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State and parameter estimation of the heat shock response system using Kalman and particle filters

Liu, Xin and Niranjan, Mahesan (2012) State and parameter estimation of the heat shock response system using Kalman and particle filters Bioinformatics, 28, (11), pp. 1501-1507. (doi:10.1093/bioinformatics/bts161). (PMID:22539674).

Record type: Article


Motivation: traditional models of systems biology describe dynamic biological phenomena as solutions to ordinary differential equations, which, when parameters in them are set to correct values, faithfully mimic observations. Often parameter values are tweaked by hand until desired results are achieved, or computed from biochemical experiments carried out in vitro. Of interest in this paper, is the use of probabilistic modelling tools with which parameters and unobserved variables, modelled as hidden states, can be estimated from limited noisy observations of parts of a dynamical system.

Results: here we focus on sequential filtering methods and take a detailed look at the capabilities of three members of this family: (a) extended Kalman filter (EKF), (b) unscented Kalman filter (UKF) and (c) the particle filter (PF), in estimating parameters and unobserved states of cellular response to sudden temperature elevation of the bacterium E.Coli. While previous literature has studied this system with the EKF, we show that parameter estimation is only possible with this method when the initial guesses are sufficiently close to the true values. The same turns out to be true for the UKF. In this thorough empirical exploration, we show that the non-parametric method of particle filtering is able to reliably estimate parameters and states, converging from initial distributions relatively far away from the underlying true values.

Supplementary information: supplementary section of the paper is available at Bioinformatics online.

Availability and implementation: software implementation of the three filters on this problem can be freely downloaded from

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e-pub ahead of print date: 26 April 2012
Organisations: Southampton Wireless Group


Local EPrints ID: 337728
ISSN: 1367-4803
PURE UUID: d221efa1-3772-42f2-8ff6-7d6fc4921685

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Date deposited: 03 May 2012 08:50
Last modified: 18 Jul 2017 06:01

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Author: Xin Liu

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