Building on my previous writings on presentism, pluralism, and “complementary science”, I develop an activist view of historiography. I begin by recognizing the inevitability of presentism. Our own purposes and perspectives do and should guide the production of our accounts of the past; like funerals, history-writing is for the living. There are different kinds of presentist history, depending on the historians’ purposes and perspectives. My particular inclination is pluralist. Science remembers its own history from a particular perspective, which views the (...) past as imperfect versions of the present; if professional historians of science shared this perspective, our work would be redundant. Instead, we can make it our task to illuminate the aspects of the past of science that scientists themselves tend to ignore and forget. History of science can also take a more productive role in the creation and improvement of scientific knowledge. Scientific progress as we know it tends to involve the shutting down of alternative paths of inquiry, resulting in a loss of potential and actual knowledge. A critical and sympathetic engagement with the past allows us to recover the lost paths, which can also suggest new paths. These points will be illustrated by a number of examples, especially from the history of chemistry and physics, including the recovery and extension of past experiments. (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)

A survey of models in immunology is conducted and distinct kinds of models are characterized based on whether models are material or conceptual, the distinctiveness of their epistemic purpose, and the criteria for evaluating the goodness of a model relative to its intended purpose. I argue that the diversity of models in interdisciplinary fields such as immunology reflects the fact that information about the phenomena of interest is gathered from different sources using multiple methods of investigation. To each model is (...) attached a description specifying how information about a phenomenon of interest has been acquired, highlighting points of commonality and difference between the methodological and epistemic histories of the information encapsulated in different models. These points of commonality and difference allow investigators to integrate findings from different models into more comprehensive explanatory accounts, as well as to troubleshoot anomalies and faulty accounts by going back to the original building blocks. (shrink)

This paper aims to identify the key characteristics of model organisms that make them a specific type of model within the contemporary life sciences: in particular, we argue that the term “model organism” does not apply to all organisms used for the purposes of experimental research. We explore the differences between experimental and model organisms in terms of their material and epistemic features, and argue that it is essential to distinguish between their representational scope and representational target. We also examine (...) the characteristics of the communities who use these two types of models, including their research goals, disciplinary affiliations, and preferred practices to show how these have contributed to the conceptualization of a model organism. We conclude that model organisms are a specific subgroup of organisms that have been standardized to fit an integrative and comparative mode of research, and that it must be clearly distinguished from the broader class of experimental organisms. In addition, we argue that model organisms are the key components of a unique and distinctively biological way of doing research using models.Keywords: Experimental organism; Genetics; Model organism; Modeling; Philosophy of biology; Representation. (shrink)

Experimental modeling in biology involves the use of living organisms (not necessarily so-called "model organisms") in order to model or simulate biological processes. I argue here that experimental modeling is a bona fide form of scientific modeling that plays an epistemic role that is distinct from that of ordinary biological experiments. What distinguishes them from ordinary experiments is that they use what I call "in vivo representations" where one kind of causal process is used to stand in for a physically (...) different kind of process. I discuss the advantages of this approach in the context of evolutionary biology. (shrink)

This paper examines how scientists conceptualize their research methodologies. Do scientists raise concerns about vague criteria and genuine uncertainties in experimental practice? If so, what sorts of issues do they identify as problematic? Do scientists acknowledge the presence of value judgments in scientific research, and do they reflect on the relation between epistemic and non-epistemic criteria for decisionmaking? We present findings from an analysis of qualitative interviews with 63 scientific researchers who talk about their views on good research practice. We (...) argue that analysts of science should care about scientists’ conceptualizations of the criteria and of the practical judgments that scientific inquiry involves. While scientists’ accounts of their own research methodologies alone do not give us a full picture of how science really works, they can point us to areas of concern. They can inspire and direct philosophical reflections about how science works. Throughout the interviews, the participating researchers provided specific examples from their own research contexts as illustrations of their methodological points. These examples reveal that scientists often struggle to evaluate the quality of their data, to figure out whether the available evidence confirms their hypothesis, whether a replication was successful, or to what extent they can rely on peer-reviewed papers. General ideas about good research methods do not directly translate into specific evaluation criteria or strategies that can guide research and help validate empirical data. (shrink)

This paper examines how scientists conceptualize their research methodologies. Do scientists raise concerns about vague criteria and genuine uncertainties in experimental practice? If so, what sorts of issues do they identify as problematic? Do scientists acknowledge the presence of value judgments in scientific research, and do they reflect on the relation between epistemic and non-epistemic criteria for decisionmaking? We present findings from an analysis of qualitative interviews with 63 scientific researchers who talk about their views on good research practice. We (...) argue that analysts of science should care about scientists’ conceptualizations of the criteria and of the practical judgments that scientific inquiry involves. While scientists’ accounts of their own research methodologies alone do not give us a full picture of how science really works, they can point us to areas of concern. They can inspire and direct philosophical reflections about how science works. Throughout the interviews, the participating researchers provided specific examples from their own research contexts as illustrations of their methodological points. These examples reveal that scientists often struggle to evaluate the quality of their data, to figure out whether the available evidence confirms their hypothesis, whether a replication was successful, or to what extent they can rely on peer-reviewed papers. General ideas about good research methods do not directly translate into specific evaluation criteria or strategies that can guide research and help validate empirical data. (shrink)

Ankeny and Leonelli propose “repertoires” as a new way to understand the stability of certain research programs as well as scientific change in general. By bringing a more complete range of social, material, and epistemic elements into one framework, they position their work as a correction for the Kuhnian impulse in philosophy of science and other areas of science studies. I argue that this “post-Kuhnian” move is not complete, and that repertoires maintain an internalist perspective, caused partly by an asymmetrical (...) emphasis on the scientists’ side of practice. If we compare “repertoires” to alternative frameworks, like “sociotechnical imaginaries” of Jasanoff and Kim, it is evident that repertoires are missing two specific things. First, I argue that the framework needs to include the role of audience, without whom the repertoires of science are unintelligible. Second, I suggest that the framework also lacks an explicit place for ethical and political imagination, which provide meaning for otherwise mechanical promotion of particular research programs. With these modifications, Ankeny and Leonelli’s framework might fulfill its post-Kuhnian potential. (shrink)

The position advanced in this paper is that the bedrock of emotional feelings is contained within the evolved emotional action apparatus of mammalian brains. This dual-aspect monism approach to brain–mind functions, which asserts that emotional feelings may reflect the neurodynamics of brain systems that generate instinctual emotional behaviors, saves us from various conceptual conundrums. In coarse form, primary process affective consciousness seems to be fundamentally an unconditional “gift of nature” rather than an acquired skill, even though those systems facilitate skill (...) acquisition via various felt reinforcements. Affective consciousness, being a comparatively intrinsic function of the brain, shared homologously by all mammalian species, should be the easiest variant of consciousness to study in animals. This is not to deny that some secondary processes cannot be evaluated in animals with sufficiently clever behavioral learning procedures, as with place-preference procedures and the analysis of changes in learned behaviors after one has induced re-valuation of incentives. Rather, the claim is that a direct neuroscientific study of primary process emotional/affective states is best achieved through the study of the intrinsic , albeit experientially refined, emotional action tendencies of other animals. In this view, core emotional feelings may reflect the neurodynamic attractor landscapes of a variety of extended trans-diencephalic, limbic emotional action systems—including SEEKING, FEAR, RAGE, LUST, CARE, PANIC, and PLAY. Through a study of these brain systems, the neural infrastructure of human and animal affective consciousness may be revealed. Emotional feelings are instantiated in large-scale neurodynamics that can be most effectively monitored via the ethological analysis of emotional action tendencies and the accompanying brain neurochemical/electrical changes. The intrinsic coherence of such emotional responses is demonstrated by the fact that they can be provoked by electrical and chemical stimulation of specific brain zones—effects that are affectively laden. For substantive progress in this emerging research arena, animal brain researchers need to discuss affective brain functions more openly. Secondary awareness processes, because of their more conditional, contextually situated nature, are more difficult to understand in any neuroscientific detail. In other words, the information-processing brain functions, critical for cognitive consciousness, are harder to study in other animals than the more homologous emotional/motivational affective state functions of the brain. (shrink)

This paper argues that research on normative issues in the life sciences will benefit from a tighter integration of philosophy of science. We examine research on ethical, legal and social issues in the life sciences (“ELSI”) and discuss three illustrative examples of normative issues that arise in different areas of the life sciences. These examples show that important normative questions are highly dependent on epistemic issues which so far have not been addressed sufficiently in ELSI, RRI and related areas of (...) research. Accordingly, we argue for the integration of research on the epistemic aspects of the relevant areas of science into ELSI research to provide a better basis for addressing normative questions. (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)

Community databases have become crucial to the collection, ordering and retrieval of data gathered on model organisms, as well as to the ways in which these data are interpreted and used across a range of research contexts. This paper analyses the impact of community databases on research practices in model organism biology by focusing on the history and current use of four community databases: FlyBase, Mouse Genome Informatics, WormBase and The Arabidopsis Information Resource. We discuss the standards used by the (...) curators of these databases for what counts as reliable evidence, acceptable terminology, appropriate experimental set-ups and adequate materials (e.g., specimens). On the one hand, these choices are informed by the collaborative research ethos characterising most model organism communities. On the other hand, the deployment of these standards in databases reinforces this ethos and gives it concrete and precise instantiations by shaping the skills, practices, values and background knowledge required of the database users. We conclude that the increasing reliance on community databases as vehicles to circulate data is having a major impact on how researchers conduct and communicate their research, which affects how they understand the biology of model organisms and its relation to the biology of other species. (shrink)

According to the received view, the transportation view, animal extrapolation consists in inductive prediction of the outcome of a mechanism in a target, based on an analogical mechanism in a model. Through an analysis of the failure of preclinical studies of TGN1412, an innovative drug, to predict the tragic consequences of its first-in-man trial in 2006, the received view is challenged by a proposed view of animal extrapolation, the chimera view. According to this view, animal extrapolation is based on a (...) hypothesis about how human organisms work, supported by the amalgamation of results drawn from various experimental organisms, and only predicting the ‘predictive grid’, that is, a global framework of the effects to be expected. (shrink)

Biomedical researchers claim there is significant biomedical information about humans which can be discovered only through experiments on intact animal systems (AMA p. 2). Although epidemiological studies, computer simulations, clinical investigation, and cell and tissue cultures have become important weapons in the biomedical scientists' arsenal, these are primarily "adjuncts to the use of animals in research" (Sigma Xi p. 76). Controlled laboratory experiments are the core of the scientific enterprise. Biomedical researchers claim these should be conducted on intact biological systems, (...) whole animals. By observing the effects of various stimuli in non-human animals, we can form legitimate expectations about the likely effects of these stimuli in humans. Perhaps more importantly, we can understand the biomedical condition's causal mechanisms. (shrink)

Animal models have received particular attention as key examples of material models. In this paper, we argue that the specificities of establishing animal models—acknowledging their status as living beings and as epistemological tools—necessitate a more complex account of animal models as materialised models. This becomes particularly evident in animal-based models of diseases that only occur in humans: in these cases, the representational relation between animal model and human patient needs to be generated and validated. The first part of this paper (...) presents an account of how disease-specific animal models are established by drawing on the example of transgenic mice models for Alzheimer’s disease. We will introduce an account of validation that involves a three-fold process including from human being to experimental organism; from experimental organism to animal model; and from animal model to human patient. This process draws upon clinical relevance as much as scientific practices and results in disease-specific, yet incomplete, animal models. The second part of this paper argues that the incompleteness of models can be described in terms of multi-level abstractions. We qualify this notion by pointing to different experimental techniques and targets of modelling, which give rise to a plurality of models for a specific disease. (shrink)

Patient-derived xenografts are currently promoted as new translational models in precision oncology. PDXs are immunodeficient mice with human tumors that are used as surrogate models to represent specific types of cancer. By accounting for the genetic heterogeneity of cancer tumors, PDXs are hoped to provide more clinically relevant results in preclinical research. Further, in the function of so-called “mouse avatars”, PDXs are hoped to allow for patient-specific drug testing in real-time. This paper examines the circulation of knowledge and bodily material (...) across the species boundary of human and personalized mouse model, historically as well as in contemporary practices. PDXs raise interesting questions about the relation between animal model and human patient, and about the capacity of hybrid or interspecies models to close existing translational gaps. We highlight that the translational potential of PDXs not only depends on representational matching of model and target, but also on temporal alignment between model development and practical uses. Aside from the importance of ensuring temporal stability of human tumors in a murine body, the mouse avatar concept rests on the possibility of aligning the temporal horizons of the clinic and the lab. We examine strategies to address temporal challenges, including cryopreservation and biobanking, as well as attempts to speed up translation through modification and use of faster developing organisms. We discuss how featured model virtues change with precision oncology, and contend that temporality is a model feature that deserves more philosophical attention. (shrink)

How effectively communities of scientists come together and co-operate is crucial both to the quality of research outputs and to the extent to which such outputs integrate insights, data and methods from a variety of fields, laboratories and locations around the globe. This essay focuses on the ensemble of material and social conditions that makes it possible for a short-term collaboration, set up to accomplish a specific task, to give rise to relatively stable communities of researchers. We refer to these (...) distinctive features as repertoires, and investigate their development and implementation across three examples of collaborative research in the life sciences. We conclude that whether a particular project ends up fostering the emergence of a resilient research community is partly determined by the degree of attention and care devoted by researchers to material and social elements beyond the specific research questions under consideration. (shrink)

This paper explores the epistemology of extrapolation from model organisms to humans in molecular medicine. We take into account two common views on the issue, the homology view and the disanalogy view. In response to both interpretations, we argue that the foundational basis of extrapolations cannot simply be provided by homology and that relevant disanalogies can, thanks to the techniques of molecular biology, be experimentally controlled and exploited to allow useful and reliable extrapolations. The case of "humanised mice" in the (...) context of cancer stem cell research provides evidence of how animal models can be construed to approximate bona fide causal analogue models of human diseases. To supplement this view we show how the epistemology of model organisms needs to take into account the engineering side of molecular medicine. Model organisms are often manipulated to create analogies or remove disanalogies with the target system. We maintain that highlighting this feature is fundamental to explain what warrants extrapolation in the search for the molecular causes of disease. (shrink)