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About OptiPa

OptiPa, an ODE modelling optimisation interface

OptiPa, an ODE modelling optimisation interface

OptiPa, is a graphical user interface built in MatLab (The MathWorks, Inc., Natick, MA, USA). OptiPa is a free software tool to simulate, calibrate and validate ODE based simulation models. OptiPa was developed for research purposes in general but with the food-related disciplines as the main focus. By making OptiPa available for free we try to reach researchers from food-related disciplines whose main training is not in the area of modelling and statistics but who are interested in extending their horizons. 

OptiPa was developed because of an urgent need for a flexible and versatile interface to estimate model parameters on ODE based models leaving only a minimum of programming for the end user. One of the innovative aspects of OptiPa is that it readily allows the user to identify the different sources of variation in a model as it allows, for instance, estimating certain model parameters either in common, per experiment, per treatment condition or per experimental unit. Analysing data this way enhances the interpretation of experimental data and the subsequent application of the model to different situations.

OptiPa can perform the following tasks:

(For a full feature list look here)

  • Simulation: This allows the user to run the model using the current parameter settings and visually compare the model outcomes against measured experimental data. The graphical and numerical output is the same as generated after an optimisation run.
  • Optimisation: This allows the user to fit the model to experimental data by optimising one or more of the model parameters. This results in both graphical, statistical and numerical output.
  • Optimisation per experiment: This allows the user to fit the model to the experimental data one experiment at a time. This results in both statistical and numerical output.
  • Calculating conditional joined confidence regions: By scanning for each pair of model parameter values the parameter space near their estimated optimal values conditional joined confidence intervals are calculated. This results in both graphical and numerical output.
  • Do a sensitivity analysis: By running the model several times with slightly different model parameter values insight can be gained in the sensitivity of the model towards the different model parameters. This results in numerical output only.
  • Bootstrapping: This allows the user the determine confidence intervals for the model parameters by repeatedly fitting the model on a large numbers of bootstrap datasets generated using a model based error resampling. This results in both graphical, statistical and numerical output.
  • Monte-Carlo simulation: Based on either an initial optimisation or some previous bootstrap results the user can generate 95% confidence intervals of the model by repeatedly simulating the model using randomly generated covarying parameter values.
  • Draw from distributions: Based on an arbitrary set of co-varying variables OptiPa can be used to generate new sets of random co-varying parameter values with the same structure as the starting set provided resulting in numerical output only.