Using Coarse-graining Cellular Automata To Research Model Of Biologic Sequences And Dynamics

Now the systemic, cybernetic and information processing methods have been widely used by biologist to observe and understand the biology process. They conceive new experiment methods based on certainty or uncertainty mathematics models and intelligent computational models. On the other hand, organism is treated by information science scholar as new research targets of complex and intelligent systems. The interaction of information science and biological science is more and more inosculate, one of its important representations is bioinformatics. There are three important sub-disciplines within bioinformatics: genome informatics, protein structural simulation, iatric design.We have made scientific researches on above three aspects of bioinformatics based on coarse-graining cellular automata. The innovative research contributions mainly include the following contents:Constructing digital coding for amino acidWe make use of similarity rule, complementaity rule, molecular recognition theory, and information theory to set up a model of digital coding for amino acids. The model reflects better amino acid chemical physical properties and degeneracy. It transforms the symbolic DNA sequences into digital genetic signals of amino acids and it opens the possibility to apply a whole range of powerful signal processing methods for analysis of amino acids.Using coarse-graining cellular automata to generate image representation for biological sequencesBy using coarse-graining cellular automata, new biological sequence visual method was put forward. It licked the shortage of other visual method that the point of the special curve corresponding to a certain nucleic acid is colligated only with the base prior to it, while the effects of all the bases behind it are totally ignored. We firstly put forward the concept of the so-called cellular automata image(CAI). Many important features, which are originally hidden in a long and complicated biological sequence, can be clearly revealed thru its cellular automata image. 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".Putting forward peculiar character analysis in RNA sequence of SARSBy using coarse-graining cellular automata, the 'V structual characteristic in SARS' CAI was firstly found. A peculiar character of RNA sequence is found in SARS, revealing particular symmetry in its sequencing, from about 3232 to 5624nt, 5703 to 7195nt, 12128 to 14470nt, 16444 to 19231nt, 19728 to 21803nt in the SARS-CoV genome sequences near 5-terminal, the number of Adenine (A) is almost equal to the number of thymine (T) in the above five sections, and the A are mostly mastered in the 5'-terminal of the segment, T are mostly in the 3'-terminal region. Comparison of symmetry between SARS and other coronaviruses shows heuristically that SARS coronavirus might come from the avian infectious bronchitis virus or porcine epidemic diarrhea virus.Investigating the prediction of subcellular location of proteinsBased on a model that takes into account the concept of pseudo amino acid, a new approach that applies Cellular automata image and amino acid composition to predict the protein subcellular location is presented in this paper. One of the remarkable merits of this approach is that many image recognition tools can be straightforwardly utilized in predicting protein subcellular location. High rates of both self-consistency and jackknife tests are obtained. The results indicate that the protein localization is considerably correlated with its C A image.? Investigating the prediction of structural class of proteinsHow to improve the prediction quality for protein structural classification by effectively incorporating the sequence-order effects is an important and challenging problem. Based on the concept of CAI, a new approach is presented. The advantage by incorporating the complexity measure factor of CAI into the pseudo amino acid composition as one of its components is that it can catch the essence of the overall sequence pattern of a protein and hence more effectively reflect its sequence-order effects. It was demonstrated thru the jackknife cross-validation test that the overall success rate by the new approach was significantly higher than those by the others.? Putting forward the model of predicting the effect on replication ratio by HBV missense mutationHepatitis B viruses (HBVs) show instantaneous and high ratio mutations when they are replicated, some sorts of which significantly affect the efficiency of virus replication through enhancing or depressing the viral replication, while others have no influence at all. Based on CAI, a novel model to predicting the effect on replication ratio by HBV mutation has been introduced firstly. The different CAIs can be gained by the HBV sequence of mutation. With the change in the CAI after mutation, we can predict the degree of effects produced by the mutation. The results show that the images thus obtained can very efficiently simulate the effects of the gene missense mutation on the virus replication. The establishment of such a predictor will no doubt expedite the process of prioritizing genes and proteins identified by genomics efforts as potential molecular targets for drug design.? Putting forward the probability coarse-graining cellular automata model for Hepatitis B viral infectionsThe basic model of hepatitis B virus (HBV) infection dynamics, are based on the assumption of well-mixed virus and cell populations. But spatial characteristics potentially play a nontrivial role in the development and outcome of a HBV infection, such as localized populations of dead cells might adversely affect the spread of infection. Here, we use a 2-D probability cellular automata model to study the evolution of HBV infection. The model takes into account the existence of different types of HBV infectious and non-infectious particles. The simulation results show that the model can be used to account for the important features of the disease, namely the wide variety of manifestations of infection and the age dependence. It is first time to simulate this feature under no change other parameters of model. And we also consider the effects of the model's parameters on the dynamics of the infection.On the other hand, we used our 2-D probability coarse-graining cellular automata model to study the evolution of HBV infection with taking medication. The space-time evolvement, self-organization course and curative effects of different medication as eliminating infectious cells, eliminating HBV and destroying thequalification of infection are simulated. The results show that the medication can inhibit HBV replication has the best curative effect. It is anticipated that probability coarse-graining Cellular Automata may also serve as a useful vehicle for drug design.