Parameter estimation methods for gene circuit modeling from time-series mRNA data: a comparative study

M. Fan, Hiroyuki Kuwahara, X. Wang, S. Wang, Xin Gao

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Parameter estimation is a challenging computational problemin the reverse engineering of biological systems. Because advances in biotechnology have facilitated wide availability of time-series gene expression data, systematic parameter esti- mation of gene circuitmodels fromsuch time-series mRNA data has become an importantmethod for quantitatively dissecting the regulation of gene expression. By focusing on themodeling of gene circuits, we examine here the perform- ance of three types of state-of-the-art parameter estimation methods: population-basedmethods, onlinemethods and model-decomposition-basedmethods. Our results show that certain population-basedmethods are able to generate high- quality parameter solutions. The performance of thesemethods, however, is heavily dependent on the size of the param- eter search space, and their computational requirements substantially increase as the size of the search space increases. In comparison, onlinemethods andmodel decomposition-basedmethods are computationally faster alternatives and are less dependent on the size of the search space. Among other things, our results show that a hybrid approach that augments computationally fastmethods with local search as a subsequent refinement procedure can substantially increase the qual- ity of their parameter estimates to the level on par with the best solution obtained fromthe population-basedmethods whilemaintaining high computational speed. These suggest that such hybridmethods can be a promising alternative to themore commonly used population-basedmethods for parameter estimation of gene circuit models when limited prior knowledge about the underlying regulatorymechanismsmakes the size of the parameter search space vastly large. © The Author 2015. Published by Oxford University Press.
Original languageEnglish (US)
Pages (from-to)987-999
Number of pages13
JournalBriefings in Bioinformatics
Issue number6
StatePublished - Mar 26 2015

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: The research reported in this publication was supported by competitive research funding from King Abdullah University of Science and Technology (KAUST), the Natural Science Foundation of Zhejiang Province of China (LQ14F010011) and the National Natural Science Foundation of China (Grant No. 61401131).


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