An agent-based modeling approach for stochastic molecular events of biochemical networks

Zhang Kuan, Qin Rui-Bin, Zheng Hao-Ran, Niu Jun-Qing

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Modeling and simulation of intracellular biochemical networks is a critical method to study the biological system behaviors. The phenomena of self-organization play a crucial role in biological systems and Agent-based modeling (ABM) has been widely viewed as a computational framework to study the complex systems. Agent-based modeling approach has tremendous potential in advancing studying the phenomena of self-organization in biochemical networks, but is still under-utilized both in theory and practice. In this study we present a new bottom-up computational modeling and simulation paradigm - Agent-Based Modeling (ABM) with Reaction Agents (ABM-RA) which models the biochemical networks based on self-organization and is a mathematical formalization of a multi-agent system for the biochemical reaction networks. Experiment results show that ABM-RA is a generic approach. It is thus a fundamentally better fit to a real biological system than the top-down approach which relies heavily on human abstractions.

Original languageEnglish (US)
Title of host publicationProceedings - 4th International Conference on Intelligent Computation Technology and Automation, ICICTA 2011
Pages759-763
Number of pages5
DOIs
StatePublished - May 20 2011
Event2011 4th International Conference on Intelligent Computation Technology and Automation, ICICTA 2011 - Shenzhen, Guangdong, China
Duration: Mar 28 2011Mar 29 2011

Publication series

NameProceedings - 4th International Conference on Intelligent Computation Technology and Automation, ICICTA 2011
Volume1

Other

Other2011 4th International Conference on Intelligent Computation Technology and Automation, ICICTA 2011
CountryChina
CityShenzhen, Guangdong
Period3/28/113/29/11

Keywords

  • Agent-based Modeling
  • Agent-based Simulation
  • Molecular Scale
  • Systems Biology

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