Microbial production of polyhydroxybutyrate with tailor-made properties: an integrated modelling approach and experimental validation

The microbial production of polyhydroxybutyrate (PHB) is a complex process in which the final quantity and quality of the PHB depend on a large number of process operating variables. Consequently, the design and optimal dynamic operation of a microbial process for the efficient production of PHB with tailor-made molecular properties is an extremely interesting problem. The present study investigates how key process operating variables (i.e., nutritional and aeration conditions) affect the biomass production rate and the PHB accumulation in the cells and its associated molecular weight distribution. A combined metabolic/polymerization/macroscopic modelling approach, relating the process performance and product quality with the process variables, was developed and validated using an extensive series of experiments and measurements. The model predicts the dynamic evolution of the biomass growth, the polymer accumulation, the consumption of carbon and nitrogen sources and the average molecular weights of the PHB in a bioreactor, under batch and fed-batch operating conditions. The proposed integrated model was used for the model-based optimization of the production of PHB with tailor-made molecular properties in Azohydromonas lata bacteria. The process optimization led to a high intracellular PHB accumulation (up to 95% g of PHB per g of DCW) and the production of different grades (i.e., different molecular weight distributions) of PHB.     

A combined metabolic/polymerization kinetic model on the microbial production of poly(3-hydroxybutyrate)

In the present work, an integrated dynamic metabolic/polymerization kinetic model is developed for the prediction of the intracellular accumulation profile and the molecular weight distribution of poly(3-hydroxybutyrate) (P(3HB) or PHB) produced in microbial cultures. The model integrates two different length/time scales by combining a polymerization kinetic model with a metabolic one. The bridging point between the two models is the concentration of the monomer unit (i.e. 3-hydroxybutyryl-CoA) produced during the central aerobic carbon metabolism. The predictive capabilities of the proposed model are assessed by the comparison of the calculated biopolymer concentration and number average molecular weight with available experimental data obtained from batch and fed-batch cultures of Alcaligenes eutrophus and Alcaligenes latus. The accuracy of the proposed model was found to be satisfactory, setting this model a valuable tool for the design of the process operating profile for the production of different polymer grades with desired molecular properties.     

Model-based intensification of a fed-batch microbial process for the maximization of polyhydroxybutyrate (PHB) production rate

An integrated metabolic–polymerization–macroscopic model, describing the microbial production of polyhydroxybutyrate (PHB) in Azohydromonas lata bacteria, was developed and validated using a comprehensive series of experimental measurements. The model accounted for biomass growth, biopolymer accumulation, carbon and nitrogen sources utilization, oxygen mass transfer and uptake rates and average molecular weights of the accumulated PHB, produced under batch and fed-batch cultivation conditions. Model predictions were in excellent agreement with experimental measurements. The validated model was subsequently utilized to calculate optimal operating conditions and feeding policies for maximizing PHB productivity for desired PHB molecular properties. More specifically, two optimal fed-batch strategies were calculated and experimentally tested: (1) a nitrogen-limited fed-batch policy and (2) a nitrogen sufficient one. The calculated optimal operating policies resulted in a maximum PHB content (94% g/g) in the cultivated bacteria and a biopolymer productivity of 4.2 g/(l h), respectively. Moreover, it was demonstrated that different PHB grades with weight average molecular weights of up to 1513 kg/mol could be produced via the optimal selection of bioprocess operating conditions.

     

An in silico erythropoiesis model rationalizing synergism between stem cell factor and erythropoietin

Stem cell factor (SCF) and erythropoietin (EPO) are two most recognized growth factors that play in concert to control in vitro erythropoiesis. However, exact mechanisms underlying the interplay of these growth factors in vitro remain unclear. We developed a mathematical model to study co-signaling effects of SCF and EPO utilizing the ERK1/2 and GATA-1 pathways (activated by SCF and EPO) that drive the proliferation and differentiation of erythroid progenitors. The model was simplified and formulated based on three key features: synergistic contribution of SCF and EPO on ERK1/2 activation, positive feedback effects on proliferation and differentiation, and cross-inhibition effects of activated ERK1/2 and GATA-1. The model characteristics were developed to correspond with biological observations made known thus far. Our simulation suggested that activated GATA-1 has a more dominant cross-inhibition effect and stronger positive feedback response on differentiation than the proliferation pathway, while SCF contributed more to the activation of ERK1/2 than EPO. A sensitivity analysis performed to gauge the dynamics of the system was able to identify the most sensitive model parameters and illustrated a contribution of transient activity in EPO ligand to growth factor synergism. Based on theoretical arguments, we have successfully developed a model that can simulate growth factor synergism observed in vitro for erythropoiesis. This hypothesized model can be applied to further computational studies in biological systems where synergistic effects of two ligands are seen.     

Lipase activity in Thermus thermophilus HB8: Purification and characterization of the extracellular enzyme

In this study, the lipolytic activity of Thermus thermophilus HB8 was examined. The addition of various oils increased the production of extracellular lipolytic activity, while a combination of olive oil and glucose increased both extracellular and intracellular lipolytic activity. The oxygen transfer rate had a significant influence on both biomass and production of extra- or intra-cellular lipolytic activity. The formation of white halos due to the hydrolysis of oleic acid ester (Tween 80) in agar plates containing Nile Blue and the formation of Ca²⁺-oleate indicated the secretion of lipase. When the cell-free supernatant of cells grown in basal reach medium or the corresponding intracellular extract were electrophoresed under denatured and renatured conditions, using ??-naphthyl acetate and Fast Blue RR, major bands at 56 kDa or 62 and 32 kDa were observed, respectively. The 56 kDa extracellular enzyme was partial purified and characterized. Its peak of activity occurred at 80°C and pH 7.0, while the T_1/2 was 1 h at 100°C. The K _m of the partial purified enzyme was 1 mM and the V _max was 0.044 U/mL/min when using p-nitrophenyl laurate as substrate. The presence of Ca²⁺ and Hg²⁺ stimulated lipase activity, whereas Zn²⁺, Co²⁺, or EDTA inhibited lipase activity. The highest activity was observed in the presence of coconut oil and p-nitrophenyl laurate (pNPL). Purified lipase was the most stable in the presence of various organic solvents, such as pentanol, chloroform and n-dodecane. Because of the superior thermostability and stability in the presence of organic solvents of T. thermophilus extracellular lipase, this lipase holds great promise for use in industrial applications.     

Modelling the Delta1/Notch1 pathway: in search of the mediator(s) of neural stem cell differentiation

The Notch1 signalling pathway has been shown to control neural stem cell fate through lateral inhibition of mash1, a key promoter of neuronal differentiation. Interaction between the Delta1 ligand of a differentiating cell and the Notch1 protein of a neighbouring cell results in cleavage of the trans-membrane protein, releasing the intracellular domain (NICD) leading to the up regulation of hes1. Hes1 homodimerisation leads to down regulation of mash1. Most mathematical models currently represent this pathway up to the formation of the HES1 dimer. Herein, we present a detailed model ranging from the cleavage of the NICD and how this signal propagates through the Delta1/Notch1 pathway to repress the expression of the proneural genes. Consistent with the current literature, we assume that cells at the self renewal state are represented by a stable limit cycle and through in silico experimentation we conclude that a drastic change in the main pathway is required in order for the transition from self-renewal to differentiation to take place. Specifically, a model analysis based approach is utilised in order to generate hypotheses regarding potential mediators of this change. Through this process of model based hypotheses generation and testing, the degradation rates of Hes1 and Mash1 mRNA and the dissociation constant of Mash1-E47 heterodimers are identified as the most potent mediators of the transition towards neural differentiation.     

Improving the accuracy of flux balance analysis through the implementation of carbon availability constraints for intracellular reactions

Constraint-based modeling methods, such as Flux Balance Analysis (FBA), have been extensively used to decipher complex, information rich -omics datasets to elicit system-wide behavioral patterns of cellular metabolism. FBA has been successfully used to gain insight in a wide range of applications, such as range of substrate utilization, product yields and to design metabolic engineering strategies to improve bioprocess performance. A well-known challenge associated with large genome-scale metabolic networks is that they result in underdetermined problem formulations. Consequently, rather than unique solutions, FBA and related methods examine ranges of reaction flux values that are consistent with the studied physiological conditions. The wider the reported flux ranges, the higher the uncertainty in the determination of basic reaction properties, limiting interpretability of and confidence in the results. Herein, we propose a new, computationally efficient approach that refines flux range predictions by constraining reaction fluxes on the basis of the elemental balance of carbon. We compared carbon constraint FBA (ccFBA) against experimentally-measured intracellular fluxes using the latest CHO GEM (iCHO1766) and were able to substantially improve the accuracy of predicted flux values compared with FBA. ccFBA can be used as a stand-alone method but is also compatible with and complimentary to other constraint-based approaches.     

Improving Embryonic Stem Cell Expansion through the Combination of Perfusion and Bioprocess Model Design

Background

High proliferative and differentiation capacity renders embryonic stem cells (ESCs) a promising cell source for tissue engineering and cell-based therapies. Harnessing their potential, however, requires well-designed, efficient and reproducible expansion and differentiation protocols as well as avoiding hazardous by-products, such as teratoma formation. Traditional, standard culture methodologies are fragmented and limited in their fed-batch feeding strategies that afford a sub-optimal environment for cellular metabolism. Herein, we investigate the impact of metabolic stress as a result of inefficient feeding utilizing a novel perfusion bioreactor and a mathematical model to achieve bioprocess improvement.

Methodology/Principal Findings

To characterize nutritional requirements, the expansion of undifferentiated murine ESCs (mESCs) encapsulated in hydrogels was performed in batch and perfusion cultures using bioreactors. Despite sufficient nutrient and growth factor provision, the accumulation of inhibitory metabolites resulted in the unscheduled differentiation of mESCs and a decline in their cell numbers in the batch cultures. In contrast, perfusion cultures maintained metabolite concentration below toxic levels, resulting in the robust expansion (>16-fold) of high quality ‘naïve’ mESCs within 4 days. A multi-scale mathematical model describing population segregated growth kinetics, metabolism and the expression of selected pluripotency (‘stemness’) genes was implemented to maximize information from available experimental data. A global sensitivity analysis (GSA) was employed that identified significant (6/29) model parameters and enabled model validation. Predicting the preferential propagation of undifferentiated ESCs in perfusion culture conditions demonstrates synchrony between theory and experiment.

Conclusions/Significance

The limitations of batch culture highlight the importance of cellular metabolism in maintaining pluripotency, which necessitates the design of suitable ESC bioprocesses. We propose a novel investigational framework that integrates a novel perfusion culture platform (controlled metabolic conditions) with mathematical modeling (information maximization) to enhance ESC bioprocess productivity and facilitate bioprocess optimization.