Combining biochemical network motifs within an ARN-agent control system

Claire E. Gerrard; John McCall; Christopher Macleod; George M. Coghill

doi:10.1109/UKCI.2013.6651281

Combining biochemical network motifs within an ARN-agent control system

Claire E. Gerrard, John McCall, Christopher Macleod, George M. Coghill

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

The Artificial Reaction Network (ARN) is an Artificial Chemistry representation inspired by cell signaling networks. The ARN has previously been applied to the simulation of the chemotaxis pathway of Escherichia coli and to the control of limbed robots. In this paper we discuss the design of an ARN control system composed of a combination of network motifs found in actual biochemical networks. Using this control system we create multiple cell-like autonomous agents capable of coordinating all aspects of their behavior, recognizing environmental patterns and communicating with other agent's stigmergically. The agents are applied to simulate two phases of the life cycle of Dictyostelium discoideum: vegetative and aggregation phase including the transition. The results of the simulation show that the ARN is well suited for construction of biochemical regulatory networks. Furthermore, it is a powerful tool for modeling multi agent systems such as a population of amoebae or bacterial colony.

Original language	English
Title of host publication	2013 13th UK Workshop on Computational Intelligence (UKCI)
Publisher	IEEE
Pages	8-15
Number of pages	8
ISBN (Print)	978-1-4799-1568-2
DOIs	https://doi.org/10.1109/UKCI.2013.6651281
Publication status	Published - Sept 2013

Publication series

Name	2013 13th UK Workshop on Computational Intelligence (UKCI)

Keywords

ARN-agent control system
Artificial Chemistry
Artificial Reaction Networks
Chemicals
Computer architecture
Logic gates
Oscillators
Proteins
Swarm Agents
Switches
artificial chemistry representation
artificial reaction network
biochemical network motifs
biochemical regulatory networks
biochemistry
cell signaling networks
chemistry computing
dictyostelium discoideum
environmental pattern recognition
multi-agent systems
multiagent systems modeling
multiple cell-like autonomous agents

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10.1109/UKCI.2013.6651281Licence: CC BY-ND

Cite this

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title = "Combining biochemical network motifs within an ARN-agent control system",

abstract = "The Artificial Reaction Network (ARN) is an Artificial Chemistry representation inspired by cell signaling networks. The ARN has previously been applied to the simulation of the chemotaxis pathway of Escherichia coli and to the control of limbed robots. In this paper we discuss the design of an ARN control system composed of a combination of network motifs found in actual biochemical networks. Using this control system we create multiple cell-like autonomous agents capable of coordinating all aspects of their behavior, recognizing environmental patterns and communicating with other agent's stigmergically. The agents are applied to simulate two phases of the life cycle of Dictyostelium discoideum: vegetative and aggregation phase including the transition. The results of the simulation show that the ARN is well suited for construction of biochemical regulatory networks. Furthermore, it is a powerful tool for modeling multi agent systems such as a population of amoebae or bacterial colony.",

keywords = "ARN-agent control system, Artificial Chemistry, Artificial Reaction Networks, Chemicals, Computer architecture, Logic gates, Oscillators, Proteins, Swarm Agents, Switches, artificial chemistry representation, artificial reaction network, biochemical network motifs, biochemical regulatory networks, biochemistry, cell signaling networks, chemistry computing, dictyostelium discoideum, environmental pattern recognition, multi-agent systems, multiagent systems modeling, multiple cell-like autonomous agents",

author = "Gerrard, {Claire E.} and John McCall and Christopher Macleod and Coghill, {George M.}",

year = "2013",

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language = "English",

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AU - Gerrard, Claire E.

AU - McCall, John

AU - Macleod, Christopher

AU - Coghill, George M.

PY - 2013/9

Y1 - 2013/9

N2 - The Artificial Reaction Network (ARN) is an Artificial Chemistry representation inspired by cell signaling networks. The ARN has previously been applied to the simulation of the chemotaxis pathway of Escherichia coli and to the control of limbed robots. In this paper we discuss the design of an ARN control system composed of a combination of network motifs found in actual biochemical networks. Using this control system we create multiple cell-like autonomous agents capable of coordinating all aspects of their behavior, recognizing environmental patterns and communicating with other agent's stigmergically. The agents are applied to simulate two phases of the life cycle of Dictyostelium discoideum: vegetative and aggregation phase including the transition. The results of the simulation show that the ARN is well suited for construction of biochemical regulatory networks. Furthermore, it is a powerful tool for modeling multi agent systems such as a population of amoebae or bacterial colony.

AB - The Artificial Reaction Network (ARN) is an Artificial Chemistry representation inspired by cell signaling networks. The ARN has previously been applied to the simulation of the chemotaxis pathway of Escherichia coli and to the control of limbed robots. In this paper we discuss the design of an ARN control system composed of a combination of network motifs found in actual biochemical networks. Using this control system we create multiple cell-like autonomous agents capable of coordinating all aspects of their behavior, recognizing environmental patterns and communicating with other agent's stigmergically. The agents are applied to simulate two phases of the life cycle of Dictyostelium discoideum: vegetative and aggregation phase including the transition. The results of the simulation show that the ARN is well suited for construction of biochemical regulatory networks. Furthermore, it is a powerful tool for modeling multi agent systems such as a population of amoebae or bacterial colony.

KW - ARN-agent control system

KW - Artificial Chemistry

KW - Artificial Reaction Networks

KW - Chemicals

KW - Computer architecture

KW - Logic gates

KW - Oscillators

KW - Proteins

KW - Swarm Agents

KW - Switches

KW - artificial chemistry representation

KW - artificial reaction network

KW - biochemical network motifs

KW - biochemical regulatory networks

KW - biochemistry

KW - cell signaling networks

KW - chemistry computing

KW - dictyostelium discoideum

KW - environmental pattern recognition

KW - multi-agent systems

KW - multiagent systems modeling

KW - multiple cell-like autonomous agents

U2 - 10.1109/UKCI.2013.6651281

DO - 10.1109/UKCI.2013.6651281

M3 - Chapter

SN - 978-1-4799-1568-2

T3 - 2013 13th UK Workshop on Computational Intelligence (UKCI)

SP - 8

EP - 15

BT - 2013 13th UK Workshop on Computational Intelligence (UKCI)

PB - IEEE

ER -

Combining biochemical network motifs within an ARN-agent control system

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