A cellular automaton model for the study of DNA sequence evolution

Cellular automata are introduced as a model for DNA structure, function and evolution. DNA is modeled as a one-dimensional cellular automaton with four states per cell. These states are the four DNA bases A, C, T and G. The four states are represented by numbers of the quaternary number system. Linear evolution rules, represented by square matrices, are considered. Based on this model a simulator of DNA evolution is developed and simulation results are presented. This simulator has a user-friendly input interface and can be used for the study of DNA evolution.

[1]  G. Sirakoulis,et al.  A cellular automaton model for the effects of population movement and vaccination on epidemic propagation , 2000 .

[2]  Durbin,et al.  Biological Sequence Analysis , 1998 .

[3]  S. K. Moore,et al.  Understanding the human genome , 1990 .

[4]  M E Jones,et al.  The application of a linear algebra to the analysis of mutation rates. , 1999, Journal of theoretical biology.

[5]  Massimo Bernaschi,et al.  A high performance simulator of the immune response , 1999, Future Gener. Comput. Syst..

[6]  J. McFadden,et al.  A quantum mechanical model of adaptive mutation. , 1999, Bio Systems.

[7]  Richard J. Gaylord,et al.  Modeling Nature: Cellular Automata Simulations with Mathematica® , 1996 .

[8]  E. T. Gawlinski,et al.  A Cellular Automaton Model of Early Tumor Growth and Invasion: The Effects of Native Tissue Vascularity and Increased Anaerobic Tumor Metabolism , 2001 .

[9]  I Karafyllidis,et al.  A model for the influence of the greenhouse effect on insect and microorganism geographical distribution and population dynamics. , 1998, Bio Systems.

[10]  John von Neumann,et al.  Theory Of Self Reproducing Automata , 1967 .

[11]  H. Schwefel Deep insight from simple models of evolution. , 2002, Bio Systems.

[12]  Sean R. Eddy,et al.  Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids , 1998 .

[13]  Christopher B. Burge,et al.  DNA Sequence Evolution with Neighbor-Dependent Mutation , 2003, J. Comput. Biol..

[14]  Y. Gaididei,et al.  Interplay of nonlinearity and geometry in a DNA-related, Klein-Gordon model with long-range dipole-dipole interaction. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  A. Bernardes,et al.  Immune network at the edge of chaos. , 1997, Journal of theoretical biology.

[16]  S M Ulam,et al.  Some ideas and prospects in biomathematics. , 1972, Annual review of biophysics and bioengineering.

[17]  Paul Manneville,et al.  Cellular Automata and Modeling of Complex Physical Systems , 1989 .

[18]  Andreas D. Baxevanis,et al.  Bioinformatics - a practical guide to the analysis of genes and proteins , 2001, Methods of biochemical analysis.

[19]  S. K. Moore Making chips to probe genes , 2001 .

[20]  S Torquato,et al.  Simulated brain tumor growth dynamics using a three-dimensional cellular automaton. , 2000, Journal of theoretical biology.

[21]  Stephen Wolfram,et al.  Cellular Automata And Complexity , 1994 .

[22]  B. Voorhees Computational Analysis of One-Dimensional Cellular Automata , 1995 .

[23]  Ioannis G. Karafyllidis,et al.  A model for the prediction of oil slick movement and spreading using cellular automata , 1997 .

[24]  Ioannis G. Karafyllidis,et al.  A model for predicting forest fire spreading using cellular automata , 1997 .