Ensemble optimization of microbial conversion of glycerol into 1, 3-propanediol by Klebsiella pneumoniae.

Using mathematical model and computer simulation to predict biological processes and optimize the target production is an important strategy for optimizing fermentation process. However, the inherent uncertainty of the kinetic model severely limits the predictive capability. In this study, optimize target production, such as productivity and yield of 1, 3-propanediol produced by Klebsiella pneumoniae using glycerol as substrate, the ensemble modeling approach was used to reduce the model's uncertainty for fermentation process as much as possible, and effectively improve its prediction performance. Firstly, through sensitivity analysis, the parameters having significant influence on the model were determined as the adjustable parameters for the ensemble modeling. After comparison, the appropriate threshold coefficient of the model error was determined, and the sampling method was used to generate as many equivalent parameter sets as possible. Each set of parameters was separately applied for the simulation, and all the predicted values were integrated for the weighted average. Therefore, the expected value of the prediction was obtained. Compared with the traditional simulation using single parameter set, the ensemble modeling method achieved the lower relative error between the prediction and the experimental value and the greatly improved model prediction performance. Moreover, the optimal productivity and yield of 1, 3-propanediol and the corresponding operating conditions were obtained, respectively. The ensemble modeling approach effectively compensates for the uncertainties of the model, making its prediction performance more practical, which is important for computer simulations to predict and guide the actual production process.

Dynamic flux balance analysis for microbial conversion of glycerol into 1,3-propanediol by Klebsiella pneumoniae.

To investigate the relationship between the yield of 1,3-propanediol (1,3-PD) and the flux variation in metabolic pathways of Klebsiella pneumoniae, an optimized calculation method was constructed on basis of dynamic flux balance analysis by combining genome-scale flux balance analysis with a kinetic model of extracellular metabolites. Through optimizing calculations, a more completely expanded metabolic pathway was obtained, which includes the previously reported metabolic pathway and additional three pathways or site: a pentose phosphate pathway (PPP) elicited at the dihydroxyacetone (DHA) node to provide more reducing equivalents; a branch of synthetic amino acids at the 3-phosphoglycerate (3PG) node; and the α-ketoglutarate site in the tricarboxylic acid (TCA) cycle leading to anabolic pathways for glutamate and other amino acids. On this basis, the relationships between the dynamic flux distribution of the important nodes in the metabolic pathway and the yield of 1,3-propanediol were analyzed. First, dynamic flux change from DHA to the PPP is positively correlated with the yield. Second, variation in flux in the TCA cycle is also positively correlated with the yield of 1,3-propanediol. In addition, the influence of the feedback loop formed by the cofactor tetrahydrofolate on the flux change of TCA in the amino acid anabolic pathway was examined. These results are of important reference value and have guiding significance for the extension of the glycerol metabolism pathway in K. pneumoniae, the rational transformation of genetic engineering in bacteria, and the optimization of metabolic pathways for industrial production.

[Stability Analysis of Susceptible-Infected-Recovered Epidemic Model].

With the range of application of computational biology and systems biology gradually expanding, the complexity of the bioprocess models is also increased. To address this difficult problem, it is required to introduce positive alternative analysis method to cope with it. Taking the dynamic model of the epidemic control process as research object, we established an evaluation model in our laboratory. Firstly, the model was solved with nonlinear programming method. The results were shown to be good. Based on biochemical systems theory, the ODE dynamic model was transformed into S-system. The eigen values of the model showed that the system was stable and contained oscillation phenomenon. Next the sensitivities of rate constant and logarithmic gains of the three key parameters were analyzed, as well as the robust of the system. The result indicated that the biochemical systems theory could be applied in different fields more widely.