Cellular automata models of chemical systems

被引：16
|
作者：
Kier, LB [1 ]
Cheng, CK
Seybold, PG
机构：
[1] Virginia Commonwealth Univ, Dept Med Chem, Richmond, VA 23298 USA
[2] Virginia Commonwealth Univ, Dept Math Sci, Richmond, VA 23298 USA
[3] Wright State Univ, Dept Chem, Dayton, OH 45435 USA
来源：
SAR AND QSAR IN ENVIRONMENTAL RESEARCH | 2000年 / 11卷 / 02期
关键词：
cellular automata; solution phenomena; kinetic models; in silico experiments; dynamic models;
D O I：
10.1080/10629360008039116
中图分类号：
O6 [化学];
学科分类号：
0703 ;
摘要：
This paper describes the use of kinematic, asynchronous, stochastic cellular automata to model liquid properties, solution phenomena and kinetic phenomena encountered in complex biological systems. Cellular automata models of dynamic phenomena represent in silico experiments designed to assess the effects of competing factors on the physical and chemical properties of solutions and other complex systems. Specific applications include solution behavior, separation of immiscible liquids, micelle formation, diffusion, membrane passage, first- and second-order chemical kinetics, enzyme activity and acid dissociation. Cellular automata is thus considered as providing an exploratory method for the analysis of dynamic phenomena and the discovery and understanding of new, unexpected phenomena.
引用
收藏
页码：79 / 102
页数：24
相关论文
共 50 条
  • [21] Modelling of bistable chemical system by cellular automata technique
    Frankowicz, M
    Kawczynski, AL
    POLISH JOURNAL OF CHEMISTRY, 1997, 71 (04) : 467 - 473
  • [22] Cellular Automata Application on Chemical Computing Logic Circuits
    Tsompanas, Michail-Antisthenis
    Chatzinikolaou, Theodoros Panagiotis
    Sirakoulis, Georgios Ch
    CELLULAR AUTOMATA (ACRI 2022), 2022, 13402 : 3 - 14
  • [23] Error as Optimization Using Cellular Automata Systems to Introduce Bias in Aggregation Models through Multigrids
    Adilenidou, Yota
    ECAADE 2015: REAL TIME - EXTENDING THE REACH OF COMPUTATION, VOL 2, 2015, : 601 - 610
  • [24] Toward robust network based complex systems: from evolutionary cellular automata to biological models
    Darabos, Christian
    Tomassini, Marco
    Di Cunto, Ferdinando
    Provero, Paolo
    Moore, Jason H.
    Giacobini, Mario
    INTELLIGENZA ARTIFICIALE, 2011, 5 (01) : 37 - 47
  • [25] Stochastic cellular automata modeling of excitable systems
    Szakaly, Tamas
    Lagzi, Istvan
    Izsak, Ferenc
    Roszol, Laszlo
    Volford, Andras
    CENTRAL EUROPEAN JOURNAL OF PHYSICS, 2007, 5 (04): : 471 - 486
  • [26] Emulating cellular automata in chemical reaction–diffusion networks
    Dominic Scalise
    Rebecca Schulman
    Natural Computing, 2016, 15 : 197 - 214
  • [27] Spatiotemporal sensitivity of systems modeled by cellular automata
    Fujarewicz, Krzysztof
    Lakomiec, Krzysztof
    MATHEMATICAL METHODS IN THE APPLIED SCIENCES, 2018, 41 (18) : 8897 - 8905
  • [28] Mining Cellular Automata Models on Protein Folding Trajectories
    Diaz, Nestor
    Tischer, Irene
    2011 6TH COLOMBIAN COMPUTING CONGRESS (CCC), 2011,
  • [29] Cellular automata and Riccati equation models for diffusion of innovations
    Renato Guseo
    Mariangela Guidolin
    Statistical Methods and Applications, 2008, 17 : 291 - 308
  • [30] Modeling evacuation of emergency vehicles by cellular automata models
    Moussa, Najem
    INTERNATIONAL JOURNAL OF MODERN PHYSICS C, 2008, 19 (06): : 947 - 956
12345