|
|
发表于 2013-3-25 00:31:59
|
显示全部楼层
The Nutritious Rice for the World (Rice) project, a World Community Grid BOINC project, ended a few weeks ago. BOINC (Berkeley Open Infrastructure for Network Computing) is a non-commercial program and infrastructure which allows volunteers to donate their computer’s spare computing resources to take part in very interesting, computing intense scientific projects. Many people around the world contributed their CPU-resources to help figure out the structure of proteins of the most common strains of rice. In the end, about 25,761 years of CPU-time were contributed to the project. IBM heavily contributed to this project through their World Community Grid (WCG) program, offering Rice a massive userbase and community .
全球营养水稻项目（The Nutritious Rice for the World project ）——一个世界公共网格（World Community Grid ，以下简称WCG） 在伯克利开放式网络平台上运行的项目在几周前刚刚结束。伯克利开放式网络平台（Berkeley Open Infrastructure for Network Computing ，以下简称BOINC）是一个非商业性的程序。作为一个重要的平台，它使志愿者们能够将自己计算机的闲置资源贡献给那些需要大量运算的有趣科研项目。为了计算出常见品种稻米中蛋白质的结构，世界各地的人将CPU资源贡献给了这个项目。最终志愿者们贡献了总计约25,761年的运算时间。在这个过程中，IBM公司通过他们的WCG向这个项目提供了大规模的用户群体与社区。
Rice is one of the most common foods in various parts of the world. It’s in the interest of us all to find varieties and breeds of rice which are most nutritious or resistant against pests; the project’s goal is to find out which varieties of rice interbreed with others to give the best results so that we’ll get new strains of rice which are harder, better, faster, stronger.
稻米是在世界范围内最为常见的食物之一。我们都有兴趣去寻找最具营养价值，或能够有效抵抗害虫的各种特性、各种品种的新型水稻。全球营养水稻项目的目标就是寻找哪两种水稻杂交能够产生最好的结果，这样我们就能够得到生命力更顽强、品质更优良、生长更快速、植株更强壮的新品种水稻。
A lot of BOINC-users who contributed to the project (like myself) are now asking themselves a lot of questions. Who are the people behind the scenes? How much work is necessary to get a project like this into operation? What was IBM’s role? What will happen with the contributed results? And after all, who will benefit from the project?
许多为这个项目贡献力量的用户（就像我自己）现在都抱有很多疑问。幕后的人是谁？有多少工作对于这类的项目的运行是必要的？IBM扮演的是什么角色？在人们共同努力下获得的成果将会被怎样处理？最终，谁将从中获益？
I think no one can give better answers than Ram Samudrala, PhD and Principal Investigator of a computational genomics research group at the University of Washington. Rocker, scientist and Emacs-admirer – he was so kind to answer me some questions about the project.
我认为没有人能比Ram Samudrala做出更好的回答了。Ram博士是华盛顿大学（University of Washington）计算基因组学研究小组的首席研究员，同时也是一名科学家、摇滚乐迷和emacs（译注：一种UNIX系统的文本编辑器）的忠实用户。他非常亲切地回答了我的几个问题。
Tell us a little about yourself and how you got involved in the Rice-project.
请您向大家介绍一下自己，并说说您是怎样接触到NRFW项目的。
Ram: I’m a professor researching computational biology at the University of Washington Seattle. My overarching interest has been to understand and model how the genome of an organism (genotype) specifies its behaviour and characteristics (phenotype). We develop computational algorithms to this end that are applied to whole genomes and we work on many organisms. Rice was specifically chosen since our collaborators at the Beijing Genomics Institute had just finished sequence (and we annotated the refined version) and I also got a $1.9 million grant from the US National Science Foundation (NSF) to predict the structure and functions of all proteins encoded by the rice genome. We developed algorithms to do this and we applied it to all rice proteins. Then IBM came along and offered us the means to redo some of our calculations on the most difficult proteins using the WCG and then we ported our code over to work on the Grid.
RAM：我在华盛顿大学是一位研究计算生物学的教授。我最大的兴趣是了解一个有机体的基因组如何确定其行为与特征（表现型），并对其进行建模。为了达成这个目标，我们开发了一种能够适用于整个基因组的算法，并利用该算法对很多有机体进行了研究。我们选择稻米的原因之一，是我们在北京华大基因研究中心（the Beijing Genomics Institute）的合作者完成了稻米全基因组的测序，另外我还从美国国家科学基金会（the US National Science Foundation，NFS）获得了190万美元的拨款用以预测由水稻基因组编码的所有蛋白质的结构与功能。为此，我们开发了能够应用于所有水稻蛋白质的算法。然后，IBM公司出现了，他们为我们提供了重新对那些复杂的蛋白质进行运算的方法——也就是利用WCG。这之后，我们便将代码移植到了WCG上。
When was the first time you considered using voluntary distributed computing for your project?
您是什么时候开始真正考虑将志愿分布式计算应用于您的项目的？
Ram: Since the days of SETI@home, and since we built our own local clusters to do structural computational biology, but porting our code to BOINC was always a
inertial challenge.
Ram：是在SETI@home兴起的那段时间，同时那也是我们为了研究计算结构生物学而建立起自己的本地服务器集群的时候，不过将我们的代码移植到BOINC上一直是一个很大的挑战。
Did you consider using other DC-infrastructures except BOINC, like distributed.net? If yes, why did you decide using BOINC?
您想过利用BOINC以外的像是distributed.net这样的分布式计算平台吗？如果有过这样的打算，那么您为什么选择了BOINC呢？
Ram: No, we used BOINC since it was what was supported by IBM WCG.
Ram：没有，因为BOINC有IBM的WCG支持，所以我们一开始就选择了BOINC。
Have you considered asking the NCSA for computing resources?
您是否考虑过请求NCSA（美国国家超级电脑应用中心）提供计算资源？
Ram: Yep, but it’s a cumbersome process, like applying for a grant, and again, porting software to work on different architectures. The barrier is that we get grant money to do research and not develop software. I have used NIST supercomputing resources in the past.
Ram：是的，但要走的程序很复杂，比如研究基金的获取、新平台下软件的移植等等。困难在于，我们只搞到了研究用的经费，却不包括软件开发的。过去，我曾经利用过NIST（美国国家标准技术研究所）的超级计算机资源。
You said you would need 200 years of computing time using your available resources. Besides voluntary distributed computing and the University of Washington, were there other universities or institutes directly contributing computing-resources to your project?
您曾指出，利用当时的资源（译注：指仅仅利用华盛顿大学的计算机时），你们需要200年的计算时间才能完成计算。除了分布式计算平台的志愿者和华盛顿大学，还有其他的大学或研究机构直接为您的项目贡献计算资源吗？
Ram: Not for this project, no.
Ram：没有了。
You were using algorithms from the Protinfo website. Which one did you actually use, how much effort did you put into customizing it for using it in BOINC? Can you tell us if those algorithms and implementation are released under a free license?
您的程序使用的是来自Protinfo网站的算法，具体究竟是哪一种算法呢？把这个算法移植到BOINC平台又费了你们多大功夫？此外，请问这些算法及其程序实现是否是在自由协议的许可下发布的？
Ram: It’s the Protinfo AB algorithm, which is our ab initio or de novo simulation protocol. IBM spent a fair amount of time porting the code to work with BOINC. The original algorithms/software are all freely available without any claim of copyright (i.e., in the public domain).
Ram：我们采用的是 Protinfo AB 算法，一种“从头计算”的模拟方法（ab initio or de novo simulation protocol，注：即不依赖经验参数的计算）。IBM花费了不少精力来移植代码到BOINC平台。原始的算法和软件是自由分发的，没有任何版权限制（即属于公有领域）。
Could you explain “de novo” and “ab initio” for non-scientists, please?
您能用通俗的语言解释一下“从头计算”（“de novo”或“ab initio”）的意思吗？
Ram: “De novo” and “ab initio” generally are translated to mean “from first principles”. In the old days, this used to mean using pure physics energy potentials for protein folding. These days, to us, it means any set of general principles that is not biased to a particular protein or organism.
Ram：从头计算，通常的解释是是“根据第一性原理（first principles）”。过去，这意味着应用纯物理的能量势进行蛋白质折叠。现在，对于我们而言，这意味着使用一套不针对特定蛋白质或有机体的普遍原理。
If the algorithms you used are under a free license, did you already manage to publish the modifications, if there are any?
既然算法是在自由协议下发布的，你们是否已经发布过自己的改良版本？
Ram: The modifications involving the porting are with IBM and they are unpublished.
Ram：涉及到平台移植的改良版本是和IBM共有的，还没有发布。
(Ed. note: Since the software was released in the public domain there’s no requirement to publish the modifications.)
（编者按：由于软件属于公有领域，任何人都可以自由地修改和重发布它，没有任何限制。）
IBM helped you out in customizing the protein-prediction algorithms for various platforms. Can you tell us how much they contributed?
IBM帮你们把蛋白质预测算法移植到了不同的平台上。请您谈谈他们提供了多大帮助。
Ram: All the customisation was done by IBM engineers. We just gave them the original software and ran sanity checks on the output. I’m a strong free software and anti IP proponent, to the degree that I encourage commercial use without restrictions on the software (people can always use the public domain versions if they want to).
Ram：所有的移植工作都是都是由IBM工程师完成的。我们只是提供了原始的软件，并对程序结果进行适当的检验。我鼓励无条件的商业化使用我的软件（人们只要有意愿，就可以随便使用公有领域中发布的版本），在这个角度上，我是个坚定的自由软件支持者和反版权人士。
How much time did you save by using the World Community Grid’s infrastructure compared to if you would’ve set it up all on your own, like other projects do?
比起独立完成（就像其他科研项目一样），利用WCG节省了多少时间呢？
Ram: IBM took about six months or so to port our software, so I presume it would’ve required that kind of an investment. Keep in mind that they had a lot of prior experience with BOINC. IBM now maintains the code and does the PR and runs the predictions for us. I’d say this would be a full time programmer/sysadm type of person and if I had that extra money, I’d rather spend it on someone doing the basic research.
Ram：IBM花了大约半年来移植软件，所以我假定这部分投资是必需的。别忘了他们有不少和BOINC打交道的经验。现在，IBM负责维护代码、搞公关并为我们运行预测程序。我要说的是，找一个全职的程序员或系统管理员大概也能完成这些事。但要有这闲钱，我宁愿雇人来搞基础研究。
If there are flaws about BOINC, which would you like to be addressed first?
如果说BOINC有什么缺陷，您首先想到了什么呢？
Ram: I can’t think of any in the way we did it with IBM, but without IBM, the PR machine has to be powerful to get people on board. It’s more than just recruiting people, but also motivating them as IBM does with badges and giving them a sense of community and providing a support infrastructure. This is hard for a research lab to do on their own (it can be done, but is it really the best use of our talents is the questions).
Ram：在和IBM合作的过程中，我实在想不出什么问题。要是没了IBM，非得找个强大的公关才能吸引人气。公关不仅仅是召集人，还要像IBM那样激发他们的热情，搞点奖励、给大家一种集体荣誉感并提供支持平台。这些事单靠实验室很难完成（其实也能完成，但把我们的智慧耗费在这种事情上大概不怎么值得）。
Programming and debugging is an iterative process. Looking at your sourcecode-repository, how many releases of the software were necessary until you got the cow flying?
编程和调试是个反复的过程。看看你们的源代码库，在确定万事具备之前，你们发布过多少个版本呢？
Ram: For this case, internally we probably had about 10 or so iterations in total, but the basic science part of the software is something that has evolved over 18 years.
Ram：对于目前这个软件，内部发布的大概有10个。但要追溯软件的基础的科学部分，可能已经演变了至少18年。
How did you do beta-testing, did you use the publicly available beta-projects at WCG? Or, were you actually just doing it in your lab?
你们是怎么做 beta 测试的？是使用的 WCG 中公开的 beta 测试项目机制，还是自己在实验室里做？
Ram: It was mostly in our group. We just submitted sequences for which we knew the answers and we did a dry run initially with the same sequences.
Ram：大部分是我们小组自己搞的。我们仅仅是提交已经知道结果的序列，用同样的序列进行测试。
I’m curious there – were these structures predicted by other algorithms or was that done the hard way, using X-ray crystallography?
我很好奇——这些序列的结果是使用其他算法得到的，还是使用更复杂的X射线结晶学方法得到的？
Ram: These were done the hard way, at the bench. These are our gold standard for when we know we’re right or wrong, so we benchmark our methods against all this. When we did the rice project, we did sequences with known answers to see how well things would work and that there was no chance of anything going wrong.
Ram：在实验室里利用复杂的方法得到的。这些结果是判断方法正确性的黄金标准，所以我们把每个数据都测试了一遍。搞水稻项目时，我们把所有已知序列都做了测序，以检查运行情况，并确保不会出现任何问题。
How was is like getting in touch with the community? Was the feedback helpful? How many people from your team were actually dealing with the community?
和志愿者团体交流的感觉如何？他们的反馈信息有用吗？您的团队中又有多少人实际负责和志愿者打交道？
Ram: At its peak, we had 3 people dealing with the community, our sysadm and project lead Michal Guerquin, our programmer and scientist Ling-Hong Hung, and myself. Opening our software to the Grid and the community definitely presented some challenges, which I believe will be the focus of our first paper. An interesting tangent of that is that we’ve had to port some of our analysis software to work on GPUs so we could handle all this data. So some good technological developments here that we’ll be writing about shortly.
Ram：最多的时候，我们有三个人负责和志愿者团体打交道：系统管理员兼项目领导 Michal Guerquin，程序员和科学家 Ling-Hong Hung，以及我本人。向 WCG 和志愿者团体公开我们的软件确实带来了一些挑战，我认为这会成为我们第一份论文的重点。扯点有趣的题外话，我们曾不得不移植一些分析软件，使其在GPU上工作，这样我们就能处理所有这样的数据。所以还会简要写一些有关这里的优秀技术开发的内容。
A lot of people are concerned about “Frankenfood”. Your project’s website explicitly states that this is not about genetic engineering, but about finding the most nutritious rice-strains for interbreeding with other rice-crops. Is there anything you’d like to explain to people who are still concerned?
许多人都很关注“转基因食品”（原文“Frankenfood”是贬义的说法）的问题。你们的项目主页明确说明，此项目的目的是寻找用于杂交的最具营养的水稻品种，与基因工程无关。但有些人仍心存疑虑，对他们您有什么要说明的吗？
Ram: We’re simply extending what farmers have been doing for millenia in a more rational way, and also what has been going on in nature for billions of years. The problem to us is scientific and all knowledge that is produced (which from our end will be completely free and transparent) can be used in various ways according to the will of the people. But we have governments and politicians to handle the deeper societal implications. What I mean by this is that people should petition their representatives, as they are doing successfully in many parts of the world, to decide where to go with genetically modified organisms, which I see as ultimately having a socioeconomic/political solution.
Ram：同样的事情，农民们已经做了上千年，自然界中更是持续了上亿年，我们只是用科学的方法拓展了它。我们关心的是科学问题，所有现有的知识（在我们看来都是自由和透明的）因人的意志不同可以有各种用途。至于社会影响，还有我们的政府和政治家应对。我的意思是，民众应当向他们的代表提出请求（因为这种方式在世界各地运作得相当成功），以决定转基因食品的未来。我认为这就是社会经济学/政治上的最终解决方案。
Your project is one of the very few with a fixed end, almost all other projects are handing out work-units for new phases. How comes that you’re finished now? Is everything from the rice-genome now analyzed from a computational point-of-view and nothing else left to do?
其他项目几乎都在准备新一阶段的任务，而你们的项目却是少数几个直接就结束了的。为什么你们现在就已经完成了？难道有关水稻基因的一切东西都已经从计算机的角度完全解析，完全无事可做了？
Ram: Not at all. We obtained a huge amount of data and we’re now pressed to analyse it. I honestly can say that we were overwhelmed with this data. My goal as a scientist though is not just to develop technical tools and produce large tables and graphs but try to come up with something tangible that is prioritised and can be tested at the bench that really changes the make up of rice in a desired manner. The computations and the Grid are the means by which we arrived at this step, but our job now is to figure out where the best low hanging fruit is （好像是个谚语，找不来等价翻译） in collaboration with rice researchers (which we are doing with researchers around the world including IRRI, Phillipines). [Ed. note: IRRI, International Rice Research Institute]
Ram：并不是这样。我们已经得到了海量的数据，现在正要着手分析。说实话，我们都要淹死在这数据海洋里了。作为一名科学家，我的目标并不仅仅是开发技术工具和制做大量的图表和数据，而且还得得到有形的成果：这些成果能够经得起实践检验、能真正向着有益的方向改进水稻的生产——这才是最重要的。网格计算只是我们达到目前阶段的一种手段，而接下来我们的任务是和水稻研究者（我们的合作者来自全球各地，包括菲律宾的IRRI）一同找出那些触手可及的改进方法。[编者注：IRRI，International Rice Research Institute，国际水稻研究所]
Focussing on the data: Now that you know how those proteins really look like, where do you draw a line and say “this protein is more nutritious than others”? My basic understanding is that the nutritious parts in rice is actually carbohydrates (starch), proteins and some fat. How do I have to imagine this analysis?
关注一下数据：既然您已经了解到了这些蛋白质的构造，那么你们又是怎样界定“这种蛋白质比其他的更具营养”呢？我有这种简单的认识：大米的营养成分主要是碳水化合物（淀粉）、蛋白质和一些脂肪。我该如何认识分析结果呢？
Ram: So the proteins we’re talking about are gene products, that carry out almost all the functions in rice (or any other organism). So we use
the protein to refer to a molecule that does this, rather than the nutrition use of the word “protein” which refers to these biological molecules broken down and aggregrated (see “Protein” and “Protein (nutrient)” in Wikipedia).
Ram：我们这里提及的蛋白质是基因的产物，它们促成了水稻（及其他一切有机体）体内几乎所有生命活动。所以这里说的蛋白质代表的是具有上述功能的分子，而非营养学里所说的“蛋白质”，后者指的是上述分子分解和聚集后的产物（详见英文维基百科的“Protein”和“Protein (nutrient)”条目）。
By nutrition we mean anything that leads to higher range of bioavailable substances like dietary minerals and vitamins. In rice, examples include elements like iron or organics like vitamin A. Incidentally the “golden rice” GMO is a product of Monsanto that has higher beta-carotene, a precursor to vitamin A (“Golden Rice” at Wikipedia). We’d like to get to something like that by crossbreeding without the use of genetic engineering, working on both micro and macronutrients.
所谓营养，我们指的是具有提高生物体所需物质（如膳食矿物质和维生素）含量水平的功能的任何东西。如大米中包含的铁元素和有机分子维生素A。顺带一说，黄金大米原来是孟山都的一项产品，其中含有大量的beta胡萝卜素（参见维基百科“黄金大米”条目）。我们希望不使用基因工程，而仅仅通过杂交的手段，得到类似的东西。
So in the end, we need to be able to create a rice strain that does have enriched nutrients and is perhaps better than current strains in terms of yield and/or hardiness. Before we go off and start crossing rice, there are a number of molecule biology bench experiments that can be done to say whether predictions we make about the activity of certain proteins will be correct so we’d do them first.
所以，我们最终要创造一种比现有品种更具营养、并且可能更高产、更耐寒的优良水稻品种。在开始杂交之前，还要先进行一系列分子生物学测验，以检验我们对特定蛋白质功能做出的预测是否正确。
Do you plan to publish all your results in an Open Access Journal?
您是否打算把全部实验结果发布到某个开放获取期刊（Open Access Journal）上？
Ram: Yep, that would be the ideal. Publishing in Open Access Journals also sometimes costs money. I’m not a big fan of the “pay to publish” model—it’s not a lot of money and some scientists have grants to do this, but it’s not a good principle.
Ram：是的，这正是我的愿望。在开放存取期刊上发布文章有时也要花钱。我并不喜欢这种“付钱即发表”的模式——虽然花不了多少钱，而且不少科学家有足够的研究基金去这么做，但这决不是什么好的模式。
Thank you very much for this interview!
感谢您接受这次采访！
Ram: Thanks; I enjoyed the questions!
Ram：谢谢。能为大家解惑我也很高兴。
Dr. Ram Samudrala is a tenured Professor at the University of Washington, Seattle. He’s head of the Nutritious Rice For The World project and one of the inventors of protein prediction algorithms. He’s a notorious contributor of scientific papers and generally a very nice guy I’d like to buy a drink.
Ram Samudrala 博士是西雅图华盛顿大学的终身教授。他是全球营养水稻项目的领导者，也是蛋白质预测算法的开发者之一。他是个“臭名远扬”的科学论文投稿者，还算是个不错的家伙啦，我愿意请他喝上一杯。 |
评分
-
查看全部评分
|
发表于 2013-2-8 17:49:03
发表于 2013-2-9 13:49:15
