'Multilevel modelling of neuronal function: combining metabotropic pathways and membrane excitability in silico for the quick screening of drugs'

Neurons work as information relays. They integrate information received from the environment at the subcellular scale, generating an appropriate electrophysiological response. The understanding of electrical processes happening at the plasma membrane level has been clarified by Hodgkin and Huxley, combining experimental recordings with mathematical modelling of equivalent electrical circuits. However, quantitative models describing the effects of cell signalling on the cellular response are lacking.

Adenosine is a neurotransmitter binding both A1 (Gi) and A2 (Gs) receptors. A1 and A2 stimulation results in altered neuronal excitability via K+ permeability changes. In this project, we aim to combine a mathematical model of adenosine receptor signal transduction (Tindall) with the Hodgkin-Huxley model of a neuron (Tsaneva-Atanasova), to generate an in-silico unified model, predictive of neuronal function following exposure to chemical stimulation. We will test the predictive capability of the model with whole-neuron patch-clamp recordings (Tamagnini).

The mathematical models formulated during the project will utilise the theory of differential equations (ordinary and partial) solved and analysed both numerically and analytically (e.g. dynamical systems theory, asymptotic methods). The successful candidate will be involved in the design and undertaking of wet laboratory experiments for testing and informing the mathematical models. The candidate will be trained in whole cell patch clamp techniques, with the value added of performing dynamic clamp to test the validity of the model on to a live system. The design and validation of the unified model is aimed at providing a tool (software) for the pre-clinical, ethical, high-throughput screening of newly developed molecules with biological activity and their action on neuronal function.