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Of the toolboxes supporting parameter- and state-dependent trigger functions, only Sloppy Cell (Myers , 2007) allows for sensitivity analysis using symbolically derived forward sensitivity equations.However, no existing toolbox supports sensitivity analysis for event-triggered observations which yield event-resolved data, e.g.Furthermore, we introduce an objective function that allows the consideration of event-triggered observations through mechanistic models.All relevant methods are implemented in the open-source software toolbox Advanced Matlab Interface for CVODES and IDAS (AMICI, originally presented by Fröhlich , 2005) and automatically handles events, which are not natively supported by CVODES or IDAS.Yet, sensitivity equations for models with parameter- and state-dependent events and event-triggered observations are not supported by existing toolboxes.In this manuscript, we describe the sensitivity equations for differential equation models with events and demonstrate how to estimate parameters from event-resolved data using event-triggered observations in parameter estimation.To exploit the predictive power of ODE models, their parameters have to be inferred from experimental data.

event-triggered state transitions and observations, and logical operations.synthesis, interconversion and degradation) as well as discrete transitions and logical operations.Transitions can be triggered by internal or external events, e.g. Exemplarily, in the firing of neurones, the event trigger is the membrane voltage reaching a threshold voltage, which results in an instantaneous decrease of the membrane voltage and an increase in the ion flux across the membrane (Izhikevich, 2003).neuronal spike trains (Perkel The shortcomings of the available software toolboxes complicate the analysis of ODE models with discrete events and logical operations, especially for gradient based parameter estimation.Most ODE models possess unknown parameters, such as reaction rates, which have to be estimated from experimental data.

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