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

Area of Science:

• Systems Biology
• Computational Biology
• Bioinformatics

Background:

• Boolean network models are widely used for in silico studies of biological network sensitivity to mutations.
• Existing tools lack the capability to analyze sensitivity against a broad spectrum of mutations, including user-defined ones.

Purpose of the Study:

• To develop RMut, an R package for comprehensive analysis of Boolean network sensitivity to various mutations.
• To enable the examination of network sensitivity to user-defined mutations, mutation areas, and duration times.

Main Methods:

• RMut employs a parallel algorithm using the OpenCL library for efficient analysis of large-scale networks.
• The package supports well-known node-based and edgetic mutations, as well as novel user-defined mutations.
• RMut allows specification of mutation area and duration for precise sensitivity analysis.

Main Results:

• Real biological networks showed highest sensitivity to overexpression/state-flip (node-based) and edge-addition/-reverse (edgetic) mutations.
• Edgetic mutations demonstrated superior predictive power for drug targets compared to node-based mutations.
• Analysis of double edge-removal mutations revealed significant synergistic effects.

Conclusions:

• RMut provides a flexible and efficient platform for analyzing network sensitivity to a wide array of mutations.
• The findings highlight the importance of considering diverse mutation types for a comprehensive understanding of network behavior.
• RMut facilitates advanced network sensitivity analysis, aiding in biological discovery and drug target identification.