This is the story, told in the light of a new analysis of historical data, of a mathematical biology problem that was explored in the 1930s in Thomas Morgan’s laboratory at the California Institute of Technology. It is one of the early developments of evolutionary genetics and quantitative phylogeny, and deals with the identification and counting of chromosomal inversions in Drosophila species from comparisons of genetic maps. A re-analysis of the data produced in the 1930s using current mathematics and computational (...) technologies reveals how a team of biologists, with the help of a renowned mathematician and against their first intuition, came to an erroneous conclusion regarding the presence of phylogenetic signals in gene arrangements. This example illustrates two different aspects of a same piece: the appearance of a mathematical in biology problem solved with the development of a combinatorial algorithm, which was unusual at the time, and the role of errors in scientific activity. Also underlying is the possible influence of computational complexity in understanding the directions of research in biology. (shrink)

The use of molecules and reactions as evidence, markers and/or traits for evolutionary processes has a history more than a century long. Molecules have been used in studies of intra-specific variation and studies of similarity among species that do not necessarily result in the analysis of phylogenetic relations. Promoters of the use of molecular data have sustained the need for quantification as the main argument to make use of them. Moreover, quantification has allowed intensive statistical analysis, as a condition and (...) a product of increasing automation. All of these analyses are subject to the methodological anxiety characteristic of a community in search of objectivity. It is in this context that scientists compared and evaluated protein and nucleic acid sequence data with other types of molecular data – including immunological, electrophoretic and hybridization data. This paper argues that by looking at long-term historical processes, such as the use of molecular evidence in evolutionary biology, we gain valuable insights into the history of science. In that sense, it accompanies a growing concern among historians for big-pictures of science that incorporate the fruitful historical research on local cases of the last decades. (shrink)

This paper argues that analyses of the ways in which Big Data has been enacted in other academic disciplines can provide us with concepts that will help understand the application of Big Data to social questions. We use examples drawn from our Science and Technology Studies analyses of -omic biology and high energy physics to demonstrate the utility of three theoretical concepts: primary and secondary inscriptions, crafted and found data, and the locus of legitimate interpretation. These help us to show (...) how the histories, organisational forms, and power dynamics of a field lead to different enactments of big data. The paper suggests that these concepts can be used to help us to understand the ways in which Big Data is being enacted in the domain of the social sciences, and to outline in general terms the ways in which this enactment might be different to that which we have observed in the ‘hard’ sciences. We contend that the locus of legitimate interpretation of Big Data biology and physics is tightly delineated, found within the disciplinary institutions and cultures of these disciplines. We suggest that when using Big Data to make knowledge claims about ‘the social’ the locus of legitimate interpretation is more diffuse, with knowledge claims that are treated as being credible made from other disciplines, or even by those outside academia entirely. (shrink)

The concept of ‘Fifth Business’ is used to analyze a minority standpoint and bring serious attention to the role of scientists who play a galvanizing role in a science but for multiple reasons appear less prominently in more common recounts of any particular development. Biochemist Ray Wu published a DNA sequencing experiment in March 1970 using DNA polymerase catalysis and specific nucleotide labeling, both of which are foundational to general sequencing methods today. The scant mention of Wu’s work from textbooks, (...) research articles, and other accounts of DNA sequencing calls into question how scientific collective memory forms. This alternative history seeks to understand why a key figure in nucleic acid sequence analysis has remained less visibly connected or peripheral to solidifying narratives about the history of DNA sequencing. The study resists predictable dismissals of Wu’s work in order to seriously examine the formation of his nucleic acid sequence analysis research program and how he shared his knowledge of sequencing during a period of rapid advancement in the field. An analysis of Wu’s work on sequencing the cohesive ends of lambda bacteriophage in the 1960s and 1970s exemplifies how a variety of individuals and groups attempted to develop protocol for sequencing the order of nucleotide base pairs comprising DNA. This historical examination of the sociality of scientific research suggests a way to understand how Wu and others contributed to the very collective memory of DNA sequencing that Wu eventually tried to repair. The study of Wu, who was a Chinese immigrant to the United States, provides a foundation for further critical scholarship on the heterogeneous histories of Asian American bioscientists, the sociality of their scientific works, and how the resulting knowledge produced is preserved, if not evenly, in a scientific field’s collective memory. (shrink)

Bioinformatics – the so-called shotgun marriage between biology and computer science – is an interdiscipline. Despite interdisciplinarity being seen as a virtue, for having the capacity to solve complex problems and foster innovation, it has the potential to place projects and people in anomalous categories. For example, valorised ‘outputs’ in academia are often defined and rewarded by discipline. Bioinformatics, as an interdisciplinary bricolage, incorporates experts from various disciplinary cultures with their own distinct ways of working. Perceived problems of interdisciplinarity include (...) difficulties of making explicit knowledge that is practical, theoretical, or cognitive. But successful interdisciplinary research also depends on an understanding of disciplinary cultures and value systems, often only tacitly understood by members of the communities in question. In bioinformatics, the ‘parent’ disciplines have different value systems; for example, what is considered worthwhile research by computer scientists can be thought of as trivial by biologists, and vice versa. This paper concentrates on the problems of reward and recognition described by scientists working in academic bioinformatics in the United Kingdom. We highlight problems that are a consequence of its cross-cultural make-up, recognising that the mismatches in knowledge in this borderland take place not just at the level of the practical, theoretical, or epistemological, but also at the cultural level too. The trend in big, interdisciplinary science is towards multiple authors on a single paper; in bioinformatics this has created hybrid or fractional scientists who find they are being positioned not just in-between established disciplines but also in-between as middle authors or, worse still, left off papers altogether. (shrink)

This article proposes a new bi-directional way of understanding the convergence of biology and computing. It argues for a reciprocal interaction in which biology and computing have shaped and are currently reshaping each other. In so doing, we qualify both the view of a natural marriage and of a digital shaping of biology, which are common in the literature written by scientists, STS, and communication scholars. The DNA database is at the center of this interaction. We argue that DNA databases (...) are spaces of convergence for computing and biology that change in form, meaning, and function from the 1960s to the 2000s. The first part of the article shows how, in the 1980s, DNA sequencing shifted from passively incorporating computers to be increasingly modeled in digital coding and decoding. Information retrieval algorithms, reciprocally, were altered according to the peculiarities of DNA in the first sequence-storage databases. The second part of the article investigates the impact of these reciprocal interactions and globalization on the organization of research centers, ways of conducting big science, and scientific values. Through convergence and new technologies such as data mining, biology and computing were transformed technologically, institutionally, and culturally into a new bio-data enterprise called genomics. (shrink)