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

01/18/2016
by   Christoph Zimmer, et al.
0

Ordinary differential equations (ODE) are widely used for modeling in Systems Biology. As most commonly only some of the kinetic parameters are measurable or precisely known, parameter estimation techniques are applied to parametrize the model to experimental data. A main challenge for the parameter estimation is the complexity of the parameter space, especially its high dimensionality and local minima. Parameter estimation techniques consist of an objective function, measuring how well a certain parameter set describes the experimental data, and an optimization algorithm that optimizes this objective function. A lot of effort has been spent on developing highly sophisticated optimization algorithms to cope with the complexity in the parameter space, but surprisingly few articles address the influence of the objective function on the computational complexity in finding global optima. We extend a recently developed multiple shooting for stochastic systems (MSS) objective function for parameter estimation of stochastic models and apply it to parameter estimation of ODE models. This MSS objective function treats the intervals between measurement points separately. This separate treatment allows the ODE trajectory to stay closer to the data and we show that it reduces the complexity of the parameter space. We use examples from Systems Biology, namely a Lotka-Volterra model, a FitzHugh-Nagumo oscillator and a Calcium oscillation model, to demonstrate the power of the MSS approach for reducing the complexity and the number of local minima in the parameter space. The approach is fully implemented in the COPASI software package and, therefore, easily accessible for a wide community of researchers.

READ FULL TEXT

page 6

page 7

research
05/02/2019

On the Smoothness of Nonlinear System Identification

New light is shed onto optimization problems resulting from prediction e...
research
07/22/2021

A local approach to parameter space reduction for regression and classification tasks

Frequently, the parameter space, chosen for shape design or other applic...
research
01/18/2021

Accelerating Derivative-Free Optimization with Dimension Reduction and Hyperparameter Learning

We consider convex, black-box objective functions with additive or multi...
research
12/20/2022

Likelihood-based generalization of Markov parameter estimation and multiple shooting objectives in system identification

This paper considers the problem of system identification (ID) of linear...
research
05/24/2020

Applying Evolutionary Metaheuristics for Parameter Estimation of Individual-Based Models

Individual-based models are complex and they have usually an elevated nu...
research
07/01/2020

Can Global Optimization Strategy Outperform Myopic Strategy for Bayesian Parameter Estimation?

Bayesian adaptive inference is widely used in psychophysics to estimate ...
research
05/15/2020

High-dimensional Bayesian Optimization of Personalized Cardiac Model Parameters via an Embedded Generative Model

The estimation of patient-specific tissue properties in the form of mode...

Please sign up or login with your details

Forgot password? Click here to reset