Optimizing ion channel models using a parallel genetic algorithm on graphical processors

We have recently shown that we can semi-automatically constrain models of voltage-gated ion channels by combining a stochastic search algorithm with ionic currents measured using multiple voltage-clamp protocols. Although numerically successful, this approach is highly demanding computationally, with optimization on a high performance Linux cluster typically lasting several days. To solve this computational bottleneck we converted our optimization algorithm for work on a graphical processing unit (GPU) using NVIDIA's CUDA. Parallelizing the process on a Fermi graphic computing engine from NVIDIA increased the speed ∼180 times over an application running on an 80 node Linux cluster, considerably reducing simulation times. This application allows users to optimize models for ion channel kinetics on a single, inexpensive, desktop "super computer," greatly reducing the time and cost of building models relevant to neuronal physiology. We also demonstrate that the point of algorithm parallelization is crucial to its performance. We substantially reduced computing time by solving the ODEs (Ordinary Differential Equations) so as to massively reduce memory transfers to and from the GPU. This approach may be applied to speed up other data intensive applications requiring iterative solutions of ODEs.

[1]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[2]  Klaus Schulten,et al.  Accelerating Molecular Modeling Applications with GPU Computing , 2009 .

[3]  Noam Peled,et al.  Constraining compartmental models using multiple voltage recordings and genetic algorithms. , 2005, Journal of neurophysiology.

[4]  John Guckenheimer,et al.  An Improved Parameter Estimation Method for Hodgkin-Huxley Models , 1999, Journal of Computational Neuroscience.

[5]  A. Hodgkin,et al.  The dual effect of membrane potential on sodium conductance in the giant axon of Loligo , 1952, The Journal of physiology.

[6]  Klaus Schulten,et al.  High performance computation and interactive display of molecular orbitals on GPUs and multi-core CPUs , 2009, GPGPU-2.

[7]  B. Sakmann,et al.  Improved patch-clamp techniques for high-resolution current recording from cells and cell-free membrane patches , 1981, Pflügers Archiv.

[8]  A. Korngreen,et al.  Recording, analysis, and function of dendritic voltage-gated channels , 2006, Pflügers Archiv.

[9]  Robert B. Ross,et al.  Using MPI-2: Advanced Features of the Message Passing Interface , 2003, CLUSTER.

[10]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1952, The Journal of physiology.

[11]  B. Sakmann,et al.  Single-Channel Recording , 1995, Springer US.

[12]  Alon Korngreen,et al.  A Numerical Approach to Ion Channel Modelling Using Whole-Cell Voltage-Clamp Recordings and a Genetic Algorithm , 2007, PLoS Comput. Biol..

[13]  A. Hodgkin,et al.  The components of membrane conductance in the giant axon of Loligo , 1952, The Journal of physiology.

[14]  Alexander Borst,et al.  Neural Simulations on Multi-Core Architectures , 2009, Front. Neuroinform..

[15]  Markus Hadwiger,et al.  Ssecrett and NeuroTrace: Interactive Visualization and Analysis Tools for Large-Scale Neuroscience Data Sets , 2010, IEEE Computer Graphics and Applications.

[16]  Nicholas T. Carnevale,et al.  Expanding NEURON's Repertoire of Mechanisms with NMODL , 2000, Neural Computation.

[17]  S. Waxman,et al.  Kinetic modeling of Nav1.7 provides insight into erythromelalgia-associated F1449V mutation. , 2011, Journal of neurophysiology.

[18]  Yongchao Liu,et al.  CUDASW++: optimizing Smith-Waterman sequence database searches for CUDA-enabled graphics processing units , 2009, BMC Research Notes.

[19]  M L Hines,et al.  Neuron: A Tool for Neuroscientists , 2001, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[20]  A. Hodgkin,et al.  Measurement of current‐voltage relations in the membrane of the giant axon of Loligo , 1952, The Journal of physiology.

[21]  Lothar Thiele,et al.  A Comparison of Selection Schemes Used in Evolutionary Algorithms , 1996, Evolutionary Computation.

[22]  Alan G. Hawkes,et al.  A Q-Matrix Cookbook , 1995 .

[23]  Nikil D. Dutt,et al.  A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors , 2009, Neural Networks.

[24]  Nicholas T. Carnevale,et al.  The NEURON Simulation Environment , 1997, Neural Computation.

[25]  A. Auerbach,et al.  Maximum likelihood estimation of aggregated Markov processes , 1997, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[26]  Fred J. Sigworth,et al.  Fitting and Statistical Analysis of Single-Channel Records , 1983 .

[27]  F. Qin,et al.  Estimating single-channel kinetic parameters from idealized patch-clamp data containing missed events. , 1996, Biophysical journal.

[28]  Bertrand Fontaine,et al.  Fitting Neuron Models to Spike Trains , 2011, Front. Neurosci..

[29]  Giorgio Valle,et al.  CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment , 2008, BMC Bioinformatics.

[30]  A. Hodgkin,et al.  Currents carried by sodium and potassium ions through the membrane of the giant axon of Loligo , 1952, The Journal of physiology.

[31]  David D. Cox,et al.  A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation , 2009, PLoS Comput. Biol..

[32]  N. Keren,et al.  Experimentally guided modelling of dendritic excitability in rat neocortical pyramidal neurones , 2009, The Journal of physiology.