一个改进的马尔科夫模型

离子通道是细胞膜里的大分子孔道,是跨越细胞膜里的蛋白质大分子,是神经、肌肉等细胞膜兴奋性的基础。人体细胞均具有完成特殊功能的离子通道,构建离子通道,尤其其门控行为的动力学模型,对于研究离子通道的相关课题具有重要意义。离子通道的开关反映了蛋白质构象变化的动力学过程。本文介绍了细胞膜离子通道的基本内容和几种常用模型,并根据Markov链对离子通道门控行为的一个二态（闭、开）模型的Markov过程进行了改进,得到了包含失活状态的离子通道门控行为的Markov模型。     

电压门控离子通道功能与结构分子模型

离子通道是细胞膜上特殊跨膜蛋白构成的亲水孔道,越来越多的证据表明其与兴奋性,腺体分泌、机体运动、甚至学习和记忆行为等重要生理现象密切相关,由此该领域成为当今年生命学科广为注目的前沿之一。本将简要介绍离子通道的分类和功能,并侧重阐明通道压控原理有压控通道的跨膜拓扑结构和功能分子模型。     

PC12细胞钾离子通道门控动力学随机建模与参数估计(Ⅰ)

本文以PC12细胞钾离子通门控机理分析为例证,探索离子通道门控动力学马尔科夫模型的建模方法,并详述建模过程,为通过研究者提供启示与借鉴．在此基础上提出PC12细胞钾离子通道门控动力学的三个备择方案,然后试用Ball等人建议的适用于片膜中只含一个通道记录的极大似然算法估计速率参数,根据AIC及SC准则将模型作了比较,选择较适宜的模型刻划PC12细胞钾离子通道门控动力学．     

环核苷酸门控离子通道门控的分子机理

环核苷酸门控离子通道(CNG)最广泛地分布于神经细胞。近年来关于 CNG 通道门控的分子机制的研究取得了很大的进步。研究表明, CNG 通道的组成及组装影响通道的特性及门控。近年来有关 CNG 突变体的研究及半胱氨酸残基亲和性的分析表明, 环核苷酸首先结合到 CNG 通道 C 端的环核苷酸结合域(CNBD)上引起 CNBD 空间构像改变, 然后 4 个亚单元发生空间构像的协调改变, CNG 通道开放。本文详细讨论了 CNG 通道的门控机制、各亚单元之间的相互作用、组装的过程及其空间构想的变化, 为 CNG 通道的进一步研究, 尤其是离子通道疾病方面提供理论指导。     

生物膜电压门控离子通道的结构和功能性构象

生物膜离子通道具有多种重要的生理功能.近年,已分离、纯化了电压门控的Na~+、Ca~(2+)和K~+通道的蛋白质组分.Na~+和Ca~(2+)通道分别由一个构成离子孔洞的主要亚单位和数目不同的其他亚单位组成,K~+通道是单一的多肽.对Na~+、Ca~(2+)通道主要亚单位和K~+通道的氨基酸序列的测定表明,它们之间有许多相似性.已分别给出了三种通道跨膜排列的二级结构图象.考虑了Na~+通道的功能特性,包括电压敏感性、通道开放动力学、门控电流、神经毒素的作用等,已提出几种Na~+通道功能性构象模型.     

一个HIV感染的动力学模型

考虑到人体免疫缺损病毒(HIV)能感染和杀每T4细胞,病毒颗粒能从感染细胞膜上发芽而出以及感染细胞能与未感染细胞融合等实验事实,建议了一个细胞水平的人体免疫缺损病毒感染的动力学模型。利用这个模型,我们对HIV感染的动力学行为进行了分析。结果表明:在潜伏期,感染细胞的溶度和病毒的溶度都渐渐地趋于一个暂态的平衡值;在表达期,病毒溶度大幅度增加,正常T4细胞溶度渐渐减少,这也许正是AIDS病致病的原因。     

离子通道数学模型

介绍了离子通道记录的记忆性及其反映这种记忆的齐次Markov模型和具有随机环境的Markov模型,并且上述模型较好的解决“Omission”问题,讨论了模型反映的离子通道记录的物理及生理机制,认为离子通道记录记忆性反映离子通道记忆性。     

电压依赖性离子通道门控的分子机制

５０年代Ｈｏｄｇｋｉｎ和Ｈｕｘｌｅｙ双通道模型及其激活与失活学说,正逐步被８０年代以来的分子生物学和电生理学研究所证实。Ｎａ＾＋、Ｋ＾＋离子通道的激活主要决定于高度保守的带正电荷氨基酸残基密集的Ｓ４段,由膜内向膜外方向的拧改锥样旋转。Ｎａ＾＋通道的失活主要与其Ⅲ－Ⅳ功能区之间的胞内连结襻的“铰链盖”样运动有关;Ｋ＾＋通的失活分Ｎ－、Ｃ－、Ｐ－三型,分别发生在Ｎ－、Ｃ－末端和Ｐ区,其Ｎ型失活与Ｎ－末     

DICE：电压门控离子通道电生理实验数据库

针对现有相关文献中离子通道电生理数据繁多且分散的特点,开发了一套电压门控离子通道电生理实验数据库。数据库中目前主要包括钠离子通道序列数据、调制剂分子结构和序列数据,并收集整理了文献中调制剂和通道相互作用时的电生理学数据和药理学数据。系统实现了数据的收集、录入、存储和查询,为后期进行数据挖掘奠定了基础。用户可以通过网址http：／／biodb．sgst．cn／DICE对数据库进行访问。     

“门控-弹簧”模型及其在果蝇机械力信号转导中的分子基础北大核心CSCD

机械力是生物体生命活动中普遍存在的一种物理刺激,高等生物对机械力的感受是听觉、触觉、压觉以及平衡觉等重要生理过程的生物学基础。生物体中的感受器细胞对机械力信号的转导是上述感受的关键,这一信号转导过程可将细胞外机械力刺激转换为细胞的电信号或者化学信号。"门控-弹簧"模型("Gating-Spring"model)在细胞水平定量地描述了机械力信号转导的原理,但是其分子基础(包括机械力信号转导通路的分子组分,结构基础以及力学原理)依然有待探索。近年来,人们以果蝇为模式生物对上述问题进行了系统的研究,取得了重要的进展。本文对"门控-弹簧"理论进行了概述,并对在果蝇系统中取得的研究进展以及有待解决的问题进行综述和展望。     

A markov classification model for metabolic pathways

Background  

This paper considers the problem of identifying pathways through metabolic networks that relate to a specific biological response. Our proposed model, HME3M, first identifies frequently traversed network paths using a Markov mixture model. Then by employing a hierarchical mixture of experts, separate classifiers are built using information specific to each path and combined into an ensemble prediction for the response.     

Specialized hidden markov model databases for microbial genomics

As hidden Markov models (HMMs) become increasingly more important in the analysis of biological sequences, so too have databases of HMMs expanded in size, number and importance. While the standard paradigm a short while ago was the analysis of one or a few sequences at a time, it has now become standard procedure to submit an entire microbial genome. In the future, it will be common to submit large groups of completed genomes to run simultaneously against a dozen public databases and any number of internally developed targets. This paper looks at some of the readily available HMM (or HMM-like) algorithms and several publicly available HMM databases, and outlines methods by which the reader may develop custom HMM targets.     

An improved P2P lookup protocol model

With the development of the peer-to-peer (P2P) technology, a lot of lookup protocols have come out. Although we can benefit from different protocols separately, our research shows that if there exists a proper model, most of the seemingly different protocols can be classified to a same framework. Since technology is a kind of human behavior, we can set up the P2P lookup general model referring to some outcomes of cognitive psychology. In this paper, we firstly study the lookup model in cognitive psychology, then introduce the model into P2P search strategy, at last we give an improved Chord arithmetic based on the binary tree.     

An improved kinetic model for lactic acid fermentation

A kinetic model for the production of lactic acid by Lactobacillus delbruckii from glucose has been developed using the batch kinetic data of Luedeking. This model incorporates the inhibitory effects of undissociated lactic acid and of hydrogen ion concentration upon cellular growth and production processes.     

修正非齐次马尔可夫模型应用的研究

修正非齐次模型是在齐次模型和非齐次模型基础上提出的适用于蛋白质编码区的马尔可夫模型。此模型可以用来分析生物物种进化和基因突变,模型中的马尔可夫度与序列进化水平相关联,转移矩阵与基因突变相关联。本文通过比较7类不同物种-1度马尔可夫链的含量,验证了生物物种进化反映在密码子使用上的特征;通过密码子位点间转移矩阵的计算,分析了基因突变在密码子不同位点上发生的可能性。     

An improved statistical model for taxonomic assignment of metagenomics

Background

With the advances in the next-generation sequencing technologies, researchers can now rapidly examine the composition of samples from humans and their surroundings. To enhance the accuracy of taxonomy assignments in metagenomic samples, we developed a method that allows multiple mismatch probabilities from different genomes.

Results

We extended the algorithm of taxonomic assignment of metagenomic sequence reads (TAMER) by developing an improved method that can set a different mismatch probability for each genome rather than imposing a single parameter for all genomes, thereby obtaining a greater degree of accuracy. This method, which we call TADIP (Taxonomic Assignment of metagenomics based on DIfferent Probabilities), was comprehensively tested in simulated and real datasets. The results support that TADIP improved the performance of TAMER especially in large sample size datasets with high complexity.

Conclusions

TADIP was developed as a statistical model to improve the estimate accuracy of taxonomy assignments. Based on its varying mismatch probability setting and correlated variance matrix setting, its performance was enhanced for high complexity samples when compared with TAMER.

     

An improved alignment-free model for dna sequence similarity metric

Background

DNA Clustering is an important technology to automatically find the inherent relationships on a large scale of DNA sequences. But the DNA clustering quality can still be improved greatly. The DNA sequences similarity metric is one of the key points of clustering. The alignment-free methodology is a very popular way to calculate DNA sequence similarity. It normally converts a sequence into a feature space based on words’ probability distribution rather than directly matches strings. Existing alignment-free models, e.g. k-tuple, merely employ word frequency information and ignore many types of useful information contained in the DNA sequence, such as classifications of nucleotide bases, position and the like. It is believed that the better data mining results can be achieved with compounded information. Therefore, we present a new alignment-free model that employs compounded information to improve the DNA clustering quality.

Results

This paper proposes a Category-Position-Frequency (CPF) model, which utilizes the word frequency, position and classification information of nucleotide bases from DNA sequences. The CPF model converts a DNA sequence into three sequences according to the categories of nucleotide bases, and then yields a 12-dimension feature vector. The feature values are computed by an entropy based model that takes both local word frequency and position information into account. We conduct DNA clustering experiments on several datasets and compare with some mainstream alignment-free models for evaluation, including k-tuple, DMk, TSM, AMI and CV. The experiments show that CPF model is superior to other models in terms of the clustering results and optimal settings.

Conclusions

The following conclusions can be drawn from the experiments. (1) The hybrid information model is better than the model based on word frequency only. (2) For DNA sequences no more than 5000 characters, the preferred size of sliding windows for CPF is two which provides a great advantage to promote system performance. (3) The CPF model is able to obtain an efficient stable performance and broad generalization.