Mapping Bacterial Metabolic Network topology vs. Nanoparticle antibacterial activity

The study of the Metabolic Networks (MNs) of those bacteria with high resistance to Nanoparticles (NPs) action may give clues for the future design of new NP with specific antibacterial activity. In our previous work, we reported that the values of p(f(n,c,j,s)=1)_pred are the probabilities with which a given NP is predicted to be active against the bacteria with a given MN_s. We can interpret these probabilities as a measure of bacterial susceptibility to NPs. Consequently, from the point of view of the MNs those bacteria with low values of p(f(n,c,j,s)=1)_pred are predicted to be very resistant to the action of the n^th NP in the j^th assay. Accordingly, a low average value p(f(n,c,j,s)=1)_avg = <p(f(n,c,j,s)=1)_pred> (average of all p(f(n,c,j,s)=1)_pred values) for the s^th bacteria vs. the same NP in different assays indicates that this specie should be very resistant to this NP in particular regardless the assay selected. In order to compare the structure of the MNs of different bacteria vs. the predicted p(f(n,c,j,s)=1)_avg we could use a single numerical parameter of MN metabolic structure (network topology). In this work we used 3 numerical parameters related to MNs metabolic structure, N_ms, <L_ins>, and <L_outs>. We used these parameters to calculate a unique parameter that fusion all this information. We are going to call this parameter as the Anabolism-Catabolism Unbalance (ACU_s) index of MN_s of the s^th bacteria specie. In this communication we discussed the use of this index. Full publication: Nanoscale. 2021 Jan 21;13(2):1318-1330. doi: 10.1039/d0nr07588d.