Dynamic ion channel activation scheduling in patch clamp on a chip

In 2002, Fertig et al. made a remarkable invention: the first successful demonstration of a patch clamp on a chip-a planar quartz-based biological chip that contains up to several hundred ion channels. This patch-clamp chip can be used in massively parallel screens for ion channel activity, thereby providing a high-throughput screening tool for drug discovery efforts. In this paper, we propose computationally efficient dynamic stochastic scheduling algorithms for activating individual ion channels in the patch-clamp chip. By formulating the ion channel activation scheduling problem as a partially observed Markov decision process with a multiarmed bandit structure, near-optimal dynamic scheduling for activation of the individual channels is achieved to optimize the information gained from the patch-clamp chip. Numerical examples using state-of-the-art algorithms developed recently in artificial intelligence and operations research are presented to illustrate these dynamic ion channel (macromolecule) activation scheduling algorithms.

[1]  V Krishnamurthy,et al.  Adaptive processing techniques based on hidden Markov models for characterizing very small channel currents buried in noise and deterministic interferences. , 1991, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

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

[3]  Michael L. Littman,et al.  Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes , 1997, UAI.

[4]  P. Whittle Multi‐Armed Bandits and the Gittins Index , 1980 .

[5]  William S. Lovejoy,et al.  Computationally Feasible Bounds for Partially Observed Markov Decision Processes , 1991, Oper. Res..

[6]  Robert H Blick,et al.  Whole cell patch clamp recording performed on a planar glass chip. , 2002, Biophysical journal.

[7]  Vikram Krishnamurthy,et al.  Time discretization of continuous-time filters and smoothers for HMM parameter estimation , 1996, IEEE Trans. Inf. Theory.

[8]  Robin J. Evans,et al.  Correction to "Hidden Markov model multiarm bandits: a methodology for beam scheduling in multitarget tracking" , 2003, IEEE Trans. Signal Process..

[9]  Lei Wu,et al.  Ion-channel assay technologies: quo vadis? , 2001, Drug discovery today.

[10]  Neri Merhav,et al.  Hidden Markov processes , 2002, IEEE Trans. Inf. Theory.

[11]  F. Sigworth,et al.  Patch clamp on a chip. , 2002, Biophysical journal.

[12]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

[13]  Robin J. Evans,et al.  Hidden Markov model multiarm bandits: a methodology for beam scheduling in multitarget tracking , 2001, IEEE Trans. Signal Process..

[14]  V. Krishnamurthy,et al.  Adaptive learning algorithms for Nernst potential and I-V curves in nerve cell membrane ion channels modeled as hidden Markov models , 2003, IEEE Transactions on NanoBioscience.

[15]  J. Bather,et al.  Multi‐Armed Bandit Allocation Indices , 1990 .

[16]  B. Sakmann,et al.  Single-channel currents recorded from membrane of denervated frog muscle fibres , 1976, Nature.