Welcome Two new Members to My efforts against childhood cancer Team!!

Today we have received two new members to our Boinc team.

Once again welcome!
LTDAkiles
My efforts against childhood cancer Team

Next project of the month: MilkyWay@home

Milkyway@Home uses the BOINC platform to harness volunteered computing resources, creating a highly accurate three dimensional model of the Milky Way galaxy using data gathered by the Sloan Digital Sky Survey. This project enables research in both astroinformatics and computer science.

In computer science, the project is investigating different optimization methods which are resilient to the fault-prone, heterogeneous and asynchronous nature of Internet computing; such as evolutionary and genetic algorithms, as well as asynchronous newton methods. While in astroinformatics, Milkyway@Home is generating highly accurate three dimensional models of the Sagittarius stream, which provides knowledge about how the Milky Way galaxy was formed and how tidal tails are created when galaxies merge.

Milkyway@Home is a joint effort between Rensselaer Polytechnic Institute's departments of Computer Science and Physics, Applied Physics and Astronomy.

World Community Grid : Help Fight Childhood Cancer

Mission
The mission of the Help Fight Childhood Cancer project is to find drugs that can disable three particular proteins associated with neuroblastoma, one of the most frequently occurring solid tumors in children. Identifying these drugs could potentially make the disease much more curable when combined with chemotherapy treatment.


Significance
Neuroblastoma is one of the most common tumors occuring in early childhood and is the most common cause of death in children with solid cancer tumors. If this project is successful, it could dramatically increase the cure rate for neuroblastoma, providing the breakthrough for this disease that has eluded scientists thus far.


Approach
Proteins (molecules which are a bound collection of atoms) are the building blocks of all life processes. They also play an important role in the progress of diseases such as cancer.

Scientists have identified three particular proteins involved with neuroblastoma, which if disabled, could make the disease much more curable by conventional methods such as chemotherapy. This project is performing virtual chemistry experiments between these proteins and each of the three million drug candidates that scientists believe could potentially block the proteins involved. A computer program called AutoDock will test if the shape of the protein and shape of each drug candidate fit together and bond in a suitable way to disable the protein.

This work consists of 9 million virtual chemistry experiments, each of which would take hours to perform on a single computer, totaling over 8,000 years of computer time. World Community Grid is performing these computations in parallel and is thus speeding up the effort dramatically. The project is expected to be completed in two years or less.

You can help by joining and crunching as much as you want.
World Community Grid

New Gpu for Boinc crunching have arrived ! Powercolor 6870

   Finally today i received new GPU for Boinc crunching.
It's Powercolor AMD 6870 1gb and i will install it to Boinc rig Two for Dnetc crunching. It should get about 220K-240K ppd (220 000-240 000 points per day). Before this latest addition my crunchers generated 400K ppd (400 000 points per day). This new gpu should give nice points per day boost to "My efforts against Childhood Cancer" Boinc Team.

SIMAP (Similarity Matrix of Proteins)

SIMAP (Similarity Matrix of Proteins) is a public database of pre-calculated protein similarities that plays a key role in many bioinformatics methods. Protein sequence comparison is the most powerful tool in computational biology for characterizing protein sequences because of the enormous amount of information that is preserved throughout the evolutionary process.

About SIMAP (Similarity Matrix of Proteins) 
The SIMAP database contains all currently published protein sequences and is continuously updated. The computational effort for keeping SIMAP up-to-date is constantly increasing. Please help to update SIMAP by calculating protein similarities on your computer. The computing power you donate supports manifold biological research projects that make use of SIMAP data.

Protein similarities are computed using the FASTA algorithm which provides optimal speed and sensitivity. Protein domains are calculated using the InterPro methods and databases. SIMAP is, to our knowledge, the only project that combines comprehensive coverage with respect to all known proteins and incremental update capabilities.

What is SIMAP used for?
Because of the huge amount of known protein sequences in public databases it became clear that most of them will not be experimentally characterized in the near future. Nevertheless, proteins that have evolved from a common ancestor often share same functions (so-called orthologs). So it is possible to infer the function of a non-characterized protein from an ortholog with known function. A well-known example is the investigations about mouse genes and proteins. Their results are also true for orthologous human genes and proteins in many cases. Protein similarities provide information about relationships between proteins and are necessary for the prediction of orthologs.

Protein domains (often called function domains) are the structural building blocks of proteins. They are responsible for the activities of a certain protein, e.g. binding of small molecules, catalytic reactions or binding other proteins in large complexes. The knowledge about protein domains is stored in huge repositories like the InterPro databases. The prediction of domains in newly sequenced proteins is based on those databases and provides a fully automatic functional annotation of these proteins. Therefore we calculate protein domains for all proteins in SIMAP, thus providing the largest system for protein function prediction worldwide.

There are many more bioinformatics methods that rely on protein similarity and domains. Our protein similarity database provides pre-computed similarity, domain data and represents the known protein space. This opens completely new perspectives compared to the commonly used method to repeatedly re-calculate such kind of data. SIMAP is regularly updated. The similarity matrix is simply being incrementally extended if new sequences occur. The use of SIMAP is completely free for education and public research.
 
Why do we need distributed computing for SIMAP?
The computational costs to calculate the similarity data depend on the square of the number of contained sequences. So the computational effort for keeping the matrix up-to-date is constantly increasing. Our internal resources that perform calculations for SIMAP for the last number of years are no longer sufficient to keep track of all new sequences. That's why we implemented a SIMAP-client for the BOINC platform (Berkeley Open Infrastructure for Network Computing) which is based on the FASTA algorithm to detect sequence similarities.The situation for proteins domains is different but of similar complexity. The computational costs are proportional to the number of sequences and the number of domain models. Due to the growth of the sequence space and the frequent updates in the domain databases, the computational effort for keeping the domain predictions up-to-date is constantly increasing.

What are the institutions behind SIMAP?
SIMAP is a joint project of the GSF National Research Center for Environment and Health, Neuherberg and Technical University Munich, Center of Life and Food Science Weihenstephan (both in Germany). Please contact Thomas Rattei (Department of Genome Oriented Bioinformatics, TU Munich).

Help Fight Childhood Cancer update!!

After screening 3,000,000 candidate chemicals by molecular imaging and cellular toxicity, our project team has finally identified 7 small chemical compounds which kill several neuroblastoma cells at very low concentration.

These compounds are currently subjected for further analyses including the toxicity test using mice as well as elucidation of the molecular mechanism how cancer cells are killed.

From: Help Fight Childhood Cancer at World Commynity Grid

My efforts against Childhood Cancer Rigs!

Boinc One:
Xigmatek Midgard,
AMD Phenom II x6 3.85GHZ,
8 gb ddr3,Intel 40gb Ssd,
AMD 5830 1gb, AMD 4850 1gb,
M4A785TD-V Evo,
Thermaltake 775 Toughpower XT
Mugen 2 B with push 2100 rpm slipstream, pull 1850 rpm Gentle Typhoon and
5x1850 rpm Gentle Typhoons in the case.

Boinc Two:

Basic mid tower case
AMD Athlon x3 one core unlocked to x4 3.2ghz
2x1gb ddr3, AMD 4850 512mb, Asus 760G Mobo
Xilence heatpipe cooler
1850 rpm Gentle Typhoon and Xigmatek 120 case fan
Modecon 620W PSU





Boinc 3 is almost ready. Just need Cpu and Mobo.