SEQUENTIAL MONTE CARLO BASED MAXIMUM LIKELIHOOD ESTIMATION FOR CALCIUM BINDING REACTIONS

Sequential Monte Carlo based maximum likelihood estimation offers a computationally efficient estimation methodology for tuning the parameter values of stochastic cell and molecular biological models. In this work, we consider a model for calcium binding reactions. The calcium binding model is simulated here with stochastic methods. Six estimation tasks are selected to test the estimation method. The tasks are constructed by using two volumes in which the calcium binding model is converted from concentrations to numbers of molecules and ions. The reference data of the calcium bindind model is simulated in these volumes using the Gillespie stochastic simulation algorithm and corrupted by three different levels of measurement noise yielding measurement data. The parameter values of

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