ArticleBMC systems biology2011
A method for zooming of nonlinear models of biochemical systems.
Article in BMC systems biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 46 citations in OpenAlex.
- A structural property for reduction of biochemical networks.Scientific reports · 2021Article
- Balanced truncation for model reduction of biological oscillators.Biological cybernetics · 2021Article
- Model reduction in mathematical pharmacology : Integration, reduction and linking of PBPK and systems biology models.Journal of pharmacokinetics and pharmacodynamics · 2018Article
- Principal process analysis of biological models.BMC systems biology · 2018Article
- Memory functions reveal structural properties of gene regulatory networks.PLoS computational biology · 2018Article
- Maximal aggregation of polynomial dynamical systems.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- Methods of Model Reduction for Large-Scale Biological Systems: A Survey of Current Methods and Trends.Bulletin of mathematical biology · 2017Article
- A combined model reduction algorithm for controlled biochemical systems.BMC systems biology · 2017Article
- Article
- A model reduction method for biochemical reaction networks.BMC systems biology · 2014Article
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Authors and funding
3 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundModels 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.
resultsIn 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 baker's yeast. Both models can be significantly reduced with the proposed method, at the same time as the interpretability is conserved.
conclusionsWe 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.