The Experts below are selected from a list of 24 Experts worldwide ranked by ideXlab platform
Joo-Youp Lee - One of the best experts on this subject based on the ideXlab platform.
-
On integrating the Droop model with the flux balance model for predicting metabolic shifts in Microalgae growth
Control, Automation and Systems (ICCAS), 2014 14th International Conference on, 2014Co-Authors: Minkyu Jeon, Boeun Kim, Mingyu Sung, Joo-Youp LeeAbstract:Identifying the mechanism for and predicting the metabolic shift between lipid accumulation and cell growth is a key research issue for microalgal biodiesel production. In this study, we propose a novel way to integrate a metabolic network model with a semi-empirical model (called “Droop model”) for predicting the lipid accumulation and cell growth simultaneously. At each time instant of mass balance model integration, the Droop model is used to predict the cell growth rate. Then, the Flux Balance Analysis (FBA) model is used to predict the rate of lipid accumulation, which is biochemically consistent with the predicted growth rate. In order to test the validity of the proposed approach, experiments are conducted for growing Microalgae Specie C. reinhardtii in a batch photo-bioreactor. Droop model's parameters are estimated using the gathered data and model predictions for the lipid contents are verified. Parameter sensitivity analysis is conducted to investigate how the various parameters affect the cell growth and lipid accumulation.
Minkyu Jeon - One of the best experts on this subject based on the ideXlab platform.
-
On integrating the Droop model with the flux balance model for predicting metabolic shifts in Microalgae growth
Control, Automation and Systems (ICCAS), 2014 14th International Conference on, 2014Co-Authors: Minkyu Jeon, Boeun Kim, Mingyu Sung, Joo-Youp LeeAbstract:Identifying the mechanism for and predicting the metabolic shift between lipid accumulation and cell growth is a key research issue for microalgal biodiesel production. In this study, we propose a novel way to integrate a metabolic network model with a semi-empirical model (called “Droop model”) for predicting the lipid accumulation and cell growth simultaneously. At each time instant of mass balance model integration, the Droop model is used to predict the cell growth rate. Then, the Flux Balance Analysis (FBA) model is used to predict the rate of lipid accumulation, which is biochemically consistent with the predicted growth rate. In order to test the validity of the proposed approach, experiments are conducted for growing Microalgae Specie C. reinhardtii in a batch photo-bioreactor. Droop model's parameters are estimated using the gathered data and model predictions for the lipid contents are verified. Parameter sensitivity analysis is conducted to investigate how the various parameters affect the cell growth and lipid accumulation.
Mingyu Sung - One of the best experts on this subject based on the ideXlab platform.
-
On integrating the Droop model with the flux balance model for predicting metabolic shifts in Microalgae growth
Control, Automation and Systems (ICCAS), 2014 14th International Conference on, 2014Co-Authors: Minkyu Jeon, Boeun Kim, Mingyu Sung, Joo-Youp LeeAbstract:Identifying the mechanism for and predicting the metabolic shift between lipid accumulation and cell growth is a key research issue for microalgal biodiesel production. In this study, we propose a novel way to integrate a metabolic network model with a semi-empirical model (called “Droop model”) for predicting the lipid accumulation and cell growth simultaneously. At each time instant of mass balance model integration, the Droop model is used to predict the cell growth rate. Then, the Flux Balance Analysis (FBA) model is used to predict the rate of lipid accumulation, which is biochemically consistent with the predicted growth rate. In order to test the validity of the proposed approach, experiments are conducted for growing Microalgae Specie C. reinhardtii in a batch photo-bioreactor. Droop model's parameters are estimated using the gathered data and model predictions for the lipid contents are verified. Parameter sensitivity analysis is conducted to investigate how the various parameters affect the cell growth and lipid accumulation.
Boeun Kim - One of the best experts on this subject based on the ideXlab platform.
-
On integrating the Droop model with the flux balance model for predicting metabolic shifts in Microalgae growth
Control, Automation and Systems (ICCAS), 2014 14th International Conference on, 2014Co-Authors: Minkyu Jeon, Boeun Kim, Mingyu Sung, Joo-Youp LeeAbstract:Identifying the mechanism for and predicting the metabolic shift between lipid accumulation and cell growth is a key research issue for microalgal biodiesel production. In this study, we propose a novel way to integrate a metabolic network model with a semi-empirical model (called “Droop model”) for predicting the lipid accumulation and cell growth simultaneously. At each time instant of mass balance model integration, the Droop model is used to predict the cell growth rate. Then, the Flux Balance Analysis (FBA) model is used to predict the rate of lipid accumulation, which is biochemically consistent with the predicted growth rate. In order to test the validity of the proposed approach, experiments are conducted for growing Microalgae Specie C. reinhardtii in a batch photo-bioreactor. Droop model's parameters are estimated using the gathered data and model predictions for the lipid contents are verified. Parameter sensitivity analysis is conducted to investigate how the various parameters affect the cell growth and lipid accumulation.
Wenjun Zhou - One of the best experts on this subject based on the ideXlab platform.
-
Lipid accumulation and metabolic analysis based on transcriptome sequencing of filamentous oleaginous Microalgae Tribonema minus at different growth phases
Bioprocess and Biosystems Engineering, 2017Co-Authors: Hui Wang, Huimin Shao, Wenjun ZhouAbstract:Filamentous oleaginous Microalgae Specie Tribonema minus is a promising feedstock for biodiesel production. However, the metabolic mechanism of lipid production in this filamentous microalgal Specie remains unclear. Here, we compared the lipid accumulation of T. minus at different growth phases, and described the de novo transcriptome sequencing and assembly and identified important pathways and genes involved in TAG production. Total lipid increased by 2.5-fold and its TAG level in total lipid reached 81.1% at stationary phase. Using the genes involved in the lipid metabolism, the TAG biosynthesis pathways were generated. Moreover, results also demonstrated that, in addition to the observed overexpression of the fatty acid synthesis pathway, TAG production at stationary growth phase was bolstered by repression of the β-oxidation pathway, up-regulation of genes that funnels acetyl-CoA to lipid biosynthesis, especially gene encoding for phospholipid:diacylglycerol acyltransferase (PDAT) which funnels DAG to TAG biosynthesis.