Enacting molecular complexity : data and health in the metabonomics laboratory

Thesis

In this dissertation, I examine how biological data practices enable researchers to interact with and enact biological life in statistical ways, and how this poses challenges to the use and integration of biological knowledge with clinical practices. Instead of considering data as a pre-existing cognitive representation of the world, I combine scholarship on the anthropology of science with scholarship from science and technology studies to consider data as a form of material practice. I consider, in other words, how data is intertwined with technologies, people, and values, such that data is used to make normative and naturalized claims about biology and disease.

To explore the generation, interpretation, and use of biological data, I focus on the field of “metabonomics”—the post-genomic study of metabolism—as it is carried out within the Biomolecular Medicine Laboratory (BMM) at Imperial College London. In doing so, I examine how metabonomics researchers use biochemical techniques and multivariate statistics to investigate metabolism and disease.

After providing an overview of the literature, central questions, and methodology that frame this dissertation, I examine how multivariate statistical practices are central to the historical identity and epistemic culture of metabonomics research at the BMM. From there, I demonstrate how multivariate statistics require and enable metabonomics to enact metabolism as an inherently complex entity. Consequently, I examine how researchers struggle to assign the categories of “normal” and “abnormal” to dynamic notions of metabolism and health. I then explore how the translation of metabonomics knowledge into clinical practices places value on multivariate forms and large volumes of information, eclipsing the importance of human interpretation and judgment. Finally, I examine how metabonomics research is used to develop personalized medicine, but in ways that make it difficult to address the health of individual patients.

Authors: Levin, N

• Publication date: 2013
• DOI: 10.5287/ora-gokp8kwop
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: Oxford University, UK
• Language: English
• Keywords: metabolomics; post-genomic; metabolism; statistics; laboratory; data; bioinformatics; science and technology studies
• Subjects: Anthropology; Medical and ecological anthropology; Statistics (social sciences); Sociology