Characterization of Ferroplasma acidiphilum growing in pure and mixed culture with Leptospirillum ferriphilum.

Biomining is defined as biotechnology for metal recovery from minerals, and is promoted by the concerted effort of a consortium of acidophile prokaryotes, comprised of members of the Bacteria and Archaea domains. Ferroplasma acidiphilum and Leptospirillum ferriphilum are the dominant species in extremely acid environments and have great use in bioleaching applications; however, the role of each species in this consortia is still a subject of research. The hypothesis of this work is that F. acidiphilum uses the organic matter secreted by L. ferriphilum for growth, maintaining low levels of organic compounds in the culture medium, preventing their toxic effects on L. ferriphilum. To test this hypothesis, a characterization of Ferroplasma acidiphilum strain BRL-115 was made with the objective of determining its optimal growth conditions. Subsequently, under the optimal conditions, L. ferriphilum and F. acidiphilum were tested growing in each other's supernatant, in order to define if there was exchange of metabolites between the species. With these results, a mixed culture in batch cyclic operation was performed to obtain main specific growth rates, which were used to evaluate a mixed metabolic model previously developed by our group. It was observed that F. acidiphilum, strain BRL-115 is a chemomixotrophic organism, and its growth is maximized with yeast extract at a concentration of 0.04% wt/vol. From the experiments of L. ferriphilum growing on F. acidiphilum supernatant and vice versa, it was observed that in both cases cell growth is favorably affected by the presence of the filtered medium of the other microorganism, proving a synergistic interaction between these species. Specific growth rates were obtained in cyclic batch operation of the mixed culture and were used as input data for a Flux Balance Analysis of the mixed metabolic model, obtaining a reasonable behavior of the metabolic fluxes and the system as a whole, therefore consolidating the model previously developed. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1390-1396, 2016.

Genome-scale reconstruction of Salinispora tropica CNB-440 metabolism to study strain-specific adaptation.

The first manually curated genome-scale metabolic model for Salinispora tropica strain CNB-440 was constructed. The reconstruction enables characterization of the metabolic capabilities for understanding and modeling the cellular physiology of this actinobacterium. The iCC908 model was based on physiological and biochemical information of primary and specialised metabolism pathways. The reconstructed stoichiometric matrix consists of 1169 biochemical conversions, 204 transport reactions and 1317 metabolites. A total of 908 structural open reading frames (ORFs) were included in the reconstructed network. The number of gene functions included in the reconstructed network corresponds to 20% of all characterized ORFs in the S. tropica genome. The genome-scale metabolic model was used to study strain-specific capabilities in defined minimal media. iCC908 was used to analyze growth capabilities in 41 different minimal growth-supporting environments. These nutrient sources were evaluated experimentally to assess the accuracy of in silico growth simulations. The model predicted no auxotrophies for essential amino acids, which was corroborated experimentally. The strain is able to use 21 different carbon sources, 8 nitrogen sources and 4 sulfur sources from the nutrient sources tested. Experimental observation suggests that the cells may be able to store sulfur. False predictions provided opportunities to gain new insights into the physiology of this species, and to gap fill the missing knowledge. The incorporation of modifications led to increased accuracy in predicting the outcome of growth/no growth experiments from 76 to 93%. iCC908 can thus be used to define the metabolic capabilities of S. tropica and guide and enhance the production of specialised metabolites.

Stoichiometric model and flux balance analysis for a mixed culture of Leptospirillum ferriphilum and Ferroplasma acidiphilum.

The oxidation process of sulfide minerals in natural environments is achieved by microbial communities from the Archaea and Bacteria domains. A metabolic reconstruction of two dominant species, Leptospirillum ferriphilum and Ferroplasma acidiphilum, which are always found together as a mixed culture in this natural environments, was made. The metabolic model, composed of 152 internal reactions and 29 transport reactions, describes the main interactions between these species, assuming that both use ferrous iron as energy source, and F. acidiphilum takes advantage of the organic compounds secreted by L. ferriphilum for chemomixotrophic growth. A first metabolic model for a mixed culture used in bacterial leaching is proposed in this article, which pretends to represent the characteristics of the mixed culture in a simplified manner. It was evaluated with experimental data through flux balance analysis (FBA) using as objective function the maximization of biomass. The growth yields on ferrous iron obtained for each microorganism are consistent with experimental data, and the flux distribution obtained allows understanding of the metabolic capabilities of both microorganisms growing together in a bioleaching process. The model was used to simulate the growth of F. acidiphilum on different substrates, to determine in silico which compounds maximize cell growth, and which are essential. Knockout simulations were carried out for L. ferriphilum and F. acidiphilum metabolic models, predicting key enzymes of central metabolism. The results of this analysis are consistent with experimental data from literature, showing a robust behavior of the metabolic model.

Analyzing redox balance in a synthetic yeast platform to improve utilization of brown macroalgae as feedstock.

Macroalgae have high potential to be an efficient, and sustainable feedstock for the production of biofuels and other more valuable chemicals. Attempts have been made to enable the co-fermentation of alginate and mannitol by Saccharomyces cerevisiae to unlock the full potential of this marine biomass. However, the efficient use of the sugars derived from macroalgae depends on the equilibrium of cofactors derived from the alginate and mannitol catabolic pathways. There are a number of strong metabolic limitations that have to be tackled before this bioconversion can be carried out efficiently by engineered yeast cells. An analysis of the redox balance during ethanol fermentation from alginate and mannitol by Saccharomyces cerevisiae using metabolic engineering tools was carried out. To represent the strain designed for conversion of macroalgae carbohydrates to ethanol, a context-specific model was derived from the available yeast genome-scale metabolic reconstructions. Flux balance analysis and dynamic simulations were used to determine the flux distributions. The model indicates that ethanol production is determined by the activity of 4-deoxy-l-erythro-5-hexoseulose uronate (DEHU) reductase (DehR) and its preferences for NADH or NADPH which influences strongly the flow of cellular resources. Different scenarios were explored to determine the equilibrium between NAD(H) and NADP(H) that will lead to increased ethanol yields on mannitol and DEHU under anaerobic conditions. When rates of mannitol dehydrogenase and DehRNADH tend to be close to a ratio in the range 1-1.6, high growth rates and ethanol yields were predicted. The analysis shows a number of metabolic limitations that are not easily identified through experimental procedures such as quantifying the impact of the cofactor preference by DEHU reductase in the system, the low flux into the alginate catabolic pathway, and a detailed analysis of the redox balance. These results show that production of ethanol and other chemicals can be optimized if a redox balance is achieved. A possible methodology to achieve this balance is presented. This paper shows how metabolic engineering tools are essential to comprehend and overcome this limitation.

Cloning, expression and decoding of the cold adaptation of a new widely represented thermolabile subtilisin-like protease.

AIMS: Cloning, expression and characterization of a new cold-adapted protease with potential biotechnological application, isolated from Antarctic bacteria. METHOD AND RESULTS: A subtilisin-like gene was isolated from several Antarctic bacterial genus using CODPEHOP-designed primers and a genome walking method. This gene encodes a precursor protein, which undergoes an autocatalytic cleavage resulting in a 34.6 kDa active cold-adapted protease with a maximum activity at 25-35°C and optimum pH of 8.0-9.0. It showed a higher catalytic efficiency at lower temperatures compared to its mesophilic counterpart. Heat-induced inactivation resulted in a very low melting point. Local packing analysis using the homology model indicated Ala284 as an important cold-adaptation determinant, which was corroborated by the site-directed mutagenesis. CONCLUSIONS: A new thermolabile subtilisin-like protease has been successfully cloned and analysed, and an important hot spot in the evolution of the cold adaptation and substrate specificity of this enzyme was identified and tested. SIGNIFICANCE AND IMPACT OF THE STUDY: This work reports a new cold-adapted protease with a vast representation amongst Antarctic genus, suggesting therefore its evolutionary success in this cold environment. Likewise, important sites for genetic potentiation have been identified, which are extrapolated to other enzymes of the same kind.

Continuous modeling of metabolic networks with gene regulation in yeast and in vivo determination of rate parameters.

A continuous model of a metabolic network including gene regulation to simulate metabolic fluxes during batch cultivation of yeast Saccharomyces cerevisiae was developed. The metabolic network includes reactions of glycolysis, gluconeogenesis, glycerol and ethanol synthesis and consumption, the tricarboxylic acid cycle, and protein synthesis. Carbon sources considered were glucose and then ethanol synthesized during growth on glucose. The metabolic network has 39 fluxes, which represent the action of 50 enzymes and 64 genes and it is coupled with a gene regulation network which defines enzyme synthesis (activities) and incorporates regulation by glucose (enzyme induction and repression), modeled using ordinary differential equations. The model includes enzyme kinetics, equations that follow both mass-action law and transport as well as inducible, repressible, and constitutive enzymes of metabolism. The model was able to simulate a fermentation of S. cerevisiae during the exponential growth phase on glucose and the exponential growth phase on ethanol using only one set of kinetic parameters. All fluxes in the continuous model followed the behavior shown by the metabolic flux analysis (MFA) obtained from experimental results. The differences obtained between the fluxes given by the model and the fluxes determined by the MFA do not exceed 25% in 75% of the cases during exponential growth on glucose, and 20% in 90% of the cases during exponential growth on ethanol. Furthermore, the adjustment of the fermentation profiles of biomass, glucose, and ethanol were 95%, 95%, and 79%, respectively. With these results the simulation was considered successful. A comparison between the simulation of the continuous model and the experimental data of the diauxic yeast fermentation for glucose, biomass, and ethanol, shows an extremely good match using the parameters found. The small discrepancies between the fluxes obtained through MFA and those predicted by the differential equations, as well as the good match between the profiles of glucose, biomass, and ethanol, and our simulation, show that this simple model, that does not rely on complex kinetic expressions, is able to capture the global behavior of the experimental data. Also, the determination of parameters using a straightforward minimization technique using data at only two points in time was sufficient to produce a relatively accurate model. Thus, even with a small amount of experimental data (rates and not concentrations) it was possible to estimate the parameters minimizing a simple objective function. The method proposed allows the obtention of reasonable parameters and concentrations in a system with a much larger number of unknowns than equations. Hence a contribution of this study is to present a convenient way to find in vivo rate parameters to model metabolic and genetic networks under different conditions.

Extension of the selection of protein chromatography and the rate model to affinity chromatography.

The rational selection of optimal protein purification sequences, as well as mathematical models that simulate and allow optimization of chromatographic protein purification processes have been developed for purification procedures such as ion-exchange, hydrophobic interaction and gel filtration chromatography. This paper investigates the extension of such analysis to affinity chromatography both in the selection of chromatographic processes and in the use of the rate model for mathematical modelling and simulation. Two affinity systems were used: Blue Sepharose and Protein A. The extension of the theory developed previously for ion-exchange and HIC chromatography to affinity separations is analyzed in this paper. For the selection of operations two algorithms are used. In the first, the value of η, which corresponds to the efficiency (resolution) of the actual chromatography and, Σ, which determines the amount of a particular contaminant eliminated after each separation step, which determines the purity, have to be determined. It was found that the value of both these parameters is not generic for affinity separations but will depend on the type of affinity system used and will have to be determined on a case by case basis. With Blue Sepharose a salt gradient was used and with Protein A, a pH gradient. Parameters were determined with individual proteins and simulations of the protein mixtures were done. This approach allows investigation of chromatographic protein purification in a holistic manner that includes ion-exchange, HIC, gel filtration and affinity separations for the first time.

Stoichiometric model and metabolic flux analysis for Leptospirillum ferrooxidans.

A metabolic model for Leptospirillum ferrooxidans was developed based on the genomic information of an analogous iron oxidizing bacteria and on the pathways of ferrous iron oxidation, nitrogen and CO(2) assimilation based on experimental evidence for L. ferrooxidans found in the literature. From this metabolic reconstruction, a stoichiometric model was built, which includes 86 reactions describing the main catabolic and anabolic aspects of its metabolism. The model obtained has 2 degrees of freedom, so two external fluxes were estimated to achieve a determined and observable system. By using the external oxygen consumption rate and the generation flux biomass as input data, a metabolic flux map with a distribution of internal fluxes was obtained. The results obtained were verified with experimental data from the literature, achieving a very good prediction of the metabolic behavior of this bacterium at steady state.

Viral vectors for the treatment of alcoholism: use of metabolic flux analysis for cell cultivation and vector production.

The HEK293 cell line has been used for the production of adenovirus vectors to be used in the potential treatment of alcoholism using a gene therapy strategy. Culture optimization and scale-up has been achieved by first adapting the cells to serum-free media and secondly by growing them in suspension. Adenovirus production after infection was increased, resulting in higher specific glucose consumption and lactate accumulation rates compared to the growth phase. We applied media design tools and Metabolic Flux Analysis (MFA) to compare the metabolic states of cells during growth and adenovirus production and to optimize culture media according to the metabolic demand of the cells in terms of glucose and glutamine concentrations. This allowed obtaining a higher maximum cell concentration and increased adenovirus production by minimizing the production of metabolites that can have an inhibitory effect on cell growth. We have proposed a stoichiometric equation for adenovirus synthesis. MFA results allowed determination of how these changes in composition affected the way cells distribute their nutrient resources during cell growth and virus production. Virus purification was successfully achieved using chromatography and Aqueous Two-Phase Systems (ATPS).

Global gene expression in recombinant and non-recombinant yeast Saccharomyces cerevisiae in three different metabolic states.

Global gene expression of two strains of Saccharomyces cerevisiae, one recombinant (P+), accumulating large amounts of an intracellular protein Superoxide Dismutase (SOD) and one non-recombinant (P-) which does not contain the recombinant plasmid, were compared in batch culture during diauxic growth when cells were growing exponentially on glucose, when they were growing exponentially on ethanol, and in the early stationary phase when glycerol was being utilized. When comparing the gene expression for P- (and P+) during growth on ethanol to that on glucose (Eth/Gluc), overexpression is related to an increase in consumption of glycerol, activation of the TCA cycle, degradation of glycogen and metabolism of ethanol. Furthermore, 97.6% of genes (80 genes) involved in the central metabolic pathway are overexpressed. This is similar to that observed by DeRisi et al. [DeRisi, J.L., Iyer, V.R. & Brown, P.O. 1997. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278:680-686.] but very different from was observed for Metabolic Flux Analysis (MFA), where the specific growth rate is lowered to ca. 40%, the fluxes in the TCA cycle are reduced to ca. 40% (to 30% in P+), glycolysis is reduced to virtually 0 and protein synthesis to ca. 50% (to 40% in P+). Clearly it is not possible to correlate in a simple or direct way, quantitative mRNA expression levels with cell function which is shown by the Metabolic Flux Analysis (MFA). When comparing the two strains in the 3 growth stages, 4 genes were found to be under or overexpressed in all cases. The products of all of these genes are expressed at the plasma membrane or cell wall of the yeast. While comparing the strains (P+/P-) when growing on glucose, ethanol and in the early stationary phase, many of the genes of the central metabolic pathways are underexpressed in P+, which is similar to the behaviour of the metabolic fluxes of both strains (MFA). Comparing the gene expression for P- (and to some extent P+) during the early stationary phase to growth on ethanol (Stat/Eth), underexpression is generalized. This shows that the switch in metabolism between ethanol and early stationary phases has an almost instantaneous effect on gene expression but a much more retarded effect on metabolic fluxes and that the "early stationary" phase represents a "late ethanol" phase from the metabolic analysis point of view since ethanol is still present and being consumed although at a much slower rate.

A stoichiometric model of Acidithiobacillus ferrooxidans ATCC 23270 for metabolic flux analysis.

A stoichiometric model of Acidithiobacillus ferrooxidans based on the sequenced genome from strain ATCC 23270 is derived and parameterized using genome/pathway databases. The model describes the main aspects of catabolism and anabolism. By the construction and utilization of the mathematical determination of the network, metabolic flux analysis is performed for such a bacterium for the first time and results are successfully verified by comparison to literature values. This first metabolic model of A. ferrooxidans is able to simulate the main aspects of metabolism and will be useful for further investigation and improvement of bioleaching procedures.

Protein purification using chromatography: selection of type, modelling and optimization of operating conditions.

To achieve a high level of purity in the purification of recombinant proteins for therapeutic or analytical application, it is necessary to use several chromatographic steps. There is a range of techniques available including anion and cation exchange, which can be carried out at different pHs, hydrophobic interaction chromatography, gel filtration and affinity chromatography. In the case of a complex mixture of partially unknown proteins or a clarified cell extract, there are many different routes one can take in order to choose the minimum and most efficient number of purification steps to achieve a desired level of purity (e.g. 98%, 99.5% or 99.9%). This review shows how an initial 'proteomic' characterization of the complex mixture of target protein and protein contaminants can be used to select the most efficient chromatographic separation steps in order to achieve a specific level of purity with a minimum number of steps. The chosen methodology was implemented in a computer- based Expert System. Two algorithms were developed, the first algorithm was used to select the most efficient purification method to separate a protein from its contaminants based on the physicochemical properties of the protein product and the protein contaminants and the second algorithm was used to predict the number and concentration of contaminants after each separation as well as protein product purity. The application of the Expert System approach was experimentally tested and validated with a mixture of four proteins and the experimental validation was also carried out with a supernatant of Bacillus subtilis producing a recombinant beta-1,3-glucanase. Once the type of chromatography is chosen, optimization of the operating conditions is essential. Chromatographic elution curves for a three-protein mixture (alpha-lactoalbumin, ovalbumin and beta-lactoglobulin), carried out under different flow rates and ionic strength conditions, were simulated using two different mathematical models. These models were the Plate Model and the more fundamentally based Rate Model. Simulated elution curves were compared with experimental data not used for parameter identification. Deviation between experimental data and the simulated curves using the Plate Model was less than 0.0189 (absorbance units); a slightly higher deviation [0.0252 (absorbance units)] was obtained when the Rate Model was used. In order to optimize operating conditions, a cost function was built that included the effect of the different production stages, namely fermentation, purification and concentration. This cost function was also successfully used for the determination of the fraction of product to be collected (peak cutting) in chromatography. It can be used for protein products with different characteristics and qualities, such as purity and yield, by choosing the appropriate parameters.

A discrete mathematical model applied to genetic regulation and metabolic networks.

This paper describes the use of a discrete mathematical model to represent the basic mechanisms of regulation of the bacteria E. coli in batch fermentation. The specific phenomena studied were the changes in metabolism and genetic regulation when the bacteria use three different carbon substrates (glucose, glycerol, and acetate). The model correctly predicts the behavior of E. coli vis-à-vis substrate mixtures. In a mixture of glucose, glycerol, and acetate, it prefers glucose, then glycerol, and finally acetate. The model included 67 nodes; 28 were genes, 20 enzymes, and 19 regulators/biochemical compounds. The model represents both the genetic regulation and metabolic networks in an inrtegrated form, which is how they function biologically. This is one of the first attempts to include both of these networks in one model. Previously, discrete mathematical models were used only to describe genetic regulation networks. The study of the network dynamics generated 8 (2(3)) fixed points, one for each nutrient configuration (substrate mixture) in the medium. The fixed points of the discrete model reflect the phenotypes described. Gene expression and the patterns of the metabolic fluxes generated are described accurately. The activation of the gene regulation network depends basically on the presence of glucose and glycerol. The model predicts the behavior when mixed carbon sources are utilized as well as when there is no carbon source present. Fictitious jokers (Joker1, Joker2, and Repressor SdhC) had to be created to control 12 genes whose regulation mechanism is unknown, since glycerol and glucose do not act directly on the genes. The approach presented in this paper is particularly useful to investigate potential unknown gene regulation mechanisms; such a novel approach can also be used to describe other gene regulation situations such as the comparison between non-recombinant and recombinant yeast strain, producing recombinant proteins, presently under investigation in our group.

Mathematical modeling of elution curves for a protein mixture in ion exchange chromatography and for the optimal selection of operational conditions.

Elution curves in ionic exchange chromatography (IEC) for a three-protein mixture (alpha-lactoalbumin, ovalbumin, and beta-lactoglobulin), carried out under different flow rates and ionic strength conditions, were simulated using two different mathematical models. These models were the Plate Model and the more fundamentally based Rate Model. Relatively low protein concentrations were used to avoid protein-protein interactions. Simulated elution curves were compared with experimental data not used for parameter identification. Deviation between experimental data and the simulated curves using the Plate Model was less than 0.0189 (absorbance units); a slightly higher deviation [0.0252 (absorbance units)] was obtained when the Rate Model was used. A cost function was built that included the effect of the different production stages, namely fermentation, purification, and concentration. These considered the effect on the performance of IEC; yield, purity, concentration and the time needed to accomplish the separation. Operational conditions in the IEC such as flow rate, ionic strength gradient and the operational time can be selected using this model in order to find the minimum cost for the protein production process depending on the characteristics of the final product desired such as purity and yield. This cost function was successfully used for the selection of the operational conditions as well as the fraction of the product to be collected (peak cutting) in IEC. It can be used for protein products with different characteristics and qualities, such as purity and yield, by choosing the appropriate parameters.

Correlation for the partition behavior of proteins in aqueous two-phase systems: effect of surface hydrophobicity and charge.

Correlations to describe the effect of surface hydrophobicity and charge of proteins with their partition coefficient in aqueous two-phase systems were investigated. Polyethylene glycol (PEG) 4000/phosphate, sulfate, citrate, and dextran systems in the presence of low (0.6% w/w) and high (8.8% w/w) levels of NaCl were selected for a systematic study of 12 proteins. The surface hydrophobicity of the proteins was measured by ammonium sulfate precipitation as the inverse of their solubility. The hydrophobicity values measured correlated well with the partition coefficients, K, obtained in the PEG/salt systems at high concentration of NaCl (r = 0.92-0.93). In PEG/citrate systems the partition coefficient correlated well with protein hydrophobicity at low and high concentrations of NaCl (r = 0.81 and 0.93, respectively). The PEG/citrate system also had a higher hydrophobic resolution than other systems to exploit differences in the protein's hydrophobicity. The surface charge and charge density of the proteins was determined over a range of pH (3-9) by electrophoretic titration curves; PEG/salt systems did not discriminate well between proteins of different charge or charge density. In the absence of NaCl, K decreased slightly with increased positive charge. At high NaCl concentration, K increased as a function of positive charge. This suggested that the PEG-rich top phase became more negative as the concentration of NaCl in the systems increased and, therefore, attracted the positively charged proteins. The effect of charge was more important in PEG/dextran systems at low concentrations of NaCl. In the PEG/dextran systems at lower concentration of NaCl, molecular weight appeared to be the prime determinant of partition, whereas no clear effect of molecular weight could be found in PEG/salt systems.

Effect of electrostatic energy on partitioning of proteins in aqueous two-phase systems.

An attempt has been made to adopt a different approach to evaluate the effect of a protein's charge on its partitioning behaviour in PEG/salt aqueous two-phase systems (ATPS). This has been done using a computer methodology (DelPhi) that allows the calculation of the electrostatic solvation energy that charged proteins present in a particular media such as aqueous polymer-salt systems. This calculation was done for the protein in each of the phases and a correlation was investigated that related the electrostatic energy difference of the protein in each of the phases and its partition coefficient in ATPS. Such correlation resulted in a statistical model that also included the effect of molecular weight and a shape factor at each particular pH. A global correlation which included the effect of pH was also found. All the correlations were statistically evaluated and gave good results.

Is there a rational method to purify proteins? From expert systems to proteomics.

The purification of recombinant proteins for therapeutic or analytical applications requires the use of several chromatographic steps in order to achieve a high level of purity. A range of techniques is available such as anion and cation exchange chromatography, which can be carried out at different pHs, and hence used at different steps, hydrophobic interaction chromatography, gel filtration and affinity chromatography. Evidently when confronted with a complex mixture of partially unknown proteins or a clarified cell extract there are many different routes one can take in order to choose the minimum and most efficient number of purification steps to achieve a desired level of purity (e.g. 98, 99.5 or 99.9%). In this review we will show how an initial "proteomic" characterization of the complex initial mixture of target protein and protein contaminants can be used to select the most efficient chromatographic separation steps in order to achieve a maximum level of purity with a minimum number of steps. The chosen methodology was implemented in a computer based expert system. The first algorithm developed was used to select the most efficient purification method to separate a protein from its contaminants based on the physicochemical properties of the protein product and the protein contaminants. The second algorithm developed was used to predict the number and concentration of contaminants after each separation as well as protein product purity. The successful application of the expert system approach, based on an initial proteomic characterization, to the practical cases of protein mixtures and clarified fermentation supernatant is presented and discussed. The purification strategy proposed was experimentally tested and validated with a mixture of four proteins and the experimental validation was also carried out with an "unknown" supernatant of Bacillus subtilis producing a recombinant beta-1,3-glucanase. The system was robust to errors <10% which is the range that can be found in the experimental determination of the properties in the database of product and contaminants. On the other hand, the system was sensitive both to larger variations (>20%) in the properties of the contaminant database and the protein product and to variations in one protein property (e.g. hydrophobicity).

Study of recombinant micro-organism populations characterized by their plasmid content per cell using a segregated model.

Numerous observations from recombinant systems have shown that properties such as the specific cell growth rate and the plasmid-free cell formation rate are related, not only to the average plasmid content per cell, but also to the plasmid distribution within a population. The plasmid distribution in recombinant cultures can have an effect on the culture productivity that cannot be modelled using average values of the overall culture. The prediction of the behaviour of a plasmid content distribution and its causes and effects can only be studied using segregated models. A segregated model that describes populations of recombinant cells characterized by their plasmid content distribution has been developed. This model includes critical causes of recombinant culture instability such as the plasmid partition mechanism at cell division, plasmid replication kinetics and the effect of the plasmid content on the specific growth rate. The segregated model allows investigation of the effect of each of these causes and that of the plasmid content distribution on the observable behaviour of a recombinant culture. The effect of two partitioning mechanisms (Gaussian distribution and binomial distribution) on culture stability was investigated. The Gaussian distribution is slightly more stable. A small plasmid replication rate constant results in a very unstable culture even after short periods of time. This instability is dramatically improved for a larger value of this constant, hence improving protein synthesis. For a very narrow initial plasmid distribution, a given plasmid replication rate and partitioning mechanism can become broad even after a relatively short period of time. In contrast, a very "broad" initial distribution gave rise to a "Gamma-like" distribution profile. If we compare the results obtained in the simulations of the segregated model with those of the non-segregated one (average model), the latter model predicts much more stable behaviour, thus these average models cannot predict culture instability with the same precision. When compared with the experimental results, the segregated model was able to predict the practical behaviour with accuracy even in a system with a high plasmid content per cell and a high rate of plasmid-free cell formation which could not be achieved with a non-segregated model.

Phase separation rates of aqueous two-phase systems: correlation with system properties.

The kinetics of phase separation in aqueous two-phase systems have been investigated as a function of the physical properties of the system. Two distinct situations for the settling velocities were found, one in which the light, organic-rich (PEG) phase is continuous and the other in which the heavier, salt-rich (phosphate) phase is continuous. The settling rate of a particular system is a crucial parameter for equipment design, and it was studied as a function of measured viscosity and density of each of the phases as well as the interfacial tension between the phases. Interfacial tension increases with increasing tie line length. A correlation that describes the rate of phase separation was investigated. This correlation, which is a function of the system parameters mentioned above, described the behavior of the system successfully. Different values of the parameters in the correlation were fitted for bottom-phase-continuous and top-phase-continuous systems. These parameters showed that density and viscosity play a role in the rate of separation in both top continuous- and bottom continuous-phase regions but are more dominant in the continuous top-phase region. The composition of the two-phase system was characterized by the tie line length. The rate of separation increased with increasing tie line length in both cases but at a faster rate when the bottom (less viscous) phase was the continuous phase. These results show that working in a continuous bottom-phase region is advantageous to ensure fast separation.