Claire E. Gerrard
Artificial reaction networks.
Gerrard, Claire E.; McCall, John; Coghill, George M.; Macleod, Christopher
Abstract
In this paper we present a novel method of simulating cellular intelligence, the Artificial Reaction Network (ARN). The ARN can be described as a modular S-System, with some properties in common with other Systems Biology and AI techniques, including Random Boolean Networks, Petri Nets, Artificial Biochemical Networks and Artificial Neural Networks. We validate the ARN against standard biological data, and successfully apply it to simulate cellular intelligence associated with the well-characterized cell signaling network of Escherichia coli chemotaxis. Finally, we explore the adaptability of the ARN, as a means to develop novel AI techniques, by successfully applying the simulated E. coli chemotaxis to a general optimization problem.
Citation
GERRARD, C.E., MCCALL, J., COGHILL, G.M. and MACLEOD, C. 2011. Artificial reaction networks. Presented at the 11th UK workshop on computational intelligence (UKCI 2011), 7-9 September 2011, Manchester, UK.
Presentation Conference Type | Conference Paper (unpublished) |
---|---|
Conference Name | 11th UK workshop on computational intelligence (UKCI 2011) |
Conference Location | Manchester, UK |
Start Date | Sep 7, 2011 |
End Date | Sep 9, 2011 |
Deposit Date | Sep 20, 2011 |
Publicly Available Date | Mar 29, 2024 |
Public URL | http://hdl.handle.net/10059/669 |
Files
GERRARD 2011 Artificial Reaction Networks
(538 Kb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by-nc-nd/4.0/
You might also like
Two-layer ensemble of deep learning models for medical image segmentation. [Article]
(2024)
Journal Article
A weighted ensemble of regression methods for gross error identification problem.
(2023)
Conference Proceeding
Towards explainable metaheuristics: feature extraction from trajectory mining.
(2023)
Journal Article
Explaining a staff rostering genetic algorithm using sensitivity analysis and trajectory analysis.
(2023)
Conference Proceeding
DEFEG: deep ensemble with weighted feature generation.
(2023)
Journal Article
Downloadable Citations
About OpenAIR@RGU
Administrator e-mail: publications@rgu.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search