基因转录调控相关数据库集成系统及其应用

通过互联网访问的有关基因转录调控的数据库集成系统及其应用 ,包括调控区 (3’和 5’调控区、内显子和外显子调控区等 )、调控单元 (启动子 ,增强子 ,沉默子等 )和转录因子结合位点相关数据库及其数据库系统的性质、组成和功能。也介绍了这些数据库和系统的查询和搜索方法以及相关开发的程序工具。这些生物信息学资源对于从事生物信息学、分子生物学、遗传工程、基因功能、生物技术、代谢工程、药物设计、病理学和药理学研究的机构及人员在教学研究方面具一定的参考价值和帮助。     

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.     

Embryogenomics: developmental biology meets genomics

Fundamental questions in developmental biology are: what genes are expressed, where and when they are expressed, what is the level of expression and how are these programs changed by the functional and structural alteration of genes? These questions have been addressed by studying one gene at a time, but a new research field that handles many genes in parallel is emerging. The methodology is at the interface of large-scale genomics approaches and developmental biology. Genomics needs developmental biology because one of the goals of genomics – collection and analysis of all genes in an organism – cannot be completed without working on embryonic tissues in which many genes are uniquely expressed. However, developmental biology needs genomics – the high-throughput approaches of genomics generate information about genes and pathways that can give an integrated view of complex processes. This article discusses these new approaches and their applications to mammalian developmental biology.     

基因合成技术研究进展

基因合成是生物学中一项最基本的、最常用的技术.对DNA调控元件、基因、途径乃至整个基因组的合成是验证生物学假设和利用生物学为人类服务的有力工具.合成生物学的快速发展对基因合成能力提出了日益迫切的需求.近年来,基于微芯片基因合成技术取得了很多令人振奋的新进展,正在向着高通量、高保真、自动化的方向发展.文中综述了DNA化学合成和基因组装及相关技术的最新研究进展和发展趋势,这些新技术正在推动着合成生物学向着更高的水平发展.     

Practical application of synthetic biology principles

Synthetic biology can be defined as the “repurposing and redesign of biological systems for novel purposes or applications, ” and the field lies at the interface of several biological research areas. This broad definition can be taken to include a variety of investigative endeavors, and successful design of new biological paradigms requires integration of many scientific disciplines including (but not limited to) protein engineering, metabolic engineering, genomics, structural biology, chemical biology, systems biology, and bioinformatics. This review focuses on recent applications of synthetic biology principles in three areas: (i) the construction of artificial biomolecules and biomaterials; (ii) the synthesis of both fine and bulk chemicals (including biofuels); and (iii) the construction of “smart” biological systems that respond to the surrounding environment.     

Reflections on the scope and the future of metabolic engineering and its connections to functional genomics and drug discovery

Concepts, experience, and tools from metabolic engineering are immediately applicable to the challenge of understanding how the genome influences phenotype. However, new experimental approaches and mathematical and computational resources are needed to maximize the contributions of metabolic engineering to general questions in functional genomics. Among the priorities are systems for studying physiology on a microscale, theoretical tools for understanding biological control systems, and metabolic simulators "in silico" which provide reasonable predictions of stimulus-response relationships at engineering and medical resolution, with incomplete information on cellular mechanisms and their parameters. Approaching cells as complex systems, already a well-established principle in metabolic engineering, is essential to surmount stagnation in the rate of pharmaceutical discovery which is still based on a naive single-target paradigm.     

Constraints-based genome-scale metabolic simulation for systems metabolic engineering

Random mutagenesis and selection approaches used traditionally for the development of industrial strains have largely been complemented by metabolic engineering, which allows purposeful modification of metabolic and cellular characteristics by using recombinant DNA and other molecular biological techniques. As systems biology advances as a new paradigm of research thanks to the development of genome-scale computational tools and high-throughput experimental technologies including omics, systems metabolic engineering allowing modification of metabolic, regulatory and signaling networks of the cell at the systems-level is becoming possible. In silico genome-scale metabolic model and its simulation play increasingly important role in providing systematic strategies for metabolic engineering. The in silico genome-scale metabolic model is developed using genomic annotation, metabolic reactions, literature information, and experimental data. The advent of in silico genome-scale metabolic model brought about the development of various algorithms to simulate the metabolic status of the cell as a whole. In this paper, we review the algorithms developed for the system-wide simulation and perturbation of cellular metabolism, discuss the characteristics of these algorithms, and suggest future research direction.     

Cell biology, chemogenomics and chemoproteomics

The scientific techniques used in molecular biological research and drug discovery have changed dramatically over the past 10 years due to the influence of genomics, proteomics and bioinformatics. Furthermore, genomics and functional genomics are now merging into a new scientific approach called chemogenomics. Advancements in the study of molecular cell biology are dependent upon "omics" researchers realizing the importance of and using the experimental tools currently available to cell biologists. For example, novel microscopic techniques utilizing advanced computer imaging allow for the examination of live specimens in a fourth dimension, viz., time. Yet, molecular biologists have not taken full advantage of these and other traditional and novel cell biology techniques for the further advancement of genomic and proteomic-oriented research. The application of traditional and novel cellular biological techniques will enhance the science of genomics. The authors hypothesize that a stronger interdisciplinary approach must be taken between cell biology (and its closely related fields) and genomics, proteomics and bio-chemoinformatics. Since there is a lot of confusion regarding many of the "omics" definitions, this article also clarifies some of the basic terminology used in genomics, and related fields. It also reviews the current status and future potential of chemogenomics and its relationship to cell biology. The authors also discuss and expand upon the differences between chemogenomics and the relatively new term--chemoproteomics. We conclude that the advances in cell biology methods and approaches and their adoption by "omics" researchers will allow scientists to maximize our knowledge about life.     

结构基因组学研究与核磁共振

各种生物的基因组DNA测序计划的完成,将结构生物学带入了结构基因组学时代.结构基因组学是对所有基因组产物结构的系统性测定,它运用高通量的选择、表达、纯化以及结构测定和计算分析手段,为基因组的每个蛋白质产物提供实验测定的结构或较好的理论模型,这将加速生命科学各个领域的研究.生物信息学、基因工程、结构测定技术等的发展为结构基因组学研究提供了保证.近年来核磁共振在技术方法上的进展,使其成为结构基因组学高通量结构分析中的一个关键方法.     

微生物多糖合成关键基因挖掘的研究进展

随着分子生物学技术的快速发展,功能基因的挖掘在微生物高产多糖合成关键途径研究中变得越来越重要,不断发展的基因挖掘方法和基因组分析工具推进了研究的深入进行。本文主要综述了近年来报道的微生物多糖生物合成途径和多糖合成途径中的关键酶,以及利用多种技术手段和分析软件工具对多糖合成关键基因进行挖掘和验证的相关研究,为微生物多糖合成关键基因的验证以及微生物高产多糖菌株的制备提供参考。     

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。     

Online analysis of development

The genomics revolution has altered the very nature of research in molecular biology, from how to find genes to how to find out what specific genes do. Given the availability of so many fully (or nearly) sequenced genomes, it is now relatively easy to track down dozens or even hundreds of genes relevant to a particular field of study. Unfortunately, up till now, the tools for determining what these genes actually do in embryos and cells have not kept pace, but the burgeoning field of bioinformatics should help correct this shortcoming and introduce the power of genomics to the study of developmental biology. In this review, some of the bioinformatics resources relevant to developmental biologists are described along with some simple approaches for applying these tools to analyzing early development.     

多组学技术及其在生命科学研究中应用概述

随着新一代测序技术、高分辨质谱技术、多组学整合分析方法及数据库的发展,组学技术正从传统的单一组学向多组学技术发展。以多组学驱动的系统生物学研究将带来生命科学研究的新范式。本文简要概述了基因组学、表观基因组学、转录组学,蛋白质组学及代谢组学的进展,重点介绍多组学技术平台的组成和功能,多组学技术的应用现状及在合成生物学及生物医学等领域的应用前景。     

"4D Biology for health and disease" workshop report

The "4D Biology Workshop for Health and Disease", held on 16-17th of March 2010 in Brussels, aimed at finding the best organising principles for large-scale proteomics, interactomics and structural genomics/biology initiatives, and setting the vision for future high-throughput research and large-scale data gathering in biological and medical science. Major conclusions of the workshop include the following. (i) Development of new technologies and approaches to data analysis is crucial. Biophysical methods should be developed that span a broad range of time/spatial resolution and characterise structures and kinetics of interactions. Mathematics, physics, computational and engineering tools need to be used more in biology and new tools need to be developed. (ii) Database efforts need to focus on improved definitions of ontologies and standards so that system-scale data and associated metadata can be understood and shared efficiently. (iii) Research infrastructures should play a key role in fostering multidisciplinary research, maximising knowledge exchange between disciplines and facilitating access to diverse technologies. (iv) Understanding disease on a molecular level is crucial. System approaches may represent a new paradigm in the search for biomarkers and new targets in human disease. (v) Appropriate education and training should be provided to help efficient exchange of knowledge between theoreticians, experimental biologists and clinicians. These conclusions provide a strong basis for creating major possibilities in advancing research and clinical applications towards personalised medicine.