Experimental modeling is the construction of theoretical models hand in hand with experimental activity. As explained in Section 1, experimental modeling starts with claims about phenomena that use abstract concepts, concepts whose conditions of realization are not yet specified; and it ends with a concrete model of the phenomenon, a model that can be tested against data. This paper argues that this process from abstract concepts to concrete models involves judgments of relevance, which are irreducibly normative. In Section 2, we (...) show, on the basis of several case studies, how these judgments contribute to the determination of the conditions of realization of the abstract concepts and, at the same time, of the quantities that characterize the phenomenon under study. Then, in Section 3, we compare this view on modeling with other approaches that also have acknowledged the role of relevance judgments in science. To conclude, in Section 4, we discuss the possibility of a plurality of relevance judgments and introduce a distinction between locally and generally relevant factors. (shrink)

Knowledge-making practices in biology are being strongly affected by the availability of data on an unprecedented scale, the insistence on systemic approaches and growing reliance on bioinformatics and digital infrastructures. What role does theory play within data-intensive science, and what does that tell us about scientific theories in general? To answer these questions, I focus on Open Biomedical Ontologies, digital classification tools that have become crucial to sharing results across research contexts in the biological and biomedical sciences, and argue that (...) they constitute an example of classificatory theory. This form of theorizing emerges from classification practices in conjunction with experimental know-how and expresses the knowledge underpinning the analysis and interpretation of data disseminated online. (shrink)

Exploratory experimentation and high-throughput molecular biology appear to have considerable affinity for each other. Included in the latter category is metagenomics, which is the DNA-based study of diverse microbial communities from a vast range of non-laboratory environments. Metagenomics has already made numerous discoveries and these have led to reinterpretations of fundamental concepts of microbial organization, evolution, and ecology. The most outstanding success story of metagenomics to date involves the discovery of a rhodopsin gene, named proteorhodopsin, in marine bacteria that were (...) never suspected to have any photobiological capacities. A discussion of this finding and its detailed investigation illuminates the relationship between exploratory experimentation and metagenomics. Specifically, the proteorhodopsin story indicates that a dichotomous interpretation of theory-driven and exploratory experimentation is insufficient and that an interactive understanding of these two types of experimentation can be usefully supplemented by another category, "natural history experimentation". Further reflection on the context of metagenomics suggests the necessity of thinking more historically about exploratory and other forms of experimentation. (shrink)

Beatty, Brandon, and Sober agree that biological generalizations, when contingent, do not qualify as laws. Their conclusion follows from a normative definition of law inherited from the Logical Empiricists. I suggest two additional approaches: paradigmatic and pragmatic. Only the pragmatic represents varying kinds and degrees of contingency and exposes the multiple relationships found among scientific generalizations. It emphasizes the function of laws in grounding expectation and promotes the evaluation of generalizations along continua of ontological and representational parameters. Stability of conditions (...) and strength of determination in nature govern projectibility. Accuracy, ontological level, simplicity, and manageability provide additional measures of usefulness. (shrink)

The concept of mechanism is analyzed in terms of entities and activities, organized such that they are productive of regular changes. Examples show how mechanisms work in neurobiology and molecular biology. Thinking in terms of mechanisms provides a new framework for addressing many traditional philosophical issues: causality, laws, explanation, reduction, and scientific change.

Eliminative reasoning seems to play an important role in the sciences, but should it be part of our best theory of science? Statistical evidence, prevalent across the sciences, causes problems for eliminative inference, supporting the view that probabilistic theories of confirmation provide a better framework for reasoning about evidence. Here I argue that deductive elimination has an important inferential role to play in science, one that is compatible with probabilistic approaches to evidence. Eliminative inferences help frame testing problems, an essential (...) step that determines the context for evaluating statistical evidence. I illustrate this process with examples from molecular evolutionary biology. (shrink)

In its last round of publications in September 2012, the Encyclopedia Of DNA Elements (ENCODE) assigned a biochemical function to most of the human genome, which was taken up by the media as meaning the end of ‘Junk DNA’. This provoked a heated reaction from evolutionary biologists, who among other things claimed that ENCODE adopted a wrong and much too inclusive notion of function, making its dismissal of junk DNA merely rhetorical. We argue that this criticism rests on misunderstandings concerning (...) the nature of the ENCODE project, the relevant notion of function and the claim that most of our genome is junk. We argue that evolutionary accounts of function presuppose functions as ‘causal roles’, and that selection is but a useful proxy for relevant functions, which might well be unsuitable to biomedical research. Taking a closer look at the discovery process in which ENCODE participates, we argue that ENCODE’s strategy of biochemical signatures successfully identified activities of DNA elements with an eye towards causal roles of interest to biomedical research. We argue that ENCODE’s controversial claim of functionality should be interpreted as saying that 80 % of the genome is engaging in relevant biochemical activities and is very likely to have a causal role in phenomena deemed relevant to biomedical research. Finally, we discuss ambiguities in the meaning of junk DNA and in one of the main arguments raised for its prevalence, and we evaluate the impact of ENCODE’s results on the claim that most of our genome is junk. (shrink)

Starting with some illustrative examples, I develop a systematic account of a specific type of experimentation--an experimentation which is not, as in the "standard view", driven by specific theories. It is typically practiced in periods in which no theory or--even more fundamentally--no conceptual framework is readily available. I call it exploratory experimentation and I explicate its systematic guidelines. From the historical examples I argue furthermore that exploratory experimentation may have an immense, but hitherto widely neglected, epistemic significance.

It is considered in some quarters that hypothesis‐driven methods are the only valuable, reliable or significant means of scientific advance. Data‐driven or ‘inductive’ advances in scientific knowledge are then seen as marginal, irrelevant, insecure or wrong‐headed, while the development of technology—which is not of itself ‘hypothesis‐led’ (beyond the recognition that such tools might be of value)—must be seen as equally irrelevant to the hypothetico‐deductive scientific agenda. We argue here that data‐ and technology‐driven programmes are not alternatives to hypothesis‐led studies in (...) scientific knowledge discovery but are complementary and iterative partners with them. Many fields are data‐rich but hypothesis‐poor. Here, computational methods of data analysis, which may be automated, provide the means of generating novel hypotheses, especially in the post‐genomic era. BioEssays 26:99–105, 2004. © 2003 Wiley Periodicals, Inc. (shrink)

Eliminative induction is a method for finding the truth by using evidence to eliminate false competitors. It is often characterized as "induction by means of deduction"; the accumulating evidence eliminates false hypotheses by logically contradicting them, while the true hypothesis logically entails the evidence, or at least remains logically consistent with it. If enough evidence is available to eliminate all but the most implausible competitors of a hypothesis, then (and only then) will the hypothesis become highly confirmed. I will argue (...) that, with regard to the evaluation of hypotheses, Bayesian inductive inference is essentially a probabilistic form of induction by elimination. Bayesian induction is an extension of eliminativism to cases where, rather than contradict the evidence, false hypotheses imply that the evidence is very unlikely, much less likely than the evidence would be if some competing hypothesis were true. This is not, I think, how Bayesian induction is usually understood. The recent book by Howson and Urbach, for example, provides an excellent, comprehensive explanation and defense of the Bayesian approach; but this book scarcely remarks on Bayesian induction's eliminative nature. Nevertheless, the very essence of Bayesian induction is the refutation of false competitors of a true hypothesis, or so I will argue. (shrink)

My aim in this article is to introduce readers to the topic of exploratory experimentation and briefly explain how the three articles that follow, by Richard Burian, Kevin Elliott, and Maureen O'Malley, advance our understanding of the nature and significance of exploratory research. I suggest that the distinction between exploratory and theory-driven experimentation is multidimensional and that some of the dimensions are continuums. I point out that exploratory experiments are typically theory-informed even if they are not theory-driven. I also distinguish (...) between research programs and experiments. Research programs that are largely exploratory, such as the ones discussed in these case studies, can involve both exploratory and theory-driven experimentation. (shrink)

In his highly regarded history of the rise of clinical trials in America, HarryMarks describes how their widespread adoption resulted largely fromthe efforts of ‘therapeutic reformers’ who sought to replace the individualexpertise of clinicians with the ‘science of controlled experiment’. Thetransition described by Marks resembles in many respects the transition fromthe ‘truth-to-nature’ objectivity of individual experts to a ‘mechanical’ formof objectivity portrayed by Daston and Galison. In particular,Marks details the passage from a regime of trust in expertise and experts to (...) aregime based on the mechanical generation of data, the elimination ofhuman judgement and the adoption of a ‘view from nowhere’ where individual and group idiosyncrasies are systematically suppressedthrough recourse to quantitative methods.[...]. (shrink)

In this article, two issues regarding mechanisms are discussed. The first concerns the relationships between “mechanism description” and “mechanism explanation.” It is proposed that it is rather plausible to think of them as two distinct epistemic acts. The second deals with the different molecular biology explanatory contexts, and it is shown that some of them require physics and its laws.