Emanuele Ratti,
"Large-scale Biology: Philosophical, Historical, and Computational Perspectives"
, in Christopher Donohue, Alan C. Love: Perspective on the Human Genome Project and Genomics, 11-2021
Original Titel:
Large-scale Biology: Philosophical, Historical, and Computational Perspectives
Sprache des Titels:
Englisch
Original Buchtitel:
Perspective on the Human Genome Project and Genomics
Original Kurzfassung:
Abstract. The relation between the ethos of large-scale projects in the life sciences and the epistemic culture of molecular biology has been the subject of heated discussions for the past 30 years. Molecular biology is typically a ?small science?, organized around a laboratory leader who decides what to pursue, placing ?bets? on different research strategies. ?Large-scale? biology has taken several forms, from centralized computational infrastructures, to big consortia distributed throughout a country and distributing efforts among many ?small? units. While several scholars have analyzed how large-scale biology has impacted the
sociological, epistemic, and the governance structure of molecular biology, works investigating how itsdiscovery strategies have been shaped by large-scale projects have been elusive. In this paper, we identify two ways in which large-scale biology could possibly influence the discovery strategies of traditional molecular biology. First, large-scale projects can be facilitators: while small labs continue to pursue their own interests, the way their hypotheses are developed is facilitated by the resources provided by large-scale projects. Second, large-scale projects can be epistemically centralizing: they set the research agenda of
molecular biology labs by constraining the choices of hypotheses to pursue in the first place. As an example of the former, we discuss the Human Genome Project from a historical and epistemic angle. As an example of the latter, we discuss The Cancer Genome Atlas. In order to explore the effects of this project on
discovery strategies, we perform a thorough computational analysis of the literature on cancer gene from
the 1980s to 2018.