A Faster DiSH: Hardware Implementation of a Discrete Cell Signaling Network Simulator

Abstract

Development of fast methods to conduct in silico experiments using computational models of cellular signaling is a promising approach toward advances in personalized medicine. However, software-based cellular network simulation has runtimes plagued by wasted CPU cycles and unnecessary processes. Hardware-based simulation affords substantial speedup, but prior attempts at hardware-based biological simulation have been limited in scope and have suffered from inaccuracies due to poor random number generation. In this work, we propose several hardware-based simulation schemes utilizing novel random update index generation techniques for step-based and roundbased stochastic simulations of cellular networks. Our results show improved runtimes while maintaining simulation accuracy compared to software implementations.

Dynamic system explanation

“…Defining models at a higher level of abstraction, by using a finite set of discrete value levels for model elements, and omitting some of the precise mechanism details, allows for capturing both direct and indirect interactions, as well as important feedback and feedforward loops within a large system network, while avoiding significant (or impractical) increase in model analysis runtime [3,16]. In general, the static structure of these networks can be illustrated as a directed graph, with the system components (i.e., model elements) as nodes, and the influences between components as directed edges [17].…”