Discriminating between rival biochemical network models: three approaches to optimal experiment design
OPEN ACCESS
Loading...
Author / Producer
Date
2010
Publication Type
Journal Article
ETH Bibliography
no
Citations
Altmetric
OPEN ACCESS
Data
Rights / License
Abstract
Background
The success of molecular systems biology hinges on the ability to use computational models to design predictive experiments, and ultimately unravel underlying biological mechanisms. A problem commonly encountered in the computational modelling of biological networks is that alternative, structurally different models of similar complexity fit a set of experimental data equally well. In this case, more than one molecular mechanism can explain available data. In order to rule out the incorrect mechanisms, one needs to invalidate incorrect models. At this point, new experiments maximizing the difference between the measured values of alternative models should be proposed and conducted. Such experiments should be optimally designed to produce data that are most likely to invalidate incorrect model structures.
Results
In this paper we develop methodologies for the optimal design of experiments with the aim of discriminating between different mathematical models of the same biological system. The first approach determines the 'best' initial condition that maximizes the L2 (energy) distance between the outputs of the rival models. In the second approach, we maximize the L2-distance of the outputs by designing the optimal external stimulus (input) profile of unit L2-norm. Our third method uses optimized structural changes (corresponding, for example, to parameter value changes reflecting gene knock-outs) to achieve the same goal. The numerical implementation of each method is considered in an example, signal processing in starving Dictyostelium amœbæ.
Conclusions
Model-based design of experiments improves both the reliability and the efficiency of biochemical network model discrimination. This opens the way to model invalidation, which can be used to perfect our understanding of biochemical networks. Our general problem formulation together with the three proposed experiment design methods give the practitioner new tools for a systems biology approach to experiment design.
Permanent link
Publication status
published
External links
Editor
Book title
Journal / series
Volume
4
Pages / Article No.
38
Publisher
BioMed Central
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
03659 - Buhmann, Joachim M. (emeritus) / Buhmann, Joachim M. (emeritus)