microbial community metabolic modelling using steadycom for a two-member community of e. coli

Microbial communities due to their numerous advantages over pure cultures can be used forbiotechnological applications. metabolic modeling of microbial community provides insights into the function ofeach member and the way it improves community behavior. a number of algorithms with different paradigmshave been developed to analyze this behavior and the interactions among members. steadycom with morereasonable formulation and assumptions has been used in this work to analyze a synthetic community of twoidentical e. coli and to compare its results with optcom and fba modeling methods. as expected, communitygrowth rate was the same as that for each member. however, growth rate predicted by steadycom was higherthan those resulted from optcom and fba. this difference was due to more atpm assumption in optcom andfba.