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Получил медаль за 45 процессорных дней

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This is a video of an interview with Dr. Alessandra Carbone, the Principal Investigator for the Help Cure Muscular Dystrophy Project, while attending Techfest 2010 in India in January of 2010.

Видео интервью с одной из организаторов проекта



http://www.youtube.com/watch?v=bXm8JupuhN4

Доктору Александре Карбоне, координатору проекта Помоги Вылечить Мускульную Дистрофию, недавно было присвоено звание "Женщина-ученый года"

Dr. Alessandra Carbone, Principal Investigator for the Help Cure Muscular Dystrophy project, was recently honored as "Woman Scientist of the Year".

Dr. Alessandra Carbone, professor at Universitй Pierre et Marie Curie (UPMC) and Principal Investigator for the Help Cure Muscular Dystrophy project, was recently honored as "Woman Scientist of the Year" by the jury of the 9th Irиne Joliot-Curie Prize from the French Department of Higher Education and Research. To read more about this great award, click here for the English version.

Пресс-релиз на 31 июля 2010

вот тут вообще подробная информация на английском: http://www.ihes.fr/~carbone/HCMDproject.htm

UPDATES for the project

July 31, 2010

Hi to all! Thought to try to explain what we are doing right now before you take some vacation, like the scientists here. Hope it will help to feel that things are improving and that the project is very active from this side!! Actually, someone new will join the group on september, Anne Lopes. Anne is assistant professor in structural bioinformatics and has a background in physical-chemistry. She is very interested in working on the protein partnership problem with the numerical approach we developed and on the data analysis of the huge amount of information you are producing!

The state of the art here is the following.

In the paper [S. Sacquin-Mora, A. Carbone and R. Lavery (2008), Identification of protein interaction partners and protein-protein interaction sites, J. Mol. Biol. 382, p1276-1289] we developed a numerical method to detect protein partners. The method was presented and tested on a small quantity of known protein complexes. As you can imagine, as soon as the data from HCMD Phase 1 arrived (THANKS TO YOUR CONTRIBUTION!!) we retested the approach to verify whether we could confirm the results on a larger dataset. This is indeed the case, the method works, and we can distinguish protein partners within the about 150 proteins tested. We observed that the signal is much less sharp when we work with 150 proteins than with 12 proteins (like in the paper) though and that some extra work should be done to improve the numerical method. Remember that for HCMD Phase 2 we shall search for partners among about 2200 proteins.

At the moment, we have improved the formula introduced in the paper and we are developing an "intelligent" approach to arrive fast and surely to identify a small number of potential partners for any protein.

Let me give you an insight on the complexity underlying the problem. It has something to do with the understanding of protein population. This is an important point to assimilate, if you like to understand a bit more of our analysis. When we consider a protein, we do not just study one protein (that is, its geometry and its physico-chemical properties: this is already taken into account in the docking algorithm running on your computers and into JET, the program that allowed us to predict protein binding sites) but we rather study its behaviour with the population of proteins that are around it (in the cell; for the HCMD phase 2, population means the 2200 proteins analyzed in your computers). In other words, when we look at a protein we hope to get a signal on its partnership by looking at her way to interact with all other proteins in the population. This means that we hope to learn from bad interactions as well as from good interactions. The information that YOU are giving us provides to us some insight on what is bad and what is good! but this is not enough and we shall use also some extra observation on the interaction of the protein within a population.

Some proteins are slippery, meaning that they do not seem to glue to any partner. Some others are gluing, meaning that they do glue to essentially everybody. Then there are many other proteins (about a half) that seem to stick on the right place with some specificity. They are the easiest to study. When we use, in our calculations, contributions coming from the entire population, one should think that these contributions come, in principle, from slippery proteins, gluing proteins and many other proteins whose behaviour is less sharply characterizable. "Noise" might enter into the calculation and we wish to reduce it. Learning from the whole set of interactions of a protein, means to learn to which group the protein belongs to. Once this is determined, the numerical criteria that we developed could be adjusted to accurately predict a partner or a small set of potential partners, whenever possible. The understanding of the whole set of behaviours that we need to take into account to know how to correctly evaluate the data coming from WCG is our goal today.

There are a few other concerns that are present in our analysis, and they have something to do with :

1. the algorithmic aspects concerning the handling of large amount of information to be combined for the "learning" approach I mentioned above.

2. the fact that on HCMD phase 2 data analysis, we use JET predictions of protein interaction in our numerical criteria instead of actual real interfaces as done in the paper cited above. This implies a loss of precision that we should consider in our numerical evaluations of the interactions.

These informations should give you some insight on the complexity of the question we face today. Hope that everyone will be feeling that we are advancing, together, for a project that runs alive and hopefully will reserve exciting surprises to all. We expect it.

Have a good summer! Alessandra

Посчитано 25000 процессорных лет, при прогнозе проекта в 96000 лет. То есть около 27%

Даааа. Такие мощности большие используются и не только в этом, но и в других проектах, и так долго всё считается, годами. Эххх

В шапку добавлены ссылки:

• Статистика % выполнения 2 фазы проекта
• Статистика % выполнения 2 фазы проекта в виде графика

На сегодня такие результаты:

Currently we have received results from 26212695 workunits.
This is 53,387,857,304 positions of 137,652,178,995
(38.78%)

судя по логам, мы идем 33% в год, то есть до завершения осталось 2 года

<-- Saphire!

перехожу на Clean Water, а потом, собрав везде где можно сапфиры, обратно на HCC

PS:

Statistics updated on 2010 October 29th : 41.19%
Statistics updated on 2010 October 22nd : 40.7%

делаем 0.5% в неделю, то есть до завершения фазы при такой скорости остается 823 дня о_О

Statistics updated on 2010 December 10th : 47.2%
Statistics updated on 2010 November 26th : 44.32%
Statistics updated on 2010 November 10th : 41.97%

http://graal.ens-lyon.fr/~nbard/WCGStats/

получается 5.2% в месяц, еще осталось 10.2 месяца = 311 дней

На оф форуме спросили про другие формы мускульных заболеваний (например Паркинсон или дистония) которые могут быть связаны с этим проектом, и предложили уточнить название проекта



http://www.worldcommunitygrid.org/forums/w...ad_thread,30785

Вот что ответила ученая из проекта:

Hi to all,

the HCMD project considers 2200 proteins and a bit more than 200 of them are known to be involved in muscular dystrophy. all remaining 2000 proteins exist in the cells but are unknown to interact (yet) with the 200 and/or to be involved in muscular dystrophy. The project will end up to point out all possible interactions of the 2200 proteins, even those that do not have something to do with MD. Hopefully the results will be of biological and medical interest to many researchers and will help the understanding of several forms of neuromuscular diseases.

With best regards,

Alessandra

бета-тест был удачным. исправлена незначительная ошибка в графике ВЮ., и версия 6.15 запущена в рассчеты

Обновление статистики - посчитано 56.83%