l-Threonine production from whey and fish hydrolysate by E. coli ATCC® 21277TM.

In this work production of l-threonine by Escherichia coli ATCC® 21277™ has been studied using a mixture of alternative low-cost substrates, which are recognized to be a major pollution problem. Whey was used as the primary carbon source, whereas Red Tilapia (Oreochromis sp.) viscera hydrolysates constituted the nitrogen source. A Box-Behnken Design was used for optimizing l-threonine and biomass production, using temperature and glucose, whey, and Red Tilapia (Oreochromis sp.) viscera hydrolysate contents as factors. Results indicate that biomass production is affected by the concentration of hydrolysate and temperature. On the other hand, l-threonine production is affected by concentration of whey, hydrolysate, and temperature. In this context, it was possible to maximize l-threonine production, but with a detriment on biomass production. The optimal conditions for biomass and l-threonine maximization (after 24 h) were identified and validated experimentally, resulting in biomass and l-threonine production of 0.767 g/L and 0.406 g/L, respectively. This work has shown the technical feasibility of using whey and Red Tilapia (Oreochromis sp.) viscera hydrolysates for the production of l-threonine by E. coli ATCC® 21277TM. Finally, the complications associated to the use of these low-cost complex substrates for the production of l-threonine by E. coli, suggest that more in detail studies (i.e. at the metabolic level) are required in order to propose strategies to increase the process productivity, before its scale up. This is a first step in our long-term goal of developing a production process for i) dealing with the pollution problems caused by those wastes, and ii) strengthen the milk and fish industries which are important poles of the Colombian economy.

Pareto domain: an invaluable source of process information.

Due to the highly competitive market and increasingly stringent environmental regulations, it is paramount to operate chemical processes at their optimal point. In a typical process, there are usually many process variables (decision variables) that need to be selected in order to achieve a set of optimal objectives for which the process will be considered to operate optimally. Because some of the objectives are often contradictory, Multi-objective optimization (MOO) can be used to find a suitable trade-off among all objectives that will satisfy the decision maker. The first step is to circumscribe a well-defined Pareto domain, corresponding to the portion of the solution domain comprised of a large number of non-dominated solutions. The second step is to rank all Pareto-optimal solutions based on some preferences of an expert of the process, this step being performed using visualization tools and/or a ranking algorithm. The last step is to implement the best solution to operate the process optimally. In this paper, after reviewing the main methods to solve MOO problems and to select the best Pareto-optimal solution, four simple MOO problems will be solved to clearly demonstrate the wealth of information on a given process that can be obtained from the MOO instead of a single aggregate objective. The four optimization case studies are the design of a PI controller, an SO2 to SO3 reactor, a distillation column and an acrolein reactor. Results of these optimization case studies show the benefit of generating and using the Pareto domain to gain a deeper understanding of the underlying relationships between the various process variables and performance objectives.

Stochastic Multilayer Optimization for an Acrylic Acid Reactor.

In this paper, a multilayer stochastic optimization approach is implemented to solve a dynamic optimization problem under uncertainties for an acrylic acid reactor. The proposed methodology handles different sources of uncertainties (internal, external, process), being a novel approach to obtain more realistic solutions in the context of process optimization. A comparison against deterministic dynamic optimization, single-layer stochastic optimization, and typical PI control loops is carried out. The results show the efficacy of the multilayer stochastic optimization approach for handling different sources of uncertainties, improving the economic profitability of the process while fulfilling the safety constraints in all of the scenarios analyzed.

TCA Cycle and Its Relationship with Clavulanic Acid Production: A Further Interpretation by Using a Reduced Genome-Scale Metabolic Model of Streptomyces clavuligerus.

Streptomyces clavuligerus (S. clavuligerus) has been widely studied for its ability to produce clavulanic acid (CA), a potent inhibitor of ß-lactamase enzymes. In this study, S. clavuligerus cultivated in 2D rocking bioreactor in fed-batch operation produced CA at comparable rates to those observed in stirred tank bioreactors. A reduced model of S. clavuligerus metabolism was constructed by using a bottom-up approach and validated using experimental data. The reduced model was implemented for in silico studies of the metabolic scenarios arisen during the cultivations. Constraint-based analysis confirmed the interrelations between succinate, oxaloacetate, malate, pyruvate, and acetate accumulations at high CA synthesis rates in submerged cultures of S. clavuligerus. Further analysis using shadow prices provided a first view of the metabolites positive and negatively associated with the scenarios of low and high CA production.

A Genome-Scale Insight into the Effect of Shear Stress During the Fed-Batch Production of Clavulanic Acid by Streptomyces Clavuligerus.

Streptomyces clavuligerus is a filamentous Gram-positive bacterial producer of the ß-lactamase inhibitor clavulanic acid. Antibiotics biosynthesis in the Streptomyces genus is usually triggered by nutritional and environmental perturbations. In this work, a new genome scale metabolic network of Streptomyces clavuligerus was reconstructed and used to study the experimentally observed effect of oxygen and phosphate concentrations on clavulanic acid biosynthesis under high and low shear stress. A flux balance analysis based on experimental evidence revealed that clavulanic acid biosynthetic reaction fluxes are favored in conditions of phosphate limitation, and this is correlated with enhanced activity of central and amino acid metabolism, as well as with enhanced oxygen uptake. In silico and experimental results show a possible slowing down of tricarboxylic acid (TCA) due to reduced oxygen availability in low shear stress conditions. In contrast, high shear stress conditions are connected with high intracellular oxygen availability favoring TCA activity, precursors availability and clavulanic acid (CA) production.

Characterization of the Metabolic Response of Streptomyces clavuligerus to Shear Stress in Stirred Tanks and Single-Use 2D Rocking Motion Bioreactors for Clavulanic Acid Production.

Streptomyces clavuligerus is a gram-positive filamentous bacterium notable for producing clavulanic acid (CA), an inhibitor of ß-lactamase enzymes, which confers resistance to bacteria against several antibiotics. Here we present a comparative analysis of the morphological and metabolic response of S. clavuligerus linked to the CA production under low and high shear stress conditions in a 2D rocking-motion single-use bioreactor (CELL-tainer ®) and stirred tank bioreactor (STR), respectively. The CELL-tainer® guarantees high turbulence and enhanced volumetric mass transfer at low shear stress, which (in contrast to bubble columns) allows the investigation of the impact of shear stress without oxygen limitation. The results indicate that high shear forces do not compromise the viability of S. clavuligerus cells; even higher specific growth rate, biomass, and specific CA production rate were observed in the STR. Under low shear forces in the CELL-tainer® the mycelial diameter increased considerably (average diameter 2.27 in CELL-tainer® vs. 1.44 µm in STR). This suggests that CA production may be affected by a lower surface-to-volume ratio which would lead to lower diffusion and transport of nutrients, oxygen, and product. The present study shows that there is a strong correlation between macromorphology and CA production, which should be an important aspect to consider in industrial production of CA.

Application of process system engineering tools to the fed-batch production of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) from a vinasses-molasses Mixture.

Fed-batch production of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) copolymer using vinasses-molasses mixture is carried out in this work by implementing different process systems engineering tools. Two fed-batch strategies are tested experimentally at 5 L scale, considering only offline information: (1) offline optimizing control and (2) exponential feeding. Application of these strategies showed that different feeding profiles result in different dynamic behaviour, influencing both, yield and biopolymer properties. As offline-based feeding strategies do not consider information of the culture status, they cannot deal with uncertainties. Therefore, a closed loop control strategy was implemented, which uses biomass and substrate information predicted online by soft-sensors. Results demonstrated the technical feasibility to produce biopolymer using a 75/25%vol. vinasses-molasses mixture. Successful implementation of the soft-sensor-based control strategy was evidenced at pilot plant scale, where sugar concentration was kept almost constant for 14 h, while obtaining the desired copolymer. Thus, proposed control strategy could be of interest at industrial-scale.

Metabolic adaptation of two in silico mutants of Mycobacterium tuberculosis during infection.

BACKGROUND: Up to date, Mycobacterium tuberculosis (Mtb) remains as the worst intracellular killer pathogen. To establish infection, inside the granuloma, Mtb reprograms its metabolism to support both growth and survival, keeping a balance between catabolism, anabolism and energy supply. Mtb knockouts with the faculty of being essential on a wide range of nutritional conditions are deemed as target candidates for tuberculosis (TB) treatment. Constraint-based genome-scale modeling is considered as a promising tool for evaluating genetic and nutritional perturbations on Mtb metabolic reprogramming. Nonetheless, few in silico assessments of the effect of nutritional conditions on Mtb's vulnerability and metabolic adaptation have been carried out. RESULTS: A genome-scale model (GEM) of Mtb, modified from the H37Rv iOSDD890, was used to explore the metabolic reprogramming of two Mtb knockout mutants (pfkA- and icl-mutants), lacking key enzymes of central carbon metabolism, while exposed to changing nutritional conditions (oxygen, and carbon and nitrogen sources). A combination of shadow pricing, sensitivity analysis, and flux distributions patterns allowed us to identify metabolic behaviors that are in agreement with phenotypes reported in the literature. During hypoxia, at high glucose consumption, the Mtb pfkA-mutant showed a detrimental growth effect derived from the accumulation of toxic sugar phosphate intermediates (glucose-6-phosphate and fructose-6-phosphate) along with an increment of carbon fluxes towards the reductive direction of the tricarboxylic acid cycle (TCA). Furthermore, metabolic reprogramming of the icl-mutant (icl1&icl2) showed the importance of the methylmalonyl pathway for the detoxification of propionyl-CoA, during growth at high fatty acid consumption rates and aerobic conditions. At elevated levels of fatty acid uptake and hypoxia, we found a drop in TCA cycle intermediate accumulation that might create redox imbalance. Finally, findings regarding Mtb-mutant metabolic adaptation associated with asparagine consumption and acetate, succinate and alanine production, were in agreement with literature reports. CONCLUSIONS: This study demonstrates the potential application of genome-scale modeling, flux balance analysis (FBA), phenotypic phase plane (PhPP) analysis and shadow pricing to generate valuable insights about Mtb metabolic reprogramming in the context of human granulomas.

First principle-based models in plant suspension cell cultures: a review.

In this work, the development and application of published models for describing the behavior of plant cell cultures is reviewed. The structure of each type of model is analyzed and the new tendencies for the modeling of biotechnological processes that can be applied in plant cell cultures are presented. This review is a tool for clarifying the main features that characterize each type of model in the field of plant cell cultures and can be used as a support on the selection of the more suitable model type, taking into account the purpose of specific research.

Structured model and parameter estimation in plant cell cultures of Thevetia peruviana.

In this work, a mechanistic model for predicting the dynamic behavior of extracellular and intracellular nutrients, biomass production, and the main metabolites involved in the central carbon metabolism in plant cell cultures of Thevetia peruviana is presented. The proposed model is the first mechanistic model implemented for plant cell cultures of this species, and includes 28 metabolites, 33 metabolic reactions, and 61 parameters. Given the over-parametrization of the model, its nonlinear nature and the strong correlation among the effects of the parameters, a parameter estimation routine based on identifiability analysis was implemented. This routine reduces the parameter's search space by selecting the most sensitive and linearly independent parameters. Results have shown that only 19 parameters are identifiable. Finally, the model was used for analyzing the fluxes distribution in plant cell cultures of T. peruviana. This analysis shows high uptake of phosphates and parallel uptake of glucose and fructose. Furthermore, it has pointed out the main central carbon metabolism routes for promoting biomass production in this cell culture.

Applying a Geospatial Visualization Based on USSD Messages to Real Time Identification of Epidemiological Risk Areas in Developing Countries: A Case of Study of Paraguay.

The identification of epidemiological risk areas is one of the major problems in public health. Information management strategies are needed to facilitate prevention and control of disease in the affected areas. This paper presents a model to optimize geographical data collection of suspected or confirmed disease occurrences using the Unstructured Supplementary Service Data (USSD) mobile technology, considering its wide adoption even in developing countries such as Paraguay. A Geographic Information System (GIS) is proposed for visualizing potential epidemiological risk areas in real time, that aims to support decision making and to implement prevention or contingency programs for public health.

Flux balance analysis in the production of clavulanic acid by Streptomyces clavuligerus.

In this work, in silico flux balance analysis is used for predicting the metabolic behavior of Streptomyces clavuligerus during clavulanic acid production. To choose the best objective function for use in the analysis, three different optimization problems are evaluated inside the flux balance analysis formulation: (i) maximization of the specific growth rate, (ii) maximization of the ATP yield, and (iii) maximization of clavulanic acid production. Maximization of ATP yield showed the best predictions for the cellular behavior. Therefore, flux balance analysis using ATP as objective function was used for analyzing different scenarios of nutrient limitations toward establishing the effect of limiting the carbon, nitrogen, phosphorous, and oxygen sources on the growth and clavulanic acid production rates. Obtained results showed that ammonia and phosphate limitations are the ones most strongly affecting clavulanic acid biosynthesis. Furthermore, it was possible to identify the ornithine flux from the urea cycle and the α-ketoglutarate flux from the TCA cycle as the most determinant internal fluxes for promoting clavulanic acid production.

Model-based identifiable parameter determination applied to a simultaneous saccharification and fermentation process model for bio-ethanol production.

In this work, a methodology for the model-based identifiable parameter determination (MBIPD) is presented. This systematic approach is proposed to be used for structure and parameter identification of nonlinear models of biological reaction networks. Usually, this kind of problems are over-parameterized with large correlations between parameters. Hence, the related inverse problems for parameter determination and analysis are mathematically ill-posed and numerically difficult to solve. The proposed MBIPD methodology comprises several tasks: (i) model selection, (ii) tracking of an adequate initial guess, and (iii) an iterative parameter estimation step which includes an identifiable parameter subset selection (SsS) algorithm and accuracy analysis of the estimated parameters. The SsS algorithm is based on the analysis of the sensitivity matrix by rank revealing factorization methods. Using this, a reduction of the parameter search space to a reasonable subset, which can be reliably and efficiently estimated from available measurements, is achieved. The simultaneous saccharification and fermentation (SSF) process for bio-ethanol production from cellulosic material is used as case study for testing the methodology. The successful application of MBIPD to the SSF process demonstrates a relatively large reduction in the identified parameter space. It is shown by a cross-validation that using the identified parameters (even though the reduction of the search space), the model is still able to predict the experimental data properly. Moreover, it is shown that the model is easily and efficiently adapted to new process conditions by solving reduced and well conditioned problems.

Modeling and parameter identification of the simultaneous saccharification-fermentation process for ethanol production.

Despite many environmental advantages of using alcohol as a fuel, there are still serious questions about its economical feasibility when compared with oil-based fuels. The bioethanol industry needs to be more competitive, and therefore, all stages of its production process must be simple, inexpensive, efficient, and "easy" to control. In recent years, there have been significant improvements in process design, such as in the purification technologies for ethanol dehydration (molecular sieves, pressure swing adsorption, pervaporation, etc.) and in genetic modifications of microbial strains. However, a lot of research effort is still required in optimization and control, where the first step is the development of suitable models of the process, which can be used as a simulated plant, as a soft sensor or as part of the control algorithm. Thus, toward developing good, reliable, and simple but highly predictive models that can be used in the future for optimization and process control applications, in this paper an unstructured and a cybernetic model are proposed and compared for the simultaneous saccharification-fermentation process (SSF) for the production of ethanol from starch by a recombinant Saccharomyces cerevisiae strain. The cybernetic model proposed is a new one that considers the degradation of starch not only into glucose but also into dextrins (reducing sugars) and takes into account the intracellular reactions occurring inside the cells, giving a more detailed description of the process. Furthermore, an identification procedure based on the Metropolis Monte Carlo optimization method coupled with a sensitivity analysis is proposed for the identification of the model's parameters, employing experimental data reported in the literature.