后基因组时代生物信息学的发展趋势

介绍生物信息学产生背景、发展过程以及研究现状，讨论了后基因组时代分子生物学的主要研究领域功能基因组学、蛋白质组学、比较基因组学、药物基因组学之间的关系。在分析基因组时代和后基因组时代生物信息学所研究内容的差异基础上．说明了基于分层递阶结构的系统结构、特征分析方法以及相应的软件系统开发将成为生物信息学发展的基本趋势之一。     

人类基因组计划与后基因组时代

2003年4月14日生命科学诞生了一个新的重要里程碑,人类基因组计划完成,后基因组时代正式来临。着重介绍了人类基因组计划的提出、目标与任务、实施与进展等方面的基本情况,讨论了后基因组时代的时间界定,分析展望了后基因组时代与人类基因组计划密切相关的生物信息学、功能基因组学、蛋白质组学、药物基因组学等几个重要研究领域 。     

水稻功能基因组学研究

水稻是迄今为止第一个被测序的农作物。随着水稻基因组测序计划的完成,以功能基因组学研究为标志的后基因组时代已经到来。综述了水稻功能基因组学的工作进展与方法,主要包括:表达序列标签(EST)c、DNA微阵列和DNA芯片、蛋白质组学、生物信息学和反向遗传学等新方法。     

后基因组时代的基因组功能注释

基因组功能注释是后基因组时代功能基因组学研究的热点领域.从基因组功能注释的研究内容与研究手段出发,重点综述了生物信息学在该领域方法学上的研究进展,并展望了今后的发展前景.     

“中国遗传学会功能基因组学研讨会”胜利闭幕

人类基因组计划圆满结束后,生命科学进入了以研究基因结构与功能为核心内容的“后基因组时代”。为了把握近两年来基因组学研究的最新动态,加强国内外同行的沟通与交流,促进功能基因组生命科学技术的发展,中国遗传学会基因组学专业委员会于2006年10月13日-15日在成都主持召开了“中国遗传学会功能基因组学研讨会”。     

元蛋白质组分析——研究微生物生态功能的新途径

随着对微生物纯化培养和元基因组学研究的不断深入,积累了大量的微生物基因组信息,元蛋白组分析将促进我们对基因组功能的理解。目前,对微生物群落的元蛋白质组(metaproteome)的研究已成为后基因组时代进一步认识微生物生态功能的有效途径。对这一新技术的介绍结合元蛋白质组的提取和鉴定方法、元蛋白质组在研究微生物生态功能上的应用进行了综述,并对这一新的研究领域所面临的困难与挑战进行了讨论。     

化学基因组学和化学蛋白质组学用于细胞自噬研究

化学基因组学和化学蛋白质组学作为后基因组时代的新技术,是以化学小分子为工具,对细胞的生理过程进行精确干扰,研究有机体和细胞的功能,同时也是新药开发的重要手段。本文综述了化学基因组学和化学蛋白质组学征自噬相关靶点的特异性小分子的发现,及小分子存自噬机理研究中的应用。     

枯草芽孢杆菌无标记遗传操作技术研究进展

枯草芽孢杆菌是革兰氏阳性菌的模式生物,长期以来在代谢工程和工业微生物领域扮演重要角色。枯草芽孢杆菌无标记遗传操作技术对后基因组时代的基因功能研究和菌株生理特性的改造起着关键作用。综述枯草芽孢杆菌无标记遗传操作所使用的负筛选标记基因,总结目前主流的无标记遗传操作的策略,并提出枯草芽孢杆菌无标记遗传操作技术面临的主要问题和发展方向。     

功能基因组学研究概述

21世纪是生物时代和信息时代,基因组学的研究已从结构基因组学转向功能基因组学,功能基因组学时代对于基因功能的研究也由单一基因转向大规模、批量分析。对功能基因组学及相关学科的概念作了概述,综述了功能基因组学的研究内容与方法,主要包括应用差异显示反转录PCR、基因表达序列分析（SAGE）、微点阵、蛋白质组学和生物信息学等方法来研究基因组表达概况、基因组多样性和模式生物学等。     

后基因组时代的工业生物技术

简述了工业生物技术的发展背景和意义,分析了基因组学和功能基因组学发展对工业生物技术的推动作用,重点介绍了本期专刊发表的代谢工程、发酵工程以及工业酶与生物催化领域的17篇论文。     

植物萜类代谢工程

植物萜类化合物不仅在植物生命活动中起重要作用,而且具有重要商业价值。随着近年来萜类代谢途径和调控机理研究的深入,代谢工程已成为提高萜类产量最有潜力的途径之一。对萜类代谢工程领域具代表性的研究结果进行了全面回顾,然后讨论了萜类代谢工程的研究方法和策略,其中重点探讨了功能基因组学方法在萜类代谢途径及调控机理研究方面的应用。     

酿酒酵母中基于CRISPR/dCas9的基因转录调控工具的开发与应用

酿酒酵母(Saccharomyces cerevisiae)是重要的模式真核微生物,广泛用于基础研究和工业发酵。基于CRISPR/dCas9系统开发的转录调控方法具有可编程、多重性和正交性等优点,在酿酒酵母的基因调控、功能基因组学、代谢工程等研究领域具有巨大潜力。本文关注酿酒酵母中CRISPR/dCas9基因转录调控工具的研究进展,阐述了不同转录调节结构域对dCas9或gRNA活性的调节,设计与优化dCas9和gRNA表达的方法,影响CRISPR/dCas9系统转录调控效率、特异性和通量的靶向性因素,最后总结了该工具在酿酒酵母代谢工程中的应用,并对该技术的未来发展提出了展望。     

The path to next generation biofuels: successes and challenges in the era of synthetic biology

Volatility of oil prices along with major concerns about climate change, oil supply security and depleting reserves have sparked renewed interest in the production of fuels from renewable resources. Recent advances in synthetic biology provide new tools for metabolic engineers to direct their strategies and construct optimal biocatalysts for the sustainable production of biofuels. Metabolic engineering and synthetic biology efforts entailing the engineering of native and de novo pathways for conversion of biomass constituents to short-chain alcohols and advanced biofuels are herewith reviewed. In the foreseeable future, formal integration of functional genomics and systems biology with synthetic biology and metabolic engineering will undoubtedly support the discovery, characterization, and engineering of new metabolic routes and more efficient microbial systems for the production of biofuels.     

Mapping phenotypic landscapes using DNA micro-arrays

Inverse metabolic engineering is a useful approach for engineering phenotypes in biological systems. The overarching objective of this approach is to combine the power of evolutionary engineering approaches with the precision of constructive metabolic engineering strategies. Often the difficulty in this approach is elucidating the genetic basis of the phenotypes that emerge as a result of evolutionary mechanisms. As a result of advances in genomics technologies, several techniques now exist that substantially improve researchers ability to identify such genes. Metabolic engineers now have the ability to map phenotypic landscapes of considerable genetic diversity, which should improve understanding of the relationships that exist among phenotype, genotype, and environment. In this mini-review, we will discuss several of such genomics tools that may be useful in developing inverse metabolic engineering strategies and, in particular, mapping phenotypic landscapes.     

Metabolic footprinting in microbiology: methods and applications in functional genomics and biotechnology

Metabolomics embraces several strategies that aim to quantify cell metabolites in order to increase our understanding of how metabolite levels and interactions influence phenotypes. Metabolic footprinting represents a niche within metabolomics, because it focuses on the analysis of extracellular metabolites. Although metabolic footprinting represents only a fraction of the entire metabolome, it provides important information for functional genomics and strain characterization, and it can also provide scientists with a key understanding of cell communication mechanisms, metabolic engineering and industrial biotechnological processes. Due to the tight and convoluted relationship between intracellular metabolism and metabolic footprinting, metabolic footprinting can provide precious information about the intracellular metabolic status. Hereby, we state that integrative information from metabolic footprinting can assist in further interpretation of metabolic networks.     

Modelling strategies for the industrial exploitation of lactic acid bacteria

Lactic acid bacteria (LAB) have a long tradition of use in the food industry, and the number and diversity of their applications has increased considerably over the years. Traditionally, process optimization for these applications involved both strain selection and trial and error. More recently, metabolic engineering has emerged as a discipline that focuses on the rational improvement of industrially useful strains. In the post-genomic era, metabolic engineering increasingly benefits from systems biology, an approach that combines mathematical modelling techniques with functional-genomics data to build models for biological interpretation and--ultimately--prediction. In this review, the industrial applications of LAB are mapped onto available global, genome-scale metabolic modelling techniques to evaluate the extent to which functional genomics and systems biology can live up to their industrial promise.     

Implementation of functional genomics for gene discovery in alkaloid producing plants

Two centuries after the discovery of the first alkaloids, many enzymes involved in plant alkaloid biosynthesis have been identified. Nevertheless, the biosynthetic pathways for most of the plant alkaloids still remain incompletely characterised and understanding the regulatory mechanisms controlling the onset and flux of alkaloid biosynthesis is virtually inexistent. This information is however crucial to allow modelling of metabolic networks and predictive metabolic engineering. In the postgenomics era, new functional genomics tools, enabling comprehensive investigations of biological systems, are continuously emerging and are now gradually being implemented in the field of plant secondary metabolism as well. Here we discuss the advances these promising new technologies have already brought and may still bring with regard to the dissection of plant alkaloid biosynthesis. Encouraging results were obtained in alkaloid producing species such as Papaver somniferum, Catharanthus roseus and Nicotiana tabacum. Therefore we anticipate that functional genomics and the knowledge it brings along, will eventually allow a better exploitation of the plant biosynthetic machinery.     

Use of genome-scale microbial models for metabolic engineering

Metabolic engineering serves as an integrated approach to design new cell factories by providing rational design procedures and valuable mathematical and experimental tools. Mathematical models have an important role for phenotypic analysis, but can also be used for the design of optimal metabolic network structures. The major challenge for metabolic engineering in the post-genomic era is to broaden its design methodologies to incorporate genome-scale biological data. Genome-scale stoichiometric models of microorganisms represent a first step in this direction.