FPGA-accelerated Attractor Computation of Scale Free Gene Regulatory Networks

Due to the large amount of experimental data provided by DNA microarrays, different kinds of computational methods have been proposed to study gene interactions. These methods are computationally very intensive. More specifically, this work focuses on an efficient approach to compute the attractor cycles in gene regulatory networks, which are modeled by Boolean graphs. This work proposes to explore the inherent parallelism of this approach by using a FPGA based implementation. In addition, we also propose a runtime framework to dynamically insert/delete edges at low cost by using a multistage interconnection network (MIN). We also show that MIN could be efficiently mapped on FPGAs by taking advantages of embedded memory modules. The MIN implementation is close to theoretical complexity O(n lg n). Moreover, we propose a heterogeneous node set, where few nodes are strongly connected and most nodes are poorly connected, which allow us to handle efficiently scale free topologies. Furthermore, our approach is synthesized once onto a FPGA and several simulations are performed, without the need of re-synthesis. Experimental results have shown acceleration gains up to three orders of magnitude compared to sequential approaches.