This post is written by UT Austin grad student Rayna Harris and postdoc Tessa Solomon-Lane
Innovative science is increasingly interdisciplinary. With our Pop-Up Institute in May 2017, we aim to expand beyond the traditional scope of interdisciplinary collaboration to make meaningful progress on questions critical to biology, medicine, and society. Pop-Up Institutes are a novel framework for collaboration being funded for the first time this year by the Vice President for Research at the University of Texas at Austin. Designed to be longer than a conference and less permanent than a research center, these Institutes will bring diverse experts together, into the same physical space, to work together. Our Institute “Seeing the Tree and the Forest: Understanding Individual and Population Variation in Biology, Medicine, and Society” focuses on the causes and consequences of individual and population variation. Variation is fundamental to such a wide range of disciplines that we have researchers coming together from biology, statistics, medicine, nutrition, public health, athletics, classics, anthropology, and more.
Individuals differ from each other in a myriad of ways, from their DNA to their behavior and lifetime health. Understanding the underlying causes of this variation across individuals and populations is critical to the success of both the individual and the population within which they live. However, the directionality of cause and consequence is complex, and the pertinent factors that underlie why individuals are the way that they are span disciplines, crossing traditional research boundaries. Human health, for example, is investigated by clinical researchers and health-care professionals, basic and applied biologists, sociologists, statisticians, and more. While genetics clearly influence individual health outcomes, health cannot be fully understood without uncovering the cognitive processes of decision-making. Decision-making is mechanistically based in the structure and signaling of the brain, but family and friends, education, and socioeconomic status all play important and overlapping roles in health-related decisions and, therefore, health outcomes. Different populations—based in ethnic and racial background, gender, sexual orientation, socioeconomic status, geographic location, and more—have differential access to education, work, and health care. These population-level metrics affect individual mental and physical health, thus shifting the state of the population. The interactions between individual and population are reciprocal, dynamic, and not well understood.
The recent explosion of interest in “Personalized Medicine” (also Precision Medicine) has underscored the complexity of the causes and consequences individual and population variation. But healthcare is not the only field facing the challenges of complexity. In fact, many of the problems currently limiting progress are shared across disciplines. For example, little is known about how phenotypic variation develops or is maintained—and at which mechanistic level (e.g., genomic, neural, physiological)—within and across populations and species. A promising and popular approach to understanding the causes of variation at the individual and population levels relies on technological advances in the collection, management, and analysis of large amounts of data (i.e., “Big Data”). For example, it is now feasible to sequence all of the genes expressed in a cancerous tumor or in the brain of a social fish. Big Data also exists at the levels of behavior, social networks, population genetics, and more. But it is not straightforward how information about tens of thousands of genes from a single individual (n=1) can be used to optimize treatment and care. Using conventional models, there is no statistical power in predicting outcomes for a single individual. Furthermore, it is a fallacy to assume that more data necessarily results in a more complete understanding or improved care. Patterns within the data may or may not be meaningful, and choices surrounding analysis and interpretation have consequences for individuals and populations. Unless the right questions can be asked of these data, sheer volume does not guarantee understanding.
For our Institute, we aim to 1) identify fundamental similarities across disciplines and the most promising, central research questions related to individual and population variation, 2) establish a unique and comprehensive research plan, and 3) develop solutions to shared problems that currently limit progress. Together, this work will launch ongoing collaborations to conduct groundbreaking research with real-world, positive outcomes for humans and society.
Over 40 trainees and faculty from across the University of Texas at Austin are already committed to working to accomplish these aims over a period of three weeks. While the Institute will officially take place in May, this diverse group has already come together for town halls and focus group meetings to hone in on our specific themes. The Big Data in Biology Symposium, now in its 5th year, will serve as the launch event for the Institute. This Opening Symposium will bring together researchers from across the University to share their research and synthesize new ideas.
In addition to the support received by the Vice President for Research at The University of Texas at Austin, this Pop-Up Institute is also supported by the Center for Computational Biology and Bioinformatics and several other units and departments, along with the BEACON Center.