基于粗粒化元胞自动机在生物序列与动力学的模型研究
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摘要
生物学家用系统、控制论与信息处理方法来观察了解生物学过程的例子越来越多。确定性、不确定性的数学模型和智能计算模型帮助他们构思新的实验方法,洞察复杂的生物系统。另一方面,系统控制论等信息科学学者也正把生物体看作又一类新的复杂系统与智能系统的研究目标。信息科学与生物科学的互动越演越列,交叉越演越密,其重要体现之一是生物信息学的研究。其研究内容包括基因组信息学、蛋白质的结构模拟以及药物设计三个重要的方面。
本文采用粗粒化元胞自动机研究生物信息中的若干模型,取得了以下方面的创新研究成果:
● 构造氨基酸数字编码模型
采用相似规则、对称规则、分子识别理论以及信息处理方法,建立了氨基酸的数字编码模型。此编码模型考虑了氨基酸的物理化学特性,且氨基酸与其数字编码是一一对应的。它可以把生物字符序列转变为二进制数字序列,这种变换使得生物信息可以用其它的数字信息处理方法来研究。
● 提出了基于粗粒化元胞自动机的生物序列可视化模型
基于粗粒化元胞自动机给出了一种新的基因序列可视化方法。与其它生物序列可视化方法作图时碱基所对应的空间点只与此碱基之前的碱基数量和种类有关所不同的是,在构图时,碱基所对应的空间点与此前后碱基的数量和种类都有关。首次提出了元胞自动机图(CAI)的概念与方法,使许多隐含在长而复杂的生物字符序列中的特征,通过CAI清楚地表现出来。提供了一个研究生物序列关键特征和辨识基因功能的新途径。
● 给出了SARS序列的一种特征分析
基于粗粒化元胞自动机序列可视化模型,首次发现了SARS病毒图像中所含有的“V”型结构,提出了局部对称性的概念。分析靠“V”型所对应的序列区,得出SARS序列5端开始第3232bp～5624bp、5703bp～7195bp:12128bp～14470bp、16444bp～19231bp、19720bp～21803bp,这五段中腺嘌呤(A)的个数与鸟嘧啶(U)基本相同,且在前半段以腺嘌呤为主,后半段以鸟嘧啶为主,而其他的所有非SARS冠状病毒都不具有此特征。根据这个特征为依据,对所有的冠状病毒进行比较,给出鸟类流行性支气管炎病毒与猪流行性腹泻病毒与SARS最接近的论断。
● 研究了蛋白质亚细胞定位预测
基于伪氨基酸成分的扩大协方差判别式算法,提出了一种新的基于粗粒化元胞自动机的蛋白质序列图和氨基酸成分的预测蛋白质亚细胞定位方法,使现有的图像识别技术可以直接地被应用于预测蛋白质亚细胞定位。Self-consistency和Jackknife测试已表明蛋白质定位与它的元胞自动机图是有联系的。
● 研究了蛋白质二级结构类型预测
与现有的预测蛋白质二级结构类型只基于氨基酸成份的方法不同,我们首
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 proteins
Based 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 the
qualification 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.
本文采用粗粒化元胞自动机研究生物信息中的若干模型,取得了以下方面的创新研究成果:
● 构造氨基酸数字编码模型
采用相似规则、对称规则、分子识别理论以及信息处理方法,建立了氨基酸的数字编码模型。此编码模型考虑了氨基酸的物理化学特性,且氨基酸与其数字编码是一一对应的。它可以把生物字符序列转变为二进制数字序列,这种变换使得生物信息可以用其它的数字信息处理方法来研究。
● 提出了基于粗粒化元胞自动机的生物序列可视化模型
基于粗粒化元胞自动机给出了一种新的基因序列可视化方法。与其它生物序列可视化方法作图时碱基所对应的空间点只与此碱基之前的碱基数量和种类有关所不同的是,在构图时,碱基所对应的空间点与此前后碱基的数量和种类都有关。首次提出了元胞自动机图(CAI)的概念与方法,使许多隐含在长而复杂的生物字符序列中的特征,通过CAI清楚地表现出来。提供了一个研究生物序列关键特征和辨识基因功能的新途径。
● 给出了SARS序列的一种特征分析
基于粗粒化元胞自动机序列可视化模型,首次发现了SARS病毒图像中所含有的“V”型结构,提出了局部对称性的概念。分析靠“V”型所对应的序列区,得出SARS序列5端开始第3232bp～5624bp、5703bp～7195bp:12128bp～14470bp、16444bp～19231bp、19720bp～21803bp,这五段中腺嘌呤(A)的个数与鸟嘧啶(U)基本相同,且在前半段以腺嘌呤为主,后半段以鸟嘧啶为主,而其他的所有非SARS冠状病毒都不具有此特征。根据这个特征为依据,对所有的冠状病毒进行比较,给出鸟类流行性支气管炎病毒与猪流行性腹泻病毒与SARS最接近的论断。
● 研究了蛋白质亚细胞定位预测
基于伪氨基酸成分的扩大协方差判别式算法,提出了一种新的基于粗粒化元胞自动机的蛋白质序列图和氨基酸成分的预测蛋白质亚细胞定位方法,使现有的图像识别技术可以直接地被应用于预测蛋白质亚细胞定位。Self-consistency和Jackknife测试已表明蛋白质定位与它的元胞自动机图是有联系的。
● 研究了蛋白质二级结构类型预测
与现有的预测蛋白质二级结构类型只基于氨基酸成份的方法不同,我们首
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 proteins
Based 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 the
qualification 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.
引文
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