Global sensitivity analysis of a model related to memory formation in synapses: Model reduction based on epistemic parameter uncertainties and related issues. (21st April 2017) - Kulasiri, Don; Liang, Jingyi; He, Yao; Samarasinghe, Sandhya
Abstract: We investigate the epistemic uncertainties of parameters of a mathematical model that describes the dynamics of CaMKII-NMDAR complex related to memory formation in synapses using global sensitivity analysis (GSA). The model, which was published in this journal, is nonlinear and complex with Ca 2+ patterns with different level of frequencies as inputs. We explore the effects of parameter on the key outputs of the model to discover the most sensitive ones using GSA and partial ranking correlation coefficient (PRCC) and to understand why they are sensitive and others are not based on the biology of the problem. We also extend the model to add presynaptic neurotransmitter vesicles release to have action potentials as inputs of different frequencies. We perform GSA on this extended model to show that the parameter sensitivities are different for the extended model as shown by PRCC landscapes. Based on the results of GSA and PRCC, we reduce the original model to a less complex model taking the most important biological processes into account. We validate the reduced model against the outputs of the original model. We show that the parameter sensitivities are dependent on the inputs and GSA would make us understand the sensitivities and the importance of the parameters. A thorough phenomenological understanding of the relationships involved is essential to interpret the results of GSA and hence for the possible model reduction. Highlights: Investigation of parameter uncertainties of a mathematical model related to memory. Exploration of parameter sensitivities on key outputs using GSA and PRCC. Model extension to add presynaptic neurotransmitter vesicles release. GSA and PRCC show that parameter sensitivities are different for extended model. Reduction of the original model to a less complex model based GSA and PRCC. Thorough phenomenological understanding is essential the possible model reduction.