Genome-scale Metabolic Models (GEMs) are constraint-based models that mathematically represent and connect the genes, reactions, and metabolites in the organism. By applying approaches such as Flux Balance Analysis, the model simulates and gives the fluxes of all the reactions in the organism within a short time.
We apply these models to various bacterial and eukaryotic cell lines. Using the data obtained from Spent media Analysis corresponding to a bioprocess, the model is constrained and simulated using Dynamic Flux Balance Analysis, which gives the intracellular fluxes at various time points. A group of experts analyzes the results to provide potential metabolic targets to improve the bioprocess or gather insights about the organism’s metabolism. This can also be extended to a set of varying conditions.
In addition, we also develop GEMs for novel or even well-studied organisms without any model in the literature. After sufficient validation, the model can predict the fluxes, either dynamically, as mentioned earlier, or simply at a single point, thus serving as a base to contextualize diverse omics data and giving insights into the organism’s metabolism.
