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What is metabolism after big data?

Principal Investigator(s): Nadine Levin, Hannah Landecker
What is metabolism after big data?

Invocations to the power and value of “big data” are ubiquitous in contemporary biomedicine.  Research is increasingly carried out with computerized tools and databases, as knowledge is generated through the management and interpretation of large data sets that include multiple formats, types, and realms of information.  With the rise of data-intensive ways of studying biology, life is suddenly made up of systems and networks, of interactions that span across space, time, and organisms.  Linear notions of disease causation, and reductionist metaphors of the informational code of life are out: instead, notions of complexity, probability, variability, and dynamism are in.

Here, metabolism—as a way of understanding how the body interacts with food, disease, and the environment over space and time—emerges as a potentially radical new object not only for thinking about the biology of cells and organisms, but also for intervening into health and disease.  This project examines how data and computational methods intersect with biology to give rise to new ways of thinking about and working with metabolism.  Although it tracks metabolism within laboratory settings, this project also think about how metabolism is central to contemporary society: it influences how we think about our bodies, ourselves, and our relationship with the world, and has captured the imagination of nutritionists, pharmaceutical companies, and the media.  This project therefore examines how, with the rise of data-intensive science, metabolism is changing as a conceptual, technological, and therapeutic object, and what consequences this has for contemporary society.

This project takes metabolomics—the post-genomic study of metabolism—as a key locale in which to investigate the role and impact of big data in biomedical research.  The centrality of big data in biomedicine is particularly evident in post-genomic or “omics” fields, where research revolves around the analysis of the statistical patterns contained within large volumes of biological data.  Metabolomics is one of several “omics”: it is the outcome of gene-environment interactions, and so can provide insight into how food, exercise, drugs, and environmental exposures affect the body.  Often cast as the lesser-known cousin of fields like genomics and epigenetics, metabolomics emerged in the late 1990s in research communities in Europe and the United States, and has since been the focus of a number of large-scale initiatives—involving, most recently, the NIH’s Common Fund Program in metabolomics—to develop “translational research” from the bench to the bedside, and to develop “personalized medicine” tailored to individual biologies.

This project entails research (ethnography, interviews) in various metabolomics laboratories, including the Computational and Systems Medicine laboratory at Imperial College London, the Chemoinformatics and Metabolism group at the European Bioinformatics Institute, the Metabolomics Core at UCLA, and the West Coast Metabolomics Center at UC Davis.

Levin, Nadine (2014). “What’s being translated in translational research? Making and making sense of data between the laboratory and the clinic.” TECNOSCIENZA: Italian Journal of Science & Technology Studies

Levin, Nadine (2014). “Multivariate statistics and the enactment of metabolic complexity.” Social Studies of Science

Levin, Nadine (2014).  “Making up “persons” in personalized medicine with metabolomics.”  Somatosphere

Levin, Nadine (2013).  An anthropology of the “metabonomics” laboratory? Conducting ethnographic research at the forefront of personalized medicine. Society, Biology, and Human Affairs, 77: 1-9

The National Science Foundation:
Grant #1431263
“The Impact of Big Data on the Science of Metabolism” 2015-2016
More info: http://www.nsf.gov/awardsearch/showAward?AWD_ID=1431263