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]

Обновление статуса проекта.

News

Apr 1, 2010

We have begun to analyze the terabytes of results that have been generated through the generous efforts of the volunteers.

Now comes the difficult part of sifting through the data to find the best models. The folding algorithm is noise and there will be many inaccurate models. We need to find the best models from the almost 7 billion models generated. This should take approximately 3-6 months using our fastest methods. After identifying the most accurate models, we then will use the information to figure out what functions these proteins perform in the rice organism. This involves comparing the structure and sequence to known proteins and is also a time consuming process. The plant genomes are not nearly as well studied as the human and mammalian genomes which makes the process all the more difficult.

We are also developing faster and more accurate technologies to examine the data. As we have mentioned in the forums, a gpu-accelerated version of the simulation process has already been developed which is several orders of magnitude faster and more accurate. We have and are extending that technology to the analyses of the model structures. We have also developed sophisticated techniques that recognise structure and sequence patterns or signatures to identify the function of the protein.

We are applying for funding to support these and other efforts to analyze the mountain of data that has been generated during this process. We too are volunteers, and it is our hope that our combined efforts in the NRW project will help develop rice strains that will make a difference in fighting malnutrition and feeding the world’s people. Finally, as the project comes to an end, we want to thank everyone for their generous contributions to this endeavor, especially those that volunteered their computers and time to generate the data. We really appreciated it.

Tentative future plans are to resubmit an application to the IBM to apply the Protinfo algorithm to proteins encoded by 1000 plant transcriptomes generated by the 1KP Project. This work in progress. Thus the efforts of the WCG volunteers and the results of this study will have a broader impact beyond rice proteomics.