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  • richardmitnick 7:51 pm on December 17, 2014 Permalink | Reply
    Tags: , , World Community Grid   

    From HCC at WCG: “New imaging tools accelerate cancer research” 

    New WCG Logo

    15 Dec 2014
    Help Conquer Cancer research team

    Summary
    The Help Conquer Cancer research team at the Ontario Cancer Institute continues to analyze the millions of protein-crystallization images processed by World Community Grid volunteers, by building new classifiers based on a combination of Grid-processed image features, and deep features learned directly from image pixels. Improvements in image classification, along with new data provided by our collaborators increase possibilities for discovering useful and interesting patterns in protein crystallization.

    Dear World Community Grid volunteers,

    Since our last Help Conquer Cancer (HCC) project update, we have continued to analyze the results that you generated. Here, we provide an update on that analysis work, and new research directions the project is taking.

    Analyzing HCC Results

    Volunteers for the HCC project received raw protein crystallization images and processed each image into a set of over 12,000 numeric image features. These features were implemented by a combination of image-processing algorithms, and refined over several generations of image-processing research leading up to the launch of HCC. The features (HCC-processed images) were then used to train a classifier that would convert each image’s features into a label describing the crystallization reaction captured in the image.

    Importantly, these thousands of features were human-designed. Most protein crystals have straight edges, for example, and so certain features were incorporated into HCC that search for straight lines. This traditional method of building an image classifier involves two types of learning: the crystallographer or image-processing expert (human), who studies the image and designs features, and the classifier (computer model), that learns to predict image labels from the designed features. The image classifier itself never sees the pixels; any improvements to the feature design must come from the human expert.

    More recently, we have applied a powerful computer-vision/machine-learning technology that improves this process by closing the feedback loop between pixels, features and the classifier: deep convolutional neural networks (CNNs). These models learn their own features directly from the image pixels; thus, they could complement human-designed features.

    CrystalNet

    We call our deep convolutional neural networks [CNN] CrystalNet. Our preliminary results suggest that it is an accurate and efficient classifier for protein crystallization images.

    In a CNN, multiple filters act like pattern detectors that are applied across the input image. A single map of the layer 1 feature maps shows the activation responses from a single filter. Deep CNNs refers to CNNs with many layers: higher-level filters stacked upon lower-level filters. Information from image pixels at the bottom of the network rises upwards through layers of filters until the “deep” features emerge from the top. Although the example shown in Figure 1 (below) has only 6 layers, more layers can be easily added. Including other image preprocessing and normalization layers, CrystalNet has 13 layers in total.

    1
    Fig. 1: Diagram of the standard convolutional neural network. For a single feature map, the convolution operation applies inner product of the same filter across the input image. 2D topography is preserved in the feature map representation. Spatial pooling performs image down-sampling of the feature maps by a factor of 2. Fully connected layers are the same as standard neural network layers. Outputs are discrete random variables or “1-of-K” codes. Element-wise nonlinearity is applied at every layer of the network.

    2
    After training, Figure 2 shows examples of the first layer filters. These filters extract interesting features useful for protein crystallography classification. Note that some of these filters look like segments of straight lines. Others resemble microcrystal-detecting filters previously designed for HCC.
    Fig. 2: Selected examples of the first-layer filters learned by our deep convolutional neural net. These filters have resemblances to human-designed feature extractors such as edge (top row), microcrystal (bottom), texture, and other detectors from HCC and computer vision generally.

    3
    Figure 3 shows CrystalNet’s crystal-detection performance across 10 image classes in the test set. CrystalNet produces an area under curve (AUC) 0.9894 for crystal class classification. At 5% false positive rate, our model can accurately detect 98% of the positive cases.

    CrystalNet can provide labels for images generated during the high-throughput process effectively, with a low miss rate and high precision for crystal detection. Moreover, CrystalNet operates in real-time, where labeling 1,536 images from a single plate only requires approximately 2 seconds. The combination of accuracy and efficiency makes a fully automated high-throughput crystallography pipeline possible, substantially reducing labor-intensive screening.

    New data from collaborators

    Our collaborators at the High-Throughput Screening Lab at the Hauptman-Woodward Medical Research Institute (HWI) supplied the original protein-crystallization image data. They continue to generate more, and are using versions of the image classifiers derived from the HCC project.

    Our research on the predictive science of protein crystallization has been limited by the information we have about the proteins being crystallized. Our research partners at HWI run crystallization trials on proteins supplied by labs all over the world. Often, protein samples are missing the identifying information that allows us to link these samples to global protein databases (e.g., Uniprot). Missing protein identifiers prevent us from integrating these samples into our data-mining system, and thereby linking the protein’s physical and chemical properties to each cocktail and corresponding crystallization response.

    Recently, however, HWI crystallographers were able to compile and share with us a complete record of all crystallization-trial proteins produced by the North-Eastern Structural Genomics (NESG) group. This dataset represents approximately 25% of all proteins processed by HCC volunteers on World Community Grid. Now all our NESG protein records are complete with each protein’s Uniprot ID, amino-acid sequence, and domain signatures.

    With more complete protein/cocktail information, combined with more accurate image labels from improved deep neural-net image classifiers, we anticipate greater success mining our protein-crystallization database. Work is ongoing.

    See the full article here.

    Please help promote STEM in your local schools.
    STEM Icon

    Stem Education Coalition

    World Community Grid (WCG) brings people together from across the globe to create the largest non-profit computing grid benefiting humanity. It does this by pooling surplus computer processing power. We believe that innovation combined with visionary scientific research and large-scale volunteerism can help make the planet smarter. Our success depends on like-minded individuals – like you.”

    WCG projects run on BOINC software from UC Berkeley.

    BOINC is a leader in the field(s) of Distributed Computing, Grid Computing and Citizen Cyberscience.BOINC is more properly the Berkeley Open Infrastructure for Network Computing.

    CAN ONE PERSON MAKE A DIFFERENCE? YOU BETCHA!!

    “Download and install secure, free software that captures your computer’s spare power when it is on, but idle. You will then be a World Community Grid volunteer. It’s that simple!” You can download the software at either WCG or BOINC.

    Please visit the project pages-
    Outsmart Ebola together

    Outsmart Ebola Together

    Mapping Cancer Markers
    mappingcancermarkers2

    Uncovering Genome Mysteries
    Uncovering Genome Mysteries

    Say No to Schistosoma

    GO Fight Against Malaria

    Drug Search for Leishmaniasis

    Computing for Clean Water

    The Clean Energy Project

    Discovering Dengue Drugs – Together

    Help Cure Muscular Dystrophy

    Help Fight Childhood Cancer

    Help Conquer Cancer

    Human Proteome Folding

    FightAIDS@Home

    World Community Grid is a social initiative of IBM Corporation
    IBM Corporation
    ibm

    IBM – Smarter Planet
    sp

     
  • richardmitnick 10:16 am on December 3, 2014 Permalink | Reply
    Tags: , , , World Community Grid   

    From WCG on Ebola 

    New WCG Logo

    Outsmart Ebola Together

    The Ebola virus is a significant global health threat and is a growing humanitarian crisis in Africa, killing thousands of victims in 2014.

    If not handled properly, an Ebola outbreak can turn into an epidemic, overwhelming regional health services and disrupting trade and the delivery of social services, causing the welfare and economy of a region to deteriorate. The ongoing viral load in the human population increases the likelihood of further mutation. Additionally, the virus’s long incubation period and our highly connected modern world could allow the virus to spread to new geographies and across oceans.

    Currently, there are no approved treatments or vaccines for this deadly disease, and the search for an effective antiviral drug to treat the disease is a high priority. While previous outbreaks have ended when the disease disappeared from the human population, the scope of the 2014 outbreak raises the possibility that the virus, rather than disappearing again, could become endemic – permanently persisting in human populations in one or more areas.

    Outsmart Ebola Together on World Community Grid aims to help researchers at The Scripps Research Institute develop a treatment for Ebola virus. The computational power donated by World Community Grid volunteers is being used to screen millions of candidate drug molecules to identify ones that can disable the Ebola virus.

    You can help researchers find a cure for Ebola by donating your computing power to this project, encouraging others to join, and also by contributing to The Scripps Research Institute’s crowdfunding campaign to secure additional resources needed to analyze the enormous volume of data generated by Outsmart Ebola Together.

    See the full article here.

    Please help promote STEM in your local schools.
    STEM Icon

    Stem Education Coalition

    World Community Grid (WCG) brings people together from across the globe to create the largest non-profit computing grid benefiting humanity. It does this by pooling surplus computer processing power. We believe that innovation combined with visionary scientific research and large-scale volunteerism can help make the planet smarter. Our success depends on like-minded individuals – like you.”

    WCG projects run on BOINC software from UC Berkeley.

    BOINC is a leader in the field(s) of Distributed Computing, Grid Computing and Citizen Cyberscience.BOINC is more properly the Berkeley Open Infrastructure for Network Computing.

    CAN ONE PERSON MAKE A DIFFERENCE? YOU BETCHA!!

    “Download and install secure, free software that captures your computer’s spare power when it is on, but idle. You will then be a World Community Grid volunteer. It’s that simple!” You can download the software at either WCG or BOINC.

    Please visit the project pages-

    Mapping Cancer Markers
    mappingcancermarkers2

    Uncovering Genome Mysteries
    Uncovering Genome Mysteries

    Say No to Schistosoma

    GO Fight Against Malaria

    Drug Search for Leishmaniasis

    Computing for Clean Water

    The Clean Energy Project

    Discovering Dengue Drugs – Together

    Help Cure Muscular Dystrophy

    Help Fight Childhood Cancer

    Help Conquer Cancer

    Human Proteome Folding

    FightAIDS@Home

    World Community Grid is a social initiative of IBM Corporation
    IBM Corporation
    ibm

    IBM – Smarter Planet
    sp

    ScienceSprings relies on technology from

    MAINGEAR computers

    Lenovo
    Lenovo

    Dell
    Dell

     
  • richardmitnick 4:31 pm on November 28, 2014 Permalink | Reply
    Tags: , , , World Community Grid   

    From DSFL at WCG: “Finding new solutions for a neglected tropical disease” 

    New WCG Logo

    28 Nov 2014
    By: Dr. Carlos Muskus López, Coordinator, Molecular Biology and Computational Unit, PECET University of Antioquia

    28 Nov 2014

    The Drug Search for Leishmaniasis (DSFL) project finished its grid-based computations over a year ago, and since then the team has been filtering the results to rank and choose the compounds that have the best potential to be effective drug candidates. We have a lot of information to give you, starting with a few upcoming developments before moving to a detailed review of results, some updates on lab-based testing, a big thank-you to our collaborators, and some other team news.

    The near future

    Our result filtering procedure is nearly finished, with approximately 96% of the data processed. This part of the project should be finished in the next 2 or 3 weeks. For more details, see the results section below.
    A paper including the results is almost ready to be submitted for publication to an international journal. We are waiting for complete filtering results, but expect to submit the paper before the end this of year.
    Within the past year, I have had the opportunity to present the DSFL project results in different local, national and international contexts, including WorldLeish5 in Brazil, the International Congress of Parasitology in Mexico City, and the XII International Congress of Microbiology, in Cartagena-Colombia.
    Other members of the DSFL team and I are helping organize the Third Colombian Congress on Computational Biology and Bioinformatics, to be held next September in Medellin. This will be a great opportunity to publicize the results of the DSFL project in deeper way and to promote World Community Grid.
    We are planning a second phase of the project which will be focused on determining how strong the interaction is between ligand and receptor. This bond can be determined by using the Binding Energy Distribution Analysis Method (BEDAM) program or another similar program.

    Detailed results

    The last year of work really helps emphasize the tremendous scale of our project. World Community Grid volunteers generated approximately 4TB of data, consisting of more than 1.5 billion records, which store information about interaction energies between a set of Leishmania proteins and a library of 600,000 compounds. During the data recompilation, a procedure was executed to filter and organize the most relevant data. A relational database was built with information about the targets, the models (snapshots of the initial set of proteins) obtained from the molecular dynamics (MD) simulations, the compounds, and the interaction energy scores between the molecules involved. The goal of this first phase was to rank and choose the best candidates based on meta-analyses which could identify the most suitable ligands for subsequent experimental validations.

    We have found some particular proteins that have been studied widely as potential molecular targets. Some of them have produced interactions with simulated free energies around -13 kcal/mol (Table 1), molecular complexes with potentially higher affinities between the external ligands and the parasite proteins.

    Table 1. List of compounds and proteins targets, including the model number obtained by MD simulations, with the better interaction energy scores.

    Score (kcal/mol) Compound Target (PBD code) MD Model (10 per target)
    -13,8 ZINC05835XXX 3P0I 20
    -13,3 ZINC33122XXX 3MJY 50
    -13,1 ZINC05835XXX 3P0I 10
    -13 ZINC12210XXX 3P0I 10
    -13 ZINC08591XXX 3MJY 50
    -13 ZINC21173XXX 3MJY 50
    -13 ZINC21887XXX 3MJY 10
    -12,9 ZINC33140XXX 2RQ8 40
    -12,8 ZINC12097XXX 2HFU 20
    -12,8 ZINC09319XXX 3MJY 50

    Based on the preliminary results, we are optimistic about finding a good candidate to treat leishmaniasis, which can be improved with further computational and experimental validations. We are preparing for a second phase of the project, which involves an extra selection filter using an adapted MD protocol to avoid false positives and consequently detect molecular hits that can behave similarly to real life. With the help of all the volunteers we hope to identify at least one new molecule capable of fighting against a neglected disease that urgently needs more effective and non-toxic chemical treatments.

    Currently the data are still under analysis in our servers. We are still filtering the best docking results. Once we analyze the data and extract the needed information, we will release the data.

    Lab-based testing and confirmation

    Now that we have nearly finished analyzing the simulations of potential compounds, we need to move to real-world testing to confirm our predictions. This requires considerable funding for lab time and materials. Unfortunately, we have had difficulty securing funding to perform this in vitro testing – we have made applications for funding from several organizations, including the Tres Cantos Open Lab Foundation and the Pathogen Box. To date, these have been unsuccessful, so we are not yet able to perform in vitro testing of all the compounds we would like to test. However, the main Colombian funding agency (Colciencias) approved funds to test between 10 and 20 compounds. Ten of the most promising compounds have been already purchased and are being tested in our lab. The best of these will be evaluated in animals before testing in clinical assays, if any.

    Thank you to our collaborators

    And finally, we want to say a huge thank-you to some of the collaborators who have helped us throughout this process.

    Working with World Community Grid for over three years has been a fantastic experience. Since the beginning of the project in September 2011, we have maintained a tight relationship with the team at World Community Grid. And of course, the volunteers who donated their computing time made this entire project possible—we can’t say thank you enough for that generosity.

    Since the beginning of the DSFL project, Dr. Stan Watowich of the University of Texas Medical Branch has been an invaluable collaborator for us. In fact, Rodrigo Ochoa (one of the members of the PECET team) learned how to run docking in his Lab in Galveston-Texas. Besides Dr. Watowich, Drs. Juan Guillermo Lalinde and Juan David Pineda, from the University of EAFIT, also in Medellin Colombia have provided extensive support to the project, providing a lot of computer time in the University of EAFIT with the Apollo-cluster. Since this collaboration, we have continued working together on this and in other projects involving computational processes.

    Finally, we started communication with Dr. Olson´s Lab at The Scripps Research Institute in La Jolla, California. They have a lot of experience in drug discovery, and are the developers of AutoDock Vina, the program we use in the DSFL project. Their BEDAM program may allow us to filter our results further based on thermodynamic parameters.

    Other team news

    Andres Flórez, one of the PECET team and currently in Heidelberg-Germany conducting his PhD, won a competition of science dance. They had to represent the doctoral thesis by means of arts, basically through dance. The name of his work was, Understanding the Role of MYCN in Neuroblastoma using a Systems Biology Approach.

    Rodrigo Ochoa got an international award for a computational tool developed at the European Molecular Biology Lab during an internship in Hinxton-England. In August 10-14 this year, Rodrigo was invited to present his work in San Francisco, California in one of the biggest events of science: 248th ACS National Meeting & Exposition. (American Chemical Society).

    We had a technical problem with our PECET Lab web site and most of the information regarding this project on our web page was lost along with the PECET information. We are working to restore the information soon.

    See the full article here.

    Please help promote STEM in your local schools.
    STEM Icon

    Stem Education Coalition

    World Community Grid (WCG) brings people together from across the globe to create the largest non-profit computing grid benefiting humanity. It does this by pooling surplus computer processing power. We believe that innovation combined with visionary scientific research and large-scale volunteerism can help make the planet smarter. Our success depends on like-minded individuals – like you.”

    WCG projects run on BOINC software from UC Berkeley.

    BOINC is a leader in the field(s) of Distributed Computing, Grid Computing and Citizen Cyberscience.BOINC is more properly the Berkeley Open Infrastructure for Network Computing.

    CAN ONE PERSON MAKE A DIFFERENCE? YOU BETCHA!!

    “Download and install secure, free software that captures your computer’s spare power when it is on, but idle. You will then be a World Community Grid volunteer. It’s that simple!” You can download the software at either WCG or BOINC.

    Please visit the project pages-

    Mapping Cancer Markers
    mappingcancermarkers2

    Uncovering Genome Mysteries
    Uncovering Genome Mysteries

    Say No to Schistosoma

    GO Fight Against Malaria

    Drug Search for Leishmaniasis

    Computing for Clean Water

    The Clean Energy Project

    Discovering Dengue Drugs – Together

    Help Cure Muscular Dystrophy

    Help Fight Childhood Cancer

    Help Conquer Cancer

    Human Proteome Folding

    FightAIDS@Home

    World Community Grid is a social initiative of IBM Corporation
    IBM Corporation
    ibm

    IBM – Smarter Planet
    sp

    ScienceSprings relies on technology from

    MAINGEAR computers

    Lenovo
    Lenovo

    Dell
    Dell

     
  • richardmitnick 3:22 pm on November 18, 2014 Permalink | Reply
    Tags: , , , , World Community Grid   

    From NOVA: “Why There’s No HIV Cure Yet” 

    [After the NOVA article, I tell you how you and your family, friends, and colleagues can help to find a cure for AIDS and other diseases]

    PBS NOVA

    NOVA

    27 Aug 2014
    Alison Hill

    Over the past two years, the phrase “HIV cure” has flashed repeatedly across newspaper headlines. In March 2013, doctors from Mississippi reported that the disease had vanished in a toddler who was infected at birth. Four months later, researchers in Boston reported a similar finding in two previously HIV-positive men. All three were no longer required to take any drug treatments. The media heralded the breakthrough, and there was anxious optimism among HIV researchers. Millions of dollars of grant funds were earmarked to bring this work to more patients.

    But in December 2013, the optimism evaporated. HIV had returned in both of the Boston men. Then, just this summer, researchers announced the same grim results for the child from Mississippi. The inevitable questions mounted from the baffled public. Will there ever be a cure for this disease? As a scientist researching HIV/AIDS, I can tell you there’s no straightforward answer. HIV is a notoriously tricky virus, one that’s eluded promising treatments before. But perhaps just as problematic is the word “cure” itself.

    Science has its fair share of trigger words. Biologists prickle at the words “vegetable” and “fruit”—culinary terms which are used without a botanical basis—chemists wrinkle their noses at “chemical free,” and physicists dislike calling “centrifugal” a force—it’s not; it only feels like one. If you ask an HIV researcher about a cure for the disease, you’ll almost certainly be chastised. What makes “cure” such a heated word?

    t
    HIV hijacks the body’s immune system by attacking T cells.

    It all started with a promise. In the early 1980s, doctors and public health officials noticed large clusters of previously healthy people whose immune systems were completely failing. The new condition became known as AIDS, for “acquired immunodeficiency syndrome.” A few years later, in 1984, researchers discovered the cause—the human immunodeficiency virus, now known commonly as HIV. On the day this breakthrough was announced, health officials assured the public that a vaccine to protect against the dreaded infection was only two years away. Yet here we are, 30 years later, and there’s still no vaccine. This turned out to be the first of many overzealous predictions about controlling the HIV epidemic or curing infected patients.

    The progression from HIV infection to AIDS and eventual death occurs in over 99% of untreated cases—making it more deadly than Ebola or the plague. Despite being identified only a few decades ago, AIDS has already killed 25 million people and currently infects another 35 million, and the World Health Organization lists it as the sixth leading cause of death worldwide.

    HIV disrupts the body’s natural disease-fighting mechanisms, which makes it particularly deadly and complicates efforts to develop a vaccine against it. Like all viruses, HIV gets inside individual cells in the body and highjacks their machinery to make thousands of copies of itself. HIV replication is especially hard for the body to control because the white blood cells it infects, and eventually kills, are a critical part of the immune system. Additionally, when HIV copies its genes, it does so sloppily. This causes it to quickly mutate into many different strains. As a result, the virus easily outwits the body’s immune defenses, eventually throwing the immune system into disarray. That gives other obscure or otherwise innocuous infections a chance to flourish in the body—a defining feature of AIDS.

    Early Hope

    In 1987, the FDA approved AZT as the first drug to treat HIV. With only two years between when the drug was identified in the lab and when it was available for doctors to prescribe, it was—and remains—the fastest approval process in the history of the FDA. AZT was widely heralded as a breakthrough. But as the movie The Dallas Buyer’s Club poignantly retells, AZT was not the miracle drug many hoped. Early prescriptions often elicited toxic side-effects and only offered a temporary benefit, as the virus quickly mutated to become resistant to the treatment. (Today, the toxicity problems have been significantly reduced, thanks to lower doses.) AZT remains a shining example of scientific bravura and is still an important tool to slow the infection, but it is far from the cure the world had hoped for.

    In three decades, over 25 highly-potent drugs have been developed and FDA-approved to treat HIV.

    Then, in the mid-1990s, some mathematicians began probing the data. Together with HIV scientists, they suggested that by taking three drugs together, we could avoid the problem of drug resistance. The chance that the virus would have enough mutations to allow it to avoid all drugs at once, they calculated, would simply be too low to worry about. When the first clinical trials of these “drug cocktails” began, both mathematical and laboratory researchers watched the levels of virus drop steadily in patients until they were undetectable. They extrapolated this decline downwards and calculated that, after two to three years of treatment, all traces of the virus should be gone from a patient’s body. When that happened, scientists believed, drugs could be withdrawn, and finally, a cure achieved. But when the time came for the first patients to stop their drugs, the virus again seemed to outwit modern medicine. Within a few weeks of the last pill, virus levels in patients’ blood sprang up to pre-treatment levels—and stayed there.

    In the three decades since, over 25 more highly-potent drugs have been developed and FDA-approved to treat HIV. When two to five of them are combined into a drug cocktail, the mixture can shut down the virus’s replication, prevent the onset of AIDS, and return life expectancy to a normal level. However, patients must continue taking these treatments for their entire lives. Though better than the alternative, drug regimens are still inconvenient and expensive, especially for patients living in the developing world.

    Given modern medicine’s success in curing other diseases, what makes HIV different? By definition, an infection is cured if treatment can be stopped without the risk of it resurfacing. When you take a week-long course of antibiotics for strep throat, for example, you can rest assured that the infection is on track to be cleared out of your body. But not with HIV.

    A Bad Memory

    The secret to why HIV is so hard to cure lies in a quirk of the type of cell it infects. Our immune system is designed to store information about infections we have had in the past; this property is called “immunologic memory.” That’s why you’re unlikely to be infected with chickenpox a second time or catch a disease you were vaccinated against. When an infection grows in the body, the white blood cells that are best able to fight it multiply repeatedly, perfecting their infection-fighting properties with each new generation. After the infection is cleared, most of these cells will die off, since they are no longer needed. However, to speed the counter-attack if the same infection returns, some white blood cells will transition to a hibernation state. They don’t do much in this state but can live for an extremely long time, thereby storing the “memory” of past infections. If provoked by a recurrence, these dormant cells will reactivate quickly.

    This near-immortal, sleep-like state allows HIV to persist in white blood cells in a patient’s body for decades. White blood cells infected with HIV will occasionally transition to the dormant state before the virus kills them. In the process, the virus also goes temporarily inactive. By the time drugs are started, a typical infected person contains millions of these cells with this “latent” HIV in them. Drug cocktails can prevent the virus from replicating, but they do nothing to the latent virus. Every day, some of the dormant white blood cells wake up. If drug treatment is halted, the latent virus particles can restart the infection.

    Latent HIV’s near-immortal, sleep-like state allows it to persist in white blood cells in a patient’s body for decades.

    HIV researchers call this huge pool of latent virus the “barrier to a cure.” Everyone’s looking for ways to get rid of it. It’s a daunting task, because although a million HIV-infected cells may seem like a lot, there are around a million times that many dormant white blood cells in the whole body. Finding the ones that contain HIV is a true needle-in-a-haystack problem. All that remains of a latent virus is its DNA, which is extremely tiny compared to the entire human genome inside every cell (about 0.001% of the size).
    Defining a Cure

    Around a decade ago, scientists began to talk amongst themselves about what a hypothetical cure could look like. They settled on two approaches. The first would involve purging the body of latent virus so that if drugs were stopped, there would be nothing left to restart the infection. This was often called a “sterilizing cure.” It would have to be done in a more targeted and less toxic way than previous attempts of the late 1990s, which, because they attempted to “wake up” all of the body’s dormant white blood cells, pushed the immune system into a self-destructive overdrive. The second approach would instead equip the body with the ability to control the virus on its own. In this case, even if treatment was stopped and latent virus reemerged, it would be unable to produce a self-sustaining, high-level infection. This approach was referred to as a “functional cure.”

    The functional cure approach acknowledged that latency alone was not the barrier to a cure for HIV. There are other common viruses that have a long-lived latent state, such as the Epstein-Barr virus that causes infectious mononucleosis (“mono”), but they rarely cause full-blown disease when reactivated. HIV is, of course, different because the immune system in most people is unable to control the infection.

    The first hint that a cure for HIV might be more than a pipe-dream came in 2008 in a fortuitous human experiment later known as the “Berlin patient.” The Berlin patient was an HIV-positive man who had also developed leukemia, a blood cancer to which HIV patients are susceptible. His cancer was advanced, so in a last-ditch effort, doctors completely cleared his bone marrow of all cells, cancerous and healthy. They then transplanted new bone marrow cells from a donor.

    Fortunately for the Berlin patient, doctors were able to find a compatible bone marrow donor who carried a unique HIV-resistance mutation in a gene known as CCR5. They completed the transplant with these cells and waited.

    For the last five years, the Berlin patient has remained off treatment without any sign of infection. Doctors still cannot detect any HIV in his body. While the Berlin patient may be cured, this approach cannot be used for most HIV-infected patients. Bone marrow transplants are extremely risky and expensive, and they would never be conducted in someone who wasn’t terminally ill—especially since current anti-HIV drugs are so good at keeping the infection in check.

    Still, the Berlin patient was an important proof-of-principle case. Most of the latent virus was likely cleared out during the transplant, and even if the virus remained, most strains couldn’t replicate efficiently given the new cells with the CCR5 mutation. The Berlin patient case provides evidence that at least one of the two cure methods (sterilizing or functional), or perhaps a combination of them, is effective.

    Researchers have continued to try to find more practical ways to rid patients of the latent virus in safe and targeted ways. In the past five years, they have identified multiple anti-latency drug candidates in the lab. Many have already begun clinical trials. Each time, people grow optimistic that a cure will be found. But so far, the results have been disappointing. None of the drugs have been able to significantly lower levels of latent virus.

    In the meantime, doctors in Boston have attempted to tease out which of the two cure methods was at work in the Berlin patient. They conducted bone marrow transplants on two HIV-infected men with cancer—but this time, since HIV-resistant donor cells were not available, they just used typical cells. Both patients continued their drug cocktails during and after the transplant in the hopes that the new cells would remain HIV-free. After the transplants, no HIV was detectable, but the real test came when these patients volunteered to stop their drug regimens. When they remained HIV-free a few months later, the results were presented at the International AIDS Society meeting in July 2013. News outlets around the world declared that two more individuals had been cured of HIV.

    Latent virus had likely escaped the detection methods available.

    It quickly became clear that everyone had spoken too soon. Six months later, researchers reported that the virus had suddenly and rapidly returned in both individuals. Latent virus had likely escaped the detection methods available—which are not sensitive enough—and persisted at low, but significant levels. Disappointment was widespread. The findings showed that even very small amounts of latent virus could restart an infection. It also meant meant that the anti-latency drugs in development would need to be extremely potent to give any hope of a cure.

    But there was one more hope—the “Mississippi baby.” A baby was born to an HIV-infected mother who had not received any routine prenatal testing or treatment. Tests revealed high levels of HIV in the baby’s blood, so doctors immediately started the infant on a drug cocktail, to be continued for life.

    The mother and child soon lost touch with their health care providers. When they were relocated a few years later, doctors learned that the mother had stopped giving drugs to the child several months prior. The doctors administered all possible tests to look for signs of the virus, both latent and active, but they didn’t find any evidence. They chose not to re-administer drugs, and a year later, when the virus was still nowhere to be found, they presented the findings to the public. It was once again heralded as a cure.

    Again, it was not to be. Just last month, the child’s doctors announced that the virus had sprung back unexpectedly. It seemed that even starting drugs as soon as infection was detected in the newborn could not prevent the infection from returning over two years later.
    Hope Remains

    Despite our grim track record with the disease, HIV is probably not incurable. Although we don’t have a cure yet, we’ve learned many lessons along the way. Most importantly, we should be extremely careful about using the word “cure,” because for now, we’ll never know if a person is cured until they’re not cured.

    Clearing out latent virus may still be a feasible approach to a cure, but the purge will have to be extremely thorough. We need drugs that can carefully reactivate or remove latent HIV, leaving minimal surviving virus while avoiding the problems that befell earlier tests that reactivated the entire immune system. Scientists have proposed multiple, cutting-edge techniques to engineer “smart” drugs for this purpose, but we don’t yet know how to deliver this type of treatment safely or effectively.

    As a result, most investigations focus on traditional types of drugs. Researchers have developed ways to rapidly scan huge repositories of existing medicines for their ability to target latent HIV. These methods have already identified compounds that were previously used to treat alcoholism, cancer, and epilepsy, and researchers are repurposing them to be tested in HIV-infected patients.
    The less latent virus that remains, the less chance there is that the virus will win the game of chance.

    Mathematicians are also helping HIV researchers evaluate new treatments. My colleagues and I use math to take data collected from just a few individuals and fill in the gaps. One question we’re focusing on is exactly how much latent virus must be removed to cure a patient, or at least to let them stop their drug cocktails for a few years. Each cell harboring latent virus is a potential spark that could restart the infection. But we don’t know when the virus will reactivate. Even once a single latent virus awakens, there are still many barriers it must overcome to restart a full-blown infection. The less latent virus that remains, the less chance there is that the virus will win this game of chance. Math allows us to work out these odds very precisely.

    Our calculations show that “apparent cures”—where patients with latent virus levels low enough to escape detection for months or years without treatment—are not a medical anomaly. In fact, math tells us that they are an expected result of these chance dynamics. It can also help researchers determine how good an anti-latency drug should be before it’s worth testing in a clinical trial.

    Many researchers are working to augment the body’s ability to control the infection, providing a functional cure rather than a sterilizing one. Studies are underway to render anyone’s immune cells resistant to HIV, mimicking the CCR5 mutation that gives some people natural resistance. Vaccines that could be given after infection, to boost the immune response or protect the body from the virus’s ill effects, are also in development.

    In the meantime, treating all HIV-infected individuals—which has the added benefit of preventing new transmissions—remains the best way to control the epidemic and reduce mortality. But the promise of “universal treatment” has also not materialized. Currently, even in the U.S., only 25% of HIV-positive people have their viral levels adequately suppressed by treatment. Worldwide, for every two individuals starting treatment, three are newly infected. While there’s no doubt that we’ve made tremendous progress in fighting the virus, we have a long way to go before the word “cure” is not taboo when it comes to HIV/AIDS.

    See the full article here.

    Did you know that you can help in the fight against AIDS? By donating time on your computer to the Fight Aids at Home project of World Community Grid, you can become a part of the solution. The work is called “crunching” because you are crunching computational data the results of which will then be fed back into the necessary lab work. We save researchers literally millions of hours of lab time in this process.
    Vsit World Community Grid (WCG) or Berkeley Open infrastructure for Network Computing (BOINC). Download the BOINC software and install it on your computer. Then visit WCG and attach to the FAAH project. The project will send you computational work units. Your computer will process them and send the results back to the project, the project will then send you more work units. It is that simple. You do nothing, unless you want to get into the nuts and bolts of the BOINC software. If you take up this work, and if you see it as valuable, please tell your family, friends and colleagues, anyone with a computer, even an Android tablet. We found out that my wife’s oncologist’s father in Brazil is a cruncher on two projects from WCG.

    This is the projects web site. Take a look.

    While you are visiting BOINC and WCG, look around at all of the very valuable projects being conducted at some of the worlds most distinguished universities and scientific institutions. You can attach to as many as you like, on one or a number of computers. You can only be a help here, particpating in Citizen Science.

    This is a look at the present and past projects at WCG:

    Please visit the project pages-

    Mapping Cancer Markers
    mappingcancermarkers2

    Uncovering Genome Mysteries
    Uncovering Genome Mysteries

    Say No to Schistosoma

    GO Fight Against Malaria

    Drug Search for Leishmaniasis

    Computing for Clean Water

    The Clean Energy Project

    Discovering Dengue Drugs – Together

    Help Cure Muscular Dystrophy

    Help Fight Childhood Cancer

    Help Conquer Cancer

    Human Proteome Folding

    FightAIDS@Home

    World Community Grid is a social initiative of IBM Corporation
    IBM Corporation
    ibm

    IBM – Smarter Planet
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  • richardmitnick 1:47 pm on November 11, 2014 Permalink | Reply
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    From DDDT at WCG: “Discovering Dengue Drugs – Together” 

    New WCG Logo

    10 Nov 2014
    By: Dr. Stan Watowich, PhD
    University of Texas Medical Branch (UTMB) in Galveston, Texas

    Summary
    For week five of our decade of discovery celebrations we’re looking back at the Discovering Dengue Drugs – Together project, which helped researchers at the University of Texas Medical Branch at Galveston search for drugs to help combat dengue – a debilitating tropical disease that threatens 40% of the world’s population. Thanks to World Community Grid volunteers, researchers have identified a drug lead that has the potential to stop the virus in its tracks.

    mic

    Dengue fever, also known as “breakbone fever”, causes excruciating joint and muscle pain, high fever and headaches. Severe dengue, known as “dengue hemorrhagic fever”, has become a leading cause of hospitalization and death among children in many Asian and Latin American countries. According to the World Health Organization (WHO), over 40% of the world’s population is at risk from dengue; another study estimated there were 390 million cases in 2010 alone.

    The disease is a mosquito-borne infection found in tropical and sub-tropical regions – primarily in the developing world. It belongs to the flavivirus family of viruses, together with Hepatitis C, West Nile and Yellow Fever.

    Despite the fact dengue represents a critical global health concern, it has received limited attention from affluent countries until recently and is widely considered to be a neglected tropical disease. Currently, no approved vaccines or treatments exist for the disease. We launched Discovering Dengue Drugs – Together on World Community Grid in 2007 to search for drugs to treat dengue infections using a computer-based discovery approach.

    In the first phase of the project, we aimed to identify compounds that could be used to develop dengue drugs. Thanks to the computing power donated by World Community Grid volunteers, my fellow researchers and I at the University of Texas Medical Branch in Galveston, Texas, screened around three million chemical compounds to determine which ones would bind to the dengue virus and disable it.

    By 2009 we had found several thousand promising compounds to take to the next stage of testing. We began identifying the strongest compounds from the thousands of potentials, with the goal of turning these into molecules that could be suitable for human clinical trials.

    We have recently made an exciting discovery using insights from Discovering Dengue Drugs – Together to guide additional calculations on our web portal for advanced computer-based drug discovery, DrugDiscovery@TACC. A molecule has demonstrated success in binding to and disabling a key dengue enzyme that is necessary for the virus to replicate.

    Furthermore, it also shows signs of being able to effectively disable related flaviviruses, such as the West Nile virus. Importantly, our newly discovered drug lead also demonstrates no negative side effects such as adverse toxicity, carcinogenicity or mutagenicity risks, making it a promising antiviral drug candidate for dengue and potentially other flavivirues. We are working with medicinal chemists to synthesize variants of this exciting candidate molecule with the goal of improving its activity for planned pre-clinical and clinical trials.

    I’d like to express my gratitude for the dedication of World Community Grid volunteers. The advances we are making, and our improved understanding of drug discovery software and its current limitations, would not have been possible without your donated computing power.

    If you’d like to help researchers make more ground-breaking discoveries like this – and have the chance of winning some fantastic prizes – take part in our decade of discovery competition by encouraging your friends to sign up to World Community Grid today. There’s a week left and the field is wide open – get started today!

    See the full article here.

    World Community Grid (WCG) brings people together from across the globe to create the largest non-profit computing grid benefiting humanity. It does this by pooling surplus computer processing power. We believe that innovation combined with visionary scientific research and large-scale volunteerism can help make the planet smarter. Our success depends on like-minded individuals – like you.”

    WCG projects run on BOINC software from UC Berkeley.

    BOINC is a leader in the field(s) of Distributed Computing, Grid Computing and Citizen Cyberscience.BOINC is more properly the Berkeley Open Infrastructure for Network Computing.

    CAN ONE PERSON MAKE A DIFFERENCE? YOU BETCHA!!

    “Download and install secure, free software that captures your computer’s spare power when it is on, but idle. You will then be a World Community Grid volunteer. It’s that simple!” You can download the software at either WCG or BOINC.

    Please visit the project pages-

    Mapping Cancer Markers
    mappingcancermarkers2

    Uncovering Genome Mysteries
    Uncovering Genome Mysteries

    Say No to Schistosoma

    GO Fight Against Malaria

    Drug Search for Leishmaniasis

    Computing for Clean Water

    The Clean Energy Project

    Discovering Dengue Drugs – Together

    Help Cure Muscular Dystrophy

    Help Fight Childhood Cancer

    Help Conquer Cancer

    Human Proteome Folding

    FightAIDS@Home

    World Community Grid is a social initiative of IBM Corporation
    IBM Corporation
    ibm

    IBM – Smarter Planet
    sp

    ScienceSprings relies on technology from

    MAINGEAR computers

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  • richardmitnick 3:13 pm on November 7, 2014 Permalink | Reply
    Tags: , , , , , World Community Grid   

    From WCG: “Decade of discovery: New precision tools to diagnose and treat cancer” 

    New WCG Logo

    3 Nov 2014
    By: Dr. David J. Foran, PhD
    Rutgers Cancer Institute of New Jersey

    Summary
    It’s week four of our 10th anniversary celebrations, and we’re following up last week’s childhood cancer feature by spotlighting another cancer project that’s helped researchers develop powerful new tools to diagnose cancer and tailor treatments to individual patients, using big data and analytics.

    no

    When it comes to cancer, a doctor’s diagnosis affects how aggressively a patient is treated, which medications might be appropriate and what levels of risk are justified. New precision medicine techniques are enabling physicians and scientists to refine diagnoses by identifying changes and patterns in individual cancers at unprecedented levels of granularity – ultimately improving treatment outcomes for patients.

    A key tool for precision medicine is tissue microarray analysis. This enables investigators to analyze large batches of tissue sample images simultaneously, so they can look for patterns and identify cancer signatures. It also provides them with a deeper understanding of cancer biology and uncovers new sub-classifications of cancer and likely patient responses – all of which influence new courses of treatment and future drug design.

    Tissue microarray analysis shows great promise, but it is not without its limitations. Pathologists typically examine the specimens visually, resulting in subjective interpretations and variations in diagnoses.

    We realized that if this method of analysis could be automated using digital pattern recognition algorithms, we could improve accuracy and reveal new patterns across large sets of data. This would make it possible for researchers to determine a patient’s type and stage of cancer more precisely, meaning they can prescribe therapies or combinations of treatments that are most likely to be effective.

    To study the feasibility of automating tissue microarray analysis, we partnered with IBM’s World Community Grid in 2006 to launch the Help Defeat Cancer project. At the time, we were pioneering a new approach that nobody else was investigating, and it was met with tremendous skepticism by many of our colleagues.

    However, with the support of more than 200,000 World Community Grid volunteers from around the globe who donated over 2,900 years of their computing time, we were able to study over 100,000 patient tissue samples to search for cancer signatures.

    Access to this vast computing power enabled our team to rapidly conduct this research under a much wider range of environmental conditions and to perform specimen analysis at much greater degrees of sensitivity.

    Thanks to World Community Grid and the Help Defeat Cancer project, we demonstrated the success of using computer-based analysis to automatically investigate and classify cancer specimens based on expression signature patterns. We were able to develop a reference library of cancer signatures that can be used to systematically analyze and compare tissue samples across large patient cohorts.

    Leveraging these experimental results, our team secured competitive funding from the National Institutes of Health (NIH) to build a clinical decision support system to automatically analyze and classify cancer specimens with improved diagnostic and prognostic accuracy. We used the core reference library of expression signatures generated through the Help Defeat Cancer project to demonstrate the proof-of-concept for the system.

    These decision support tools are now being tested and refined by investigators from the Rutgers Cancer Institute of New Jersey, Stony Brook University School of Medicine, University of Pittsburgh Medical Center and Emory University. They are exploring how the tools can aid clinical decision-making, plus are pursuing further investigative research. Together, our ultimate aim is to refine these tools sufficiently so they can be certified for routine clinical use in diagnosing and treating patients.

    Although the Help Defeat Cancer project has completed its research on World Community Grid, we continue to investigate the findings and they have contributed to some significant new beginnings. At Rutgers Cancer Institute of New Jersey, physicians and scientists – aided by high-performance computing resources – are analyzing genomes and human tissues, and identifying cancer patterns, faster than ever before.

    In collaboration with our research partners at the Rutgers Discovery Informatics Institute (RDI2) and RUCDR Infinite Biologics (the world’s largest university-based biorepository, located within the Human Genetics Institute of New Jersey), the Rutgers Cancer Institute is shaping a revolution in how best to determine cancer therapy for patients – a vast improvement from the time-intensive, trial-and-error approach that doctors have faced for years. To date, only a fraction of known cancer biomarkers have been examined. The long-term goal is to create a library of biomarkers and their expression patterns so that, in the future, physicians can consult the library to help diagnose cancer patients and provide them with the most effective treatment.

    I would like to express my gratitude to Stanley Litow, Robin Willner, and Jen Crozier from IBM and to World Community Grid’s Advisory Board for supporting the Help Defeat Cancer project. I’d also like to extend my special thanks to the IBM World Community Grid team members who contributed to the success of the project – I hope to have the opportunity to work with them again in the near future.

    Additionally, I would like to acknowledge the NIH, Department of Defense and IBM for supporting this research – and give credit to those individuals from my laboratory and partnering institutions who were involved in the early experiments and the initial design and development of the imaging and computational tools, which we then used throughout the project. And, of course, a very big thank you to all the World Community Grid volunteers – without their support, our accomplishments with Help Defeat Cancer would not have been possible.

    The Help Defeat Cancer project has completed its analysis on World Community Grid – but another innovative project, Mapping Cancer Markers, is currently running and needs your help. Help us celebrate a decade of discovery on World Community Grid by sharing this story and encouraging your friends to donate their unused computing power to cutting-edge cancer research.

    Here’s to another decade of discovery.

    See the full article here.

    World Community Grid (WCG) brings people together from across the globe to create the largest non-profit computing grid benefiting humanity. It does this by pooling surplus computer processing power. We believe that innovation combined with visionary scientific research and large-scale volunteerism can help make the planet smarter. Our success depends on like-minded individuals – like you.”

    WCG projects run on BOINC software from UC Berkeley.

    BOINC is a leader in the field(s) of Distributed Computing, Grid Computing and Citizen Cyberscience.BOINC is more properly the Berkeley Open Infrastructure for Network Computing.

    CAN ONE PERSON MAKE A DIFFERENCE? YOU BETCHA!!

    “Download and install secure, free software that captures your computer’s spare power when it is on, but idle. You will then be a World Community Grid volunteer. It’s that simple!” You can download the software at either WCG or BOINC.

    Please visit the project pages-

    Mapping Cancer Markers
    mappingcancermarkers2

    Uncovering Genome Mysteries
    Uncovering Genome Mysteries

    Say No to Schistosoma

    GO Fight Against Malaria

    Drug Search for Leishmaniasis

    Computing for Clean Water

    The Clean Energy Project

    Discovering Dengue Drugs – Together

    Help Cure Muscular Dystrophy

    Help Fight Childhood Cancer

    Help Conquer Cancer

    Human Proteome Folding

    FightAIDS@Home

    World Community Grid is a social initiative of IBM Corporation
    IBM Corporation
    ibm

    IBM – Smarter Planet
    sp

    ScienceSprings relies on technology from

    MAINGEAR computers

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  • richardmitnick 4:52 pm on November 6, 2014 Permalink | Reply
    Tags: , , , , World Community Grid   

    From FAAH at WCG: “Teamwork yields experimental support for FightAIDS@Home calculations” 

    New WCG Logo

    By: The FightAIDS@Home research team
    6 Nov 2014

    Summary
    Imaging studies have now confirmed some of the computational predictions made during FightAIDS@Home, providing important confirmation of our methodology and the value of your computational results. This work is ongoing, but promises to increase our understanding of how HIV protease can be disrupted.

    site
    The “exo-site” discovered in HIV protease (shown here in green), showing the original bound 4d9 fragment (shown here as red and orange sticks) and the volume (shown as the orange mesh) that is being targeted by FightAIDS@Home. (image credit: Stefano Forli, TSRI)

    Our lab at the Scripps Research Institute, La Jolla, is part of the HIV Interaction and Viral Evolution (HIVE) Center – a group of investigators with expertise in HIV crystallography, virology, molecular biology, biochemistry, synthetic chemistry and computational biology. This means that we have world-class resources available to verify and build upon our computational work, including the nuclear magnetic resonance (NMR) facility at the Scripps Research Institute, Florida. NMR is a technique for determining the molecular structure of a chemical sample, and therefore is very useful for validating some of the predictions made during the computational phase of FightAIDS@Home.

    We’re excited to announce that our collaborators at Scripps Florida have now optimized their NMR experiments and have been able to characterize the binding of promising ligands with the prospective allosteric sites on the HIV protease. These sites represent new footholds in the search for therapies that defeat viral drug resistance. The NMR experiment allows us to detect the location of the interactions between the candidate inhibitors and the protein, but unlike X-ray crystallography experiments, these interactions are measured in solution, which better represents the biological environment.

    In fact, the first results from the NMR experiments validated the exo site we so thoroughly investigated in FightAIDS@Home. As a result, we now have experimental evidence that a small molecule binds to the exo site in solution with structural effects that seem to perturb the dynamic behavior of protease, even with a known inhibitor in the active site.

    There are many more NMR experiments still to run, but another advantage of NMR over crystallography is that it does not require the lengthy step of growing diffraction-quality crystals. This allows higher experimental throughput, so we look forward to experimental confirmation of many more compounds in much shorter time. So far we have shipped 15 compounds to test and another batch is going to be sent this week. The new compounds will help to validate another potential interaction site on one of HIV protease’s two movable “flaps”.

    Once the validation is completed, we will proceed to test a number of compounds that we identified in different FightAIDS@Home experiments for all of the target protease allosteric sites.

    As always, thank you for your support! This research would not be possible without your valuable computing time.

    The Scripps research team needs your help to continue making progress on developing new treatments for AIDS! Take part in our decade of discovery competition by encouraging your friends to sign up to World Community Grid today to start donating their computer or mobile device’s computing power to FightAIDS@Home. There’s just over a week left and some great prizes are up for grabs – get started today!

    Here’s to another decade of discovery.

    See the full article here.

    World Community Grid (WCG) brings people together from across the globe to create the largest non-profit computing grid benefiting humanity. It does this by pooling surplus computer processing power. We believe that innovation combined with visionary scientific research and large-scale volunteerism can help make the planet smarter. Our success depends on like-minded individuals – like you.”

    WCG projects run on BOINC software from UC Berkeley.

    BOINC is a leader in the field(s) of Distributed Computing, Grid Computing and Citizen Cyberscience.BOINC is more properly the Berkeley Open Infrastructure for Network Computing.

    CAN ONE PERSON MAKE A DIFFERENCE? YOU BETCHA!!

    “Download and install secure, free software that captures your computer’s spare power when it is on, but idle. You will then be a World Community Grid volunteer. It’s that simple!” You can download the software at either WCG or BOINC.

    Please visit the project pages-

    Mapping Cancer Markers
    mappingcancermarkers2

    Uncovering Genome Mysteries
    Uncovering Genome Mysteries

    Say No to Schistosoma

    GO Fight Against Malaria

    Drug Search for Leishmaniasis

    Computing for Clean Water

    The Clean Energy Project

    Discovering Dengue Drugs – Together

    Help Cure Muscular Dystrophy

    Help Fight Childhood Cancer

    Help Conquer Cancer

    Human Proteome Folding

    FightAIDS@Home

    World Community Grid is a social initiative of IBM Corporation
    IBM Corporation
    ibm

    IBM – Smarter Planet
    sp

    ScienceSprings relies on technology from

    MAINGEAR computers

    Lenovo
    Lenovo

    Dell
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  • richardmitnick 4:51 pm on October 27, 2014 Permalink | Reply
    Tags: , , , , , World Community Grid   

    From Mapping Cancer Markers at WCG: “Early-stage results from the Mapping Cancer Markers team” 

    New WCG Logo

    27 Oct 2014
    The Mapping Cancer Markers research team

    The Princess Margaret Cancer Foundation Mapping Cancer Markers team has nearly finished establishing their benchmarks – a crucial step for their research and other related medical research around the world. See their in-depth update for the latest news about their efforts to help predict, identify and treat cancer.

    Summary
    Thanks to your help, the Mapping Cancer Markers team is nearly finished with benchmarking their first set of genetic markers. In this update, the team presents an in-depth review of what they’ve accomplished thus far, and what significance this early work will have for cancer research at their lab and elsewhere.

    The Mapping Cancer Markers (MCM) team would like to extend a huge thank you to World Community Grid members everywhere. As of October 27, 2014, we have surpassed 89,000 years of computation, a goal that simply would not be possible without your help.

    We are happy to report that we have begun to analyze the results using a high-throughput analytics package to assess the fitness and landscape of gene signature sizes between 5 and 25 genes. This analysis has shown that smaller signatures usually comprise different genes compared to larger signatures (i.e., you cannot “build” a larger signature from small ones), and that those genes are targeting many different signaling cascades and biological processes.

    Analytics

    To get a better understanding of how much data our team is receiving, we’d like to briefly introduce one of the tools that we have adopted to analyze the incoming results. From the very beginning of the project, it was clear that analyzing such a large, ongoing flow of data would be a challenge. Thus, we started to use the IBM® InfoSphere® Streams real-time analytics platform to streamline the analysis pipeline. When complete, our Streams application will run continuously, processing members’ work units in real time as we receive them. We currently have the core analysis framework implemented and running on a subset of the MCM results. We will continue to add additional layers of analysis, and fine-tune our system until it is running at full capacity. For that reason, we have dedicated one of our main compute servers (IBM Power® 780) to analyzing MCM results.

    Results

    Pictured below is a sampling (a very small fraction) of some of the ongoing work that will establish a benchmark for further experiments. Each dot in both of the graphs is a potential lung-cancer biomarker. These graphics are distilled from thousands of MCM results sent back by World Community Grid members.

    mar

    mar2

    Most of the dots have very little significance; this is expected because not everything shuts down or is activated in cancer. In other words, the graphics show differences between the disease state and the non-disease state, so we expect some things to be different, but not everything. For those reasons, most biomarkers cannot significantly differentiate cancer from non-cancer samples – this is represented by the haze of dots along the zero line. We show two graphs to illustrate the difference between shorter and longer gene signatures. Some genes that are more predictive in the shorter signature sizes do not necessarily hold their predictive power when considering more genes per signature. Most importantly, in each analysis, a few biomarkers frequently appear in high-scoring signatures. Our analysis wades through massive amounts of data to recognize those few markers that stand out.

    The first half of the “benchmarking” experiment involves determining the performance of markers as the size of the signature changes. For instance, when we compare successful 5-marker signatures against 20-marker signatures, which markers are consistently useful? Which ones increase or diminish in predictive power? Is there an optimum size for signatures? And most importantly, can we identify seemingly minor players that are critical, but not yet in clinical use that can discriminate between normal and disease states?

    graph

    After surveying the first several billion signatures, we have identified the highest-ranking combinations and underlying single genes. After separating those genes by signature size, we can see how some genes remain important regardless of the size, and how other genes “appear” to be important but are only showing up as single events. Considering we have not yet analyzed the complete data set, we have identified the genes by their known functions rather than names, to eliminate any bias towards known markers. However, even by their functions, we can see that many important signaling cascades and biological processes are affected. The most notable of these is “Cellular Fate and Organization”, which makes sense. Sometimes, when an organism loses the ability to naturally kill defective cells, it leads to uncontrolled growth, one of the hallmarks of cancer.

    Network Analysis of Major Genes:

    To further analyze the nature of our top-performing genes, we can identify their inter-relations in biological networks. We currently maintain one of the largest curated protein-protein interaction databases, which enables us to determine whether our genes (when converted to proteins) are known to interact with other important biomarkers, and in turn, what biological processes may be involved. The graph below shows one such network; nodes in the graph represent genes, edges are physical protein interactions. Node color highlights biological function as described in the legend. Use of biological networks can reveal very small subtleties of how the mechanisms of disease function and elucidate how our proteins may be causing problems; thus, eventually leading to understanding how cancer starts, progresses and how can we treat it.

    tre

    In the above network, 20 out of 24 important proteins we have identified on World Community Grid (right hand side) can be linked through known protein interactions and 56 other proteins (left hand side). We have also conducted a short analysis of the 4 proteins not yet identified using a software prediction package and found those to have significant partners. Those interactions will be evaluated in the near future. The 20 proteins noted above, strikingly, do not interact directly, however, 4 of them show very high interactivity, and can be considered as hubs. From other analyses we know that “hub proteins” are often critical, as they affect many signaling cascades and biological processes. When such proteins malfunction, catastrophic changes often result. On the other hand, proteins with low interactivity could be useful as clinical biomarkers. If they are known to only interact with a few other proteins, then their activity may help to identify particular states of cancer, while having less background “noise”. As a whole we can see that for the most part, our genes of interest are targeting mostly “genome maintenance” and “cellular fate and organization” proteins, which make up about 70% of the interacting proteins (left hand side). This is a good indication that most of the pathways affected are in those major categories, which is consistent with how we understand lung cancer to progress.

    Funding & Fundraising:

    This past August, we completed our 4th successful Team Ian Ride for Cancer Informatics Research. We were able to raise over $80,000 for cancer research in the name of a former Jurisica student, Ian Van Toch.

    Part of this funding is used for the best student paper award at the ISMB conference, and for supporting Cancer Informatics interns.

    We also support a special seminar series at Princess Margaret Cancer Center, and the recent presentation by Dr. Wan Lam from BC Cancer Agency discussed “Multi-dimensional Analysis of Lung Cancer Genomes”.

    See the full article here.

    World Community Grid (WCG) brings people together from across the globe to create the largest non-profit computing grid benefiting humanity. It does this by pooling surplus computer processing power. We believe that innovation combined with visionary scientific research and large-scale volunteerism can help make the planet smarter. Our success depends on like-minded individuals – like you.”

    WCG projects run on BOINC software from UC Berkeley.

    BOINC is a leader in the field(s) of Distributed Computing, Grid Computing and Citizen Cyberscience.BOINC is more properly the Berkeley Open Infrastructure for Network Computing.

    CAN ONE PERSON MAKE A DIFFERENCE? YOU BETCHA!!

    “Download and install secure, free software that captures your computer’s spare power when it is on, but idle. You will then be a World Community Grid volunteer. It’s that simple!” You can download the software at either WCG or BOINC.

    Please visit the project pages-

    Mapping Cancer Markers
    mappingcancermarkers2

    Uncovering Genome Mysteries
    Uncovering Genome Mysteries

    Say No to Schistosoma

    GO Fight Against Malaria

    Drug Search for Leishmaniasis

    Computing for Clean Water

    The Clean Energy Project

    Discovering Dengue Drugs – Together

    Help Cure Muscular Dystrophy

    Help Fight Childhood Cancer

    Help Conquer Cancer

    Human Proteome Folding

    FightAIDS@Home

    World Community Grid is a social initiative of IBM Corporation
    IBM Corporation
    ibm

    IBM – Smarter Planet
    sp

    ScienceSprings relies on technology from

    MAINGEAR computers

    Lenovo
    Lenovo

    Dell
    Dell

     
  • richardmitnick 5:28 pm on October 23, 2014 Permalink | Reply
    Tags: , , World Community Grid   

    From CEP at WCG: “A productive summer for the Clean Energy Project” 

    New WCG Logo

    23 Oct 2014
    By: The Clean Energy Project team
    Harvard University

    Summary
    The Clean Energy Project team has an end-of-summer update for all the World Community Grid volunteers. Several changes to the database and work units were put in place over the summer. The team sends a big thank-you to the volunteers who make this work possible, as well as to the lab’s summer students and the departing CEP web developer.

    Hi all!

    The time has come for another update on the The Clean Energy Project – Phase 2 (CEP) on World Community Grid.

    Wow, it has been a busy and productive summer! Our redesign of the database is complete, and all new jobs are being created from, and their results being stored into, the new design. This will give us a much more quickly searchable database, capable of storing a wider variety of data – very exciting! The data that has been produced so far is being parsed into this structure as well, and is also being recompressed using a more efficient algorithm. We estimate that this recompression will save us a significant amount of storage space, meaning we can now store more results than ever!

    We were very lucky to have three brilliant students work on the CEP over the summer: Kewei, Trevor and Daniel. They were mainly focused on harnessing the power of machine learning techniques to improve how we generate molecules. Their research was very promising, and we hope to write it up into a paper or two in the near future – well done, guys! In fact, two of them (Kewei and Trevor) have agreed to continue working with us during term time, and we hope to get many more exciting projects done. We will keep you all posted on those as details emerge.

    As you have probably seen in the forums, we have had a redesign of the structure of the work units. We want to thank everyone for their patience while we sorted out all the “teething” problems, but they now seem to be working well. The reason for these changes was to allow us to try and move onto slightly different families of molecules which we have identified as being particularly interesting. It is important for the CEP to be constantly updating the molecular libraries so we can really live on the cutting edge, and hopefully discover the next “blockbuster” Organic Photovoltaic molecule (the type of molecule the CEP is looking for). To do this, we have to push up against the limits of what is possible on the grid, and we really appreciate the patience of the crunchers when we occasionally push too hard!

    We have also changed the way that we build the molecules for these libraries. This was done in order to prioritize molecules that are more synthesizable (i.e. easier for our experimental friends to make in a lab). This is a win-win, because we are also able to sample a more diverse area of chemical space.

    Thanks to all the crunchers and our friends at IBM; without you the project literally would not happen!

    We would also like to take a moment to give a big thank you to Carolina Roman-Salgado, our awesome web developer. She is moving to California at the end of September, and so will be leaving the CEP. Carolina has been absolutely fantastic in working with the CEP database and molecularspace.org (where our results are all hosted for public access), and has recently been working on an update, which we hope to release soon. Aside from her brilliant work, we will really miss having Carolina around the office – please don’t wait too long before you come visit, Carolina; you will always be welcome here!

    Your Harvard CEP Team

    See the full article here.

    World Community Grid (WCG) brings people together from across the globe to create the largest non-profit computing grid benefiting humanity. It does this by pooling surplus computer processing power. We believe that innovation combined with visionary scientific research and large-scale volunteerism can help make the planet smarter. Our success depends on like-minded individuals – like you.”

    WCG projects run on BOINC software from UC Berkeley.

    BOINC is a leader in the field(s) of Distributed Computing, Grid Computing and Citizen Cyberscience.BOINC is more properly the Berkeley Open Infrastructure for Network Computing.

    CAN ONE PERSON MAKE A DIFFERENCE? YOU BETCHA!!

    “Download and install secure, free software that captures your computer’s spare power when it is on, but idle. You will then be a World Community Grid volunteer. It’s that simple!” You can download the software at either WCG or BOINC.

    Please visit the project pages-

    Say No to Schistosoma

    GO Fight Against Malaria

    Drug Search for Leishmaniasis

    Computing for Clean Water

    The Clean Energy Project

    Discovering Dengue Drugs – Together

    Help Cure Muscular Dystrophy

    Help Fight Childhood Cancer

    Help Conquer Cancer

    Human Proteome Folding

    FightAIDS@Home

    World Community Grid is a social initiative of IBM Corporation
    IBM Corporation
    ibm

    IBM – Smarter Planet
    sp

    ScienceSprings relies on technology from

    MAINGEAR computers

    Lenovo
    Lenovo

    Dell
    Dell

     
  • richardmitnick 3:36 pm on October 21, 2014 Permalink | Reply
    Tags: , World Community Grid   

    From WCG: 1,000,000 years of data processing 

    New WCG Logo

    We didn’t plan it this way, but it couldn’t have been better if we tried. The total runtime donated by our amazing volunteers has reached 1 million years.
    Thank you for making this possible, but even more thanks for what this represents: important, humanitarian research that would have been impossible without your help. And please, sign up today to participate in the new Uncovering Genome Mysteries project!

    World Community Grid (WCG) brings people together from across the globe to create the largest non-profit computing grid benefiting humanity. It does this by pooling surplus computer processing power. We believe that innovation combined with visionary scientific research and large-scale volunteerism can help make the planet smarter. Our success depends on like-minded individuals – like you.”

    WCG projects run on BOINC software from UC Berkeley.

    BOINC is a leader in the field(s) of Distributed Computing, Grid Computing and Citizen Cyberscience.BOINC is more properly the Berkeley Open Infrastructure for Network Computing.

    CAN ONE PERSON MAKE A DIFFERENCE? YOU BETCHA!!

    “Download and install secure, free software that captures your computer’s spare power when it is on, but idle. You will then be a World Community Grid volunteer. It’s that simple!” You can download the software at either WCG or BOINC.

    Please visit the project pages-

    Uncovering Genome Mysteries
    Uncovering Genome Mysteries

    Say No to Schistosoma

    GO Fight Against Malaria

    Drug Search for Leishmaniasis

    Computing for Clean Water

    The Clean Energy Project

    Discovering Dengue Drugs – Together

    Help Cure Muscular Dystrophy

    Help Fight Childhood Cancer

    Help Conquer Cancer

    Human Proteome Folding

    FightAIDS@Home

    World Community Grid is a social initiative of IBM Corporation
    IBM Corporation
    ibm

    IBM – Smarter Planet
    sp

    ScienceSprings relies on technology from

    MAINGEAR computers

    Lenovo
    Lenovo

    Dell
    Dell

     
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