Bioenergetics-based modelling of microbial ecosystems for biotechnological applications

Abstract

The bioenergetics analysis and mathematical modelling of several bioprocesses with industrial interest aiming for waste materials recovery, is conducted in this Thesis. The objective is to mechanistically understand the physical limits of the processes together with the ecological interactions established in their different microbial ecosystems. This new knowledge could lead towards an improvement of the bioprocesses control increasing their efficiency. Three mathematical models have been developed based on bioenergetics and minimizing the empirical information necessary. Firstly, a novel metabolic energy-based model has been developed that accurately predicts the experimentally observed changes in product spectrum with pH variations when glucose is fermented in acidogenic conditions. The results are mechanistically explained analysing, under different environmental conditions, the impact that variable proton motive potential and active transport energy costs have in terms of energy harvest over products yielding. Secondly, several bioenergetics analyses to investigate the potential reversibility of specific anaerobic pathways of interest (more reduced products yielding with higher energy density) have been developed. Thermodynamics of the different steps in biochemical pathways are analysed and combined with assumptions concerning kinetic and physiological constraints to evaluate if the pathways are potentially reversible by imposing changes in process conditions. And thirdly, a last model is presented based on the assumption that mixed cultures are composed by undefined species competing for the energetic resources available and limited by the fundamental trade-off between yield and rate of energy harvest per unit of substrate. In this model, the competition between existing and non-experimentally reported microbial catabolic activities, is simulated. Successful ecological relations of competition or collaboration are predicted under the hypothesis of maximum energy harvest rate and in line with experimental observations.