Category: Tools and Techniques

Understanding Complex Diseases Through Computation

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Scientists developed a computational method that could help identify various subtypes of complex diseases. Credit: Stock image

Complex diseases such as diabetes, cancer and asthma are caused by the intricate interplay of genetic, environmental and lifestyle factors that vary among affected individuals. As a result, the same medications may not work for every patient. Now, scientists have shown that a computational method capable of analyzing more than 100 clinical variables for a large group of people can identify various subtypes of asthma, which could ultimately lead to more targeted and personalized treatments. The research team, led by Wei Wu Exit icon of Carnegie Mellon University and Sally Wenzel of the University of Pittsburgh, used a computational approach developed by Wu to identify several patient clusters consistent with known subtypes of asthma, as well as a possible new subtype of severe asthma that does not respond well to conventional drug treatment. If supported by further studies, the researchers’ proposed approach could help improve the understanding, diagnosis and treatment not just of asthma but of other complex diseases.

This work also was funded by NIH’s National Heart, Lung, and Blood Institute.

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Meet Jeff Shaman

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Jeff Shaman
Jeff Shaman
Field: Climatology
Works at: Columbia University’s Mailman School of Public Health, N.Y.
Favorite high school subject: Biology
First job: Guide at the Franklin Institute in Philadelphia, Pa.
Alternative career: Opera singer
Credit: Anne Foulke

Before he wrote any scientific papers, Jeff Shaman wrote operas. At the premiere of one of his operas, an 80-minute story about psychoanalysis, reviewers said the work “crackle[d] with invention.”

After 4 years of training to become an opera singer, Shaman realized that the work wouldn’t offer him career stability. He started thinking about his other interests. After college, where he majored in biology with a focus on ecology, he had volunteered to help with HIV clinical trials and developed a fascination with understanding infectious diseases. He wondered if the quantitative tools and methods used to study the physical sciences—another interest area—could inform how contagions spread and possibly even lead to systems for monitoring or predicting their transmission.

So Shaman returned to school—this time, for advanced degrees in earth and environmental sciences. He now studies the relationship between soil wetness and mosquito-borne diseases such as malaria in Africa and West Nile in Florida.

“I love science—probing questions, thinking about problems, finding solutions, pursuing my ideas,” says Shaman.

His Findings

A few years ago, Shaman took some of his scientific compositions in another direction by focusing on seasonal flu outbreaks. For more than 60 years, researchers have linked seasonal flu outbreaks with environmental data like humidity and temperature. Shaman analyzed this work and figured out that absolute humidity, rather than relative humidity, was the best predictor of outbreaks. Now he’s applied state-of-the-art mathematical modeling and real-time observational estimates of influenza incidence to predict when outbreaks will likely occur.

His forecasting technique mimics that used by meteorologists to predict weather conditions like temperatures, precipitation and even hurricane landfall. Shaman’s version incorporates variables like how transmissible a virus is, the number of days people are contagious and sick, and how much humidity is in the air.

The flu forecasts build on a series of studies in which Shaman and his colleagues used data from previous influenza seasons to test their predictions and improve reliability of their model. The work culminated with real-time predictions for 108 cities during the 2012-2013 influenza season. The forecasts could reliably estimate the peaks of flu outbreaks up to 9 weeks before they occurred.

For the 2013-2014 flu season, the researchers continued to make weekly predictions. But instead of first publishing the results in a scientific journal, they posted them on a newly launched influenza forecasts Web site Exit icon where the public could view the projections.

“People understand the limitations and capabilities of weather forecasts,” says Shaman. “Our hope is that people will develop a similar familiarity with the flu forecasts and use that information to make sensible decisions.” For instance, the prediction of high influenza activity may motivate them to get vaccinated and practice other flu-prevention measures.

As he waits for the start of the next flu season, Shaman continues to tweak his forecast system to improve its reliability. He’s also beginning to address other questions, such as how to predict multiple outbreaks of different influenza strains and how to predict the spread of other respiratory illnesses.

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Meet Ravi Iyengar

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Ravi Iyengar
Ravi Iyengar
Fields: Systems pharmacology and systems biology
Works at: Mount Sinai School of Medicine, New York, NY
Favorite sports team: Yankees
Favorite subject in high school: Math
Recently read book: The Signal and the Noise by Nate Silver
Credit: Pedro Martinez, Systems Biology Center New York

Ravi Iyengar, a professor at Mount Sinai School of Medicine, stood in an empty lecture hall, primed to tell thousands of students about systems biology, a holistic approach to studying fundamental life processes. To prepare for this moment, he had spent 4 months reading hundreds of scientific papers and distilling the research into understandable nuggets. But that day, his only student was a videographer.

Together, they recorded 15 different lectures about systems biology—many related to Iyengar’s own research—that thousands of people would stream or download as part of a MOOC, or massive open online course.

Trained in biochemistry, Iyengar built his research career around studying molecules and developing a list of all the parts that help nerve, kidney and skin cells to function. As he obtained more information, he realized he needed to know how all the components worked together. To achieve this comprehensive understanding, Iyengar turned to computational techniques and mathematical analyses—cornerstones of systems biology.

For more than a decade, he has been using and developing systems biology approaches to explore a range of biomedical questions, from very basic to translational ones with immediate relevance to human health.

Iyengar’s Findings

In his earlier work, Iyengar used mathematical analyses to show that molecules within cells connect with one another to form switches that produce cellular memory. This may allow, for instance, an immune cell to remember a foreign object and secrete an antibody. In recent work, he and his team developed a mathematical model showing that the shape of a cell influences the flow of information across the membrane, possibly contributing to disease states and offering a way to study and identify them under the microscope. In another study, they analyzed a database of drug side effects to find combinations of medications that produce fewer adverse reactions and then created a cell biology interaction network that explains why a certain drug pair had this beneficial outcome. The approach could point to other combinations of FDA-approved drugs that reduce serious side effects and thereby guide clinical practice.

“Systems biology is a powerful way to explore important biological and medical questions, and it’s relevant to many fields of science,” said Iyengar. But he added that the majority of educational institutions, including liberal arts and community colleges, don’t have systems biology courses. So, Iyengar teamed with colleagues to create a series of MOOCs.

The first course, offered last summer and taught by Iyengar, presented all the facets of systems biology. The syllabus included lessons on genomics and bioinformatics, fields that have contributed to systems biology; gathering and integrating data; and the use of modeling in drug development.

“My goal was for the students to get the general gestalt of systems biology,” explained Iyengar, who directs an NIH-funded center focused on the systems-level study of medicine and therapeutics.

In total, more than 12,000 participants watched at least one video lecture, 3,000 submitted one or more of the weekly quizzes and 1,800 took a mid-term or final exam. The online discussions forum included nearly 400 topics with about 5,000 posts. The students, most enrolled in a graduate program or working full-time, had some training in the biological, biomedical, computer and information sciences.

“The stats tell me that many people are in fields adjacent to systems biology and don’t have access to more traditional systems biology courses,” concluded Iyengar. “Through the MOOC, we can reach them in a substantial way.”

The second course, which covers network analysis, wrapped up in early December, and the third course, which covers dynamical modeling methods, began in January. Iyengar plans to offer the intro course again in late March.

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Local Flu Forecasts Posted on New Web Site

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Incidence of influenza during the week starting 12/29/2013 (top); influenza incidence forecasts for selected cities (bottom). Credit: Columbia Prediction of Infectious Diseases.
Incidence of influenza during the week starting 12/29/2013 (top); influenza incidence forecasts for selected cities (bottom). Credit: Columbia Prediction of Infectious Diseases Exit icon.

News articles this weekend reported an uptick in flu cases in many parts of the country. When will your area be hardest hit? Infectious disease experts at Columbia University have launched an influenza forecast Web site Exit icon that gives weekly predictions for rates of flu infection in 94 U.S. cities. The predictions indicate the number of cases in Chicago; Atlanta; Washington, D.C.; and Los Angeles will peak this week, with New York City, Boston, Miami and Providence peaking in following weeks. The forecasts are updated every Friday afternoon, so check back then for any changes.

The forecasting approach, which adapts techniques used in modern weather prediction, relies on real-time observational data of people with influenza-like illness, including those who actually tested positive for flu. The researchers have spent the last couple of years developing the forecasting system and testing it—first retrospectively predicting flu cases from 2003-2008 in New York City and then in real time during the 2012-2013 influenza season in 108 cities.

“People have become acclimated to understanding the capabilities and limitations of weather forecasts,” said Jeffrey Shaman Exit icon, who’s led the flu forecasting project. “Making our forecasts available on the Web site will help people develop a similar familiarity and comfort.” Shaman and his team are hoping that, just as rainy forecasts prompt more people to carry umbrellas, an outlook for high influenza activity may motivate them to get vaccinated and practice other flu-prevention measures.

This work also was funded by NIH’s National Institute of Environmental Health Sciences.

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