从诺贝尔奖看化学和物理技术对生命科学发展的贡献

20世纪下半叶是生命科学突飞猛进的时代,生命科学尤其是分子生物学的发展与应用化学和物理技术分不开,特别是与一些获得诺贝尔奖的技术密切相关.从色谱技术与电泳技术、同位素示踪技术、X射线衍射与像重组技术、质谱与核磁共振技术、DNA测序与PCR技术等方面就化学和物理技术对生命科学发展的贡献进行了论述,并说明化学和物理技术在生命科学研究中的重要性.     

多维液相色谱及其在生命科学中的应用

介绍了一种适用于复杂体系样品分析的分离技术-多维液相色谱。对该技术的原理、分类、模式的选择做了介绍,并引证了其在蛋白质组研究、药物研究等复杂体系样品分析领域的最新应用及发展。     

生命科学与技术人才培养的探讨

本文分析了生命科学与技术人才的培养目标及计划、生命科学与技术与生物科学和生物技术的比较及其生命科学与技术人才的培养策略,期望对生命科学与技术人才的培养提供借鉴。     

从百年诺贝尔生理学或医学奖看世界生命科学发展

本文运用文献计量学的方法,对1901～2004年的诺贝尔生理学或医学奖的获奖者从空间、时间和学科分布等角度进行统计分析,以便了解生命科学领域国际诺贝尔奖人才培养情况、机构获奖情况、学科领域发展情况,思考我国生命科学领域科技人才发展之道,打造一流科研机构,合理进行学科发展布局。     

数学方法与生命科学的发展

数学方法在科学研究中有着非常重要的作用，以哈维血液循环理论的建立，达尔文提出进化论和孟德尔发现分离规律和自由组合规律等具体案例为背景，分析了数学方法在生物学史上对生物学理论发展的推动应用，还对生物学应用教学方法的现状和前景作了介绍。     

从20世纪诺贝尔奖看生命科学发展

20世纪是生命科学突飞猛进的世纪。物理和化学广泛而又深刻地渗入生命科学,全面改变了生命科学的面貌。1900年,随着孟德尔(G．Mendel,1882-1884)发现的遗传定律被重新提出,生物学迈进第2个阶段——实验生物学阶段。诺贝尔生理或医学奖就是在这个时期开始的。1953年美国科学家沃森(J．D．Wat-     

中南林业科技大学生命科学与技术学院

中南林业科技大学生命科学与技术学院成立于2001年,现有1个一级学科博士后科研流动站——生物学,2个博士点——生态学和植物学,1个一级学科硕士点——生物学,15个硕士点——发酵工程、微生物学、植物学、动物学、遗传学、生物化学与分子生物学等。     

耗散结构理论与生命科学的发展

耗散结构理论与生命科学的发展孙毅当代,由于耗散结构理论与其它学科的交叉,促进了我们对诸多学科的新认识。这种新认识的实质在于加深了我们对事物本质的揭示。耗散结构理论是普利高津领导的布鲁塞尔学派建立的关于在远离平衡点的状态下,可能出现稳定化的有序的耗散结...     

电泳亲和色谱技术分离蛋白质

亲和色谱利用亲和配体与目标组分间的特异性结合作用实现对目标组分的纯化,该分离方法分辨率高,在生物物质的分析和分离领域得到日益广泛的应用［１］。亲和色谱在分离过程每一步操作中,液相主体中的溶质分子必须经过一系列扩散过程才能进入到固定相颗粒孔内完成吸附或...     

A short guide to technology development in cell biology

New technologies drive progress in many research fields, including cell biology. Much of technological innovation comes from “bottom-up” efforts by individual students and postdocs. However, technology development can be challenging, and a successful outcome depends on many factors. This article outlines some considerations that are important when embarking on a technology development project. Despite the challenges, developing a new technology can be extremely rewarding and could lead to a lasting impact in a given field.As is true for many fields of research, cell biology has always been propelled forward by technological innovations (Botstein, 2010). Thanks to these advances we now have access to microscopes and other equipment with exquisite resolution and sensitivity, a variety of methods to track and quantify biological molecules, and many ingenious tools to manipulate genes, molecules, organelles, and cells. In addition, we have hardware and software that enable us to analyze our data, and build models of cells and their components.Naturally, even today’s technologies have limitations, and hence there is always need for improvements and for completely novel approaches that create new opportunities. Cell biology is one of the research areas with many chances for individual young scientists to invent and develop such new technologies. Numerous recent examples illustrate that such “bottom-up” efforts can be highly successful across all areas in cell biology; e.g., as a handy vector for RNA interference (Brummelkamp et al., 2002); as methods for visualization of protein–protein or protein–DNA interactions (Roux et al., 2012; Kind et al., 2013); as tools to study chromatin (van Steensel et al., 2001), ribonucleoprotein complexes (Ule et al., 2003), or translation (Ingolia et al., 2009); or as tags for sensitive protein detection (Tanenbaum et al., 2014), just to name a few examples.As a student or postdoc, you may similarly conceive an idea for a new method or tool. Usually this idea is inspired by a biological question that you are trying to address in your ongoing research project. You might then also realize that the new method, at least on paper, may have additional applications. Yet, the development of a new technique typically requires a substantial effort. Should you halt or delay your ongoing research and embark on the development of this new technique? And if so, what is the best strategy to minimize the risks and maximize the chance of success? How do you get the most out of the investment that it takes to develop the method? Here I will discuss some issues that students and postdocs might want to consider when venturing into the development of a new technique.

To develop or not to develop

Development of a new technique can take one to five years of full-time effort, and hence can be a risky endeavor for a young scientist. The decision to start such a project therefore requires careful weighing of the pros and cons (see text box). In essence, there are four main considerations.

Points to consider before starting to develop a new technology.

•Literature search: Does a similar technology already exist? Is there published evidence for or against its feasibility?•How much time and effort will it take?•What is the chance of success?•Are you in the right environment to develop the technology?•Are simple assays available for testing and optimization?•How important are the biological questions that can be addressed?•How broadly applicable will the technology be?•What are the advantages compared with existing methods?•Is the timing right (will there be substantial interest in the technology)?•Is there potential for future applications/modifications that will further enhance the technology?•How easy will it be for other researchers to use the technology?First, conduct a thorough literature survey to ensure that the method has not been developed by others already, and to search for indications that the method may or may not work. The second consideration is the potential impact of the new technology. Impact is often difficult to predict, but it is linked to how broadly applicable the technology will be. Will the new technology only provide an answer to your specific biological question, or will it be more widely applicable? It may be helpful to ask: how many other scientists will be interested in using the technology, or at least will profit substantially from the resulting biological data or knowledge? If the answer is “about five,” then the impact will likely be low; if the answer is “possibly hundreds,” then it will certainly be worth the investment. This potential impact must be balanced against the third consideration, which is the estimated amount of time and effort it takes to develop the technology. The fourth major consideration is: What is the chance that my technique will actually work and what is the risk of failure? There is no general answer to this question, but below I will outline strategies to reduce the risk of failure and minimize the associated loss of time and effort. For this I will consider the common phases of technology development (Fig. 1).Open in a separate windowFigure 1.Flow diagram showing the typical phases of technology development.

Quick proof-of-principle

An adage that is often heard in the biotechnology industry is “fail fast.” It is OK if a project turns out to be unsuccessful, as long as the failure becomes obvious soon after the start. This way the lost investment will be minimal. In an academic setting, it may also be good to prevent finding yourself empty-handed after years of work. As a rule of thumb, I suggest that one should aim to obtain a basic proof-of-principle within approximately four months of full-time work. If after this period there still is no indication that the method may eventually work, then it may be wise to terminate the project, because further efforts are then also likely to be too time-consuming. It is thus advisable to schedule a “continue/terminate” decision point about four months after the start of the project—and stick to it. Note that at this stage the proof-of-principle evidence may be rudimentary, but it is crucial that it is convincing enough to be a firm basis for the next step: optimization.

Optimization cycles

Obtaining the first proof-of-principle evidence is a reason to celebrate, but usually it is still a long way toward a robust, generally applicable method. Careful optimization is required, through iterations of systematic tuning of parameters and testing of the performance. This can be the most time-consuming phase of technology development. To keep the cycle time of the iterative optimizations short, it is essential that a quick, easy readout is chosen. This readout should be based on a simple assay that ideally requires no more than 1–2 d. It is important that the required equipment is readily accessible; for example, if for each iteration you have to wait for several weeks to get access to an overbooked shared FACS or sequencing machine, or if you depend on the goodwill of a distant collaborator who has many other things on his mind, then the optimization process will be slow and frustrating. If your technology consists of a lengthy protocol with multiple steps, try to optimize each step individually (separated from the rest of the protocol), and include good positive and negative controls.Remember that statistical analysis is your ally: it is a tool to distinguish probable signals from random noise and thus enables you to make rational decisions in the optimization process (did condition A really yield better results than condition B?). Assays with quantitative readouts are easier to analyze statistically and are therefore preferable.

Version 1.0: Reaping the first biological insights

During the optimization process it is helpful to define an endpoint that will result in “version 1.0” of the technology. Typically this is when the technology is ready to address its first interesting biological question. Once you have reached this point, it may be useful to temporarily refrain from further optimization of the technology, and focus on applying it to this biological question. This has two purposes. First, it subjects the technology to a real-life test that may expose some of its shortcomings, which then need to be addressed in further optimization cycles. Second, it may yield biological data that illustrates the usefulness of the technology, which may inspire other scientists to adopt the method. If you are based in a strictly technology-oriented laboratory, collaboration with a colleague who is an expert in the biological system at hand may expedite this phase and help to work out bugs in the methodology.If version 1.0 performs well in this biological test, it may be time to publish the method. For senior postdocs, this may also be a good moment to start your own laboratory. A new technology is usually a perfect basis for such a step.

Disseminating and leveraging the technology

When, upon publication, other scientists adopt your new technology, they will often implement improvements and new applications, which makes the technology attractive to yet more scientists. This snowball effect is one of the hallmarks of a high-impact technology. An extreme example is the recently developed CRISPR–Cas9 technology (Doudna and Charpentier, 2014), for which improvements and new applications are currently reported almost on a weekly basis. What can you do to get such a snowball rolling?First, it helps to publish the new technology in a widely read or Open Access journal, to present it at conferences, and to initiate collaborations in order to reach a broad group of potential users. Second, the threshold for others to use the new technology must be as low as possible. Thus, implementation of the technology must be simple, and users must have easy access to detailed protocols. A website with troubleshooting advice, answers to frequently asked questions, and (if applicable) software for download will also help. Depending on the complexity of the technology, it may be worth considering whether to organize hands-on training, perhaps in the form of a short course. This may seem like a big investment, but it can substantially contribute to the snowball effect.Third, materials and software required for the technology should be readily available. Technology transfer offices of research institutes often insist on the signing of a material transfer agreement (MTA) before materials such as plasmids can be shared. But all too often this leads to a substantial administrative burden and delays of weeks or even months. Free “no-strings-attached” sharing of reagents is often the best way to promote your technology—and scientific progress in general.

Patents and the commercial route

Before publication of the technology, you may consider protecting the intellectual property by filing a patent application. Most academic institutes do this, but often the associated costs are high and the ultimate profits uncertain, in part because it can be difficult to enforce protection of a patented technology (how do you prove that your technology was used by someone else?). That said, some technologies or associated materials may be more effectively scaled up and disseminated through a commercial route than via purely academic channels. Specific companies may have distribution infrastructure or technical expertise that is hard to match in an academic laboratory. Founding your own company may also be a way to give the technology more leverage, as it provides access to funds not available in an academic setting. In these cases, timely filing of a patent application may be essential. Note that in certain countries one cannot apply for a patent once the technology has been publicly disclosed (e.g., at a conference).

Competing technologies

Often different technologies for the same purpose are invented independently and more or less simultaneously. It is therefore quite likely that sooner or later an alternative technology emerges in the literature, or appears on the commercial market. This is sometimes referred to as “competing technology,” but in an academic setting this is somewhat of a misnomer, as solid science requires multiple independent methods to cross-validate results. Moreover, it is extremely rare that two independent technologies cover exactly the same spectrum of applications. For example, one technology may have a higher resolution, but the other may be superior in sensitivity. The sudden emergence of a competing technology can however have strategic consequences, and it is important to carefully define the advantages of your technology and focus on these strengths.

A bright future for technology development

New technologies generally consist of a new combination of available technologies, or apply newly discovered fundamental principles. Because the pool of available knowledge and tools continues to expand, the opportunities to devise and test new methods will only improve. This is further facilitated by the increasing quality of basic methods and tools to build on. Thus, there is a bright future for technology development. With a carefully designed strategy, the risks associated with such efforts can be minimized and the overall impact maximized. In the end, it is extremely gratifying to apply a “home-grown” technology to exciting biological questions, and to see other laboratories use it.     

生物科学的发展与《生物课程标准》

全日制义务教育《生物课程标准》的制订 ,主要是从初中生终身发展的需要、社会需求和生物科学发展 3个方面综合考虑的。此文试从生物科学的发展说明确定课程内容标准的依据。2 0世纪是自然科学发展史上最为辉煌的时代 ,生物科学是自然科学中发展最迅速的学科。因为生物科学与人类生存、人民健康、社会发展密切相关 ,必然成为2 1世纪初的主导学科。在 2 0世纪生物科学的发展中有许多重大突破 ,出现了许多新观念、新思想、新成果和新技术。特别是 2 0世纪 5 0年代以来 ,随着数理科学广泛深入地渗透到生物科学以及一些先进的仪器设备和研究技术…     

Bridging genomics technology and biology

A report on the UK Genome Science Meeting, held at the University of Nottingham, UK, 2–4 September 2013.This year’s newly named UK Genome Science Meeting was the fourth edition of what was previously known as the UK Next Generation Sequencing Meeting. The renaming reflects technological developments that continue to redefine the meaning of next generation sequencing, and the fact that high-throughput sequencing is now a common tool in the scientific community. Indeed, an enormous diversity of topics was presented at this compact 3-day meeting, ranging from evolving technologies and bioinformatics, through to evolutionary genomics, metagenomics and clinical applications, amongst others. The meeting succeeded in portraying how, in relatively few years, new sequencing technologies have revolutionized the way we do basic research in just about every subject area, and are quickly making their way through translational research, and into the clinic and field.     

纳米技术与生物学实验的微型化

纳米技术是近年来发展迅速的新技术,有着几乎无限的潜力。一方面生物分子的尺度是纳米与亚纳米级的,纳米技术可以从生物科学学到许多生物分子作用的奥秘;另一方面,纳米技术为生物学的研究提供新材料、新方法,使生物学研究可以多快好省地进行。本文简单介绍几种利用纳米技术开展生物学研究的思路与方法。     

从DNA合成技术角度阐述合成生物学

合成生物学是一个拥有巨大潜力的新兴学科,合成生物学技术的发展将会对未来生物、医药、农业、能源、材料和环保等方面产生巨大的推进作用。基因合成是合成生物学中最基本和使用最多的一种技术手段,合成生物学的快速发展对基因合成能力提出了空前需求。综述基因合成技术的发展历史、现状和未来趋势,探讨基因合成技术存合成生物学以及整个生命科学研究中的应用和重要意义。     

System biology and synthetic biology modify drug discovery and development

Life Sciences are built on observations. Right now, a more systemic approach allowing to integrate the different organizational levels in Biology is emerging. Such an approach uses a set of technologies and strategies allowing to build models that appear to be more and more predictive (omics, bioinformatics, integrative biology, computational biology…). Those models accelerate the rational development of new therapies avoiding an engineering based only on trials and errors. This approach both holistic and predictive radically modifies the discovery and development modalities used today in health industries. Moreover, because of the apparition of new jobs at the interface of disciplines, of private and public sectors and of life sciences and engineering sciences, this implies to rethink the training programs in both their contents and their pedagogical tools.