A great role model for collaborative science: meet the OpenWorm

A great role model for collaborative science: meet the OpenWorm
A couple of weeks ago I had the pleasure to meet the team of OpenWorm in two live meetings that the they held in the UK.

– from their website:

OpenWorm aims to build the first comprehensive computational model of the Caenorhabditis elegans (C. elegans) [..] With only a thousand cells, it solves basic problems such as feeding, mate-finding and predator avoidance. [..] 

We are using a bottom-up approach, aimed at observing the worm behaviour emerge from a simulation of data derived from scientific experiments carried out over the past decade. [..] We are engineering Geppetto, an open-source simulation platform, to be able to run these different models together. We are also forging new collaborations with universities and research institutes to collect data that fill in the gaps.

Rigorous predictive models are the cornerstone of science and engineering. Predictive models took us to the moon. Unfortunately, today, there are no comprehensive predictive models of living cells and tissues. Consequently, the entire field of biology and medicine is in a kind of “pre-mathematical” era. A revolution in the biosciences driven by simulation-based research, using predictive models running on high performance computing architectures with flexible user interfaces, is now possible. Simulating a living organism will have impacts on understanding mechanisms of disease, drug discovery and development, synthetic biology, bioengineering, and neuroscience. This is so important to us. 

+Matteo Cantarelli - member of the OpenWorm team - says: "It was 2007 when +Giovanni Idili and I started - naively - talking about simulating the worm. We were approaching the problem after having hit common limits of artificial intelligence. We never got to write any code for the worm at that time, we just had lengthy conversations and paper reading sessions together."

At that time +Stephen Larson was involved in an open-source, 3-D virtual environment project developed by a team of researchers from UC San Diego: the Whole Brain Catalog  About two years after that attempt to simulate the worm with his friend Matteo, Giovanni Idili tweeted on the Whole Brain Catalog's account, these words: "new year's resolution: simulate the whole C. elegans brain (302 neurons)!" - Stephen got the message and for a while he thoroughly pondered this idea.
In fact, Stephen gave a talk @IgniteSanDiego in 2010 where he officially launched the idea. He stated that if we want to get a really high-level A.I. (like Star Trek's Lieutenant Data), we should probably start with a simpler organism.  Even though it is “simpler”, reproducing the functions of C. elegans in a simulation reveals its complexity. The hermaphrodite form has just 302 neurons and the connectome has been already mapped. After this talk, a lot of people were excited about this idea, so Stephen proposed a survey to reach people around the world who were already interested in this subject. "The key was to find people that were already working on worms and convince them to work together. There are plenty of people around that are excited to do what you have in mind, you just have to find them and show them that working together is better than working separately.." Matteo and his friend Giovanni enthusiastically replied that they wanted to "make the worm". At that point, a series of Skype calls began where the initial team was figuring out the best way to reach this goal. Stephen says: "A good resource that I found useful was  Karl Fogel's Producing Open Source Software free book, which, beyond coding, shows you how to deal with getting started, permission statements, transparency, and so on.. I've learned a lot from this book, and the choice of the name - OpenWorm - was meant to be simple and understandable as they suggest.. "

"There's plenty of science that still happens in non-english language, so that there's still a lot of talent that isn't talking to the rest of the world."

Another serendipitous event was the finding of a YouTube video of a team in Siberia - Andrey Palyanov and Sergey Khayrulin - who had taken the neurons of C. elegans and created a virtual physical body. The team contacted them through the YouTube messaging system because at that time there were no references on the video. At the end they also joined the team. Stephen: "I realized that there's a lot of scientific communities within Russia and outside Russia that is still rather disconnected. There's plenty of science that still happens in non-english language, so that there's still a lot of talent that isn't talking to the rest of the world. So for us, that was a really great find.."

Then Google +Hangout came in, so they had a broader audience and it was easier to handle conference calls with more people.
They currently meet every two weeks on Google hangout. A great practice they have is to discuss high profile papers related to the simulation of biology with the authors, and stream the talks on YouTube. You can take a look on this Journal Club on their channel. Stephen, Giovanni, Matteo and Mike @+OpenWorm Live in Cambridge, UKMatteo: "At the beginning it was Stephen, Giovanni, Tim and I, if I remember correctly. First, I started building an ontology with Stephen to try identify the high-level requirements for simulating the worm. What was needed, what physical aspects we had to simulate and to gain a better understanding of the big picture. After that I worked to transform the requirements we gathered into technology by designing the architecture of the simulation engine, what we recently named Geppetto, and took a first stab at implementing the different modules. During this time Giovanni was writing the first module for the simulation engine with a proof of concept of a simple Hodgkin-Huxley neuronal simulator that leveraged GPU".Stephen: "Other people eventually joined the project. Christian Grove from Caltech who contributed with the 3-D rendering of the worm. Then, a group from INCF,  from which we decide to use NeuroML, a standard language for the model, so then we get connected to the NeuroML community where more people came to the project." He also stressed the concept to have an organizational structure, for instance OpenWorm is structured like the Bazaar theorized by Eric S. Raymond, where there's a central coordinating platform but it’s actually run by a series of sub-project that has a common direction.

“Chance handed me a perfect way to test my theory, in the form of an open-source project that I could consciously try to run in the bazaar style. So I did—and it was a significant success.” -- from The Cathedral and the Bazaar by Eric S. Raymond

"...you start to write down on a piece of paper all the things that comes into your mind, and you try to connect them, and all the aspects between them. When you do such a brainstorming you might realize that some stuff are related, and that this and that could be grouped together. So, if you're creative enough, you start to find some larger theme that cover all this stuff, and that gives you the ability to work on all the things you're actually interested in at same time..."

Stephen gives other suggestions to people that wants to commit themselves in a collaborative project. I think that his suggestions can be true for - pretty much - any young scientist who wants to approach this kind of career without being disappointed at the end of the day. In my opinion this could really work in general for any scientist.  "I think you got to find something that you're truly excited about. [..]  I think that the challenge early on is that is really easy to be excited in something for about two months and then you give up. It means that probably you're not really interested in it, or you can try to discard this idea for a while and return back on it later. Sometimes stuff works better later."
So, after you have done your homework, you start to write down on a piece of paper all the things that comes into your mind.  When you do this brainstorming you might realize that some things are related. [..] and you may start to find some larger theme that covers all this stuff.  That gives you the ability to work on all the things you're actually interested in at same time. For me OpenWorm turned into that. I was interested data visualization, artificial life simulation, neuroinformatics, something that is biologically relevant... but there was a lot of things in my previous projects that didn't actually allowed me to stretch into all these topics. OpenWorm was for me the key project to focus on all the things I was truly excited about at the same time."

OpenWorm is made of many other people, most of which aren't mentioned here. But all of them are incredibly committed to what they're doing. I think that this is one of the best projects to join if you'd like to work with skilled people with a genuine passion for Artificial Intelligence. OpenWorm is definitely something that is truly visionary. Part of the OpenWorm team and fans @+OpenWorm Live in London, UK

"All the code we produce in the OpenWorm project is Open Source and available on GitHub. This makes it easy for everyone to look at and play with.Detailed documentation for each sub-project is available from the Projects page."

Varshney LR, Chen BL, Paniagua E, Hall DH, & Chklovskii DB (2011). Structural properties of the Caenorhabditis elegans neuronal network. PLoS computational biology, 7 (2) PMID: 21304930

Palyanov A, Khayrulin S, Larson SD, & Dibert A (2011). Towards a virtual C. elegans: a framework for simulation and visualization of the neuromuscular system in a 3D physical environment. In silico biology, 11 (3-4), 137-47 PMID: 22935967

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Biologists create first predictive computational model of gene networks that control development of sea-urchin embryos

ScienceDaily (Aug. 29, 2012) — As an animal develops from an embryo, its cells take diverse paths, eventually forming different body parts -- muscles, bones, heart. In order for each cell to know what to do during development, it follows a genetic blueprint, which consists of complex webs of interacting genes called gene regulatory networks.

Biologists at the California Institute of Technology (Caltech) have spent the last decade or so detailing how these gene networks control development in sea-urchin embryos. Now, for the first time, they have built a computational model of one of these networks.

This model, the scientists say, does a remarkably good job of calculating what these networks do to control the fates of different cells in the early stages of sea-urchin development -- confirming that the interactions among a few dozen genes suffice to tell an embryo how to start the development of different body parts in their respective spatial locations. The model is also a powerful tool for understanding gene regulatory networks in a way not previously possible, allowing scientists to better study the genetic bases of both development and evolution.

"We have never had the opportunity to explore the significance of these networks before," says Eric Davidson, the Norman Chandler Professor of Cell Biology at Caltech. "The results are amazing to us."

The researchers described their computer model in a paper in the Proceedings of the National Academy of Sciences that appeared as an advance online publication on August 27.

The model encompasses the gene regulatory network that controls the first 30 hours of the development of endomesoderm cells, which eventually form the embryo's gut, skeleton, muscles, and immune system. This network -- so far the most extensively analyzed developmental gene regulatory network of any animal organism -- consists of about 50 regulatory genes that turn one another on and off.

To create the model, the researchers distilled everything they knew about the network into a series of logical statements that a computer could understand. "We translated all of our biological knowledge into very simple Boolean statements," explains Isabelle Peter, a senior research fellow and the first author of the paper. In other words, the researchers represented the network as a series of if-then statements that determine whether certain genes in different cells are on or off (i.e., if gene A is on, then genes B and C will turn off).

By computing the results of each sequence hour by hour, the model determines when and where in the embryo each gene is on and off. Comparing the computed results with experiments, the researchers found that the model reproduced the data almost exactly. "It works surprisingly well," Peter says.

Some details about the network may still be uncovered, the researchers say, but the fact that the model mirrors a real embryo so well shows that biologists have indeed identified almost all of the genes that are necessary to control these particular developmental processes. The model is accurate enough that the researchers can tweak specific parts -- for example, suppress a particular gene -- and get computed results that match those of previous experiments.

Allowing biologists to do these kinds of virtual experiments is precisely how computer models can be powerful tools, Peter says. Gene regulatory networks are so complex that it is almost impossible for a person to fully understand the role of each gene without the help of a computational model, which can reveal how the networks function in unprecedented detail.

Studying gene regulatory networks with models may also offer new insights into the evolutionary origins of species. By comparing the gene regulatory networks of different species, biologists can probe how they branched off from common ancestors at the genetic level.

So far, the researchers have only modeled one gene regulatory network, but their goal is to model the networks responsible for every part of a sea-urchin embryo, to build a model that covers not just the first 30 hours of a sea urchin's life but its entire embryonic development. Now that this modeling approach has been proven effective, Davidson says, creating a complete model is just a matter of time, effort, and resources.

The title of the PNAS paper is "Predictive computation of genomic logic processing functions in embryonic development." In addition to Peter and Davidson, the other author on the PNAS paper is Emmanuel Faure, a former Caltech postdoctoral scholar who is now at the École Polytechnique in France. This work was supported by the National Institute of Child Health and Human Development.

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The above story is reprinted from materials provided by California Institute of Technology. The original article was written by Marcus Woo.

Note: Materials may be edited for content and length. For further information, please contact the source cited above.

Journal Reference:

I. S. Peter, E. Faure, E. H. Davidson. Predictive computation of genomic logic processing functions in embryonic development. Proceedings of the National Academy of Sciences, 2012; DOI: 10.1073/pnas.1207852109

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Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.

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New statistical model lets patient's past forecast future ailments

ScienceDaily (June 4, 2012) — Analyzing medical records from thousands of patients, statisticians have devised a statistical model for predicting what other medical problems a patient might encounter.

Like how Netflix recommends movies and TV shows or how Amazon.com suggests products to buy, the algorithm makes predictions based on what a patient has already experienced as well as the experiences of other patients showing a similar medical history.

"This provides physicians with insights on what might be coming next for a patient, based on experiences of other patients. It also gives a predication that is interpretable by patients," said Tyler McCormick, an assistant professor of statistics and sociology at the University of Washington.

The algorithm will be published in an upcoming issue of the journal Annals of Applied Statistics. McCormick's co-authors are Cynthia Rudin, Massachusetts Institute of Technology, and David Madigan, Columbia University.

McCormick said that this is one of the first times that this type of predictive algorithm has been used in a medical setting. What differentiates his model from others, he said, is that it shares information across patients who have similar health problems. This allows for better predictions when details of a patient's medical history are sparse.

For example, new patients might lack a lengthy file listing ailments and drug prescriptions compiled from previous doctor visits. The algorithm can compare the patient's current health complaints with other patients who have a more extensive medical record that includes similar symptoms and the timing of when they arise. Then the algorithm can point to what medical conditions might come next for the new patient.

"We're looking at each sequence of symptoms to try to predict the rest of the sequence for a different patient," McCormick said. If a patient has already had dyspepsia and epigastric pain, for instance, heartburn might be next.

The algorithm can also accommodate situations where it's statistically difficult to predict a less common condition. For instance, most patients do not experience strokes, and accordingly most models could not predict one because they only factor in an individual patient's medical history with a stroke. But McCormick's model mines medical histories of patients who went on to have a stroke and uses that analysis to make a stroke prediction.

The statisticians used medical records obtained from a multiyear clinical drug trial involving tens of thousands of patients aged 40 and older. The records included other demographic details, such as gender and ethnicity, as well as patients' histories of medical complaints and prescription medications.

They found that of the 1,800 medical conditions in the dataset, most of them -- 1,400 -- occurred fewer than 10 times. McCormick and his co-authors had to come up with a statistical way to not overlook those 1,400 conditions, while alerting patients who might actually experience those rarer conditions.

They came up with a statistical modeling technique that is grounded in Bayesian methods, the backbone of many predictive algorithms. McCormick and his co-authors call their approach the Hierarchical Association Rule Model and are working toward making it available to patients and doctors.

"We hope that this model will provide a more patient-centered approach to medical care and to improve patient experiences," McCormick said.

The work was funded by a Google Ph.D. fellowship awarded to McCormick and by the National Science Foundation.

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The above story is reprinted from materials provided by University of Washington, via Newswise.

Note: Materials may be edited for content and length. For further information, please contact the source cited above.

Journal Reference:

Tyler H. McCormick, Cynthia Rudin and David Madigan. Bayesian Hierarchical Rule Modeling for Predicting Medical Conditions. Annals of Applied Statistics, 2012 [link]

Note: If no author is given, the source is cited instead.

Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.

View the original article here

Cyberwarfare, conservation and disease prevention could benefit from new network model

ScienceDaily (July 10, 2012) — Computer networks are the battlefields in cyberwarfare, as exemplified by the United States' recent use of computer viruses to attack Iran's nuclear program. A computer model developed at the University of Missouri could help military strategists devise the most damaging cyber attacks as well as guard America's critical infrastructure. The model also could benefit other projects involving interconnected groups, such as restoring ecosystems, halting disease epidemics and stopping smugglers.

"Our model allows users to identify the best or worst possible scenarios of network change," said Tim Matisziw, assistant professor of geography and engineering at MU. "The difficulty in evaluating a networks' resilience is that there are an infinite number of possibilities, which makes it easy to miss important scenarios. Previous studies focused on the destruction of large hubs in a network, but we found that in many cases the loss of smaller facilities can be just as damaging. Our model can suggest ways to have the maximum impact on a network with the minimum effort."

Limited resources can hinder law enforcement officers' ability to stop criminal organizations. Matisziw's model could help design plans which efficiently use a minimum of resources to cause the maximum disruption of trafficking networks and thereby reduce flows of drugs, weapons and exploited people. In a similar fashion, disease outbreaks could be mitigated by identifying and then blocking important links in their transmission, such as airports.

However, there are some networks that society needs to keep intact. After the breakdown of such a network, the model can be used to evaluate what could have made the disruption even worse and help officials prevent future problems. For example, after an electrical grid failure, such as the recent blackout in the eastern United States, future system failures could be pinpointed using the model. The critical weak points in the electrical grid could then be fortified before disaster strikes.

The model also can determine if a plan is likely to create the strongest network possible. For example, when construction projects pave over wetland ecosystems, the law requires that new wetlands be created. However, ecologists have noted that these new wetlands are often isolated from existing ecosystems and have little value to wildlife. Matisziw's model could help officials plan the best places for new wetlands so they connect with other natural areas and form wildlife corridors or stretches of wilderness that connect otherwise isolated areas and allow them to function as one ecosystem.

Matisziw's model was documented in the publicly available journal PLoS ONE. Making such a powerful tool widely available won't be a danger, Matisziw said. To use his model, a network must be understood in detail. Since terrorists and other criminals don't have access to enough data about the networks, they won't be able to use the model to develop doomsday scenarios.

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Story Source:

The above story is reprinted from materials provided by University of Missouri-Columbia, via EurekAlert!, a service of AAAS.

Note: Materials may be edited for content and length. For further information, please contact the source cited above.

Journal Reference:

Timothy C. Matisziw, Tony H. Grubesic, Junyu Guo. Robustness Elasticity in Complex Networks. PLoS ONE, 2012; 7 (7): e39788 DOI: 10.1371/journal.pone.0039788

Note: If no author is given, the source is cited instead.

Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.

View the original article here

New statistical model lets patient's past forecast future ailments

ScienceDaily (June 4, 2012) — Analyzing medical records from thousands of patients, statisticians have devised a statistical model for predicting what other medical problems a patient might encounter.

Like how Netflix recommends movies and TV shows or how Amazon.com suggests products to buy, the algorithm makes predictions based on what a patient has already experienced as well as the experiences of other patients showing a similar medical history.

"This provides physicians with insights on what might be coming next for a patient, based on experiences of other patients. It also gives a predication that is interpretable by patients," said Tyler McCormick, an assistant professor of statistics and sociology at the University of Washington.

The algorithm will be published in an upcoming issue of the journal Annals of Applied Statistics. McCormick's co-authors are Cynthia Rudin, Massachusetts Institute of Technology, and David Madigan, Columbia University.

McCormick said that this is one of the first times that this type of predictive algorithm has been used in a medical setting. What differentiates his model from others, he said, is that it shares information across patients who have similar health problems. This allows for better predictions when details of a patient's medical history are sparse.

For example, new patients might lack a lengthy file listing ailments and drug prescriptions compiled from previous doctor visits. The algorithm can compare the patient's current health complaints with other patients who have a more extensive medical record that includes similar symptoms and the timing of when they arise. Then the algorithm can point to what medical conditions might come next for the new patient.

"We're looking at each sequence of symptoms to try to predict the rest of the sequence for a different patient," McCormick said. If a patient has already had dyspepsia and epigastric pain, for instance, heartburn might be next.

The algorithm can also accommodate situations where it's statistically difficult to predict a less common condition. For instance, most patients do not experience strokes, and accordingly most models could not predict one because they only factor in an individual patient's medical history with a stroke. But McCormick's model mines medical histories of patients who went on to have a stroke and uses that analysis to make a stroke prediction.

The statisticians used medical records obtained from a multiyear clinical drug trial involving tens of thousands of patients aged 40 and older. The records included other demographic details, such as gender and ethnicity, as well as patients' histories of medical complaints and prescription medications.

They found that of the 1,800 medical conditions in the dataset, most of them -- 1,400 -- occurred fewer than 10 times. McCormick and his co-authors had to come up with a statistical way to not overlook those 1,400 conditions, while alerting patients who might actually experience those rarer conditions.

They came up with a statistical modeling technique that is grounded in Bayesian methods, the backbone of many predictive algorithms. McCormick and his co-authors call their approach the Hierarchical Association Rule Model and are working toward making it available to patients and doctors.

"We hope that this model will provide a more patient-centered approach to medical care and to improve patient experiences," McCormick said.

The work was funded by a Google Ph.D. fellowship awarded to McCormick and by the National Science Foundation.

Share this story on Facebook, Twitter, and Google:

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Story Source:

The above story is reprinted from materials provided by University of Washington, via Newswise.

Note: Materials may be edited for content and length. For further information, please contact the source cited above.

Journal Reference:

Tyler H. McCormick, Cynthia Rudin and David Madigan. Bayesian Hierarchical Rule Modeling for Predicting Medical Conditions. Annals of Applied Statistics, 2012 [link]

Note: If no author is given, the source is cited instead.

Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.

View the original article here

Cyberwarfare, conservation and disease prevention could benefit from new network model

ScienceDaily (July 10, 2012) — Computer networks are the battlefields in cyberwarfare, as exemplified by the United States' recent use of computer viruses to attack Iran's nuclear program. A computer model developed at the University of Missouri could help military strategists devise the most damaging cyber attacks as well as guard America's critical infrastructure. The model also could benefit other projects involving interconnected groups, such as restoring ecosystems, halting disease epidemics and stopping smugglers.

"Our model allows users to identify the best or worst possible scenarios of network change," said Tim Matisziw, assistant professor of geography and engineering at MU. "The difficulty in evaluating a networks' resilience is that there are an infinite number of possibilities, which makes it easy to miss important scenarios. Previous studies focused on the destruction of large hubs in a network, but we found that in many cases the loss of smaller facilities can be just as damaging. Our model can suggest ways to have the maximum impact on a network with the minimum effort."

Limited resources can hinder law enforcement officers' ability to stop criminal organizations. Matisziw's model could help design plans which efficiently use a minimum of resources to cause the maximum disruption of trafficking networks and thereby reduce flows of drugs, weapons and exploited people. In a similar fashion, disease outbreaks could be mitigated by identifying and then blocking important links in their transmission, such as airports.

However, there are some networks that society needs to keep intact. After the breakdown of such a network, the model can be used to evaluate what could have made the disruption even worse and help officials prevent future problems. For example, after an electrical grid failure, such as the recent blackout in the eastern United States, future system failures could be pinpointed using the model. The critical weak points in the electrical grid could then be fortified before disaster strikes.

The model also can determine if a plan is likely to create the strongest network possible. For example, when construction projects pave over wetland ecosystems, the law requires that new wetlands be created. However, ecologists have noted that these new wetlands are often isolated from existing ecosystems and have little value to wildlife. Matisziw's model could help officials plan the best places for new wetlands so they connect with other natural areas and form wildlife corridors or stretches of wilderness that connect otherwise isolated areas and allow them to function as one ecosystem.

Matisziw's model was documented in the publicly available journal PLoS ONE. Making such a powerful tool widely available won't be a danger, Matisziw said. To use his model, a network must be understood in detail. Since terrorists and other criminals don't have access to enough data about the networks, they won't be able to use the model to develop doomsday scenarios.

Share this story on Facebook, Twitter, and Google:

Other social bookmarking and sharing tools:

Story Source:

The above story is reprinted from materials provided by University of Missouri-Columbia, via EurekAlert!, a service of AAAS.

Note: Materials may be edited for content and length. For further information, please contact the source cited above.

Journal Reference:

Timothy C. Matisziw, Tony H. Grubesic, Junyu Guo. Robustness Elasticity in Complex Networks. PLoS ONE, 2012; 7 (7): e39788 DOI: 10.1371/journal.pone.0039788

Note: If no author is given, the source is cited instead.

Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.

View the original article here