Dana Matthiessen

It is argued that once biological systems reach a certain level of complexity, mechanistic explanations provide an inadequate account of many relevant phenomena. In this article, I evaluate such claims with respect to a representative programme in systems biological research: the study of regulatory networks within single-celled organisms. I argue that these networks are amenable to mechanistic philosophy without need to appeal to some alternate form of explanation. In particular, I claim that we can understand the mathematical modelling techniques of systems biologists as part of a broader practice of constructing and evaluating mechanism schemas. This argument is elaborated by considering the case of bacterial chemotactic networks, where some research has been interpreted as explaining phenomena by means of abstract design principles.

This paper argues against general claims for the epistemic superiority of experiment over observation. It does so by dissociating the benefits traditionally attributed to experiment from physical manipulation. In place of manipulation, we argue that other features of research methods do confer epistemic advantages in comparison to methods in which they are diminished. These features better track the epistemic successes and failures of scientific research, cross-cut the observation/experiment distinction, and nevertheless explain why manipulative experiments are successful when they are.

This paper proposes an account of accurate scientific representation in terms of techniques that produce data from a target phenomenon. I consider an approach to accurate representation that abstracts from such epistemic factors, justified by a thesis I call Ontic Priority. This holds that criteria for representational accuracy depend on a pre-established account of the nature of the relation between a model and its target phenomenon. I challenge Ontic Priority, drawing on the observation that many working scientists do not have access to information allowing them to describe this relation between model and target or compare them accordingly. I critique the ability of an ontic-first approach to provide accuracy criteria in such cases and present historical support for an alternative according to which a model is accurate if and only if integrating the model into a theory of the data acquisition process yields well-fitting predictions of patterns in the data.

This paper tracks the development of Boyle’s conception of the natural world in terms of the popular “book of nature” trope. Boyle initially spoke of the creatures and phenomena of nature in a spiritual and moral register, as emblems of divine purpose, but gradually shifted from this ideographic view to an alphabetical account, which at times became posed in explicitly cryptographic terms. I explain this transition toward cryptographic metaphors in terms of Boyle’s social and intellectual milieu and their concordance with the reductive and conjectural character of the mechanical philosophical program.

In this paper I analyze the process by which modelers in systems biology arrive at an adequate representation of the biological structures thought to underlie data gathered from high-throughput experiments. Contrary to views that causal claims and explanations are rare in systems biology, I argue that in many studies of gene regulatory networks modelers aim at a representation of causal structure. In addressing modeling challenges, they draw on assumptions informed by theory and pragmatic considerations in a manner that is guided by an interventionist conception of causal structure. While doubts have been raised about the applicability of this notion of causality to complex biological systems, it is here seen to be an adequate guide to inquiry.