Author:
Mélykúti Bence,August Elias,Papachristodoulou Antonis,El-Samad Hana
Abstract
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 L
2 (energy) distance between the outputs of the rival models. In the second approach, we maximize the L
2-distance of the outputs by designing the optimal external stimulus (input) profile of unit L
2-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.
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computer Science Applications,Molecular Biology,Modelling and Simulation,Structural Biology
Reference39 articles.
1. Murray JD: Mathematical biology. 2003, 1-2: Springer-Verlag, New York,
2. Szallasi Z, Stelling J, Periwal V, : System modeling in cellular biology: from concepts to nuts and bolts. 2006, MIT Press, Cambridge,
3. Cornish-Bowden A: Fundamentals of enzyme kinetics. 2004, Portland Press, London,
4. Heinrich R, Schuster S: The regulation of cellular systems. 1996, Chapman & Hall, New York,
5. Fell D: Understanding the control of metabolism. 1997, Portland Press, London,
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