High Performance Computing of Gene Regulatory Networks using a Message-Passing Model

TL;DR

This paper presents a highly efficient MATLAB/Octave implementation of the PANDA algorithm for gene regulatory network reconstruction, significantly improving speed and scalability for large biological datasets.

Contribution

The authors recast PANDA's similarity equations as matrix operations, creating a shorter, more readable, and faster MATLAB/Octave version that enhances scalability for big data applications.

Findings

M-code implementation is 20-80 times faster than C-code.

The new implementation is shorter and more modifiable.

Efficiency increases with larger network models.

Abstract

Gene regulatory network reconstruction is a fundamental problem in computational biology. We recently developed an algorithm, called PANDA (Passing Attributes Between Networks for Data Assimilation), that integrates multiple sources of 'omics data and estimates regulatory network models. This approach was initially implemented in the C++ programming language and has since been applied to a number of biological systems. In our current research we are beginning to expand the algorithm to incorporate larger and most diverse data-sets, to reconstruct networks that contain increasing numbers of elements, and to build not only single network models, but sets of networks. In order to accomplish these "Big Data" applications, it has become critical that we increase the computational efficiency of the PANDA implementation. In this paper we show how to recast PANDA's similarity equations as matrix operations. This allows us to implement a highly readable version of the algorithm using the MATLAB/Octave programming language. We find that the resulting M-code much shorter (103 compared to 1128 lines) and more easily modifiable for potential future applications. The new implementation also runs significantly faster, with increasing efficiency as the network models increase in size. Tests comparing the C-code and M-code versions of PANDA demonstrate that this speed-up is on the order of 20-80 times faster for networks of similar dimensions to those we find in current biological applications.