Abstract
There have been many in silico studies based on a Boolean network model to investigate network sensitivity against gene or interaction mutations. However, there are no proper tools to examine the network sensitivity against many different types of mutations, including user-defined ones. To address this issue, we developed RMut, which is an R package to analyze the Boolean network-based sensitivity by efficiently employing not only many well-known node-based and edgetic mutations but also novel user-defined mutations. In addition, RMut can specify the mutation area and the duration time for more precise analysis. RMut can be used to analyze large-scale networks because it is implemented in a parallel algorithm using the OpenCL library. In the first case study, we observed that the real biological networks were most sensitive to overexpression/state-flip and edge-addition/-reverse mutations among node-based and edgetic mutations, respectively. In the second case study, we showed that edgetic mutations can predict drug-targets better than node-based mutations. Finally, we examined the network sensitivity to double edge-removal mutations and found an interesting synergistic effect. Taken together, these findings indicate that RMut is a flexible R package to efficiently analyze network sensitivity to various types of mutations. RMut is available at https://github.com/csclab/RMut.
Highlights
Many different types of mutations have been used to investigate dynamic behaviors of biological networks; these have focused on essential components identification [1, 2], genetic interactions prediction [3], network intervention [4], and the relationship between dynamic and structural properties [5,6,7]
We compared 10 different mutations predefined in RMut over real biological networks, and found that the networks are most sensitive to overexpression/state-flip and edge-addition/-reverse mutations among node-based and edgetic mutations, respectively
We developed RMut, which is an efficient R package to investigate the network sensitivity for both predefined node-based and edgetic mutations
Summary
Many different types of mutations have been used to investigate dynamic behaviors of biological networks; these have focused on essential components identification [1, 2], genetic interactions prediction [3], network intervention [4], and the relationship between dynamic and structural properties [5,6,7]. Many computational tools have been developed to support in silico simulations based on these mutations. CABeRNET, a recent Cytoscape app, can assess the dynamics of a network via state-flip, knockout, and overexpression mutations [8]. PANET was developed for parallel analysis of sensitivity-related dynamics against state-flip and rule-flip mutations in large-scale networks [9]. BooleSim [10], Cell Collective [11], and GINsim [12] can manipulate dynamic simulations by employing knockout and overexpression mutations. GDSCalc [13] can evaluate the stability of network dynamics
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