Anya Plutynski

A Companion to the Philosophy of Biology

List of Figures. List of Tables. Notes on Contributors. Acknowledgments . Introduction: Sahotra Sarkar (University of Texas) and Anya Plutynski (University of Utah). I. Molecular Biology and Genetics: . II.1. Gene Concepts: Hans-Jorg Rheinberger (Max Planck Institute for the History of Science) and Staffan Muller-Wulle (University of Exeter). II.2. Biological Information: Stefan Artmann (University of Jena). II.3. Heredity and Heritability: Richard Lewontin (Harvard University). II.4. Genomics, Proteomics, and Beyond: Sahotra Sarkar (University of Texas). II. Evolution: . III.1. Darwinism and Neo-Darwinism: James G. Lennox (University of Pittsburgh). III.2. Systematics and Taxonomy: Marc Ereshefsky (University of Calgary). III.3. Population Genetics: Christopher Stephens (University of British Columbia). III.4. The Units and Levels of Selection: Samir Okasha (University of Bristol). III.5. Molecular Evolution: Michael R. Dietrich (Dartmouth College). III.6. Speciation and Macroevolution: Anya Plutynski (University of Utah). III.7. Adaptationism: Peter Godfrey-Smith (Harvard University) and Jon F. Wilkins (Harvard University). III. Developmental Biology: . IV.1. Phenotypic Plasticity and Reaction Norms: Jonathan M. Kaplan (University of Tennessee). IV.2. Explaining the Ontogeny of Form: Philosophical Issues: Alan C. Love (University of Minnesota). IV.3. Development and Evolution: Ron Amundson (University of Hawaii). IV. Medicine: . V.1. Self and Nonself: Moira Howes (Trent University). V.2. Health and Disease: Dominic Murphy (Caltech). V. Ecology: . VI.1. Population Ecology: Mark Colyvan (University of Sydney). VI.2.Complexity, Diversity, and Stability: James Justus (University of Texas, Austin). VI.3. Ecosystems: Kent A. Peacock (University of Lethbridge). VI.4. Biodiversity: Its Meaning and Value: Bryan G. Norton (Georgia Institute of Technology). VI. Mind and Behavior: . VII.1. Ethology, Sociobiology, and Evolutionary Psychology: Paul E. Griffiths (University of Pittsburgh). VII.2. Cooperation: J. McKenzie Alexander (London School of Economics). VII.3. Language and Evolution: Derek Bickerton (University of Hawaii). VII. Experimentation, Theory, and Themes: . VIII.1. What is Life?: Mark A. Bedau (Reed College). VIII.2. Experimentation: Marcel Weber (University of Hanover). VIII.3. Laws and Theories: Marc Lange (University of North Carolina, Chapel Hill). VIII.4. Models: Jay Odenbaugh (Lewis and Clark College). VIII.5. Function and Teleology: Justin Garson (University of Texas, Austin). VIII.6. Reductionism in Biology: Alexander Rosenberg (Duke University). Index

An integrative view on sex differences in brain tumors

Sex differences in human health and disease can range from undetectable to profound. Differences in brain tumor rates and outcome are evident in males and females throughout the world and regardless of age. These observations indicate that fundamental aspects of sex determination can impact the biology of brain tumors. It is likely that optimal personalized approaches to the treatment of male and female brain tumor patients will require recognizing and understanding the ways in which the biology of their tumors can differ. It is our view that sex-specific approaches to brain tumor screening and care will be enhanced by rigorously documenting differences in brain tumor rates and outcomes in males and females, and understanding the developmental and evolutionary origins of sex differences. Here we offer such an integrative perspective on brain tumors. It is our intent to encourage the consideration of sex differences in clinical and basic scientific investigations.

Four Problems of Abduction: A Brief History

Debates concerning the character, scope, and warrant of abductive inference have been active since Peirce first proposed that there was a third form of inference, distinct from induction and deduction. Abductive reasoning has been dubbed weak, incoherent, and even nonexistent. Part, at least, of the problem of articulating a clear sense of abductive inference is due to difficulty in interpreting Peirce. Part of the fault must lie with his critics, however. While this article will argue that Peirce indeed left a number of puzzles for interpreters, it will also contend that interpreters should be careful to distinguish discussion of the formal and strictly epistemic question of whether and how abduction is a sound form of inference from discussions of the practical goals of abduction, as Peirce understood them. This article will trace a history of critics and defenders of Peirce’s notion of abduction and discuss how Peirce both fueled the confusion and in fact anticipated and responded to several recurring objections.

Explanatory unification and the early synthesis

The object of this paper is to reply to Morrisons ([2000]) claim that while ‘structural unity’ was achieved at the level of the mathematical models of population genetics in the early synthesis, there was explanatory disunity. I argue to the contrary, that the early synthesis effected by the founders of theoretical population genetics was unifying and explanatory both. Defending this requires a reconsideration of Morrisons notion of explanation. In Morrisons view, all and only answers to ‘why’ questions which include the ‘cause or mechanism’ for some phenomenon count as explanatory. In my view, mathematical demonstrations that answer ‘how possibly’ and ‘why necessarily’ questions may also count as explanatory. The authors of the synthesis explained how evolution was possible on a Mendelian system of inheritance, answered skepticism about the sufficiency of selection, and thus explained why and how a Darwinian research program was warranted. While today we take many of these claims as obvious, they required argument, and part of the explanatory work of the formal sciences is providing such arguments. Surely, Fisher and Wright had competing views as to the optimal means of generating adaptation. Nevertheless, they had common opponents and a common unifying and explanatory goal that their mathematical demonstrations served. 1. Introduction: Morrisons challenge2. Fisher v. Wright revisited3. The early synthesis4. Conclusion: unification and explanation reconciled Introduction: Morrisons challenge Fisher v. Wright revisited The early synthesis Conclusion: unification and explanation reconciled

Explanation in Classical Population Genetics

The recent literature in philosophy of biology has drawn attention to the different sorts of explanations proffered in the biological sciences—we have molecular, biomedical, and evolutionary explanations. Do these explanations all have a common structure or relation that they seek to capture? This paper will answer in the negative. I defend a pluralistic and pragmatic approach to explanation. Using examples from classical population genetics, I argue that formal demonstrations, and even strictly “mathematical truths,” may serve as explanatory in different historical contexts.

Strategies of Model Building in Population Genetics

In 1966, Richard Levins argued that there are different strategies in model building in population biology. In this paper, I reply to Orzack and Sober’s (1993) critiques of Levins and argue that his views on modeling strategies apply also in the context of evolutionary genetics. In particular, I argue that there are different ways in which models are used to ask and answer questions about the dynamics of evolutionary change, prospectively and retrospectively, in classical versus molecular evolutionary genetics. Further, I argue that robustness analysis is a tool for, if not confirmation, then something near enough, in this discipline.

What and How Do Cancer Systems Biologists Explain

In this article, we argue, first, that there are very different research projects that fall under the heading of “systems biology of cancer.” While they share some general features, they differ in their aims and theoretical commitments. Second, we argue that some explanations in systems biology of cancer are concerned with properties of signaling networks (such as robustness or fragility) and how they may play an important causal role in patterns of vulnerability to cancer. Further, some systems biological explanations are compelling illustrations of how “top-down” and “bottom-up” approaches to the same phenomena may be integrated.

Ethics in Biomedical Research and Practice

Biomedical research raises a host of ethical questions of import to biology education. This chapter covers ethical questions “intrinsic” to the research: e.g., ethical proscriptions on what kinds of research may be conducted, as well as questions “extrinsic” to research: about which research is prioritized and why, how biomedical research is funded and related considerations of allocation and distributive justice. Research ethics is the branch of biomedical ethics that concerns the responsible conduct of research – including, but not limited to: the ethical treatment of human and non-human subjects, avoiding conflicts of interest, the fair representation of authorship, and the scientist as a responsible member of society. The first part of this chapter will focus more narrowly on the ethics of research on human and non-human subjects. After the Nazi “experiments” on vulnerable populations during WWII, the Nuremberg trials and Code that resulted (1947) codified a set of norms for research on human subjects necessary to protect vulnerable populations from abuse. Until relatively recently, vulnerable populations (prisoners, soldiers) were viewed as optimal candidates for biomedical research, and were invited to participate in medical research that posed serious harms and had very little benefit, often to them as patients, and sometimes to science, in general. The most famous example of this is the Tuskegee syphilis study, in which 400 African-American men with untreated syphilis were left untreated and observed over the course of decades, even after treatment became available. With respect to the “extrinsic” issues, a variety of economists, philosophers, sociologists, and biomedical researchers have brought attention to the fact that the overwhelming majority of biomedical research is directed toward diseases that by and large affect the wealthy. Whereas historically, biomedical research was often conducted in non-profit or government sector, a larger percentage of such research today is conducted in the private sector. This raises questions about potential conflicts of interest – e.g., concerning whether clinicians and clinician researchers are unduly influenced by profit in prioritizing some research projects over others, and, whether efficacy of new drugs or treatment regimes is exaggerated and risk minimized as a result. At the end of this chapter, several proposals for addressing these issues will be reviewed. Addressing these ethical issues is important to biology education, because students from a variety of disciplines need to situate biomedical research in social and ethical context, and reflect on its larger import.