(567x) A Novel Approach to Find Genes Susceptible of Transcription Control for the Improvement of a Microbial Fuel Cell Performance Fueled by Pseudomonas Aeruginosa and Based On Experimental Design

The rising demand of energy in the planet is augmenting the needs of cheap and renewable technologies that could fit the scale where the energy is desirable. Microbial fuel cells constitute an interesting proposal due to its versatility in terms of sources and its capability of providing energy to small scale dispositives. There exist several approximations aiming to increase its performance such as designing materials suitable for electron transport in the cathode and anode so the losses decrease or finding materials that reduce the resistance in the proton membrane exchange. Nevertheless, few efforts have been made in order to increase the electron shuttle synthesis by means of metabolic engineering the cell. On the other hand, mostly all reports regarding metabolite optimization based on flux balance analysis (FBA) aim to determine the global optimum in a mathematical graceful way. Then, it is necessary to experimentally test these candidates and this leads to the realization of the big gap between FBA models and experimental results. Here in this work, we proposed a novel approach to establish genes susceptible for transcription based on a bi-level framework and a second factorial design applied to optimize piocianine in the cell. We found that the metabolism is limited with the production of biomass in two scenarios: same solution as the first level problem and maximum of transport metabolites but no production of biomass (zero growth). For maximum deletion of 3 genes, the programming was modified including restrictions for the biomass flux (switching first level problem as a restriction for the second level problem) to tackle biomass limitations found in the bi-level approach. Double factorial design: one factorial design for gene selection and a second factorial design for level of gene over ? expression was referenced with the first level solution or the maximum production in the model for the metabolism (when the first level solution is zero). A 25 factorial design for both models. The results are accessed thorough 3-D plots replacing numbers representing the inner factorial design (over ? expression). This approximation does not provide the best solution, but illustrates a variety of scenarios in with the transport metabolites flux is increased. Finally, some genes were tested in the laboratory by using P. aeruginosa single chamber fule cell.