Genome-scale modeling drives 70-fold improvement of intracellular heme production in Saccharomyces cerevisiae
GEM method used for detecting the first round of candidates for metabolic engineering. Taken from the original publication: https://doi.org/10.1073/pnas.2108245119Abstract
This came from one of the collaborations I worked on during my PhD. Following a similar approach than the one presented in previous publications (for acetyl/malonyl-CoA and tryptophan), using a variation of a method called flux scanning based on enforced objective flux (FSEOF), we identified reactions that play an important role in heme (an important cofactor for food additives) production in S. cerevisiae. These targets were validated experimentally, and the correct predictions were combined in different configurations using an algorithm based on simulations from the enzyme-constrained model of S. cerevisiae (generated using the GECKO method). The final product, as the title suggests, increased by 70 times the production of heme, showing the success of a computationally-driven method for finding succesful metabolic engineering strategies.
