چكيده به لاتين
A microbial community refers to coexistence of several microorganisms along with the effective interactions among them. In this thesis, the growth of several microbial communities with different interactions of competition and commensalism is investigated using metabolic modeling, compartment approach (maximizing growth rate at community level) and bi-level optimization approach (maximizing growth rate for each member and the entire community by OptCom algorithm simultaneously). In the community consisting of two identical bacteria competing for 10 mmol (gDWC h) -1 glucose, the compartment approach resulted in biomass production of 0.8312 h-1 by only one member, while OptCom algorithm predicted growth rate of 0.4156 h-1 for each member. Findings suggest that the results of the bi-level approach are more consistent with the reality of the community where both members are able to grow. In this microbial community, similar behavior pattern compared to the single microorganism model was obtained. For communities in which members have different growth rates, both approaches allocate carbon sources to members with higher growth rates unless another constraint is applied. Considering such a community consists members with different growth rates both approaches assigned biomass producion to the member with the higher growth rate with biomass production of 0.59882 h-1. In another designed community, with the commensalism interaction, the carbon source was consumed only by one microorganism leading to its growth, while the growth of second microorganism was based on ethanol, the product of the first microorganism. The overall growth rate of the community was investigated in two states, (A) ethanol production, 8.2795 mmol (gDWC h) -1 which according to the calculations is the amount of ethanol produced by this microorganism as a single microorganism and (B) the state of unrestricted ethanol production calculated by the algorithm. The growth rate was calculated to be 0.4786 and 0.7819 h-1 for states A and B respectively. In another microbial community, by removing the vital intracellular reaction of Isocitrate dehydrogenase, E. coli microorganism can no longer survive and grow, but by placing this defective microorganism together with another microorganism capable of exchanging products of the said reaction, both microorganisms will be able to yeild biomass in the form of a microbial community. In the last case studied, a community with many interactions among its three members is presented, both approaches succeeded in modeling the community and predicting the overall growth rate of 2.4878 h-1 for the community. Although the two approaches calculated the same overall growth rate, compartment approach was proved to be superior due to the lower computational cost. The advantage of using OptCom algorithm is when two members of the community are identical. In this case substrate will be split evenly and the predicted growth rates will be the same.