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Ordinary differential equations (ODE) are widely used for modeling in SystemsBiology. As most commonly only some of the kinetic parameters are measurable orprecisely known, parameter estimation techniques are applied to parametrize themodel to experimental data. A main challenge for the parameter estimation isthe complexity of the parameter space, especially its high dimensionality andlocal minima.  Parameter estimation techniques consist of an objective function, measuringhow well a certain parameter set describes the experimental data, and anoptimization algorithm that optimizes this objective function. A lot of efforthas been spent on developing highly sophisticated optimization algorithms tocope with the complexity in the parameter space, but surprisingly few articlesaddress the influence of the objective function on the computational complexityin finding global optima. We extend a recently developed multiple shooting forstochastic systems (MSS) objective function for parameter estimation ofstochastic models and apply it to parameter estimation of ODE models. This MSSobjective function treats the intervals between measurement points separately.This separate treatment allows the ODE trajectory to stay closer to the dataand we show that it reduces the complexity of the parameter space.  We use examples from Systems Biology, namely a Lotka-Volterra model, aFitzHugh-Nagumo oscillator and a Calcium oscillation model, to demonstrate thepower of the MSS approach for reducing the complexity and the number of localminima in the parameter space. The approach is fully implemented in the COPASIsoftware package and, therefore, easily accessible for a wide community ofresearchers.

Reducing local minima in fitness landscapes of parameter estimation by using piecewise evaluation and state estimation