The University of Southampton
University of Southampton Institutional Repository

Biologically driven neural platform invoking parallel electrophoretic separation and urinary metabolite screening

Biologically driven neural platform invoking parallel electrophoretic separation and urinary metabolite screening
Biologically driven neural platform invoking parallel electrophoretic separation and urinary metabolite screening
This work reveals a computational framework for parallel electrophoretic separation of complex biological macromolecules and model urinary metabolites. More specifically, the implementation of a particle swarm optimization (PSO) algorithm on a neural network platform for multiparameter optimization of multiplexed 24-capillary electrophoresis technology with UV detection is highlighted. Two experimental systems were examined: (1) separation of purified rabbit metallothioneins and (2) separation of model toluene urinary metabolites and selected organic acids. Results proved superior to the use of neural networks employing standard back propagation when examining training error, fitting response, and predictive abilities. Simulation runs were obtained as a result of metaheuristic examination of the global search space with experimental responses in good agreement with predicted values. Full separation of selected analytes was realized after employing optimal model conditions. This framework provides guidance for the application of metaheuristic computational tools to aid in future studies involving parallel chemical separation and screening. Adaptable pseudo-code is provided to enable users of varied software packages and modeling framework to implement the PSO algorithm for their desired use.
1618-2642
2367–2375
Page, Tessa
d650dc79-64eb-4f14-b16c-86266cdeefc8
Nguyen, Huong Thi Huynh
a88966f0-cfe5-4d9e-b104-f54854b41f6c
Hilts, Lindsey
af1107f7-cc8b-4d7f-a66a-5731bd798d62
Ramos, Lorena
fd2d0613-5b61-471c-b59a-e491c91cf25a
Hanrahan, Grady
d789e81b-775f-40c0-86a5-6bcabe0dd434
Page, Tessa
d650dc79-64eb-4f14-b16c-86266cdeefc8
Nguyen, Huong Thi Huynh
a88966f0-cfe5-4d9e-b104-f54854b41f6c
Hilts, Lindsey
af1107f7-cc8b-4d7f-a66a-5731bd798d62
Ramos, Lorena
fd2d0613-5b61-471c-b59a-e491c91cf25a
Hanrahan, Grady
d789e81b-775f-40c0-86a5-6bcabe0dd434

Page, Tessa, Nguyen, Huong Thi Huynh, Hilts, Lindsey, Ramos, Lorena and Hanrahan, Grady (2012) Biologically driven neural platform invoking parallel electrophoretic separation and urinary metabolite screening. Analytical and Bioanalytical Chemistry, 403 (8), 2367–2375. (doi:10.1007/s00216-012-5719-y).

Record type: Article

Abstract

This work reveals a computational framework for parallel electrophoretic separation of complex biological macromolecules and model urinary metabolites. More specifically, the implementation of a particle swarm optimization (PSO) algorithm on a neural network platform for multiparameter optimization of multiplexed 24-capillary electrophoresis technology with UV detection is highlighted. Two experimental systems were examined: (1) separation of purified rabbit metallothioneins and (2) separation of model toluene urinary metabolites and selected organic acids. Results proved superior to the use of neural networks employing standard back propagation when examining training error, fitting response, and predictive abilities. Simulation runs were obtained as a result of metaheuristic examination of the global search space with experimental responses in good agreement with predicted values. Full separation of selected analytes was realized after employing optimal model conditions. This framework provides guidance for the application of metaheuristic computational tools to aid in future studies involving parallel chemical separation and screening. Adaptable pseudo-code is provided to enable users of varied software packages and modeling framework to implement the PSO algorithm for their desired use.

This record has no associated files available for download.

More information

Accepted/In Press date: 5 January 2012
Published date: 1 June 2012

Identifiers

Local EPrints ID: 468971
URI: http://eprints.soton.ac.uk/id/eprint/468971
ISSN: 1618-2642
PURE UUID: df0c8d32-4e26-4462-8fd7-56ce2de95ff2
ORCID for Tessa Page: ORCID iD orcid.org/0000-0002-5575-7049

Catalogue record

Date deposited: 02 Sep 2022 18:41
Last modified: 17 Mar 2024 04:12

Export record

Altmetrics

Contributors

Author: Tessa Page ORCID iD
Author: Huong Thi Huynh Nguyen
Author: Lindsey Hilts
Author: Lorena Ramos
Author: Grady Hanrahan

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×