S-system parameter estimation for noisy metabolic profiles using Newton-flow analysis

Kutalik, Z., Tucker, W. and Moulton, V. ORCID: https://orcid.org/0000-0001-9371-6435 (2007) S-system parameter estimation for noisy metabolic profiles using Newton-flow analysis. IET Systems Biology, 1 (3). pp. 174-180. ISSN 1751-8849

Full text not available from this repository. (Request a copy)

Abstract

Biochemical systems are commonly modelled by systems of ordinary differential equations (ODEs). A particular class of such models called S-systems have recently gained popularity in biochemical system modelling. The parameters of an S-system are usually estimated from time-course profiles. However, finding these estimates is a difficult computational problem. Moreover, although several methods have been recently proposed to solve this problem for ideal profiles, relatively little progress has been reported for noisy profiles. We describe a special feature of a Newton-flow optimisation problem associated with S-system parameter estimation. This enables us to significantly reduce the search space, and also lends itself to parameter estimation for noisy data. We illustrate the applicability of our method by applying it to noisy time-course data synthetically produced from previously published 4- and 30-dimensional S-systems. In addition, we propose an extension of our method that allows the detection of network topologies for small S-systems. We introduce a new method for estimating S-system parameters from time-course profiles. We show that the performance of this method compares favorably with competing methods for ideal profiles, and that it also allows the determination of parameters for noisy profiles

Item Type:	Article
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Computational Biology > Computational biology of RNA (former - to 2018) Faculty of Science > Research Groups > Computational Biology > Phylogenetics (former - to 2018) Faculty of Science > Research Groups > Computational Biology Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre
Related URLs:	http://ieeexplore.ieee.org/xpl/freeabs_a... http://www.math.uu.se/~warwick/main/pape...
Depositing User:	Vishal Gautam
Date Deposited:	10 Mar 2011 10:10
Last Modified:	16 Jun 2023 00:03
URI:	https://ueaeprints.uea.ac.uk/id/eprint/23695
DOI:

Actions (login required)

View Item