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Dr Igor Khovanov

Brownian dynamics models of ion permutation and selectivity

Ion channels are natural nanotubes in cellular membranes that control a vast range of biological functions in health and disease. They are highly selective with respect to the charge and/or size of the ions passing through them. They are a primary target of the pharmaceutical industry. Analogous to nano- scale transistors, they are present in the membranes of all biological cells. Accordingly, their main properties - structure, conformational changes, selectivity, conductivity, and gating - are subject to intensive, ever-growing, fundamental and applied research.

The molecular structures of some channels have been determined by crystallographic analysis, including the KcsA channel that discriminates (selects) between Na+ and K+. These structures provide the experimental information needed for molecular modelling (molecular dynamics) of the dynamical features of the observed selectivity. However this molecular dynamics modelling is very time consuming and a simpler description is desirable. Such description can be formed by considering ions as particles moving in potential fields under stochastic forces and it is called Brownian dynamics.

In this project it is suggested to develop Brownian dynamics models of ion permutation (conduction) on the base of analysis of ion trajectories coming from molecular dynamics simulations. The project assumes leaning and performing molecular dynamics simulations using HPC, and then developing approaches to obtain Brownian dynamics model.

Modelling of Brain-Heart-Respiratory interaction

Biomedical signals contain important information about the state of living systems. For example, heart rhythm reflects the state of the regulatory neural system, the cardiovascular system and additionally there is a strong modulatory component coming from the respiration. Ultimately nervous system controls all rhythms.

A proper processing of these signals allows in many instances to obtain useful physiological and clinical information. Simultaneous recording and analysis of signals from several subsystems lead to integrative information and reveal interactions between subsystems. This project aims to conduct simultaneous recording ECG, respiration and EEG signals, and then analyse the measurement data and develop the corresponding model to describe brain-heart-respiration interactions.

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