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Jan 15, 2013 (Vol. 33, No. 2)

The Schadt Equation

Mathematics + 21st Century Computational Power = Safer and More Effective Healthcare

  • Click Image To Enlarge +
    Dr. Eric Schadt is an expert on the generation and integration of large-scale sequence variation, molecular profiling, and clinical data in disease populations.

    Eric Schadt, Ph.D., may be a scientist, and he may be the director of the $100 million enterprise, The Icahn Institute for Genomics and Multiscale Biology at Mount Sinai, but these days he’s thinking like a hedge fund manager.

    However, even though he has $50 million worth of computational and data cyberinfrastructure available to him at Mount Sinai, he is definitely not acting like a hedge fund manager. That is, he’s not using the extraordinary computing power and mathematical techniques available at the institute to make real-time decisions on what stocks to buy, sell, or hold.

    “Instead of asking what companies to bet on,” he explained in a recent interview, “we’re using these mathematical techniques to bet on which patients require treatment, and for those requiring treatment, what’s the best treatment for them.”

    He expects that using the mathematical techniques developed for Wall Street, that in five to ten years, “Medicine will be so personalized, and the healthcare provider will have such a fine-grained understanding of what has perturbed the network of networks that resulted in the individual’s disease, that the physician will have both the knowledge to predict the course of the individual’s disease and the tools to treat or even prevent it.”

    Ultimately, Dr. Schadt expects this mathematical approach to reduce humankind’s overall disease burden.

  • No Simple Linear Pathways in Biology

    Why have the mathematical techniques used in finance suddenly become so important in medicine? According to Dr. Schadt, for most of mankind’s history, we’ve tried to explain the complex and the incomprehensible by telling ourselves fairly straight-forward linear stories, the kind that don’t require a lot of math. We explained lightning as Zeus hurling light bolts from the sky, or more recently, we explained type 2 diabetes as a linearly ordered pathway, starting with a defective gene and ending with the disease.

    However, people with the same defective gene, whether it’s a gene for type 2 diabetes or virtually any other disease, often have remarkably different outcomes. With many single-gene disorders, there is a spectrum of phenotypic expressions, with some individuals barely touched by the disease while others experience the most extreme and horrific versions of it.

    The linear explanation doesn’t account for this variety of outcomes, and it can’t because it doesn’t take into account the full, nonlinear complexity of diseases.

    “DNA variation,” notes Dr. Schadt, “is just one dimension among many that define living systems. DNA doesn’t directly cause disease, but instead it has effects at the molecular level. It changes transcription. It changes proteins and it changes metabolite levels.

    “Those different variables aren’t acting in isolation. They’re acting in a network, and it’s this network that senses the genetic and environmental perturbations that cause shifts in the system that lead to disease.”

    He goes on to say that it requires the tools of advanced mathematics and unprecedented computing power to take the deeper snapshots of biological processes needed for constructing and generating models that will predict states of the system.

    The phrase “multiscale biology” in the institute’s name comes from its goal of modeling the complexity of living systems, understanding not only all of the processes taking place at the molecular, cellular, tissue, organ, organismal, and community levels, but also how information flows between these levels.

    “We generate big data (DNA sequencing, RNA sequencing, and so on), but then we also pull in lots of big data generated by others,” says Dr. Schadt. “We then have access to all of the clinical data from electronic medical records, and then we integrate the data to build complex network models that can tell us how best to diagnose and treat disease.”

    Dr. Schadt points out that, for example, in the case of an individual with liver cancer, “The information on the liver tumor for just this one individual can lead to a terabyte or more of raw data. However, if we’re trying to find out where a given individual’s sequence is abnormal, we may want to compare it to similar information from 1,000 other people. Now you have a petabyte of information—that is one followed by 15 zeroes—that has to be analyzed for just one type of cancer.”

    And for a still deeper understanding, researchers need to look at a hierarchy of levels, at multiple scales, from the small molecular level to the cellular to the tissue to the organism and then up to the large community level.

    “As we move away from single genes and into networks, the complexity increases exponentially, and the scales of data we generate will exceed everything that is in the digital universe today,” continues Dr. Schadt. “We used to think a terabyte was a big number, and now we have petabytes and even exabytes of data.”

  • Disrupted Molecular Networks

    Click Image To Enlarge +
    Omental Adipose Molecular Network: A causal gene network constructed from omental adipose tissue collected from individuals in a morbidly obese cohort (body mass index > 45). The blue/pink balls (or nodes) represent genes that were monitored in omental fat tissue across 800 individuals. Edges between the nodes represent causal associations between the corresponding genes. All of the edges in the network are directed, meaning they convey causal information regarding what genes cause others to change. The pink nodes in the network represent a special inflammatory/immune subnetwork of interest that we have identified in many studies as causal for many common human diseases such as Alzheimer’s disease, obesity, diabetes, heart disease, asthma, COPD, IBD, and different types of cancer.

    The goal of Dr. Schadt and his colleagues is to tie all this data together to create more accurate models of diseases.

    “We know that in the case of an individual, the disease is likely to have been caused by many changes in many genes that disrupted many different networks defining biological processes, and the goal is to discover and understand these disruptions.

    “After we increase our understanding of the diseases,” he continues, “the question will be how to counter the perturbations in the disease-associated networks that operate in a network of networks. With this new information, derived from advanced computational techniques, we should be able to predict the current state of an individual’s disease, how likely it is to progress in the next year, what treatments are most likely to help his or her subtype, and which targets we can develop for new therapies that would be effective on the individual’s subtype.”

    Dr. Schadt has an example of how, in the last four years, these new approaches have worked in practice. Our increased knowledge of the molecular causes of diabetes has changed how scientists and clinicians look at that disease. Previously, researches believed the major cause of diabetes was insulin resistance, and the treatment was to lower glucose levels.

    “We now have found, by uncovering many of the molecular causes of diabetes, that most common forms of diabetes are related to beta cell dysfunction, and are more akin to type 2 diabetes,” he explains. “The pressing questions now are, how do we proliferate more normal beta cells and how do we prevent inflammatory processes from adversely affecting beta cell function and/or proliferation?”

    With this and other knowledge, Dr. Schadt hopes that scientists will discover, on an individualized basis, that there are food types or diets that might correct the course of an individual’s type 2 diabetes. For instance, different types of sugar or fats have different effects on molecular functioning.

    “The goal is to build maps of perturbagens that will cause changes in an individual’s molecular functioning, and with this, discover if the individual is on a healthy or unhealthy course, or if they already have the disease, what can we do to get the individual out of the disease state,” he says.

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