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Open Access Methodology article

Zooming of states and parameters using a lumping approach including back-translation

Mikael Sunnåker1, Henning Schmidt1, Mats Jirstrand1* and Gunnar Cedersund1234*

Author Affiliations

1 Fraunhofer-Chalmers Research Centre for Industrial Mathematics, 412 88 Gothenburg, Sweden

2 Department of Clinical and Experimental Medicine, Diabetes and Integrative Systems Biology, Linköping University, 581 85 Linköping, Sweden

3 Freiburg Institute of Advanced Sciences, Freiburg University, D79104, Freiburg, Germany

4 Department of Mathematical Sciences, Gothenburg University, 41296, Gothenburg, Sweden

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BMC Systems Biology 2010, 4:28  doi:10.1186/1752-0509-4-28

Published: 18 March 2010

Abstract

Background

Systems biology models tend to become large since biological systems often consist of complex networks of interacting components, and since the models usually are developed to reflect various mechanistic assumptions of those networks. Nevertheless, not all aspects of the model are equally interesting in a given setting, and normally there are parts that can be reduced without affecting the relevant model performance. There are many methods for model reduction, but few or none of them allow for a restoration of the details of the original model after the simplified model has been simulated.

Results

We present a reduction method that allows for such a back-translation from the reduced to the original model. The method is based on lumping of states, and includes a general and formal algorithm for both determining appropriate lumps, and for calculating the analytical back-translation formulas. The lumping makes use of efficient methods from graph-theory and ϵ-decomposition and is derived and exemplified on two published models for fluorescence emission in photosynthesis. The bigger of these models is reduced from 26 to 6 states, with a negligible deviation from the reduced model simulations, both when comparing simulations in the states of the reduced model and when comparing back-translated simulations in the states of the original model. The method is developed in a linear setting, but we exemplify how the same concepts and approaches can be applied to non-linear problems. Importantly, the method automatically provides a reduced model with back-translations. Also, the method is implemented as a part of the systems biology toolbox for matlab, and the matlab scripts for the examples in this paper are available in the supplementary material.

Conclusions

Our novel lumping methodology allows for both automatic reduction of states using lumping, and for analytical retrieval of the original states and parameters without performing a new simulation. The two models can thus be considered as two degrees of zooming of the same model. This is a conceptually new development of model reduction approaches, which we think will stimulate much further research and will prove to be very useful in future modelling projects.