A framework for scalable parameter estimation of gene circuit models using structural information
MetadataShow full item record
AbstractMotivation: Systematic and scalable parameter estimation is a key to construct complex gene regulatory models and to ultimately facilitate an integrative systems biology approach to quantitatively understand the molecular mechanisms underpinning gene regulation. Results: Here, we report a novel framework for efficient and scalable parameter estimation that focuses specifically on modeling of gene circuits. Exploiting the structure commonly found in gene circuit models, this framework decomposes a system of coupled rate equations into individual ones and efficiently integrates them separately to reconstruct the mean time evolution of the gene products. The accuracy of the parameter estimates is refined by iteratively increasing the accuracy of numerical integration using the model structure. As a case study, we applied our framework to four gene circuit models with complex dynamics based on three synthetic datasets and one time series microarray data set. We compared our framework to three state-of-the-art parameter estimation methods and found that our approach consistently generated higher quality parameter solutions efficiently. Although many general-purpose parameter estimation methods have been applied for modeling of gene circuits, our results suggest that the use of more tailored approaches to use domain-specific information may be a key to reverse engineering of complex biological systems. The Author 2013.
CitationKuwahara H, Fan M, Wang S, Gao X (2013) A framework for scalable parameter estimation of gene circuit models using structural information. Bioinformatics 29: i98-i107. doi:10.1093/bioinformatics/btt232.
PublisherOxford University Press (OUP)
PubMed Central IDPMC3694671
The following license files are associated with this item:
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact email@example.com
- Parameter estimation methods for gene circuit modeling from time-series mRNA data: a comparative study.
- Authors: Fan M, Kuwahara H, Wang X, Wang S, Gao X
- Issue date: 2015 Nov
- Hybrid regulatory models: a statistically tractable approach to model regulatory network dynamics.
- Authors: Ocone A, Millar AJ, Sanguinetti G
- Issue date: 2013 Apr 1
- Data2Dynamics: a modeling environment tailored to parameter estimation in dynamical systems.
- Authors: Raue A, Steiert B, Schelker M, Kreutz C, Maiwald T, Hass H, Vanlier J, Tönsing C, Adlung L, Engesser R, Mader W, Heinemann T, Hasenauer J, Schilling M, Höfer T, Klipp E, Theis F, Klingmüller U, Schöberl B, Timmer J
- Issue date: 2015 Nov 1
- Recent developments in parameter estimation and structure identification of biochemical and genomic systems.
- Authors: Chou IC, Voit EO
- Issue date: 2009 Jun
- KDDN: an open-source Cytoscape app for constructing differential dependency networks with significant rewiring.
- Authors: Tian Y, Zhang B, Hoffman EP, Clarke R, Zhang Z, Shih IeM, Xuan J, Herrington DM, Wang Y
- Issue date: 2015 Jan 15