Multi-Scale, Integrative Model Development using High-Performance Computer Architectures

Abstract

In the field of biophysical modeling, it has often been desirable to build models that can run in real-time on a standard desktop workstation, but this is becoming more difficult to achieve. The complexity of molecular model components is increasing. Models of protein kinetics are evolving into large Markov chains where there were once a handful of Hodgekin-Huxley gating variables or algebraic equations. Additionally, models are integrating more modules for many aspects of cellular regulation, greatly increasing the number of states and expanding the range of relevant timescales. These models achieve mechanistic accuracy at the cost of greatly increased computation. Approximations may be made to decrease simulation time, but with some sacrifice of simulation accuracy. A simplified model of cardiac excitation-contraction (EC) coupling such as the coupled L-type Ca2+ channel-Ryanodine Receptor (LCC-RyR) model can provide a reasonable facsimile of EC coupling gain by modeling only a single LCC-RyR pair per cardiac dyad, far less than what is observed experimentally. To produce more detailed output the number of channels modeled per dyad can be increased, leading to an exponential growth in the number of states and compute time.Increased computing power is becoming more readily available in the form of multi-core processors, cluster computing, and general purpose graphics processing units (GPUs.) As the cost of such advanced computation decreases, the added benefit of including the fully detailed biophysical mechanisms in these models outweighs the computational cost of maintaining the model's complexity. The methods used here show how implementation of the coupled LCC-RyR model on the parallel GPU architecture can lead to significant speedup in simulation time. Use of the GPU also provides a beneficial scaleup in performance as models comprised of more states can be simulated on a larger machine in less time.