Nutritious Rice For The World [complete], рассчет популяции нового, более питательного риса Налаштування

[Rilian]

Nutritious Rice for the World

проект стартовал 12 мая 2008

Полезные ссылки:

• Интервью с Dr. Ram Samudrala, координатором проекта

Как присоединиться читайте в главном топике World Community Grid

Изучается строение протеинов риса, чтобы потом, не используя генeтические модификации, а просто за счет скрещивания, выводить новые виды. :flowers:

Mission
The objective of this project is to predict the structure of proteins of major strains of rice. The intent is to help farmers breed better rice strains with higher crop yields, promote greater disease and pest resistance, and utilize a full range of bioavailable nutrients that can benefit people around the world, especially in regions where hunger is a critical concern.

Determining the structure of proteins is an extremely difficult and expensive process. However, it is possible to computationally predict a protein's structure from its corresponding DNA sequence. The Computational Biology Research Group at the University of Washington has developed state of the art software to accomplish this. The difficulty is, there are thousands of distinct proteins found in rice. This presents a computational challenge that a single computer cannot solve within a reasonable timeframe. Therefore, volunteers of World Community Grid are invited to assist in this daunting task. Through collaboration with agricultural researchers and farmers, the hope is to eventually improve global rice yields and quality.

Significance
Hunger and malnutrition are the top risks to health worldwide. Nearly 30 percent of the world's population suffers from some form of malnutrition [1]. Every year, 10 million people die of hunger and hunger-related diseases. In fact, more people die from hunger and malnutrition annually than from AIDS, malaria, and tuberculosis combined [2].

Rice is the main food staple of more than half the world's population. 20 percent of the total food energy intake for every man, woman, and child in the world comes from rice. In Asia alone, more than 2 billion people get up to 70 percent of their daily dietary energy from rice and its by-products [3].

Improving strains of rice to yield larger, more resilient, and nutritionally-optimized harvests will positively impact the lives of billions of people.

Approach
Making better strains of rice has traditionally been accomplished through cross breeding of strains to produce hybrids with the best features. However, this is limited to crossing strains with easily observable traits.

Complex traits (such as high yield, disease resistance, or nutrient content) come from complex biochemical interactions of individual component proteins. Identifying such proteins and understanding their properties and interactions gives farmers the opportunity to affect these traits in a refined manner by choosing more subtle candidates for cross breeding. Predicting the structure of proteins can provide insight into the roles they play in the biochemistry of these traits.

Как работает рассчетное ядро
ВЮ содержит некоторые исходные параметры состояния белков. Каждое новое вычисление берет исходные параметры и генерирует новые значения в пределах некоторой нормы, и каждое такое состояние считается примерно 2 минуты на среднем по мощности компьютере. После каждого рассчета ВЮ сохраняется (чекпоинт) и проверяет, превышен ли интервал в 6 часов. Если да - ВЮ отправляется на сервер. Иначе - считается дальше. Таким образом мощные компьютеры могут посчитать примерно 500 состояний за ВЮ, а слабые компьютеры около 200. Соответственно, мощные компьютеры получают больше очков за то же самое время.



График проекта


Це повідомлення відредагував Rilian: May 20 2010, 20:26

[Rilian]

Sorry about not updating for so long.

The lab has received some funding and I am now working full-time on NRW rather than in my spare time. I am very happy to be back in Seattle.

We are about to receive some new CPU/GPU servers to analyze the data and there should be something soon. We are also applying for funds to really upgrade our cluster in anticipation of the 1000 plant proteome (1KP) project.

To make things clear, the GPUs are being used to analyze the data internally. We do have a GPU-aware protein folding client as well. If World Community Grid is ready to go with GPUs we are ready to utilize GPUs in the 1KP project. If not, we can proceed using CPUs with fewer candidate structures per sequence.

Thanks for you patience. I will be updating this thread much more regularly as we get results.

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NRFW predicts the structures of rice proteins.

These structures can be compared with proteins with known function. Similarity in structure implies similariy of function. Similarity of sequence to proteins of known function also implies similarity of function to those proteins. Unfortunately with rice, most sequences are dissimilar to anything that has been studied. However combining both structural and sequence similarity information allows us to assign the function of a rice protein/gene more accurately.

Geneticists/breeders use that information to develop better strains of rice as in the projects that you mentioned. So these are not competing projects but projects that can use the information in NRFW.

IRRI is also helping us focus on examining proteins/genes that they believe are most interesting for their work.

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We can run the exact same client on both GPU/CPU but ideally thats something that we might want to change. It makes more sense to leverage the capabilities of GPUs to do some pre-analysis as well as generating structures.

The ATI cards are just as capable in terms of double precision math - it's just that the early acceptance of CUDA means that ATI owners were left out. As you may know, we don't need double precision so we can gain an additional 2-5x speedup and we have tested the folding software on ATI GPUs. OpenCL is supported by both NVIDIA and ATI.

I agree wholehearted with your sentiments and if anything, you may be understating the amount of useful GPUs out there. Even stock and budget GPUs for movie playing are becoming quite powerful.

Hong
Nutritious Rice for the World Scientist

http://www.worldcommunitygrid.org/forums/wcg/viewthread_thread,29075_lastpage,yes#312061