Adaptation of Scenedesmus Dimorphus to Brackish Water

Microalgae is a promising biofuel feedstock for replacement of conventional transportation fuels. Microalgae does not require arable land for cultivation, and the biofuel production rate per acre of land is an order of magnitude greater than that needed for crop-based production methods. Though microalgae to biofuel processes are attractive, none have proven commercially successful due to the high costs of algae dewatering. Moreover, the scarcity of fresh water in many parts of the world prevents development of this process because of competition with drinking water supplies. Our lab has developed an efficient dewatering method using an inclined gravity settler. It is possible to adapt the freshwater Scenedesmus dimorphus to brackish water, without significantly changing growth rate nor lipid content. In this research, we investigate whether this saltwater-adapted algae species can be dewatered using the gravity settling method. The settling velocity was measured by a settling column using absorbance at 600 nm to measure cell concentration. Our preliminary results yielded settling velocities of 0.76 cm/h, which is similar to earlier measurements of 0.87 cm/h for freshwater S. dimorphus. Based on these settling velocity measurements, we have found that saltwater acclimation will have minimal effect on the separation efficiency of S. dimorphus.https://engagedscholarship.csuohio.edu/u_poster_2013/1011/thumbnail.jp

A Simple Apparatus for Measuring Cell Settling Velocity

Accurate cell settling velocity determination is critical for perfusion culture using a gravity settler for cell retention. We have developed a simple apparatus (a \u27settling column\u27) for measuring settling velocity and have validated the procedure with 15-μm polystyrene particles with known physical properties. The measured settling velocity of the polystyrene particles is within 4% of the value obtained using the traditional Stokes\u27 law approach. The settling velocities of three hybridoma cell lines were measured, resulting in up to twofold variation among cell lines, and the values decreased as the cell culture aged. The settling velocities of the nonviable cells were 33-50% less than the corresponding viable cells. The significant variation of settling velocities among cell populations and growth phases confirms the necessity of routine measurement of this property during long-term perfusion culture

Network Analysis of Intermediary Metabolism Using Linear Optimization. I. Development of Mathematical Formalism

Analysis of metabolic networks using linear optimization theory allows one to quantify and understand the limitations imposed on the cell by its metabolic stoichiometry, and to understand how the flux through each pathway influences the overall behavior of metabolism. A stoichiometric matrix accounting for the major pathways involved in energy and mass transformations in the cell was used in our analysis. The auxiliary parameters of linear optimization, the so-called shadow prices, identify the intermediates and cofactors that cause the growth to be limited on each nutrient. This formalism was used to examine how well the cell balances its needs for carbon, nitrogen, and energy during growth on different substrates. The relative values of glucose and glutamine as nutrients were compared by varying the ratio of rates of glucose to glutamine uptakes, and calculating the maximum growth rate. The optimum value of this ratio is between 2-7, similar to experimentally observed ratios. The theoretical maximum growth rate was calculated for growth on each amino acid, and the amino acids catabolized directly to glutamate were found to be the optimal nutrients. The importance of each reaction in the network can be examined both by selectively limiting the flux through the reaction, and by the value of the reduced cost for that reaction. Some reactions, such as malic enzyme and glutamate dehydrogenase, may be inhibited or deleted with little or no adverse effect on the calculated cell growth rate

Optimal Selection of Metabolic Fluxes for in Vivo Measurement. I. Development of Mathematical Methods

The measurement of uptake and secretion rates is often not sufficient to allow the calculation of all internal metabolic fluxes. Measurements of internal fluxes are needed and these additional measurements are used in conjunction with mass-balance equations to calculate the complete metabolic flux map. A method is presented that identifies the fluxes that should be selected for experimental measurement, and the fluxes that can be computed using the mass-balance equations. The criterion for selecting internal metabolic fluxes for measurement is that the values of the computed fluxes should have low sensitivity to experimental error in the measured fluxes. A condition number indicating the upper bound on this sensitivity, is calculated based on stoichiometry alone. The actual sensitivity is dependent on both the flux measurements and the error in flux measurements, as well as the stoichiometry. If approximate physiologic ranges of fluxes are known a realistic sensitivity can be computed. The exact sensitivity cannot be calculated since the experimental error is usually unknown. The most probable value of the actual sensitivity for a given selection of measured fluxes is estimated by selecting a large number of representative error vectors and calculating the actual sensitivity for each of these. A frequency distribution of actual sensitivities is thus obtained giving a representative range of actual sensitivities for a particular experimental situation

Network Analysis of Intermediary Metabolism Using Linear Optimization. I. Development of Mathematical Formalism

Analysis of metabolic networks using linear optimization theory allows one to quantify and understand the limitations imposed on the cell by its metabolic stoichiometry, and to understand how the flux through each pathway influences the overall behavior of metabolism. A stoichiometric matrix accounting for the major pathways involved in energy and mass transformations in the cell was used in our analysis. The auxiliary parameters of linear optimization, the so-called shadow prices, identify the intermediates and cofactors that cause the growth to be limited on each nutrient. This formalism was used to examine how well the cell balances its needs for carbon, nitrogen, and energy during growth on different substrates. The relative values of glucose and glutamine as nutrients were compared by varying the ratio of rates of glucose to glutamine uptakes, and calculating the maximum growth rate. The optimum value of this ratio is between 2-7, similar to experimentally observed ratios. The theoretical maximum growth rate was calculated for growth on each amino acid, and the amino acids catabolized directly to glutamate were found to be the optimal nutrients. The importance of each reaction in the network can be examined both by selectively limiting the flux through the reaction, and by the value of the reduced cost for that reaction. Some reactions, such as malic enzyme and glutamate dehydrogenase, may be inhibited or deleted with little or no adverse effect on the calculated cell growth rate

Optimal Selection of Metabolic Fluxes for in Vivo Measurement. I. Development of Mathematical Methods

The measurement of uptake and secretion rates is often not sufficient to allow the calculation of all internal metabolic fluxes. Measurements of internal fluxes are needed and these additional measurements are used in conjunction with mass-balance equations to calculate the complete metabolic flux map. A method is presented that identifies the fluxes that should be selected for experimental measurement, and the fluxes that can be computed using the mass-balance equations. The criterion for selecting internal metabolic fluxes for measurement is that the values of the computed fluxes should have low sensitivity to experimental error in the measured fluxes. A condition number indicating the upper bound on this sensitivity, is calculated based on stoichiometry alone. The actual sensitivity is dependent on both the flux measurements and the error in flux measurements, as well as the stoichiometry. If approximate physiologic ranges of fluxes are known a realistic sensitivity can be computed. The exact sensitivity cannot be calculated since the experimental error is usually unknown. The most probable value of the actual sensitivity for a given selection of measured fluxes is estimated by selecting a large number of representative error vectors and calculating the actual sensitivity for each of these. A frequency distribution of actual sensitivities is thus obtained giving a representative range of actual sensitivities for a particular experimental situation

Network Analysis of Intermediary Metabolism Using Linear Optimization. II. Interpretation of Hybridoma Cell Metabolism

Analysis of metabolic networks using linear optimization theory allows one to quantify and understand the limitations imposed on the cell by its metabolic stoichiometry, and to understand how the flux through each pathway influences the overall behavior of metabolism. A stoichiometric matrix accounting for the major pathways involved in energy and mass transformations in the cell was used in our analysis. The auxiliary parameters of linear optimization, the so-called shadow prices, identify the intermediates and cofactors that cause the growth to be limited on each nutrient. This formalism was used to examine how well the cell balances its needs for carbon, nitrogen, and energy during growth on different substrates. The relative values of glucose and glutamine as nutrients were compared by varying the ratio of rates of glucose to glutamine uptakes, and calculating the maximum growth rate. The optimum value of this ratio is between 2–7, similar to experimentally observed ratios. The theoretical maximum growth rate was calculated for growth on each amino acid, and the amino acids catabolized directly to glutamate were found to be the optimal nutrients. The importance of each reaction in the network can be examined both by selectively limiting the flux through the reaction, and by the value of the reduced cost for that reaction. Some reactions, such as malic enzyme and glutamate dehydrogenase, may be inhibited or deleted with little or no adverse effect on the calculated cell growth rate

Optimal Selection of Metabolic Fluxes for in vivo Measurement. II. Application to Escherichia coli and Hybridoma Cell Metabolism

A method of analysis was presented in part I of this series for determining the fluxes in a biochemical network that are the optimal choices for experimental measurement. This algorithm is applied to two important biological models: Escherichia coli and a hybridoma cell line (167.4G5.3). Our results show that potentially poor choices for in vivo measurement of metabolic fluxes exist for both model systems. For the subset of reactions in E. coli that was studied, the condition number of the augmented stoichiometric matrix reveals that a 60-fold amplification of experimental error during computations is possible. The biochemical network of the hybridoma cell is more compelex than the E. coli system, and thus results in much larger possible error amplification—up to 100 000-fold. The physiological situations appear to have sensitivities that are less than 1/4 to 1/10 of those estimated by the condition number, and the maximum sensitivities are proportional to the condition number. These maximum sensitivities calculated using estimates of the fluxes and the worst possible error vector are upper bounds on the system\u27s actual sensitivity. By examining the effect of measurement error on the sensitivity, the most probable sensitivity is calculated. These results indicate that an approximate two-fold increase in sensitivity of the E. coli system is likely when the worst set of fluxes are measured rather than the best set. The most likely sensitivity of the hybridoma system can range three orders of magnitude, depending on the set of fluxes that are measured. The propagation of experimental error during computations can be diminished for both systems by increasing the number of flux measurements over and above the minimum number of experimental measurements. The findings from these two model systems indicate that the calculation of the condition number can be a useful method for efficient experimental design, and that the usefulness of this method increases as the order of the system increases

A Computer Model of Gluconeogenesis and Lipid Metabolism in the Perfused Liver

A mathematical model of the perfused rat liver was developed to predict intermediate metabolite concentrations and fluxes in response to changes in various substrate concentrations in the perfusion medium. The model simulates gluconeogenesis in the liver perfused separately with lactate and pyruvate and the combination of these substrates with fatty acids (oleate). The model consists of key reactions representing gluconeogenesis, glycolysis, fatty acid metabolism, tricarboxylic acid cycle, oxidative phosphorylation, and ketogenesis. Michaelis-Menten-type kinetic expressions, with control by ATP/ADP, are used for many of the reactions. For key regulated reactions (fructose-1,6-bisphosphatase, phosphofructokinase, pyruvate carboxylase, pyruvate dehydrogenase complex, and pyruvate kinase), rate expressions were developed that incorporate allosteric effectors, specific substrate relationships (e.g., cooperative binding), and/or phosphorylation/dephosphorylation using in vitro enzyme activity data and knowledge of the specific mechanisms. The model was independently validated by comparing model predictions with 10 sets of experimental data from 7 different published works, with no parameter adjustments. The simulations predict the same trends, in terms of stimulation of substrate uptake by fatty acid addition, as observed experimentally. In general, the major metabolic indicators calculated by the model are in good agreement with experimental results. For example, the simulated glucose/pyruvate mass yield is 43% compared with the average of 45% reported in the literature. The model accurately predicts the specific time constants of the glucose response (2.5–4 min) and the dynamic behavior of substrate and product fluxes. It is expected that this model will be a useful tool for analyzing the complex relationships between carbohydrate and fat metabolism

Fractionation of Cell Mixtures Using Acoustic and Laminar Flow Fields

A fractionation method applicable to different populations of cells in a suspension is reported. The separation was accomplished by subjecting the suspension to a resonant ultrasonic field and a laminar flow field propagating in orthogonal directions within a thin, rectangular chamber. Steady, laminar flow transports the cell suspension along the chamber, while the ultrasonic field causes the suspended cells to migrate to the mid-plane of the chamber at rates related to their size and physical properties. A thin flow splitter positioned near the outlet divides the effluent cell suspension into two product streams, thereby allowing cells that respond faster to the acoustic field to be separated from those cells that respond more slowly. Modeling of the trajectories of individual cells through the chamber shows that by altering the strength of the flow relative to that of the acoustic field, the desired fractionation can be controlled. Proof-of-concept experiments were performed using hybridoma cells and Lactobacillus rhamnosus cells. The two populations of cells could be effectively separated using this technique, resulting in hybridoma/Lactobacillus ratios in the left and right product streams, normalized to the feed ratio, of 6.9 ± 1.8 and 0.39 ± 0.01 (vol/vol), respectively. The acoustic method is fast, efficient, and could be operated continuously with a high degree of selectivity and yield and with low power consumption