumption phase due to A. eutrophus. The lactate concentration could be estimated on-line by the amount of NaOH solution and HCl solution supplied to keep the culture pH at constant level. Several mixed culture experiments were conducted to see the dynamics of the system. Finally, a mathematical model which can describe the dynamic behavior of the present mixed culture was developed and the model parameters were tuned for fitting the experimental data. The model may be used for several purposes such as control, optimization, and understanding process dynamics etc.

Application of cybernetic models to metabolic engineering: investigation of storage pathways

A cybernetic model is proposed to examine generic features of storage pathways. This model is capable of describing synthesis of carbon and non-carbon storage polymers. The effect of environmental conditions is evaluated using storage polymer level as a fraction of total biomass as a gauge of pathway performance. The base wild-type pathway is then analyzed to determine the effect of genetic alterations upon system performance. Proposed modifications are tested using the cybernetic model as a diagnostic tool to ascertain the ramifications of potential genetic alterations. A methodology is developed within the cybernetic framework to describe alterations of enzyme activity and over-expression of pathway enzymes. Copyright 1998 John Wiley & Sons, Inc.

Multi-inhibition kinetic model for the growth ofAlcaligenes eutrophus

During the growth phase of Alcaligenes eutrophus, it was observed that the concentrations of glucose and ammonium ions are inhibitory at a certain level, which affects the specific growth rate. Moreover, it has been established from previous work that (an) unidentified substance(s) inhibit(s) the growth at biomass concentrations above 10 g∙L−1. A model taking into account these multiple inhibitory factors is proposed to predict the growth of A. eutrophus. The model parameters were determined by batch experiments except for the parameter associated with inhibition at high biomass concentration. This parameter was estimated by a classical optimization method. The validity of the model was tested by comparing the model predictions with experimental data obtained in batch and transient continuous fermentations and with literature data.Key words: A. eutrophus, inhibitory product, high biomass concentration.