Zooming of states and parameters using a lumping approach including back-translation
Artikel i vetenskaplig tidskrift, 2010

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 -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.

biochemical reaction networks

lumping

systems theory

model reduction

Författare

Mikael Sunnåker

Henning Schmidt

Mats Jirstrand

Chalmers, Kemi- och bioteknik, Livsvetenskaper

Gunnar Cedersund

BMC Systems Biology

1752-0509 (eISSN)

Vol. 4 28

Ämneskategorier

Biokemi och molekylärbiologi

Beräkningsmatematik

Systemvetenskap

Styrkeområden

Livsvetenskaper och teknik (2010-2018)

DOI

10.1186/1752-0509-4-28

Mer information

Skapat

2017-10-07