Using cellular automata to generate image representation for biological sequences

Abstract

A novel approach to visualize biological sequences is developed based on cellular automata (Wolfram, S. Nature 1984, 311, 419-424), a set of discrete dynamical systems in which space and time are discrete. By transforming the symbolic sequence codes into the digital codes, and using some optimal space-time evolvement rules of cellular automata, a biological sequence can be represented by a unique image, the so-called cellular automata image. Many important features, which are originally hidden in a long and complicated biological sequence, can be clearly revealed thru its cellular automata image. With biological sequences entering into databanks rapidly increasing in the post-genomic era, it is anticipated that the cellular automata image will become a very useful vehicle for investigation into their key features, identification of their function, as well as revelation of their "fingerprint". It is anticipated that by using the concept of the pseudo amino acid composition (Chou, K.C. Proteins: Structure, Function, and Genetics, 2001, 43, 246-255), the cellular automata image approach can also be used to improve the quality of predicting protein attributes, such as structural class and subcellular location.

REVIEW : Recent advances in developing web-servers for predicting protein attributes

“…As a first step, each of the 20 native amino acids in a protein sequence is represented by a 5-digit strain according to the binary coding as defined in [163]. Thus, a protein consisting of N amino acids can be converted to a sequence with 5N digits (or grids); i.e,, 1 2 5…”

“…N as defined in [163]. Suppose the time for each updated step is consecutively expressed by 0, 1, 2, ,    t , we have g (1) g (1) g (1) g (1) g (2) g (2) g (2) g (2)…”

“…The image thus evolved is called the cellular automaton image for the protein sequence concerned. The advantage of using the cellular automaton image to represent the protein is that it can help us visualize some special features hidden in its long and complex sequence [163]. For instance, the cellular automata images for proteins from a same GPCR family share a similar texture pattern (Figure 12), while those from different GPCR families have different texture patterns (Figure 13).…”

Probabilistic Pharmaceutical Modelling: A Comparison Between Synchronous and Asynchronous Cellular Automata

“…Although it is common for various families of CA update mechanisms to operate under periodic boundary conditions [12,13], pharmaceutical models benefit from using the fixed equivalent, as drug movement across the matrix boundaries is used as a direct method for calculating release rates. To satisfy the condition that the models need to mimic the non-homogeneousness of the physical device, with exact distributions of polymer and drug properties not available from experimental data, the model establishes the initial cell states using stochastic distributions within the known device geometry.…”

Organisms can essentially be classified according to two codon patterns

“…Various graphical approaches have been successfully used; for example, to study enzyme-catalyzed systems (Chou 1983(Chou , 1989(Chou , 1990Chou et al 1979;Kuzmic et al 1992;Lin and Neet 1990;Zhou and Deng 1984), protein folding kinetics (Chou 1990), codon usage (Chou and Zhang 1992;Sorimachi and Okayasu 2003;2004a;2005a, b;2008a, b, c;Zhang and Chou 1993), and HIV reverse transcriptase inhibition mechanisms (Althaus et al 1993a-c;Chou et al 1994). Cellular automaton images (Wolfram 1984(Wolfram , 2002 have also been used to represent biological sequences (Xiao et al 2005b), to predict protein subcellular localization (Xiao et al 2006b), predict transmembrane regions in proteins (Diao et al 2008), predict the effect on replication ratio by HBV virus gene missense mutation (Xiao et al 2005a), and to study hepatitis B viral infections (Xiao et al 2006a). Graphic approaches have been used recently to represent DNA sequences (for example, Qi et al 2007b), investigate p53 stress response networks (Qi et al 2007a), analyze the network structure of the amino acid metabolism (Shikata et al 2007), study cellular signaling networks (Diao et al 2007) and proteomics (González-Díaz et al 2008), and for a systematic biology analysis of the Drosophila phagosome (Stuart et al 2007).…”