Biomedical subjects

Vikram Krishnamurthy

Publications and source records attributed to Vikram Krishnamurthy.

5 recordsLinked to original sources

PubMed2006

Adaptive Brownian dynamics simulation for estimating potential mean force in ion channel permeation.

Ion channels are biological nanotubes formed by large protein molecules in the cell membrane. This paper presents a novel multiparticle simulation methodology, which we call adaptive controlled Brownian dynamics, for estimating the force experienced by a permeating ion at each discrete position along the ion-conducting pathway. The profile of this force, commonly known as the potential of mean force, results from the electrostatic interactions between the ions in the conduit and all the charges carried by atoms forming the channel the protein, as well as the induced charges on the protein wall. The current across the channel is solely determined by the potential of mean force encountered by the permeant ions. The simulation algorithm yields consistent estimates of this profile. The algorithm operates on an angstrom unit spatial scale and femtosecond time scale. Numerical simulations on the gramicidin ion channel show that the algorithm yields the potential of mean force profile that accurately reproduces experimental observations.

Adaptation, Physiological↗

PubMed2006

Controlled hidden markov models for dynamically adapting patch clamp experiment to estimate Nernst potential of single-ion channels.

This paper presents novel kernel-based stochastic learning algorithms for controlling the kinetics of single-ion channels in a patch clamp experiment. The algorithms yield efficient estimates of the equilibrium (Nernst) potential of an ion channel. The equilibrium potential of an ion channel is the applied external potential difference required to maintain electrochemical equilibrium across the ion channel. The algorithm adaptively controls the exploration of the learning algorithm to achieve an optimal balance between exploration and exploitation. An important feature of the resulting algorithm is that it is guaranteed to minimize the experimental effort. We illustrate the efficiency of the algorithms for the experimentally determined current voltage curve of a bi-ionic single potassium ion channel.

Adaptation, Physiological↗

PubMed2005

Brownian dynamics simulation for modeling ion permeation across bionanotubes.

The principles underlying Brownian dynamics (BD), its statistical consistency, and algorithms for practical implementation are outlined here. The ability to compute current flow across ion channels confers a distinct advantage to BD simulations compared to other simulation techniques. Thus, two obvious applications of BD ion channels are in calculation of the current-voltage and current-concentration curves, which can be directly compared to the physiological measurements to assess the reliability of the model and predictive power of the method. We illustrate how BD simulations are used to unravel the permeation dynamics in two biological ion channels-the KcsA K+ channel and CIC Cl- channel.

Cell Membrane↗

PubMed2004

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.

Algorithms↗

PubMed2003

Adaptive learning algorithms for nernst potential and I-V curves in nerve cell membrane ion channels modeled as hidden Markov models.

We present discrete stochastic optimization algorithms that adaptively learn the Nernst potential in membrane ion channels. The proposed algorithms dynamically control both the ion channel experiment and the resulting hidden Markov model signal processor and can adapt to time-varying behavior of ion channels. One of the most important properties of the proposed algorithms is their self-learning capability--they spend most of the computational effort at the global optimizer (Nernst potential). Numerical examples illustrate the performance of the algorithms on computer-generated synthetic data.

Algorithms↗