Constraints and Spandrels in Gould's Structure of Evolutionary Theory

Gould's Structure ofEvolutionary Theory argues that Darwinism hasundergone significant revision. Although Gouldsucceeds in showing that hierarchicalapproaches have expanded Darwinism, hiscritique of adaptationism is less successful. Gould claims that the ubiquity of developmentalconstraints and spandrels has forced biologiststo soften their commitment to adaptationism. Iargue that Gould overstates his conclusion; hisprincipal claims are compatible with at leastsome versions of adaptationism. Despite thisweakness, Gould's discussion of adaptationism –particularly his discussions of the exaptivepool and cross-level spandrels – shouldprovoke new work in evolutionary theory and thephilosophy of biology.     

Adaptationism and Engineering

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.     

Engineering and evolvability

Comparing engineering to evolution typically involves adaptationist thinking, where well-designed artifacts are likened to well-adapted organisms, and the process of evolution is likened to the process of design. A quite different comparison is made when biologists focus on evolvability instead of adaptationism. Here, the idea is that complex integrated systems, whether evolved or engineered, share universal principles that affect the way they change over time. This shift from adaptationism to evolvability is a significant move for, as I argue, we can make sense of these universal principles without making any adaptationism claims. Furthermore, evolvability highlights important aspects of engineering that are ignored in the adaptationist debates. I introduce some novel engineering examples that incorporate these key neglected aspects, and use these examples to challenge some commonly cited contrasts between engineering and evolution, and to highlight some novel resemblances that have gone unnoticed.     

A Philosophical Evaluation of Adaptationism as a Heuristic Strategy

Adaptationism has prompted many a debate in philosophy of biology but the focus is usually on empirical and explanatory issues rather than methodological adaptationism (MA). Likewise, the context of evolutionary biology has provided the grounding for most discussions of the heuristic role of adaptationism. This paper extends the debate by drawing on case studies from physiology and systems biology to discuss the productive and problematic aspects of adaptationism in functional as well as evolutionary studies at different levels of biological organization. Gould and Lewontin’s Spandrels-paper famously criticized adaptationist methodology for implying a risk of generating ‘blind spots’ with respect to non-selective effects on evolution. Some have claimed that this bias can be accommodated through the testing of evolutionary hypotheses. Although this is an important aspect of overcoming the pitfalls of adaptationism, I argue that the issue of methodological biases is broader than the question of testability. I demonstrate the productivity of adaptationist heuristics but also discuss the deeper problematic aspects associated with the imperialistic tendencies of the strong account of MA.     

Optimality modeling in a suboptimal world

The fate of optimality modeling is typically linked to that of adaptationism: the two are thought to stand or fall together (Gould and Lewontin, Proc Relig Soc Lond 205:581–598, 1979; Orzack and Sober, Am Nat 143(3):361–380, 1994). I argue here that this is mistaken. The debate over adaptationism has tended to focus on one particular use of optimality models, which I refer to here as their strong use. The strong use of an optimality model involves the claim that selection is the only important influence on the evolutionary outcome in question and is thus linked to adaptationism. However, biologists seldom intend this strong use of optimality models. One common alternative that I term the weak use simply involves the claim that an optimality model accurately represents the role of selection in bringing about the outcome. This and other weaker uses of optimality models insulate the optimality approach from criticisms of adaptationism, and they account for the prominence of optimality modeling (broadly construed) in population biology. The centrality of these uses of optimality models ensures a continuing role for the optimality approach, regardless of the fate of adaptationism.

A Kantian Stance on the Intentional Stance

I examine the way in which Daniel Dennett (1987, 1995) uses his 'intentional'and 'design' stances to make the claim that intentionality is derived fromdesign. I suggest that Dennett is best understood as attempting to supplyan objective, nonintentional, naturalistic rationale for our use of intentionalconcepts. However, I demonstrate that his overall picture presupposesprior application of the intentional stance in a preconditional, ineliminable,'sense-giving' role. Construed as such, Dennett's account is almostidentical to the account of biological teleology offered by Kant in TheCritique of Judgement, with the consequence that Dennett's naturalism isuntenable. My conclusions lead to doubts concerning the legitimacy of anyaccount attempting to naturalise intentionality by extracting normativityfrom biology and also point to a novel account of biological function.     

Synthetic biology advancing clinical applications

The 'omics' era, with its identification of genetic and protein components, has combined with systems biology, which provided insights into network structures, to set the stage for synthetic biology, an emerging interdisciplinary life science that uses engineering principles. By capitalizing on an iterative design cycle that involves molecular and computational biology tools to assemble functional designer devices from a comprehensive catalogue of standardized biological components with predictable functions, synthetic biology has significantly advanced our understanding of complex control dynamics that program living systems. Such insights, collected over the past decade, are priming a variety of synthetic biology-inspired biomedical applications that have the potential to revolutionize drug discovery and production technologies, as well as treatment strategies for infectious diseases and metabolic disorders.     

Maynard Smith,optimization, and evolution

Maynard Smith’s defenses of adaptationism and of the value of optimization theory in evolutionary biology are both criticized. His defense does not adequately respond to the criticism of adaptationism by Gould and Lewontin. It is also argued here that natural selection cannot be interpreted as an optimization process if the objective function to be optimized is either (i) interpretable as a fitness, or (ii) correlated with the mean population fitness. This result holds even if fitnesses are frequency-independent; the problem is further exacerbated in the frequency-dependent context modeled by evolutionary game theory. However, Eshel and Feldman’s new results on “long-term” evolution may provide some hope for the continuing relevance of the game-theoretic framework. These arguments also demonstrate the irrelevance of attempts by Intelligent Design creationists to use computational limits on optimization algorithms as evidence against evolutionary theory. It is pointed out that adaptation, natural selection, and optimization are not equivalent processes in the context of biological evolution. It is a pleasure to dedicate this paper to the memory of John Maynard Smith. Thanks are due to James Justus and Samir Okasha for comments on an earlier draft.     

Engineering excellence in breakthrough biomedical technologies: bioengineering at the University of California, Riverside

The Department of Bioengineering at the University of California, Riverside (UCR), was established in 2006 and is the youngest department in the Bourns College of Engineering. It is an interdisciplinary research engine that builds strength from highly recognized experts in biochemistry, biophysics, biology, and engineering, focusing on common critical themes. The range of faculty research interests is notable for its diversity, from the basic cell biology through cell function to the physiology of the whole organism, each directed at breakthroughs in biomedical devices for measurement and therapy. The department forges future leaders in bioengineering, mirroring the field in being energetic, interdisciplinary, and fast moving at the frontiers of biomedical discoveries. Our educational programs combine a solid foundation in bio logical sciences and engineering, diverse communication skills, and training in the most advanced quantitative bioengineering research. Bioengineering at UCR also includes the Bioengineering Interdepartmental Graduate (BIG) program. With its slogan Start-Grow-Be-BIG, it is already recognized for its many accomplishments, including being third in the nation in 2011 for bioengineering students receiving National Science Foundation graduate research fellowships as well as being one of the most ethnically inclusive programs in the nation.     

Seven types of adaptationism

Godfrey-Smith (2001) has distinguished three types of adaptationism. This article builds on his analysis, and revises it in places, by distinguishing seven varieties of adaptationism. This taxonomy allows us to clarify what is at stake in debates over adaptationism, and it also helps to cement the importance of Gould and Lewontin’s ‘Spandrels’ essay. Some adaptationists have suggested that their essay does not offer any coherent alternative to the adaptationist programme: it consists only in an exhortation to test adaptationist hypotheses more thoroughly than was usual in the 1970s. Here it is argued that the ‘Spandrels’ paper points towards a genuinely non-adaptationist methodology implicit in much evolutionary developmental biology. This conclusion helps to expose the links between older debates over adaptationism and more recent questions about the property of evolvability.

Adaptationism: Hypothesis or Heuristic?

Elliott Sober (1987, 1993) and Orzack and Sober (forthcoming) argue that adaptationism is a very general hypothesis that can be tested by testing various particular hypotheses that invoke natural selection to explain the presence of traits in populations of organisms. In this paper, I challenge Sobers claim that adaptationism is an hypothesis and I argue that it is best viewed as a heuristic (or research strategy). Biologists would still have good reasons for employing this research strategy even if it turns out that natural selection is not the most important cause of evolution.     

A new paradigm for graduate research and training in the biomedical sciences and engineering

98Emphasis on the individual investigator has fostered discovery for centuries, yet it is now recognized that the complexity of problems in the biomedical sciences and engineering requires collaborative efforts from individuals having diverse training and expertise. Various approaches can facilitate interdisciplinary interactions, but we submit that there is a critical need for a new educational paradigm for the way that we train biomedical engineers, life scientists, and mathematicians. We cannot continue to train graduate students in isolation within single disciplines, nor can we ask any one individual to learn all the essentials of biology, engineering, and mathematics. We must transform how students are trained and incorporate how real-world research and development are done-in diverse, interdisciplinary teams. Our fundamental vision is to create an innovative paradigm for graduate research and training that yields a new generation of biomedical engineers, life scientists, and mathematicians that is more diverse and that embraces and actively pursues a truly interdisciplinary, team-based approach to research based on a known benefit and mutual respect. In this paper, we describe our attempt to accomplish this via focused training in biomechanics, biomedical optics, mathematics, mechanobiology, and physiology. The overall approach is applicable, however, to most areas of biomedical research.     

Buyer beware: robustness analyses in economics and biology

Theoretical biology and economics are remarkably similar in their reliance on mathematical models, which attempt to represent real world systems using many idealized assumptions. They are also similar in placing a great emphasis on derivational robustness of modeling results. Recently philosophers of biology and economics have argued that robustness analysis can be a method for confirmation of claims about causal mechanisms, despite the significant reliance of these models on patently false assumptions. We argue that the power of robustness analysis has been greatly exaggerated. It is best regarded as a method of discovery rather than confirmation.     

Reverend Paley's naturalist revival

This paper analyzes the remarkable popularity of William Paley's argument from design among contemporary naturalists in biology and the philosophy of science. In philosophy of science Elliott Sober has argued that creationism should be excluded from the schools not because it is not science but because it is 'less likely' than evolution according to fairly standard confirmation theory. Creationism is said to have been a plausible scientific option as presented by Paley but no longer to be acceptable according to the same standards that once approved it. In biology C. G. Williams and Richard Dawkins have seen in Paley a proto-adaptationist. This paper shows that the historical assumptions of Sober's arguments are wrong and that the philosophical arguments themselves take alternatives to science to be alternatives in science and conflate the null hypothesis, chance, with a competing explanatory hypothesis. It is also shown that the similarity of Paley's adaptationism to that of contemporary biology is not what it is made out to be.     

Testing Methods: The Evaluation of Discovery Operations in Evolutionary Biology

Scientific discovery requires both abstract, theoretically defined concepts and discovery operations formed by sets of rules that permit the empirical detection of instances of those concepts. In this paper, I examine the ontological status of discovery operations and the tests employed to evaluate them in evolutionary biology. Attention is drawn to the distinction between nomothetic (universal, predictive) and ideographic (historical, retrodictive) discovery operations, and between complementary and exclusive discovery operations. Three types of tests of discovery operations are commonly employed in evolutionary biology. Theoretical tests aim to show that a discovery operation is inconsistent with accepted, well-corroborated, empirical theories. Empirical tests evaluate the performance of competing discovery operations in terms of their results when applied to the same empirical data sets. Philosophical tests aim to show that an operation is inconsistent with logical and epistemological principles. Appropriately designed theoretical and philosophical tests of ideographic discovery operations may be scientifically valid. Empirical tests, however, are incapable of evaluating the scientific merits of competing discovery operations. Nonetheless, empirical comparisons (not tests ) of competing discovery operations may provide insight into the ways discovery operations may be misleading and therefore may play an important role in stimulating critical debate and eventually establishing a scientifically optimal operation. In practice, theoretical and philosophical tests are often combined to test competing discovery operations as rigorously as possible.     

Constraining the adaptationism debate

This contribution to the adaptationism debate elaborates the nature of constraints and their importance in evolutionary explanation and argues that the adaptationism debate should be limited to the issue of how to privilege causes in evolutionary explanation. I argue that adaptationist explanations are deeply conceptually dependent on developmental constraints, and explanations that appeal to constraints are dependant on the results of natural selection. I suggest these explanations should be integrated into the framework of historical causal explanation. Each strategy explicitly appeals to some aspect of the evolutionary process, while implicitly appealing to others. Thus, adaptationists and anti-adaptationists can offer complementary causal explanations of the same explanandum. This eliminates much of the adaptationism debate and explains why its adversaries regularly agree with each other more than they would like. The adaptationism issue that remains is a species of the general issue of how to privilege causes in explanation. I show how a proposed solution to this general problem might be brought to bear on evolutionary explanations, and investigate some difficulties that might arise due to the nature of the evolutionary process.     

From Reductionism to Reintegration: Solving society’s most pressing problems requires building bridges between data types across the life sciences

Decades of reductionist approaches in biology have achieved spectacular progress, but the proliferation of subdisciplines, each with its own technical and social practices regarding data, impedes the growth of the multidisciplinary and interdisciplinary approaches now needed to address pressing societal challenges. Data integration is key to a reintegrated biology able to address global issues such as climate change, biodiversity loss, and sustainable ecosystem management. We identify major challenges to data integration and present a vision for a “Data as a Service”-oriented architecture to promote reuse of data for discovery. The proposed architecture includes standards development, new tools and services, and strategies for career-development and sustainability.

Data integration is key to the reintegration of biology and the pursuit of global issues such as climate change, biodiversity loss, and sustainable ecosystem management. This Essay defines the primary challenges in data integration and presents a vision for a "Data as a Service" (DaaS) oriented architecture that enables frictionless data reuse, hypothesis testing, and discovery.     

Synthetic biology, inspired by synthetic chemistry

The topic synthetic biology appears still as an 'empty basket to be filled'. However, there is already plenty of claims and visions, as well as convincing research strategies about the theme of synthetic biology. First of all, synthetic biology seems to be about the engineering of biology - about bottom-up and top-down approaches, compromising complexity versus stability of artificial architectures, relevant in biology. Synthetic biology accounts for heterogeneous approaches towards minimal and even artificial life, the engineering of biochemical pathways on the organismic level, the modelling of molecular processes and finally, the combination of synthetic with nature-derived materials and architectural concepts, such as a cellular membrane. Still, synthetic biology is a discipline, which embraces interdisciplinary attempts in order to have a profound, scientific base to enable the re-design of nature and to compose architectures and processes with man-made matter. We like to give an overview about the developments in the field of synthetic biology, regarding polymer-based analogs of cellular membranes and what questions can be answered by applying synthetic polymer science towards the smallest unit in life, namely a cell.