Date Published: March 19, 2019
Publisher: Public Library of Science
Author(s): Hung-Cuong Trinh, Yung-Keun Kwon, José Carlos M. Mombach.
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.
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 , network intervention , and the relationship between dynamic and structural properties [5–7]. In addition, many computational tools have been developed to support in silico simulations based on these mutations. For example, CABeRNET, a recent Cytoscape app, can assess the dynamics of a network via state-flip, knockout, and overexpression mutations . PANET was developed for parallel analysis of sensitivity-related dynamics against state-flip and rule-flip mutations in large-scale networks . BooleSim , Cell Collective , and GINsim  can manipulate dynamic simulations by employing knockout and overexpression mutations. GDSCalc  can evaluate the stability of network dynamics upon a state-flip mutation. BoolNet  can investigate network sensitivity via state-flip, knockout, and overexpression mutations.
This section is organized into four subsections. A Boolean network model employed in this study is first introduced. The next two subsections present predefined mutations that have been widely used in previous studies, and user-defined mutations based on a Java template implementation, respectively. Finally, two network sensitivity measures used in this study are defined.
To allow analysis of large-scale networks, the sensitivity is calculated in parallel using the OpenCL library as in our previous tool–PANET . Specifically, we assign each initial state included in a set of random initial states S in Eq (2) to processing elements of a central processing unit or graphics processing unit where the wild-type and the mutant attractors are computed in parallel.
To demonstrate the usefulness of RMut, we conducted three case studies using the following real biological networks.
Although it is ideal to specify all the update rule based on real regulatory relations, most of them are not available, particularly in the case of large-scale biological networks. In this regard, we employed NCFs to randomly specify the update rule in this study. It is known that NCFs can represent various types of regulatory interactions [18, 19, 79–81]. For example, 133 out of 139 rules compiled from a dataset about a transcriptional regulatory network  and 39 out of 42 rules inferred from a dataset about signaling pathways  were NCFs. Despite these supports, we note that the accurate representation of the regulatory interaction can be limited in our tool. Another issue to be discussed about our Boolean network model is the synchronous update scheme. In fact, it is very likely that the genes in the real signaling networks are regulated in an asynchronous manner. However, it is required to properly specify some unknown parameters to implement the asynchronous scheme such as the number of genes to be updated in a single step and a strategy to choose an update sequence. To avoid this problem, we employed the synchronous update scheme which can be another limitation of our tool.
We developed RMut, which is an efficient R package to investigate the network sensitivity for both predefined node-based and edgetic mutations. Moreover, new user-defined mutations can be easily embedded using a Java template. RMut also provides more precise analysis by specifying the mutation area and the duration time. We implemented RMut in a parallel algorithm using the OpenCL library to analyze large-scale networks. In this study, we demonstrated the usefulness of RMut through three case studies. First, we compared 10 different mutations predefined in RMut over real biological networks and found that the 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 observed that edgetic mutations can predict drug-targets better than node-based mutations. Interestingly, edge-attenuation, which has not been considered in previous tools, showed high performance in drug-target prediction. Finally, we compared double and single edge-removal mutations based on network sensitivity values, and found an interesting synergy effect even for a pair of susceptible edges. Taken together, these findings indicate that RMut can be a useful tool to efficiently analyze network sensitivity against various types of mutations. In future, RMut could be extended to employ arbitrary update-rules or asynchronous update-scheme in the Boolean network model, and provide more visualization features for the analysis.