Quantifying the metabolic capabilities of engineered Zymomonas mobilis using linear programming analysis

Tsantili, IC; Karim, MN; Klapa, MI

dc.contributor.author	Tsantili, IC	en
dc.contributor.author	Karim, MN	en
dc.contributor.author	Klapa, MI	en
dc.date.accessioned	2014-03-01T01:26:57Z
dc.date.available	2014-03-01T01:26:57Z
dc.date.issued	2007	en
dc.identifier.issn	1475-2859	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18306
dc.subject	Bacterial Growth	en
dc.subject	Biomass Production	en
dc.subject	Cost Efficiency	en
dc.subject	Energy Source	en
dc.subject	Genetics	en
dc.subject	Metabolic Engineering	en
dc.subject	Metabolic Network	en
dc.subject	Metabolic Pathway	en
dc.subject	Redox Potential	en
dc.subject	Linear Program	en
dc.subject.classification	Biotechnology & Applied Microbiology	en
dc.subject.other	alcohol	en
dc.subject.other	accuracy	en
dc.subject.other	alcohol production	en
dc.subject.other	anaerobic metabolism	en
dc.subject.other	article	en
dc.subject.other	bacterial cell	en
dc.subject.other	bacterial growth	en
dc.subject.other	bacterial metabolism	en
dc.subject.other	bacterial strain	en
dc.subject.other	biomass production	en
dc.subject.other	cell growth	en
dc.subject.other	controlled study	en
dc.subject.other	gene deletion	en
dc.subject.other	in vivo study	en
dc.subject.other	metabolic engineering	en
dc.subject.other	nonhuman	en
dc.subject.other	oxidation reduction potential	en
dc.subject.other	simulation	en
dc.subject.other	stoichiometry	en
dc.subject.other	system analysis	en
dc.subject.other	Zymomonas mobilis	en
dc.subject.other	Bacteria (microorganisms)	en
dc.subject.other	Zymomonas mobilis	en
dc.title	Quantifying the metabolic capabilities of engineered Zymomonas mobilis using linear programming analysis	en
heal.type	journalArticle	en
heal.identifier.primary	10.1186/1475-2859-6-8	en
heal.identifier.secondary	http://dx.doi.org/10.1186/1475-2859-6-8	en
heal.identifier.secondary	8	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	Background: The need for discovery of alternative, renewable, environmentally friendly energy sources and the development of cost-efficient, ""clean"" methods for their conversion into higher fuels becomes imperative. Ethanol, whose significance as fuel has dramatically increased in the last decade, can be produced from hexoses and pentoses through microbial fermentation. Importantly, plant biomass, if appropriately and effectively decomposed, is a potential inexpensive and highly renewable source of the hexose and pentose mixture. Recently, the engineered (to also catabolize pentoses) anaerobic bacterium Zymomonas mobilis has been widely discussed among the most promising microorganisms for the microbial production of ethanol fuel. However, Z. mobilis genome having been fully sequenced in 2005, there is still a small number of published studies of its in vivo physiology and limited use of the metabolic engineering experimental and computational toolboxes to understand its metabolic pathway interconnectivity and regulation towards the optimization of its hexose and pentose fermentation into ethanol. Results: In this paper, we reconstructed the metabolic network of the engineered Z. mobilis to a level that it could be modelled using the metabolic engineering methodologies. We then used linear programming (LP) analysis and identified the Z. mobilis metabolic boundaries with respect to various biological objectives, these boundaries being determined only by Z. mobilis network's stoichiometric connectivity. This study revealed the essential for bacterial growth reactions and elucidated the association between the metabolic pathways, especially regarding main product and byproduct formation. More specifically, the study indicated that ethanol and biomass production depend directly on anaerobic respiration stoichiometry and activity. Thus, enhanced understanding and improved means for analyzing anaerobic respiration and redox potential in vivo are needed to yield further conclusions for potential genetic targets that may lead to optimized Z. mobilis strains. Conclusion: Applying LP to study the Z. mobilis physiology enabled the identification of the main factors influencing the accomplishment of certain biological objectives due to metabolic network connectivity only. This first-level metabolic analysis model forms the basis for the incorporation of more complex regulatory mechanisms and the formation of more realistic models for the accurate simulation of the in vivo Z. mobilis physiology. © 2007 Tsantili et al; licensee BioMed Central Ltd.	en
heal.publisher	BIOMED CENTRAL LTD	en
heal.journalName	Microbial Cell Factories	en
dc.identifier.doi	10.1186/1475-2859-6-8	en
dc.identifier.isi	ISI:000245327800001	en
dc.identifier.volume	6	en