Using Cellular Automata to Learn About the Immune System
Autor: | Rita Maria Zorzenon dos Santos |
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Rok vydání: | 1998 |
Předmět: |
Immune repertoire
Collective behavior Theoretical computer science Computer science Complex system Human immunodeficiency virus (HIV) General Physics and Astronomy Statistical and Nonlinear Physics Nonlinear Sciences::Cellular Automata and Lattice Gases medicine.disease_cause Cellular automaton Computer Science Applications Complex dynamics Immune system Stochastic cellular automaton Computational Theory and Mathematics medicine Mathematical Physics Simulation |
Zdroj: | International Journal of Modern Physics C. :793-799 |
ISSN: | 1793-6586 0129-1831 |
DOI: | 10.1142/s0129183198000716 |
Popis: | Cellular automata are very simple systems that can exhibit complex dynamics on its time evolution. Over the last decade there have been many applications of cellular automata to modeling of biological systems. Those applications have been stimulated by the study of complex systems which has brought many insights into the cooperative and global behavior of the biological systems. Along with this discussion we present two different applications of deterministic and also of probabilistic cellular automata that are used to model the dynamics involved in cooperative and collective behavior of the immune system. In the first example, we use a deterministic cellular automata to model the time evolution of the immune repertoire, as a network, according to the Jerne's theory. Using this model we could reproduce some recent experimental results about immunization and aging of the immune system. In the second example, we use a probabilistic cellular automata model to study the evolution of HIV infection and the onset of AIDS. The results are in excellent agreement with experimental data obtained from infected patients. Besides the examples above, other interesting applications, such as models for cancer and recurrent epidemics, are being considered in the present framework. |
Databáze: | OpenAIRE |
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