RMut: R package for a Boolean sensitivity analysis against various types of mutations

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.

Fig 1. An illustrative example of the predefined mutations implemented in RMut.

(a) An example node subject to mutations. Let v be a node with four incoming links from a set of nodes u₁,u₂,u₃, and u₄, and f be the update rule of v. The arrows and bar-headed lines represent positive and negative interactions, respectively. (b) Changes of the update function by node-based mutations subject to node v. The update rule f is modified to f′ by each of five node-based mutations. (c) Changes of the update function by edgetic mutations subject to (u₅,v)∉E or (u₂,v)∈E in the case of the edge-addition and the other edgetic mutations, respectively.

Fig 2. User-defined mutations in RMut.

(a) A Java template for implementation of a user-defined mutation. (b) An example of reimplementing the rule-flip mutation using the template.

Fig 3. An example of network sensitivity analysis using RMut.

Fig 4. Average sensitivities based on the predefined mutations.

(a)-(c) Results of HSN, CCSN, and AMRN networks, respectively, using the identicalness-based sensitivity. (d)-(f) Results of HSN, CCSN, and AMRN networks, respectively, using the similarity-based sensitivity. In each subfigure, Y-axis values represent the average sensitivity values.

Fig 5. Results of drug-targets prediction based on network sensitivity analysis using RMut.

(a)-(b) Comparison of average mutation-susceptibility values between groups of drug-targets and non-drug-targets over node-based and edgetic mutations, respectively, using the identicalness-based sensitivity. (c) AUC values in drug-target prediction using the identicalness-based sensitivity. (d)-(e) Comparison of average mutation-susceptibility values between groups of drug-targets and non-drug-targets over node-based and edgetic mutations, respectively, using the similarity-based sensitivity. (f) AUC values in drug-target prediction using the similarity-based sensitivity. In all sub-figures, the error bars represent 95% confidence intervals.

Fig 6. Scalability by parallel computation in RMut.

We compared the running times for calculating average sensitivity of the HSN in three modes: serial computation, parallel computation on multi-core CPU (denoted as “parallel CPU”), and parallel computation on GPU (denoted as “parallel GPU”). The knockout mutation is considered and the number of initial states varied from 100 to 2000.