Artificial reaction networks

Claire Gerrard; John McCall; George MacLeod Coghill; Chris Macleod

Artificial reaction networks

Claire Gerrard, John McCall, George MacLeod Coghill, Chris Macleod

Robert Gordon University

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

6 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings of the 11th UK Workshop on Computational Intelligence (UKCI 2011)
Place of Publication	Manchester, UK
Publisher	University of Manchester
Pages	20-25
Number of pages	6
Publication status	Published - Sept 2011
Event	11th UK Workshop on Computational Intelligence - Manchester, United Kingdom Duration: 7 Sept 2011 → 9 Sept 2011

Conference

Conference	11th UK Workshop on Computational Intelligence
Country/Territory	United Kingdom
City	Manchester
Period	7/09/11 → 9/09/11

Access to Document

http://ukci.cs.manchester.ac.uk/files/Proceedings.pdfLicence: Unspecified

Cite this

@inproceedings{66b819c623904aa9981c514773f6a9c4,

title = "Artificial reaction networks",

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.",

author = "Claire Gerrard and John McCall and Coghill, {George MacLeod} and Chris Macleod",

year = "2011",

month = sep,

language = "English",

pages = "20--25",

booktitle = "Proceedings of the 11th UK Workshop on Computational Intelligence (UKCI 2011)",

publisher = "University of Manchester",

note = "11th UK Workshop on Computational Intelligence ; Conference date: 07-09-2011 Through 09-09-2011",

}

TY - GEN

T1 - Artificial reaction networks

AU - Gerrard, Claire

AU - McCall, John

AU - Coghill, George MacLeod

AU - Macleod, Chris

PY - 2011/9

Y1 - 2011/9

N2 - 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.

AB - 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.

M3 - Published conference contribution

SP - 20

EP - 25

BT - Proceedings of the 11th UK Workshop on Computational Intelligence (UKCI 2011)

PB - University of Manchester

CY - Manchester, UK

T2 - 11th UK Workshop on Computational Intelligence

Y2 - 7 September 2011 through 9 September 2011

ER -

Artificial reaction networks

Abstract

Conference

Access to Document

Fingerprint

Cite this