ACCELERATION OF BOOLEAN GENE REGULATORY NETWORKS ANALYSIS USING FPGA

Abstract

Gene expression does not occur arbitrarily and spontaneously, it obeys certain patterns that can be expressed as a connected graph or network. The disclosure of these patterns requires a large amount of experimental research and accumulation of necessary statistical information. Then this information is subjected to mathematical processing, which involves significant computing resources and takes a lot of time. Boolean networks are often used as the basis for building mathematical models in those calculations. Recently, models based on Boolean networks have increasingly grown in size and complexity causing increased demands on traditional software solutions and computing tools. Field-programmable gate arrays (FPGAs) are a powerful and reconfigurable platform for implementing efficient and high-performance computing. The use of FPGA will significantly speed up the process of calculating sequential chains of gene states, both through the use of hardware acceleration in the calculation of logical dependencies, and through the implementation of an array of parallel computing cores, each of which can perform its own individual task. Another solution that can significantly simplify the work of researchers of gene regulation networks is the creation of a universal computing architecture that will allow dynamic reconfiguration of its internal structure when the task or logical dependencies for the current Boolean network change. Such a solution will relieve the researcher of the need to perform the entire set of actions for the technological preparation of a new FPGA configuration, from making changes to the HDL code that describes the network to uploading the updated configuration to the hardware accelerator. The article discusses how to use FPGA for the implementation and modeling of arbitrary Boolean networks, describes the concept of a universal reconfigurable architecture of a logical dependency calculating core for an arbitrary Boolean network and proposes a practical implementation of such a calculating core for modeling gene regulation networks.