Detection of spreader nodes and ranking of interacting edges in Human-SARS-CoV protein interaction network

Sovan Saha; Piyali Chatterjee; Subhadip Basu; Mita Nasipuri

The SIS Epidemic Model [30] is used in this proposed methodology as a way of modeling SARS-CoV epidemic by classifying the proteins in SARS-CoV-human PPIN based on their disease/infection status. SIS actually refers to Susceptible, Infected and Susceptible states which are generally considered as the three probable states that a protein in a PPIN can fall into due to SARS-CoV. 1) S -The susceptible proteins who are not yet infected but are at risk for getting infected. In general every protein in PPIN is initially in susceptible state. 2) I -Infected proteins who are infected and are capable of transmitting the disease to other proteins. 3) S -proteins who have recovered and again become susceptible. Infection rate of the network, recovery rate ( general assumption is any one out of the infected proteins gets recovered in one day) and total number of proteins are usually provided as input to SIS. If a protein gets infected and it has many neighbors, then any neighbor can get infected or may not be. So the final result is generated after 50 iterations for each infected protein. The total no. of susceptible after 50 iterations in the neighborhood of each infected protein divided by the total number of proteins in the network generates the infection capability of the infected protein. Thus the spreader nodes identified by spreadability index are validated by the infection rate as generated by SIS for them. It can be observed from Table 1 to Table 5 that the proposed methodology has the highest SIS infection rate of 2.46 (see Table 1 ) in comparison to others for their corresponding top 10 spreader nodes in the synthetic network as shown in Figure 1 .