Reverse Engineering of Genome-Scale Biological Networks
Availability of genome-scale data sets in biology present a great opportunity as well as a challenge for computational biologists. Simulation and model based analysis on such large-scale dynamical systems pose compute-intensive problems. A reverse-engineering algorithm optimized for parallel architectures has been developed to study these dynamical systems. The parallel architecture and processing power of Graphics processing units (GPUs) provide a platform to carry out genome-scale simulations. We show that genome-scale networks can be inferred using this reverse-engineering algorithm in a matter of days on a single Tesla K20 GPU.