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A method for zooming of nonlinear models of biochemical systems
Fraunhofer Chalmers Research Centre for Industrial Math.
Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
Fraunhofer Chalmers Research Centre for Industrial Math.
2011 (English)In: BMC Systems Biology, ISSN 1752-0509, Vol. 5, no 140Article in journal (Refereed) Published
Abstract [en]

Background: Models of biochemical systems are typically complex, which may complicate the discovery of cardinal biochemical principles. It is therefore important to single out the parts of a model that are essential for the function of the system, so that the remaining non-essential parts can be eliminated. However, each component of a mechanistic model has a clear biochemical interpretation, and it is desirable to conserve as much of this interpretability as possible in the reduction process. Furthermore, it is of great advantage if we can translate predictions from the reduced model to the original model. less thanbrgreater than less thanbrgreater thanResults: In this paper we present a novel method for model reduction that generates reduced models with a clear biochemical interpretation. Unlike conventional methods for model reduction our method enables the mapping of predictions by the reduced model to the corresponding detailed predictions by the original model. The method is based on proper lumping of state variables interacting on short time scales and on the computation of fraction parameters, which serve as the link between the reduced model and the original model. We illustrate the advantages of the proposed method by applying it to two biochemical models. The first model is of modest size and is commonly occurring as a part of larger models. The second model describes glucose transport across the cell membrane in bakers yeast. Both models can be significantly reduced with the proposed method, at the same time as the interpretability is conserved. less thanbrgreater than less thanbrgreater thanConclusions: We introduce a novel method for reduction of biochemical models that is compatible with the concept of zooming. Zooming allows the modeler to work on different levels of model granularity, and enables a direct interpretation of how modifications to the model on one level affect the model on other levels in the hierarchy. The method extends the applicability of the method that was previously developed for zooming of linear biochemical models to nonlinear models.

Place, publisher, year, edition, pages
BioMed Central , 2011. Vol. 5, no 140
National Category
Medical and Health Sciences
URN: urn:nbn:se:liu:diva-71647DOI: 10.1186/1752-0509-5-140ISI: 000295800800001OAI: diva2:451815
Funding Agencies|European Commission|005137201142|||Swedish Foundation for Strategic Research through the Gothenburg Mathematical Modelling Centre||Swedish Research Council||Lions||Available from: 2011-10-27 Created: 2011-10-27 Last updated: 2011-10-31

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