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|Title:||In Silico Analysis to Explore the Effect of Various Carbon Sources on Ethanol Production in Zymomonas mobilis|
Genome-scale metabolic network
|Source:||Widiastuti, H.,Lee, D.-Y.,Karimi, I.A. (2012). In Silico Analysis to Explore the Effect of Various Carbon Sources on Ethanol Production in Zymomonas mobilis. Computer Aided Chemical Engineering 30 : 1382-1386. ScholarBank@NUS Repository. https://doi.org/10.1016/B978-0-444-59520-1.50135-4|
|Abstract:||In the last decade, ethanol has gained popularity as a transportation fuel alternative due to its high octane number, low cetane number and high heat of vaporization. Additionally, ethanol produced via biological process has the advantage of being carbon neutral. Nevertheless, bioethanol only makes up less than 10% of the total gasoline consumption per annum. One of the factors that limit the bioethanol usage is its high production cost due to the utilization of food crops as feedstock. Hence, alternative feedstock such as lignocellulosic biomass becomes more interesting. However, most bioethanol producers could only ferment hexose sugars (glucose and fructose), while lignocellulosic biomass composes of almost equal amount of hexose and pentose sugars.Among various bioethanol producers, Zymomonas mobilis has acquired some special interest because of its high ethanol yield (5-10% more ethanol per fermented glucose) and ethanol tolerance. In spite of having many advantages, the wild strain of Z. mobilis could only ferment glucose, fructose, and sucrose. Therefore, recent studies have focused on the development of recombinant strains of Z. mobilis that were capable of utilizing pentose sugars. These studies have so far been limited to time and resources consuming experimental work. Thus, the use of biological model can enable a systematic approach for Z. mobilis strain improvement. To this end, we utilized the genome-scale metabolic network of Z. mobilis ATCC31821 to investigate the effects of various carbon sources on metabolic activity in Z. mobilis for biomass growth and ethanol production. Constraints-based flux analysis utilizing the model was conducted to quantify the maximum yield for ethanol production. The corresponding flux distributions fueled by different carbon sources under investigation were compared with respect to theoretical yield and energy utilization, thereby identifying the indispensable pathways for achieving optimal ethanol production on each carbon source. © 2012 Elsevier B.V.|
|Source Title:||Computer Aided Chemical Engineering|
|Appears in Collections:||Staff Publications|
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