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Creative Minds: Building Better Computational Models of Common Disease

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Hilary Finucane

Hilary Finucane

Not so long ago, Hilary Finucane was a talented young mathematician about to complete a master’s degree in theoretical computer science. As much as she enjoyed exploring pure mathematics, Finucane had begun having second thoughts about her career choice. She wanted to use her gift for numbers in a way that would have more real-world impact.

The solution to her dilemma was, literally, standing right by her side. Her husband Yakir Reshef, also a mathematician, was developing a new algorithm at the Broad Institute of MIT and Harvard, Cambridge, MA, to improve detection of unexpected associations in large data sets. So, Finucane helped the Broad team with modeling biomedical topics ranging from the gut microbiome to global health. That work led to her co-authoring a paper in the journal Science [1], providing a strong start to what’s shaping up to be a rewarding career in computational biology.

Studies of Dogs, Mice, and People Provide Clues to OCD

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Chances are you know someone with obsessive-compulsive disorder (OCD). It’s estimated that more than 2 million Americans struggle with this mental health condition, characterized by unwanted recurring thoughts and/or repetitive behaviors, such as excessive hand washing or constant counting of objects. While we know that OCD tends to run in families, it’s been frustratingly difficult to identify specific genes that influence OCD risk.

Now, an international research team, partly funded by NIH, has made progress thanks to an innovative genomic approach involving dogs, mice, and people. The strategy allowed them to uncover four genes involved in OCD that turn out to play a role in synapses, where nerve impulses are transmitted between neurons in the brain. While more research is needed to confirm the findings and better understand the molecular mechanisms of OCD, these findings offer important new leads that could point the way to more effective treatments.

Creative Minds: Using Machine Learning to Understand Genome Function

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Anshul Kundaje

Anshul Kundaje / Credit: Nalini Kartha

Science has always fascinated Anshul Kundaje, whether it was biology, physics, or chemistry. When he left his home country of India to pursue graduate studies in electrical engineering at Columbia University, New York, his plan was to focus on telecommunications and computer networks. But a course in computational genomics during his first semester showed him he could follow his interest in computing without giving up his love for biology.

Now an assistant professor of genetics and computer science at Stanford University, Palo Alto, CA, Kundaje has received a 2016 NIH Director’s New Innovator Award to explore not just how the human genome sequence encodes function, but also why it functions in the way that it does. Kundaje even envisions a time when it might be possible to use sophisticated computational approaches to predict the genomic basis of many human diseases.

Creative Minds: Modeling Neurobiological Disorders in Stem Cells

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Feng Zhang

Feng Zhang

Most neurological and psychiatric disorders are profoundly complex, involving a variety of environmental and genetic factors. Researchers around the world have worked with patients and their families to identify hundreds of possible genetic leads to learn what goes wrong in autism spectrum disorder, schizophrenia, and other conditions. The great challenge now is to begin examining this growing cache of information more systematically to understand the mechanism by which these gene variants contribute to disease risk—potentially providing important information that will someday lead to methods for diagnosis and treatment.

Meeting this profoundly difficult challenge will require a special set of laboratory tools. That’s where Feng Zhang comes into the picture. Zhang, a bioengineer at the Broad Institute of MIT and Harvard, Cambridge, MA, has made significant contributions to a number of groundbreaking research technologies over the past decade, including optogenetics (using light to control brain cells), and CRISPR/Cas9, which researchers now routinely use to edit genomes in the lab [1,2].

Zhang has received a 2015 NIH Director’s Transformative Research Award to develop new tools to study multiple gene variants that might be involved in a neurological or psychiatric disorder. Zhang draws his inspiration from nature, and the microscopic molecules that various organisms have developed through the millennia to survive. CRISPR/Cas9, for instance, is a naturally occurring bacterial defense system that Zhang and others have adapted into a gene-editing tool.

Cardiometabolic Disease: Big Data Tackles a Big Health Problem

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Cardiometabolic risk lociMore and more studies are popping up that demonstrate the power of Big Data analyses to get at the underlying molecular pathology of some of our most common diseases. A great example, which may have flown a bit under the radar during the summer holidays, involves cardiometabolic disease. It’s an umbrella term for common vascular and metabolic conditions, including hypertension, impaired glucose and lipid metabolism, excess belly fat, and inflammation. All of these components of cardiometabolic disease can increase a person’s risk for a heart attack or stroke.

In the study, an international research team tapped into the power of genomic data to develop clearer pictures of the complex biocircuitry in seven types of vascular and metabolic tissue known to be affected by cardiometabolic disease: the liver, the heart’s aortic root, visceral abdominal fat, subcutaneous fat, internal mammary artery, skeletal muscle, and blood. The researchers found that while some circuits might regulate the level of gene expression in just one tissue, that’s often not the case. In fact, the researchers’ computational models show that such genetic circuitry can be organized into super networks that work together to influence how multiple tissues carry out fundamental life processes, such as metabolizing glucose or regulating lipid levels. When these networks are perturbed, perhaps by things like inherited variants that affect gene expression, or environmental influences such as a high-carb diet, sedentary lifestyle, the aging process, or infectious disease, the researchers’ modeling work suggests that multiple tissues can be affected, resulting in chronic, systemic disorders including cardiometabolic disease.

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