This paper considers the applicability of standard accounts of causation to living systems. In particular it examines critically the increasing tendency to equate causal explanation with the identification of a mechanism. A range of differences between living systems and paradigm mechanisms are identified and discussed. While in principle it might be possible to accommodate an account of mechanism to these features, the attempt to do so risks reducing the idea of a mechanism to vacuity. It is proposed that the solution (...) to this problem requires the development of a philosophical account of process adequate to apply to living systems. (shrink)

The scientific study of living organisms is permeated by machine and design metaphors. Genes are thought of as the ‘‘blueprint’’ of an organism, organisms are ‘‘reverse engineered’’ to discover their functionality, and living cells are compared to biochemical factories, complete with assembly lines, transport systems, messenger circuits, etc. Although the notion of design is indispensable to think about adaptations, and engineering analogies have considerable heuristic value (e.g., optimality assumptions), we argue they are limited in several important respects. In particular, the (...) analogy with human-made machines falters when we move down to the level of molecular biology and genetics. Living organisms are far more messy and less transparent than human-made machines. Notoriously, evolution is an opportunistic tinkerer, blindly stumbling on ‘‘designs’’ that no sensible engineer would come up with. Despite impressive technological innovation, the prospect of artificially designing new life forms from scratch has proven more difficult than the superficial analogy with ‘‘programming’’ the right ‘‘software’’ would suggest. The idea of applying straightforward engineering approaches to living systems and their genomes— isolating functional components, designing new parts from scratch, recombining and assembling them into novel life forms—pushes the analogy with human artifacts beyond its limits. In the absence of a one-to-one correspondence between genotype and phenotype, there is no straightforward way to implement novel biological functions and design new life forms. Both the developmental complexity of gene expression and the multifarious interactions of genes and environments are serious obstacles for ‘‘engineering’’ a particular phenotype. The problem of reverse-engineering a desired phenotype to its genetic ‘‘instructions’’ is probably intractable for any but the most simple phenotypes. Recent developments in the field of bio-engineering and synthetic biology reflect these limitations. Instead of genetically engineering a desired trait from scratch, as the machine/engineering metaphor promises, researchers are making greater strides by co-opting natural selection to ‘‘search’’ for a suitable genotype, or by borrowing and recombining genetic material from extant life forms. (shrink)

Co‐option of the eye developmental gene regulatory network may have led to the appearance of novel functional traits on the wings of flies and butterflies. The first trait is a recently described wing organ in a species of extinct midge resembling the outer layers of the midge's own compound eye. The second trait is red pigment patches on Heliconius butterfly wings connected to the expression of an eye selector gene, optix. These examples, as well as others, are discussed regarding the (...) type of empirical evidence and burden of proof that have been used to infer gene network co‐option underlying the origin of novel traits. A conceptual framework describing increasing confidence in inference of network co‐option is proposed. Novel research directions to facilitate inference of network co‐option are also highlighted, especially in cases where the pre‐existent and novel traits do not resemble each other. (shrink)

The scientific study of living organisms is permeated by machine and design metaphors. Genes are thought of as the ‘‘blueprint’’ of an organism, organisms are ‘‘reverse engineered’’ to discover their func- tionality, and living cells are compared to biochemical factories, complete with assembly lines, transport systems, messenger circuits, etc. Although the notion of design is indispensable to think about adapta- tions, and engineering analogies have considerable heuristic value (e.g., optimality assumptions), we argue they are limited in several important respects. In (...) particular, the analogy with human-made machines falters when we move down to the level of molecular biology and genetics. Living organisms are far more messy and less transparent than human-made machines. Notoriously, evolution is an oppor- tunistic tinkerer, blindly stumbling on ‘‘designs’’ that no sensible engineer would come up with. Despite impressive technological innovation, the prospect of artificially designing new life forms from scratch has proven more difficult than the superficial analogy with ‘‘programming’’ the right ‘‘software’’ would sug- gest. The idea of applying straightforward engineering approaches to living systems and their genomes— isolating functional components, designing new parts from scratch, recombining and assembling them into novel life forms—pushes the analogy with human artifacts beyond its limits. In the absence of a one-to-one correspondence between genotype and phenotype, there is no straightforward way to imple- ment novel biological functions and design new life forms. Both the developmental complexity of gene expression and the multifarious interactions of genes and environments are serious obstacles for ‘‘engi- neering’’ a particular phenotype. The problem of reverse-engineering a desired phenotype to its genetic ‘‘instructions’’ is probably intractable for any but the most simple phenotypes. Recent developments in the field of bio-engineering and synthetic biology reflect these limitations. Instead of genetically engi- neering a desired trait from scratch, as the machine/engineering metaphor promises, researchers are making greater strides by co-opting natural selection to ‘‘search’’ for a suitable genotype, or by borrowing and recombining genetic material from extant life forms. (shrink)

In recent years, the concept of evolvability has been gaining in prominence both within evolutionary developmental biology (evo-devo) and the broader field of evolutionary biology. Despite this, there remains considerable disagreement about what evolvability is. This article offers a solution to this problem. I argue that, in focusing too closely on the role played by evolvability as an explanandum in evo-devo, existing philosophical attempts to clarify the evolvability concept have been overly narrow. Within evolutionary biology more broadly, evolvability offers a (...) robust explanation for the evolutionary trajectories of populations. Evolvability is an abstract, robust, dispositional property of populations, which captures the joint causal influence of their internal features on the outcomes of evolution (as opposed to the causal influence of selection, which is often characterized as external). When considering the nature of the physical basis of this disposition, it becomes clear that the many existing definitions of evolvability at play within evo-devo should be understood as capturing only aspects of a much broader phenomenon. 1 Introduction2 The Problem of Evolvability3 The Theoretical Role of Evolvability in Evolutionary Biology3.1 The explanatory targets of evolutionary biology3.2 Selection-based explanations3.3 Lineage explanations3.4 Evolvability-based explanations3.5 What properties must evolvability have?4 What Evolvability Really Is4.1 Making sense of ft4.2 Making sense of x and b5 What of the Limbs? The Power of E6 Conclusion. (shrink)

Genes are often described by biologists using metaphors derived from computa- tional science: they are thought of as carriers of information, as being the equivalent of ‘‘blueprints’’ for the construction of organisms. Likewise, cells are often characterized as ‘‘factories’’ and organisms themselves become analogous to machines. Accordingly, when the human genome project was initially announced, the promise was that we would soon know how a human being is made, just as we know how to make airplanes and buildings. Impor- tantly, (...) modern proponents of Intelligent Design, the latest version of creationism, have exploited biologists’ use of the language of information and blueprints to make their spurious case, based on pseudoscientific concepts such as ‘‘irreducible complexity’’ and on flawed analogies between living cells and mechanical factories. However, the living organ- ism = machine analogy was criticized already by David Hume in his Dialogues Concerning Natural Religion. In line with Hume’s criticism, over the past several years a more nuanced and accurate understanding of what genes are and how they operate has emerged, ironically in part from the work of computational scientists who take biology, and in particular developmental biology, more seriously than some biologists seem to do. In this article we connect Hume’s original criticism of the living organism = machine analogy with the modern ID movement, and illustrate how the use of misleading and outdated metaphors in science can play into the hands of pseudoscientists. Thus, we argue that dropping the blueprint and similar metaphors will improve both the science of biology and its understanding by the general public. (shrink)

This paper attempts to elucidate three characteristics of causal relationships that are important in biological contexts. Stability has to do with whether a causal relationship continues to hold under changes in background conditions. Proportionality has to do with whether changes in the state of the cause “line up” in the right way with changes in the state of the effect and with whether the cause and effect are characterized in a way that contains irrelevant detail. Specificity is connected both to (...) David Lewis’ notion of “influence” and also with the extent to which a causal relation approximates to the ideal of one cause–one effect. Interrelations among these notions and their possible biological significance are also discussed. (shrink)

This paper explores the connections between inheritance systems, evolvability and modularity. I argue that the transmission of symbiotic micro-organisms is an inheritance system, and one that is evolutionarily significant because symbionts generate biologically crucial aspects of their hosts’ organisation through modular developmental pathways. More specifically, I develop and defend five theses.

A number of philosophers and ‘evolutionary psychologists’ have argued that attacks on adaptationism in contemporary biology are misguided. These thinkers identify anti-adaptationism with advocacy of non-adaptive modes of explanation. They overlook the influence of anti-adaptationism in the development of more rigorous forms of adaptive explanation. Many biologists who reject adaptationism do not reject Darwinism. Instead, they have pioneered the contemporary historical turn in the study of adaptation. One real issue which remains unresolved amongst these methodological advances is the nature of (...) ‘phylogenetic inertia’. To what extent is an adaptive explanation needed for the persistence of a trait as well as its origin? (shrink)

This paper explores the question of whether all or most explanations in biology are, or ideally should be, ‘mechanistic’. I begin by providing an account of mechanistic explanation, making use of the interventionist ideas about causation I have developed elsewhere. This account emphasizes the way in which mechanistic explanations, at least in the biological sciences, integrate difference‐making and spatio‐temporal information, and exhibit what I call fine‐tunedness of organization. I also emphasize the role played by modularity conditions in mechanistic explanation. I (...) will then argue, in agreement with John Dupré, that, given this account, it is plausible that many biological systems require explanations that are relatively non‐mechanical or depart from expectations one associates with the behaviour of machines. (shrink)

Co‐option of the eye developmental gene regulatory network may have led to the appearance of novel functional traits on the wings of flies and butterflies. The first trait is a recently described wing organ in a species of extinct midge resembling the outer layers of the midge's own compound eye. The second trait is red pigment patches on Heliconius butterfly wings connected to the expression of an eye selector gene, optix. These examples, as well as others, are discussed regarding the (...) type of empirical evidence and burden of proof that have been used to infer gene network co‐option underlying the origin of novel traits. A conceptual framework describing increasing confidence in inference of network co‐option is proposed. Novel research directions to facilitate inference of network co‐option are also highlighted, especially in cases where the pre‐existent and novel traits do not resemble each other. (shrink)

Like Laland et al., I think Mayr’s distinction is problematic, but I identify a further problem with it. I argue that Mayr’s distinction is a false dichotomy, and obscures an important question about evolutionary change. I show how this question, once revealed, sheds light on some debates in evo-devo that Laland et al.’s analysis cannot, and suggest that it provides a different view about how future integration between biological disciplines might proceed.

In recent years, biologists have increasingly been asking whether the ability to evolve — the evolvability — of biological systems, itself evolves, and whether this phenomenon is the result of natural selection or a by-product of other evolutionary processes. The concept of evolvability, and the increasing theoretical and empirical literature that refers to it, may constitute one of several pillars on which an extended evolutionary synthesis will take shape during the next few years, although much work remains to be done (...) on how evolvability comes about. (shrink)

In a now classic paper published in 1991, Alberch introduced the concept of genotype–phenotype (G!P) mapping to provide a framework for a more sophisticated discussion of the integration between genetics and developmental biology that was then available. The advent of evo-devo first and of the genomic era later would seem to have superseded talk of transitions in phenotypic space and the like, central to Alberch’s approach. On the contrary, this paper shows that recent empirical and theoretical advances have only sharpened (...) the need for a different conceptual treat- ment of how phenotypes are produced. Old-fashioned metaphors like genetic blueprint and genetic programme are not only woefully inadequate but positively misleading about the nature of G!P, and are being replaced by an algorithmic approach emerging from the study of a variety of actual G!P maps. These include RNA folding, protein function and the study of evolvable soft- ware. Some generalities are emerging from these disparate fields of analysis, and I suggest that the concept of ‘developmental encoding’ (as opposed to the classical one of genetic encoding) provides a promising computational–theoretical underpinning to coherently integrate ideas on evolvability, modularity and robustness and foster a fruitful framing of the G!P mapping problem. (shrink)

This paper describes a pattern of explanation prevalent in the biological sciences that I call a ‘lineage explanation’. The aim of these explanations is to make plausible certain trajectories of change through phenotypic space. They do this by laying out a series of stages, where each stage shows how some mechanism worked, and the differences between each adjacent stage demonstrates how one mechanism, through minor modifications, could be changed into another. These explanations are important, for though it is widely accepted (...) that there is an ‘incremental constraint’ on evolutionary change, in an important class of cases it is difficult to see how to satisfy this constraint. I show that lineage explanations answer important questions about evolutionary change, but do so by demonstrating differences between individuals rather than invoking population processes, such as natural selection.1. Introduction2. Turning a ‘Scale’ into a ‘Plume’3. Lineage Explanations in Biology3.1. The evolution of eyes3.2. The evolution of feathers4. The Two Dimensions of a Lineage Explanation4.1. The production dimension4.2. The continuity dimension4.3. The dual role of the parts5. Constraining the Explanations6. Operational and Generative Lineages7. Explaining Change Without Populations8. Conclusion. (shrink)

This paper considers the applicability of standard accounts of causation to living systems. In particular it examines critically the increasing tendency to equate causal explanation with the identification of a mechanism. A range of differences between living systems and paradigm mechanisms are identified and discussed. While in principle it might be possible to accommodate an account of mechanism to these features, the attempt to do so risks reducing the idea of a mechanism to vacuity. It is proposed that the solution (...) to this problem requires the development of a philosophical account of process adequate to apply to living systems. (shrink)

Preface ix 1 Meaning and the Means to an Understanding of Ends 2 Why Is an Eye? 21 3 Adaptationism and Engineering 39 4 On Five "-Isms" 67 5 Function, Selection, and Explanation 87 6 Deflating Function 119 7 Artifacts and Organisms 139 References 167 Index 177.

The rights and wrongs of adaptationism areoften discussed by appeal to what I call theartefact model. Anti-adaptationistscomplain that the use of optimality modelling,reverse engineering and other techniques areindicative of a mistaken and outmoded beliefthat organisms are like well-designedartefacts. Adaptationists (e.g. Dennett 1995)respond with the assertion that viewingorganisms as though they were well designed isa fruitful, perhaps necessary research strategyin evolutionary biology. Anti-adaptationistsare right when they say that techniques likereverse engineering are liable to mislead. This fact does not undermine the artefact (...) modelprecisely because the same techniques misleadus for the same reasons when they are appliedunreflectively to artefacts. Thoseadaptationists who hold only that it isworthwhile to investigate organisms as thoughthey were artefacts and thoseanti-adaptationists who criticise simplisticdesign models have far more in common than thelabels attached to their positions mightsuggest. (shrink)