Elias S. Manolakos
Time: 4/3/13 2-3 pm
Rapid advances in Systems Biology call for the development of efficient methods and tools for analyzing the complex dynamics of large-scale biomolecular networks to unravel the “epigenetic landscape” of cells and predict how it can be manipulated by intervention strategies (e.g. combinatorial drug design for personalized medicine, stem cell therapies etc). Stochastic simulation is the method of choice for analyzing the dynamics of biological systems while accounting for their inherently stochastic behavior due to intrinsic and extrinsic “noise”. However, Stochastic Simulation Algorithms (SSA) are very demanding computationally and only small biomolecular networks can be analyzed in a reasonable amount of time today using a modern computer. Inferring from high throughput data “biological circuits” of increasing complexity and simulating their dynamical behavior under different conditions motivates the need for new flexible and efficient computation strategies that exploit modern hardware technologies to deliver scalable performance per unit power, without sacrificing simulation accuracy. Such solutions will allow us to analyze efficiently systems such as cross-talking metabolic pathways, whole-species gene regulatory networks, biofilm formation mechanisms etc. We present the design of a scalable Multiprocessor Systems-on-Chip architecture implementing Gillespie’s SSA in reconfigurable hardware. Our MPSoC architecture can deliver performance (Mega-Reactions/sec) and throughput (MReaction cycles/sec) that is increasing linearly with the number of processors in the SoC. It can handle the simulation of very large biomolecular networks with up to m = 16K reactions (of up to the 3rd order) using a moderate size FPGA. The MPSoCs can be configured to use the available processors to split the reactions of a Single Simulation run and execute them In Parallel (SSIP mode), or to execute Multiple independent Simulation runs In Parallel (MSIP mode). We have synthesized and verified several MPSoC instances with up to N=8 Processing Elements for Xilinx Virtex 5 and Virtex 6 FPGAs, reaching clock frequencies up to 320 MHz and delivering performance that exceeds by 2 orders of magnitude that of software based simulators running on Intel Core 2 and i7 CPUs at frequencies higher than 2GHz. In addition, we have developed a configurable fully parametric soft IP core of the architecture, expressed in VHDL, and a completely automated design flow that can be used to synthesize the most appropriate MPSoC instance for a given biomodel of any complexity (captured in SBML). Moreover, we have developed a Hardware Abstraction Layer API in Python which allows user applications running in a host PC to view the SoC running in the FPGA just as a component for efficient stochastic simulations. We are in the process of developing a StochSoCs based flexible simulation platform for systems biology modeling studies accessible over the internet.
Elias S. Manolakos is a Visiting Scholar at the Wyss Institute for Biologically Inspired Engineering, Harvard University and the Director of the Multidisciplinary Program "Information Technologies in Medicine and Biology", University of Athens. Before returning to Greece he was with the faculty of the ECE Dept. of Northeastern University, Boston, where he directed the Communications and DSP Center for Research and graduate studies, promoting student-centered innovation through academia-industry collaboration.