Tuesday, 12th March 2024 Using non-linear mixed effects and SciML modelling to fit Calmodulin chemical reaction networks Abstract: Calmodulin (CaM) is one of the key neuronal calcium binding proteins that is essential in calcium signalling (Xia and Storm, 2005; https://doi.org/10.1038/nrn1647). Reflecting its importance, there are many different computational kinetic calcium-CaM models (Heil et al., 2018; https://doi.org/10.1101/254094). However, there has been no quantitative comparison and evaluation of these models, with some publications not reporting their training and validation performance. Moreover, there is no comparison on a common benchmark data set as is common practice in other modeling domains. We quantitatively compare how well different binding schemes, with either published parameters or parameters we find ourselves, fit the data from calcium uncaging experiments (Faas et al. 2011, https://doi.org/10.1038/nn.2746). To fit the model parameters we used the Julia programming language and the Pumas.jl software package which implements powerful training algorithms for non-linear mixed effects modeling and SciML. Event type: Workshop Date: Tuesday, 12th March Time: 11:00 Location: G.03 Speaker(s): Domas Linkevicius Chair: Javier Alfaro This article was published on 2024-11-22
