Sabina Leonelli

Scientific classification has long been recognized as involving a specific style of reasoning and doing research, and as occasionally affecting the development of scientific theories. However, the role played by classificatory activities in generating theories has not been closely investigated within the philosophy of science. I argue that classificatory systems can themselves become a form of theory, which I call classificatory theory, when they come to formalize and express the scientific significance of the elements being classified. This is particularly evident in some of the classification practices used in contemporary experimental biology, such as bio-ontologies used to classify genomic data and typologies used to classify “normal” stages of development in developmental biology. In this paper, I explore some characteristics of classificatory theories and ways in which they differ from other types of scientific theories and other components of scientific epistemology, such as models and background assumptions

This paper aims to identify the key characteristics of model organisms that make them a specific type of model within the contemporary life sciences: in particular, we argue that the term “model organism” does not apply to all organisms used for the purposes of experimental research. We explore the differences between experimental and model organisms in terms of their material and epistemic features, and argue that it is essential to distinguish between their representational scope and representational target. We also examine the characteristics of the communities who use these two types of models, including their research goals, disciplinary affiliations, and preferred practices to show how these have contributed to the conceptualization of a model organism. We conclude that model organisms are a specific subgroup of organisms that have been standardized to fit an integrative and comparative mode of research, and that it must be clearly distinguished from the broader class of experimental organisms. In addition, we argue that model organisms are the key components of a unique and distinctively biological way of doing research using models.Keywords: Experimental organism; Genetics; Model organism; Modeling; Philosophy of biology; Representation

Community databases have become crucial to the collection, ordering and retrieval of data gathered on model organisms, as well as to the ways in which these data are interpreted and used across a range of research contexts. This paper analyses the impact of community databases on research practices in model organism biology by focusing on the history and current use of four community databases: FlyBase, Mouse Genome Informatics, WormBase and The Arabidopsis Information Resource. We discuss the standards used by the curators of these databases for what counts as reliable evidence, acceptable terminology, appropriate experimental set-ups and adequate materials (e.g., specimens). On the one hand, these choices are informed by the collaborative research ethos characterising most model organism communities. On the other hand, the deployment of these standards in databases reinforces this ethos and gives it concrete and precise instantiations by shaping the skills, practices, values and background knowledge required of the database users. We conclude that the increasing reliance on community databases as vehicles to circulate data is having a major impact on how researchers conduct and communicate their research, which affects how they understand the biology of model organisms and its relation to the biology of other species.

This paper discusses what it means and what it takes to integrate data in order to acquire new knowledge about biological entities and processes. Maureen O’Malley and Orkun Soyer have pointed to the scientific work involved in data integration as important and distinct from the work required by other forms of integration, such as methodological and explanatory integration, which have been more successful in captivating the attention of philosophers of science. Here I explore what data integration involves in more detail and with a focus on the role of data-sharing tools, like online databases, in facilitating this process; and I point to the philosophical implications of focusing on data as a unit of analysis. I then analyse three cases of data integration in the field of plant science, each of which highlights a different mode of integration: inter-level integration, which involves data documenting different features of the same species, aims to acquire an interdisciplinary understanding of organisms as complex wholes and is exemplified by research on Arabidopsis thaliana; cross-species integration, which involves data acquired on different species, aims to understand plant biology in all its different manifestations and is exemplified by research on Miscanthus giganteus; and translational integration, which involves data acquired from sources within as well as outside academia, aims at the provision of interventions to improve human health and is exemplified by research on Phytophtora ramorum. Recognising the differences between these efforts sheds light on the dynamics and diverse outcomes of data dissemination and integrative research; and the relations between the social and institutional roles of science, the development of data-sharing infrastructures and the production of scientific knowledge