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.