Cell modeling with reusable agent-based formalisms

被引:0
|
作者
Ken Webb
Tony White
机构
[1] Webb Primordion,School of Computer Science
[2] Carleton University,undefined
来源
Applied Intelligence | 2006年 / 24卷
关键词
Agent-based modeling; Cell simulation; Architectural reuse;
D O I
暂无
中图分类号
学科分类号
摘要
Biologists are building increasingly complex models and simulations of cells and other biological entities, and are looking at alternatives to traditional representations. Making use of the object-oriented (OO) paradigm, the Unified Modeling Language (UML) and Real-time Object-Oriented Modeling (ROOM) visual formalisms, and the Rational Rose RealTime (RRT) visual modeling tool, we summarize a previously-described multi-step process for constructing top-down models of cells. We first construct a simple model of a cell using an architecture in which all objects are containers, agents, or passive objects. We then reuse these architectural principles and components to extend our simple cell model into a more complex cell, the goal being to demonstrate that encapsulation familiar to artificial intelligence researchers can be employed by systems biologists in their models. A red blood cell is embedded in a straight-forward manner within a larger system, which is in turn iteratively embedded within still larger systems, including a blood vessel, a circulatory system, a human being, and a simple ecology. Each complexity increment reuses the same architectural principles, including the use of agents, each of which continuously either moves passive small molecules between containers, or transforms these passive objects from one type into another. We show how it is possible to start with a direct diagrammatic representation of a biological structure such as a cell, using terminology familiar to biologists, and by following a process of gradually adding more and more detail, arrive at a system with structure and behavior of arbitrary complexity that can run and be observed on a computer.
引用
收藏
页码:169 / 181
页数:12
相关论文
共 50 条
  • [31] AGENT-BASED MODELING AND SIMULATION OF BIOMOLECULAR REACTIONS
    Vallurupalli, Vaishali
    Purdy, Carla
    SCALABLE COMPUTING-PRACTICE AND EXPERIENCE, 2007, 8 (02): : 185 - 196
  • [32] Modeling and theorizing with agent-based sustainable development
    Secchi, D.
    Grimm, V.
    Herath, D. B.
    Homberg, F.
    ENVIRONMENTAL MODELLING & SOFTWARE, 2024, 171
  • [33] Exploring the Origins of Prejudice with Agent-Based Modeling
    Van Rooy, Dirk
    AGENT-BASED APPROACHES IN ECONOMIC AND SOCIAL COMPLEX SYSTEMS VIII, 2015, : 15 - 26
  • [34] Self-organization and agent-based modeling
    Sato, Yoshimichi
    SOCIOLOGICAL THEORY AND METHODS, 2006, 21 (01) : 1 - 10
  • [35] Agent-based modeling for the tumor microenvironment (TME)
    Weerasinghe, Hasitha N.
    Burrage, Pamela M.
    Nicolau, Dan V.
    Burrage, Kevin
    Mathematical Biosciences and Engineering, 2024, 21 (11) : 7621 - 7647
  • [36] On agent-based modeling and computational social science
    Conte, Rosaria
    Paolucci, Mario
    FRONTIERS IN PSYCHOLOGY, 2014, 5
  • [37] Modeling on the Table: Agent-Based Modeling in Elementary School with NetTango
    Olson, Izabel C.
    Horn, Michael S.
    PROCEEDINGS OF IDC 2011: THE 10TH INTERNATIONAL CONFERENCE ON INTERACTION DESIGN AND CHILDREN (IDC2011), 2011, : 189 - 192
  • [38] Large-Scale Agent-Based Modeling with Repast HPC: A Case Study in Parallelizing an Agent-Based Model
    Collier, Nicholson
    Ozik, Jonathan
    Macal, Charles M.
    EURO-PAR 2015: PARALLEL PROCESSING WORKSHOPS, 2015, 9523 : 454 - 465
  • [39] Modeling the demographic situation in the regions by agent-based approach
    Timushev, Evgeny N.
    Dubrovskaya, Yulia V.
    Kozonogova, Elena V.
    VOPROSY EKONOMIKI, 2024, (04): : 127 - 147
  • [40] An Agent-Based Approach to Artificial Stock Market Modeling
    Vanfossan, Samuel
    Dagli, Cihan H.
    Kwasa, Benjamin
    COMPLEX ADAPTIVE SYSTEMS, 2020, 168 : 161 - 169