Genes are thought to have evolved from long-lived and multiply-interactive molecules in the early stages of the origins of life. However, at that stage there were no replicators, and the distinction between interactors and replicators did not yet apply. Nevertheless, the process of evolution that proceeded from initial autocatalytic hypercycles to full organisms was a Darwinian process of selection of favourable variants. We distinguish therefore between Neo-Darwinian evolution and the related Weismannian and Central Dogma divisions, on the one hand, and (...) the more generic category of Darwinian evolution on the other. We argue that Hull’s and Dawkins’ replicator/interactor distinction of entities is a sufficient, but not necessary, condition for Darwinian evolution to take place. We conceive the origin of genes as a separation between different types of molecules in a thermodynamic state space, and employ a notion of reproducers. (shrink)

This paper assesses Sarkar's ([2003]) deflationary account of genetic information. On Sarkar's account, genes carry information about proteins because protein synthesis exemplifies what Sarkar calls a ‘formal information system’. Furthermore, genes are informationally privileged over non-genetic factors of development because only genes enter into arbitrary relations to their products (in virtue of the alleged arbitrariness of the genetic code). I argue that the deflationary theory does not capture four essential features of the ordinary concept of genetic information: intentionality, exclusiveness, asymmetry, (...) and causal relevance. It is therefore further removed from what is customarily meant by genetic information than Sarkar admits. Moreover, I argue that it is questionable whether the account succeeds in demonstrating that information is theoretically useful in molecular genetics. Introduction Sarkar's Information System The Pre-theoretic Features of Genetic Information 3.1 Intentionality 3.2 Exclusiveness 3.3 Asymmetry 3.4 Causal relevance Theoretical Usefulness Conclusion CiteULike Connotea Del.icio.us What's this? (shrink)

According to a recent suggestion, the names of gene taxa should be conceived of as referring to individuals with concrete genes as their parts, just as the names of biological species are often understood as denoting individuals with organisms as their parts. Although prima facie this suggestion might advance the debate on gene concepts in a similar way as the species-are-individuals thesis advanced the debate on species concepts, I argue that the principal arguments in support of the gene-individuality thesis are (...) much less compelling than the parallel arguments in the species case. In addition, I argue that the notion of biological function invoked in the gene-individuality thesis (selected effect) is not the one that biologists actually use when individuating genes. Contra the gene-individuality thesis, I argue that gene names refer to kinds, defined primarily (though not exclusively) by causal-role functions. (shrink)

Molecular biologists exploit information conveyed by mechanistic models for experimental purposes. In this article, I make sense of this aspect of biological practice by developing Keller’s idea of the distinction between ‘models of’ and ‘models for’. ‘Models of (phenomena)’ should be understood as models representing phenomena and are valuable if they explain phenomena. ‘Models for (manipulating phenomena)’ are new types of material manipulations and are important not because of their explanatory force, but because of the interventionist strategies they afford. This (...) is a distinction between aspects of the same model. In molecular biology, models may be treated either as ‘models of’ or as ‘models for’. By analysing the discovery and characterization of restriction–modification systems and their exploitation for DNA cloning and mapping, I identify the differences between treating a model as a ‘model of’ or as a ‘model for’. These lie in the cognitive disposition of the modeller towards the model: a modeller will look at a model as a ‘model of’ if interested in its explanatory force, or as a ‘model for’ if interested in the material manipulations it can possibly afford. (shrink)

According to the variety of evidence thesis items of evidence from independent lines of investigation are more confirmatory, ceteris paribus, than, for example, replications of analogous studies. This thesis is known to fail (Bovens and Hartmann; Claveau). However, the results obtained by Bovens and Hartmann only concern instruments whose evidence is either fully random or perfectly reliable; instead, for Claveau, unreliability is modelled as deterministic bias. In both cases, the unreliable instrument delivers totally irrelevant information. We present a model that (...) formalizes both reliability and unreliability differently. Our instruments either are reliable, but affected by random error, or are biased but not deterministically so.Bovens and Hartmann’s results are counter-intuitive in that in their model a long series of consistent reports from the same instrument does not raise suspicion of ‘too-good-to-be-true’ evidence. This happens precisely because they contemplate neither the role of systematic bias, nor unavoidable random error of reliable instruments. In our model, the variety of evidence thesis fails as well, but the area of failure is considerably smaller than for Bovens and Hartmann and Claveau, and holds for (the majority of) realistic cases (that is, where biased instruments are very biased). The essential mechanism that triggers variety of evidence thesis failure is the rate of false to true positives for the two kinds of instruments. Our emphasis is on modelling beliefs about sources of knowledge and their role in hypothesis confirmation in interaction with dimensions of evidence, such as variety and consistency. (shrink)

The Hodgkin–Huxley (HH) model of the action potential is a theoretical pillar of modern neurobiology. In a number of recent publications, Carl Craver ([2006], [2007], [2008]) has argued that the model is explanatorily deficient because it does not reveal enough about underlying molecular mechanisms. I offer an alternative picture of the HH model, according to which it deliberately abstracts from molecular specifics. By doing so, the model explains whole-cell behaviour as the product of a mass of underlying low-level events. The (...) issue goes beyond cellular neurobiology, for the strategy of abstraction exhibited in the HH case is found in a range of biological contexts. I discuss why it has been largely neglected by advocates of the mechanist approach to explanation. 1 Introduction2 A Primer on the HH Model2.1 The basic qualitative picture2.2 The quantitative model3 Interlude: What Did Hodgkin and Huxley Think?4 Craver’s View4.1 Mechanistic explanation4.2 Sketches4.3 Craver's view: The HH model as a mechanism sketch5 An Alternative View of the HH Model5.1 Another look at the equations5.2 The discrete-gating picture5.3 The road paved by Hodgkin and Huxley5.4 Summary and comparison to Craver6 Conclusion: The HH Model and Mechanistic Explanation6.1 Sketches and abstractions6.2 Why has aggregative abstraction been overlooked? (shrink)

Many biological investigations are organized around a small group of species, often referred to as ‘model organisms’, such as the fruit fly Drosophila melanogaster. The terms ‘model’ and ‘modelling’ also occur in biology in association with mathematical and mechanistic theorizing, as in the Lotka–Volterra model of predator-prey dynamics. What is the relation between theoretical models and model organisms? Are these models in the same sense? We offer an account on which the two practices are shown to have different epistemic characters. (...) Theoretical modelling is grounded in explicit and known analogies between model and target. By contrast, inferences from model organisms are empirical extrapolations. Often such extrapolation is based on shared ancestry, sometimes in conjunction with other empirical information. One implication is that such inferences are unique to biology, whereas theoretical models are common across many disciplines. We close by discussing the diversity of uses to which model organisms are put, suggesting how these relate to our overall account. 1 Introduction2 Volterra and Theoretical Modelling3 Drosophila as a Model Organism4 Generalizing from Work on Model Organisms5 Phylogenetic Inference and Model Organisms6 Further Roles of Model Organisms6.1 Preparative experimentation6.2 Model organisms as paradigms6.3 Model organisms as theoretical models6.4 Inspiration for engineers6.5 Anchoring a research community7 Conclusion. (shrink)

The inapplicability of variations on theory reduction in the context of genetics and their irrelevance to ongoing research has led to an anti-reductionist consensus in philosophy of biology. One response to this situation is to focus on forms of reductive explanation that better correspond to actual scientific reasoning (e.g. part–whole relations). Working from this perspective, we explore three different aspects (intrinsicality, fundamentality, and temporality) that arise from distinct facets of reductive explanation: composition and causation. Concentrating on these aspects generates new (...) forms of reductive explanation and conditions for their success or failure in biology and other sciences. This analysis is illustrated using the case of protein folding in molecular biology, which demonstrates its applicability and relevance, as well as illuminating the complexity of reductive reasoning in a specific biological context. (shrink)

I distinguish two forms of pluralism about biological functions, between-discipline pluralism and within-discipline pluralism. Between-discipline pluralism holds that different theories of function are appropriate for different subdisciplines of biology and psychology. I provide reasons for rejecting this view. Instead, I recommend within-discipline pluralism, which emphasizes the plurality of function concepts at play within any given subdiscipline of biology and psychology.

This article reconsiders the theoretical role of the genetic code. By drawing on published and unpublished sources from the 1950s, I analyse how the code metaphor was actually employed by the scientists who first promoted its use. The analysis shows that the term ‘code’ picked out mechanism sketches, consisting of more or less detailed descriptions of ordinary molecular components, processes, and structural properties of the mechanism of protein synthesis. The sketches provided how-possibly explanations for the ordering of amino acids by (...) nucleic acids. I argue that employing the code metaphor was justified in virtue of its descriptive-denotational and explanatory roles, and because it highlighted a similarity with conventional codes that was particularly salient at the time. 1 Introduction2 Coding Schemes in the 1950s2.1 The research problem: Determining amino acid sequences2.2 The solution: Mapping schemes or ‘codes’3 The Code Metaphor Played Descriptive and Explanatory Roles4 Theness of Codes and the Expendability of the Code Metaphor5 The Role of Arbitrariness6 Conclusions. (shrink)

Completeness is an important but misunderstood norm of explanation. It has recently been argued that mechanistic accounts of scientific explanation are committed to the thesis that models are complete only if they describe everything about a mechanism and, as a corollary, that incomplete models are always improved by adding more details. If so, mechanistic accounts are at odds with the obvious and important role of abstraction in scientific modelling. We respond to this characterization of the mechanist’s views about abstraction and (...) articulate norms of completeness for mechanistic explanations that have no such unwanted implications. _1_ Introduction _2_ A Balancing Act: When Do Details Matter? _3_ The Norms of Causal Explanation _4_ The Norms of Constitutive Explanation _5_ Salmon-Completeness _6_ From More Details to More Relevant Details _7_ Non-explanatory Virtues of Abstraction _8_ From Explanatory Models to Explanatory Knowledge _9_ Mechanistic Completeness Reconsidered _10_ Conclusion. (shrink)

Both clinical research and basic science rely on the epistemic practice of extrapolation from surrogate models, to the point that explanatory accounts presented in review papers and biology textbooks are in fact composite pictures reconstituted from data gathered in a variety of distinct experimental setups. This raises two new challenges to previously proposed mechanistic-similarity solutions to the problem of extrapolation: one pertaining to the absence of mechanistic knowledge in the early stages of research and the second to the large number (...) of extrapolations underpinning explanatory accounts. An analysis of the strategies deployed in experimental research supports the conclusion that while results from validated surrogate models are treated as a legitimate line of evidence supporting claims about target systems, the overall structure of research projects also demonstrates that extrapolative inferences are not considered definitive or sufficient evidence, but only partially justified hypotheses subjected to further testing. 1 Introduction2 Surrogate Models2.1 What exactly is a surrogate model?2.2 Why use surrogate models?3 Prior Validation of Surrogate Models3.1 The validation and ranking of surrogate models in the early stages of basic research3.2 The validation of surrogate models in later stages of basic research and in clinical research4 ‘Big Picture’ Accounts and the Extrapolations Underpinning Them4.1 The mosaic nature of mechanistic descriptions in basic science4.2 Challenges for mechanistic-similarity-based validation protocols5 Retrospective Testing of Extrapolated Knowledge5.1 Holistic confirmation5.2 Fallback strategies6 Conclusions. (shrink)