MCP Brain-IT: Parameter estimation, uncertainty analysis and global sensitivity analysis of intracellular neuronal models

Within this project we develop a workflow combining uncertainty analysis with global sensitivity analysis and apply this on intracellular models of synapses.

Dynamical models describing e.g. intracellular phenomena are increasing in size and complexity as more information are contained from experiments. These models are often over-parameterized with respect to the quantitative data used for parameter estimation, resulting in uncertainty in the individual parameter estimates as well as in the predictions made from the model. Within this project we develop a workflow combining uncertainty analysis with global sensitivity analysis and apply this on intracellular models of synapses. Uncertainty analysis helps in specifying the assumptions made during modeling as well as pointing out in weaker parts of the model that are important targets for further experiments. When combined with global sensitivity analysis, uncertainty analysis can also make predictions on groups of parameters that are important for specific behaviours.