Copula particle filter algorithm for inferring parameters of regulatory network models with noisy observation data

Zhimin Deng, Xingan Zhang, Tianhai Tian

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Biological experimental data normally contain noise due to probabilistic character of biochemical reactions and environmental fluctuations. It has been widely assumed that the observed data is the corresponding system states plus a white noise whose variance is a constant. In addition, all observations are assumed to be independent to each other. However, recently biological studies suggested that the randomness in experimental data may depend on system state and observations of different variables may be highly correlated. To address these issues, this work proposes a new model in which the variance of noise is a function of system state. We design a copula particle filter algorithm that is characterized by using copula density functions in place of multivariable normal density functions. The combination of the noise model and copula particle filter leads to a novel algorithm whose performance is rigorously evaluated by inferring unknown parameters in mathematical models. We test three linear/nonlinear functions to fit the noisy data and numerical results suggest that the nonlinear sigmoid function is the best function to represent the state-dependent variance of noise. Our proposed method has better accuracy of estimated parameters than the widely use the Liu-West filter and copula particle filter algorithm. Numerical results suggest that our proposed method is superior to the other two widely filter algorithms and is very effective for parameter estimation in biochemical network models under noisy dependent observations.
Original languageEnglish
Title of host publicationProceedings 2016 IEEE International Conference on Bioinformatics and Biomedicine
EditorsTianhai Tian, Yadong Wang, Qinghua Jiang, Xiaohua Hu, Yunlong Liu, Shinichi Morishita, Kevin Burrage, Qian Zhu, Jiangning Song, Guohua Wang
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages570-575
Number of pages6
ISBN (Electronic)9781509016105
ISBN (Print)9781509016105
DOIs
Publication statusPublished - 2016
EventIEEE International Conference on Bioinformatics and Biomedicine 2016 - Kylin Villa Resort, Shenzhen, China
Duration: 15 Dec 201618 Dec 2016
Conference number: 8th
https://cci.drexel.edu/ieeebibm/bibm2016/

Conference

ConferenceIEEE International Conference on Bioinformatics and Biomedicine 2016
Abbreviated titleBIBM 2016
CountryChina
CityShenzhen
Period15/12/1618/12/16
Internet address

Keywords

  • Parameter inference
  • Copula particle filter
  • variance
  • genetic regulation

Cite this

Deng, Z., Zhang, X., & Tian, T. (2016). Copula particle filter algorithm for inferring parameters of regulatory network models with noisy observation data. In T. Tian, Y. Wang, Q. Jiang, X. Hu, Y. Liu, S. Morishita, K. Burrage, Q. Zhu, J. Song, ... G. Wang (Eds.), Proceedings 2016 IEEE International Conference on Bioinformatics and Biomedicine (pp. 570-575). [7822583] Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/BIBM.2016.7822583