Biophysical networks underlying the robustness of .. (CanaloHmics)
Biophysical networks underlying the robustness of neuronal excitability
Start date: May 1, 2014,
End date: Apr 30, 2019
The mammalian nervous system is in some respect surprisingly robust to perturbations, as suggested by the virtually complete recovery of brain function after strokes or the pre-clinical asymptomatic phase of Parkinson’s disease. Ultimately though, cognitive and behavioral robustness relies on the ability of single neurons to cope with perturbations, and in particular to maintain a constant and reliable transfer of information.So far, the main facet of robustness that has been studied at the neuronal level is homeostatic plasticity of electrical activity, which refers to the ability of neurons to stabilize their activity level in response to external perturbations. But neurons are also able to maintain their function when one of the major ion channels underlying their activity is deleted or mutated: the number of ion channel subtypes expressed by most excitable cells by far exceeds the minimal number of components necessary to achieve function, offering great potential for compensation when one of the channel’s function is altered. How ion channels are dynamically co-regulated to maintain the appropriate pattern of activity has yet to be determined.In the current project, we will develop a systems-level approach to robustness of neuronal activity based on the combination of electrophysiology, microfluidic single-cell qPCR and computational modeling. We propose to i) characterize the electrical phenotype of dopaminergic neurons following different types of perturbations (ion channel KO, chronic pharmacological treatment), ii) measure the quantitatives changes in ion channel transcriptome (40 voltage-dependent ion channels) associated with these perturbations and iii) determine the mathematical relationships between quantitative changes in ion channel expression and electrical phenotype. Although focused on dopaminergic neurons, this project will provide a general framework that could be applied to any type of excitable cell to decipher its code of robustness.
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