Science is moving beyond big data to provide meaningful insight that’s responsive enough to make a real difference.


With recent advances in information biology research, policy-makers, healthcare professionals, scientists, and others working to solve real-world problems now have evidence at their fingertips that can help them make more informed decisions. The practice of processing massive amounts of data is coming into its own, so that researchers are finally able to apply their work to problems in a way that has a direct and immediate impact.

“Big data is something we’ve been living for 20 years,” says Stephen Eubank, deputy director of the Biocomplexity Institute of Virginia Tech’s Network Dynamics and Simulation Science Laboratory (NDSSL.) “It’s more than just a lot of data, it’s data that was never meant to work together. And when we create synthetic information systems, we try to find ways to make that data work together and provide insight that decision-makers can use.”

This applied approach drives the Biocomplexity Institute’s research. Mathematicians and computational theorists study the “pure” science of interacting systems, but they do so in tandem with life scientists, epidemiologists, sociologists, economists, psychologists, and social scientists. The big problems faced by society require all disciplines to work together, providing information that affects individuals all the way up to governments and global crisis decision-making.

“The theory is not sitting in isolation — it’s strongly tied to applications,” says Madhav Marathe, director of NDSSL. “The theoretical work we do is really based on the problems that are posed to us, and we work on transitioning that into tools that scientists and analysts can use without having to become computer experts.”

Those tools foster a relationship between scientists and policy-makers that’s stronger and more direct than it’s ever been before.

“It really opens up this delicate issue of balance between science and supporting policy and analysis,” Marathe says. “Science by its very nature is slow-moving; people want to do things very thoroughly. Policy-makers, especially when an epidemic or other event is unfolding, do not have time to get perfect answers, and how to balance this becomes a very interesting question. So, we try to solve problems as best we can. We state our assumptions, but then when the event is done, we go back and reprise the research program for a good number of years, before the next event happens.”

That balancing act may blur the lines between traditional science that happens behind lab-room doors and the quick answers decision-makers so desperately need to solve global-scale problems. Information biology is opening up all kinds of opportunities that point to a new era of well-informed judgments with immediate impact.

The Future of Scientific Research


How will information biology affect the future of scientific research? Or of laboratory science? Or decision-making and policy?

In the past, research was largely driven by curiosity. Scientists would ask a question about a subject and design a method for finding the answers, with work that often followed the conventions and practices of their academic discipline. Any significant findings could be interpreted for use outside of scholarship, but the practice of research itself was largely an academic one.

To move the converation forward, we must consider how interacting systems at all levels—the microbiological, the human, the larger society—will play a role in how our communities will continue to evolve.

“Every community has a unique phenotype—a set of observable characteristics that are influenced by its environment,” says Sallie Keller, director of the Biocomplexity Institute’s Social and Decision Analytics Laboratory (SDAL.)  “A community may like their current phenotype or may want to change. Just as these variations in phenotypes are important in the evolution of species, variations in community phenotypes are important in determining a community’s evolutionary trajectory.”

With this new approach, science doesn’t have to look to find a use for its answers. The research is motivated directly by the problems themselves, and designed to provide the most effective real-world solutions. This isn’t science nudging at the boundaries of its disciplines, it’s fundamentally trans-disciplinary, because real problems don’t fit neatly along subject lines.

Information biology leads to a clearer understanding of how networked systems influence life in all its complexity, providing better medical treatments for patients with genetic disorders, faster response times, more nuanced understanding of how we as humans are affected by our ecology, and, ultimately, more informed decisions.

“These massively interacting networked systems pose enormous challenges to science, but are central to major problems in the realities of modern life,” says Chris Barrett, executive director of the Biocomplexity Institute. “We’ve changed, and we must continue to change, how we approach integrated life science to understand how things influence, interact, and affect one another on these massive scales.”

The goal is that, in transforming research so that it can be applied directly to society’s biggest problems, we can provide new answers to some of life’s most serious questions.  

Published by Tiffany Trent, January 19, 2016
Tags: Global Synthetic Information Systems