The question is no longer, “What is the story hidden in our genomes?” but rather, “What does it mean for human life?” For the first time, we’re starting to build tools that can find the answers.
Since successfully sequencing the human genome in 2003, scientists have had access to a vast new amount of information about the basic components of human life. At first, many believed it would be the key to unlocking all the secrets of heredity and disease, of developing new cures, and preventing genetic disorders.
The progress made in the years since has been both inspiring and humbling. We know enough now to realize that we’ve only been looking at part of the puzzle. The paths to curing diseases like diabetes and cancer are steep. What we thought was a systematic process of probing the genome has unfolded into a labyrinth of complex interactions: determining the effects of interaction, nature, environment, and the countless chains of cause and effect that enable illness to find a foothold in the human body.
The questions become: How can we study disease in a way that takes into account individual genes as well as a person’s biological, environmental, and social contexts? How can that vast array of information be filtered and properly interpreted to solve the complex issues of modern life?
Enter information biology.
From the microbiological level to the macro level of policy, information biology spans a spectrum of research to uncover answers to complex questions.
From Molecules to Massively Interacting Systems
Bioinformatics began in the 1960s as a computationally enabled approach to genetic research. It started with breakthroughs like Frederick Sanger’s method for sequencing insulin and Michael Waterman’s algorithm for mapping DNA. Their work focused on peering into our genomes, to see what they might tell us.
After decades of studying genomes and advances in high-performance computing, we now know that genes and their contents are really just one factor in a complex network of influences. To better explain the whole context, scientists are layering data from different sources using a new approach called information biology.
Information biology applies computational, algorithmic, mathematical, and statistical methods to understand biological systems. Researchers use it to build an informational account of biological causes, as opposed to a strictly biochemical account.
High-performance computing technology like institute's Shadowfax hybrid cluster enables researchers to run simulations at an unprecedented level of detail and scale.
In the event of an epidemic, traditional bioinformatics can help us understand how the virus behaves in a closed system. But an information biology approach to studying that epidemic would incorporate other data about the affected area, like census records, maps, and traffic patterns, to paint a more complete contextual picture of the virus’ total impact.
Technological advances from the last decade have made this approach possible, including mechanisms that record and provide access to data, mobility, and high-performance computing infrastructure. Researchers are developing methods to process enormous amounts of relevant data in seconds and get answers to complex questions more quickly. In a biosocial context, the implications of these advances are staggering.
A New Way of Thinking About Old Problems
The Biocomplexity Institute of Virginia Tech is focused on building information tools driven by real-world problems that need immediate answers. Synthesizing previously disconnected areas of research into a single body of data is a massive task, but could mean systematic advances for science that affect everything from public policy to how we think about the bacteria in our own gut.
The Network Dynamics and Simulation Science Laboratory (NDSSL) and the Nutritional Immunology and Molecular Medicine Laboratory (NIMML) have been working to determine the most effective treatment for bacterial infections in the human gastrointestinal tract. NIMML analyzes the bacteria, then NDSSL uses the data to create a computational model simulating the body’s inflammatory response to infection.
Together, they found that attempts to kill off the bacteria would cause an imbalance in a patient’s microbiome, the community of microscopic organisms that resides in the human body. The simulations show negative side effects so disruptive that it would actually be more effective to treat the inflammation rather than try to eliminate the infection itself. Since current drug therapies often have negative side effects, the laboratories are working to discover which natural pathways would be best to aid in treating inflammation.
Researchers used localized readings of air quality data to track a simulated population's ozone exposure as it moved throughout a "normal" day.
Scientists at the Biocomplexity Institute of Virginia Tech are also doing information biology research on the macro level. The Social Decision and Analytics Laboratory (SDAL) has helped the city of Houston, Texas, provide better air quality by showing how their population dynamics affect ozone production. The analysis examined seemingly disparate types of data, and has helped them make decisions about their environmental policies.
Projects like these show the range of what’s at stake. With information biology research taking on vast quantities of data, we can bring new interpretations to old problems that have never been possible before.