Study of the production of poly(hydroxybutyrate-co-hydroxyhexanoate) and poly(hydroxybutyrate-co-hydroxyvalerate-co-hydroxyhexanoate) in Rhodospirillum rubrum

Dehazing redox homeostasis to foster purple bacteria biotechnology

Purple non-sulfur bacteria (PNSB) show great potential for environmental and industrial biotechnology, producing microbial protein, biohydrogen, polyhydroxyalkanoates (PHAs), pigments, etc. When grown photoheterotrophically, the carbon source is typically more reduced than the PNSB biomass, which leads to a redox imbalance. To mitigate the excess of electrons, PNSB can exhibit several 'electron sinking' strategies, such as CO₂ fixation, N₂ fixation, and H₂ and PHA production. The lack of a comprehensive (over)view of these redox strategies is hindering the implementation of PNSB for biotechnology applications. This review aims to present the state of the art of redox homeostasis in phototrophically grown PNSB, presenting known and theoretically expected strategies, and discussing them from stoichiometric, thermodynamic, metabolic, and economic points of view.

Dynamic modeling of Rhodospirillum rubrum PHA production triggered by redox stress during VFA photoheterotrophic assimilations

Polyhydroxyalkanoates (PHA) represent an environmentally friendly alternative to petroleum based plastics for a broad range of applications from packaging to biomedical devices. In the prospect of an industrial PHA production, it is highly valuable to accurately control the incorporation of different repeating units into the polymer, to produce a polyester with specific material characteristics. In this study, we develop macroscopic dynamic models predicting the polymer production and composition when mixtures containing up to four volatile fatty acids (VFA) are used as substrates. These models successfully reproduce the sequential (and preferential) substrate consumption and polymer production/reconsumption patterns, experimentally observed during biomass growth, thanks to simple kinetic structures based on Monod and inhibition factors. These models can serve as a basis for numerical simulation and process analysis, as well as process intensification through model-based optimization and control.