Near-optimal experimental design for model selection in systems biology

Near-optimal experimental design for model selection in systems biology

MOTIVATION Biological systems are understood through iterations of modeling and experimentation. Not all experiments, however, are equally valuable for predictive modeling. This study introduces an efficient method for experimental design aimed at selecting dynamical models from data. Motivated by biological applications, the method enables the design of crucial experiments: it determines a hig...

Supplementary Information of “ Near - optimal Experimental Design for Model Selection in Systems Biology ”

The aim of the introduced method for experimental design is that of model discrimination. In its application to biochemical reaction network modeling, each element of the hypothesis class F consists of an alternative reaction network. Such hypotheses offer hypothetical explanations for the studied biochemical process. Models are identified with functions f ∈ F , which define vector fields for a...

Model Selection in Systems Biology Depends on Experimental Design

Experimental design attempts to maximise the information available for modelling tasks. An optimal experiment allows the inferred models or parameters to be chosen with the highest expected degree of confidence. If the true system is faithfully reproduced by one of the models, the merit of this approach is clear - we simply wish to identify it and the true parameters with the most certainty. Ho...

Simulation Methods for Optimal Experimental Design in Systems Biology

To obtain a systems-level understanding of a biological system, the authors conducted quantitative dynamic experiments from which the system structure and the parameters have to be deduced. Since biological systems have to cope with different environmental conditions, certain properties are often robust with respect to variations in some of the parameters. Hence, it is important to use optimal ...

Prediction and Optimal Experimental Design in Systems Biology Models