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 proceedingChapter

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 languageEnglish
Title of host publication2013 13th UK Workshop on Computational Intelligence (UKCI)
PublisherIEEE
Pages8-15
Number of pages8
ISBN (Print)978-1-4799-1568-2
DOIs
Publication statusPublished - Sep 2013

Publication series

Name2013 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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  • Cite this

    Gerrard, C. E., McCall, J., Macleod, C., & Coghill, G. M. (2013). Combining biochemical network motifs within an ARN-agent control system. In 2013 13th UK Workshop on Computational Intelligence (UKCI) (pp. 8-15). (2013 13th UK Workshop on Computational Intelligence (UKCI)). IEEE. https://doi.org/10.1109/UKCI.2013.6651281