Master's Thesis
Industrial fermentation leverages microorganisms for the synthesis of valuable products. These processes serve as a pivotal technological asset for reducing our dependence on chemicals and products derived from fossil fuels.
Employing high-density fermentation strategies proves to be a cost-effective approach for achieving optimal yields in biomass, extracellular metabolites, intracellular components, or modified substrates. The rate in which fermented products are generated is contingent upon various factors such as concentration of microorganisms, cell density, cellular components, enzymes, temperature, and pH. These systems are difficult to design, operate, and scale up and down, which has a direct impact in crucial parameters such as product titer, rate, and yield. The absence of high-quality fermentation data hinders optimization and prediction strategies that could otherwise mitigate these challenges.
Our goal is to establish a standardized and aggregated database, consolidating high-density fermentation data for accessibility within the scientific community.
The database will harmonize a previously generated comprehensive dataset from more than 600 fermentations using different strains under varied conditions to produce melatonin and tryptophan. Furthermore, this resource will allow the analysis and visualization of these experiments as well as streamlining the integration of new fermentation datasets. By compiling comprehensive data, we aim to expedite the development of "fermenterphiles" and enhance bioproduction, marking a significant stride toward sustainable industrial practices.
Design of the FermentDB data model.
