分子进化生物学中序列分析方法的新进展

简要介绍了分子进化生物学中序列分析方法的最新进展,特别强调了似然比检验和贝叶斯推论在分子进化和系统发育假说检验中的重要性,并介绍了新方法的一些成功应用,同时还给出了一些重要的信息资源。     

DNA分子进化的研究基础和策略

从研究方法和技术手段等方面讨论了分子进化研究上的一些重大问题．阐述了基因的基本结构及DNA分子的进化方式：净化选择、中性进化和分化选择。探讨了利用DNA分子进化数据构建系统树的原理,评价了NJ、UPGMAM、MP和ML4种算法在构建进化树时的合理性与不足,以及进化树可靠性检验的方法,并对未来分子进化生物学的发展加以展望。     

纤毛虫分子系统发育学的研究进展

在回顾纤毛虫分子系统发育学产生发展历史的基础上,介绍了随着20年中RFLP、RAPD和DNA序列分析等分子生物学技术作为该学科的主要研究方法在种群遗传多样性与进化、种上阶元系统发育学两方面取得的研究成果和近期研究进展,最后在探讨纤毛虫分子系统发育学存在的一些问题和解决方法的同时,预测了纤毛虫分子系统发育学今后将极大地推动真核生物的起源与进化,内共生等重要生物进化问题的研究。     

丝氨酸蛋白酶超家族分子结构进化研究

采用刚体结构比较法进行蛋白质的结构比较,根据结构比较分数构建分子进化树, 研究丝氨酸蛋白酶超家族分子的进化规律。对分子进化树进行了一些初步分析,得到了一些有意义的结果。根据蛋白质的进化,可以比较精确的确定某物种的进化地位,对于物种的分类具有重要意义。通过对超家族分子进化的研究可以了解蛋白质超家族不同蛋白质之间的亲缘关系和蛋白质之间的进化差异,对于蛋白质工程分子设计提供帮助,对蛋白质结构预测具有一定意义     

植物分子群体遗传学研究动态

分子群体遗传学是当代进化生物学研究的支柱学科, 也是遗传育种和关于遗传关联作图和连锁分析的基础理论学科。分子群体遗传学是在经典群体遗传的基础上发展起来的, 它利用大分子主要是DNA序列的变异式样来研究群体的遗传结构及引起群体遗传变化的因素与群体遗传结构的关系, 从而使得遗传学家能够从数量上精确地推知群体的进化演变, 不仅克服了经典的群体遗传学通常只能研究群体遗传结构短期变化的局限性, 而且可检验以往关于长期进化或遗传系统稳定性推论的可靠程度。同时, 对群体中分子序列变异式样的研究也使人们开始重新审视达尔文的以“自然选择”为核心的进化学说。到目前为止, 分子群体遗传学已经取得长足的发展, 阐明了许多重要的科学问题, 如一些重要农作物的DNA多态性式样、连锁不平衡水平及其影响因素、种群的变迁历史、基因进化的遗传学动力等, 更为重要的是, 在分子群体遗传学基础上建立起来的新兴的学科如分子系统地理学等也得到了迅速的发展。文中综述了植物分子群体遗传研究的内容及最新成果。     

肌动蛋白与真核生物的进化

以肌动蛋白氨基酸取代与真核生物进化年代呈线性关系为依据，收集原生生物界，真菌界，植物界和动物界等四界７４种生物的１２８个肌动蛋白序列，通过对其氨基酸序列同源性进行比较，制作出肌动蛋白的分子进化树，并依此进化树从分子水平对真核生物的进化进行一些探讨，从总体上看，肌动蛋白分子进化树较地地反映了真核生物间的进化关系，为确定某些生物的进化位置及进化关系提供了分子证据。     

分子遗传标记技术及其在昆虫科学中的应用

分子遗传标记是随着聚合酶链式反应 (PCR)和Southern杂交等分子生物学技术的飞速发展而出现的遗传学标记技术 ,它突破了以往形态标记 ,细胞学标记和同工酶标记等表达型标记的局限性 ,在揭示物种的遗传变异性研究中发挥着独特的优势。分子遗传标记目前已出现了几十种 ,可依其涉及的位点和反映的多态性的基础分为多位点分子标记和单位点分子标记 ,多位点分子标记反映核苷酸序列的多态性 ,单位点分子标记反映基因座上等位基因的多态性。本文对一些常用的分子标记技术的特点和它们在昆虫系统进化、昆虫分类、昆虫生态、生物防治和特定基因标记等研究中的应用作了介绍。     

植物系统学研究中常用基因分子标记特点分析

阐述了目前用于植物系统学和进化研究的一些常用的ＤＮＡ分子标记技术,分析了各自的使用范围和应用特点,提出在使用这些分子标记来研究分子系统学时必须注意的问题。     

分子进化研究:Ⅰ.细胞色素C分子进化的模糊聚类分析法研究

本文在强调生物系统自身特点的基础上,针对分子进化本身所具有的模糊性,运用模糊聚类分析的原理和方法,就29种动物细胞色素c一级结构间的进化关系进行了研究,作出了相应的分子系统树并运用硬划分法对该树进行了修改。同时,与其它一些研究的结果进行了比较,讨论了在生物大分子一级结构间进行比较、分析的局限性和分子进化研究中的一些有待解决的问题。     

分子钟及其存在的问题

有关分子钟的理论是目前分子进化研究中的热点问题之一。虽然分子钟假说自提出之后对其合理性的怀疑一直存在 ,但目前分子钟在分子系统发育关系重建中有着越来越广泛而深入的应用 ;为了帮助人们消除对分子钟的误解及了解应用中可能存在的问题 ,本文简述了分子钟的概念、对分子钟假说合理性的挑战、引起分子异速进化的原因、如何建立正确的分子钟以及如何在系统关系重建时合理地应用这个概念的一些简要原则     

分子模拟方法及其在分子生物学中的应用

常用的分子模拟方法有 :量子力学法、分子力学方法、蒙特卡洛法和分子动力学法。四种方法各有优势 ,共同成为分子模拟的组成部分。综述了分子模拟法在分子生物学中的应用 ,最后介绍了分子模拟的发展方向 ,并预测了其未来的发展趋势。常用的分子模拟方法有 :量子力学法、分子力学方法、蒙特卡洛法和分子动力学法。四种方法各有优势 ,共同成为分子模拟的组成部分。综述了分子模拟法在分子生物学中的应用 ,最后介绍了分子模拟的发展方向 ,并预测了其未来的发展趋势。     

Statistical methods for detecting molecular adaptation

The past few years have seen the development of powerful statistical methods for detecting adaptive molecular evolution. These methods compare synonymous and nonsynonymous substitution rates in protein-coding genes, and regard a nonsynonymous rate elevated above the synonymous rate as evidence for darwinian selection. Numerous cases of molecular adaptation are being identified in various systems from viruses to humans. Although previous analyses averaging rates over sites and time have little power, recent methods designed to detect positive selection at individual sites and lineages have been successful. Here, we summarize recent statistical methods for detecting molecular adaptation, and discuss their limitations and possible improvements.     

Novel computational biology methods and their applications to drug discovery

Computational biology methods are now firmly entrenched in the drug discovery process. These methods focus on modeling and simulations of biological systems to complement and direct conventional experimental approaches. Two important branches of computational biology include protein homology modeling and the computational biophysics method of molecular dynamics. Protein modeling methods attempt to accurately predict three-dimensional (3D) structures of uncrystallized proteins for subsequent structure-based drug design applications. Molecular dynamics methods aim to elucidate the molecular motions of the static representations of crystallized protein structures. In this review we highlight recent novel methodologies in the field of homology modeling and molecular dynamics. Selected drug discovery applications using these methods conclude the review.     

Detection of infectious agents in laboratory rodents: traditional and molecular techniques

Methods to detect infectious agents in laboratory animals have traditionally been serological and culture based. Molecular methods to detect infectious agents in laboratory animals are being used more routinely. Confusion as to when and how to use molecular methods abounds. In this review, we present a guide to the weaknesses and strengths of using traditional and molecular methods for the detection of infectious agents in laboratory animals.     

Experimental design and quantitative analysis of microbial community multiomics

Studies of the microbiome have become increasingly sophisticated, and multiple sequence-based, molecular methods as well as culture-based methods exist for population-scale microbiome profiles. To link the resulting host and microbial data types to human health, several experimental design considerations, data analysis challenges, and statistical epidemiological approaches must be addressed. Here, we survey current best practices for experimental design in microbiome molecular epidemiology, including technologies for generating, analyzing, and integrating microbiome multiomics data. We highlight studies that have identified molecular bioactives that influence human health, and we suggest steps for scaling translational microbiome research to high-throughput target discovery across large populations.     

A systems biology approach to prediction of oncogenes and molecular perturbation targets in B‐cell lymphomas

The computational identification of oncogenic lesions is still a key open problem in cancer biology. Although several methods have been proposed, they fail to model how such events are mediated by the network of molecular interactions in the cell. In this paper, we introduce a systems biology approach, based on the analysis of molecular interactions that become dysregulated in specific tumor phenotypes. Such a strategy provides important insights into tumorigenesis, effectively extending and complementing existing methods. Furthermore, we show that the same approach is highly effective in identifying the targets of molecular perturbations in a human cellular context, a task virtually unaddressed by existing computational methods. To identify interactions that are dysregulated in three distinct non‐Hodgkin's lymphomas and in samples perturbed with CD40 ligand, we use the B‐cell interactome (BCI), a genome‐wide compendium of human B‐cell molecular interactions, in combination with a large set of microarray expression profiles. The method consistently ranked the known gene in the top 20 (0.3%), outperforming conventional approaches in 3 of 4 cases.     

Statistical modeling and visualization of molecular profiles in cancer

Current cancer classifications using morphological criteria produce heterogeneous classes with variable prognosis and clinical course. By measuring gene expression for thousands of genes in a single hybridization experiment, microarrays have the potential to contribute to more effective classifications based on molecular information. This gives hope to improve both prognosis and treatment. Statistical methods for molecular classification have focused on using high dimensional representations of molecular profiles to identify subclasses. These can be noisy, unstable, and highly platform-specific. In this article, we emphasize the notion of molecular profiles based on latent categories signifying under-, over-, and baseline expression. Following this approach, we can generate results that are more easily interpretable, more easily translated into clinical tools, more robust to noise, and less platform-dependent. We illustrate both the methods and the associated software for molecular class discovery on a data set of 244 microarrays comprising six known leukemia classes.