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)

It is one of the central aims of the philosophy of science to elucidate the meanings of scientific terms and also to think critically about their application. The focus of this essay is the scientific term predict and whether there is credible evidence that animal models, especially in toxicology and pathophysiology, can be used to predict human outcomes. Whether animals can be used to predict human response to drugs and other chemicals is apparently a contentious issue. However, when one empirically (...) analyzes animal models using scientific tools they fall far short of being able to predict human responses. This is not surprising considering what we have learned from fields such evolutionary and developmental biology, gene regulation and expression, epigenetics, complexity theory, and comparative genomics. (shrink)

. . . Unlike Dewey, he has provided detailed incisive argumentation, and has shown just where the dogmas and dualisms break down." -- Richard Rorty, The Yale Review.

Inferences like these are known as extrapolations.

This paper defends an inferential conception of scientific representation. It approaches the notion of representation in a deflationary spirit, and minimally characterizes the concept as it appears in science by means of two necessary conditions: its essential directionality and its capacity to allow surrogate reasoning and inference. The conception is defended by showing that it successfully meets the objections that make its competitors, such as isomorphism and similarity, untenable. In addition the inferential conception captures the objectivity of the cognitive representations (...) used by science, it sheds light on their truth and completeness, and it explains the source of the analogy between scientific and artistic modes of representation. (shrink)

Critics of animal modeling have advanced a variety of arguments against the validity of the practice. The point of one such form of argument is to establish that animal modeling is pointless and therefore immoral. In this article, critical arguments of this form are divided into three types, the pseudoscience argument, the disanalogy argument, and the predictive validity argument. I contend that none of these criticisms currently succeed, nor are they likely to. However, the connection between validity and morality is (...) important, suggesting that critical efforts would be instructive if they addressed it in a more nuanced way. (shrink)

Many standard philosophical accounts of scientific practice fail to distinguish between modeling and other types of theory construction. This failure is unfortunate because there are important contrasts among the goals, procedures, and representations employed by modelers and other kinds of theorists. We can see some of these differences intuitively when we reflect on the methods of theorists such as Vito Volterra and Linus Pauling on the one hand, and Charles Darwin and Dimitri Mendeleev on the other. Much of Volterra's and (...) Pauling's work involved modeling; much of Darwin's and Mendeleev's did not. In order to capture this distinction, I consider two examples of theory construction in detail: Volterra's treatment of post-WWI fishery dynamics and Mendeleev's construction of the periodic system. I argue that modeling can be distinguished from other forms of theorizing by the procedures modelers use to represent and to study real-world phenomena: indirect representation and analysis. This differentiation between modelers and non-modelers is one component of the larger project of understanding the practice of modeling, its distinctive features, and the strategies of abstraction and idealization it employs. (shrink)

Extrapolation from a well-understood base population to a less-understood target population can fail if the base and target populations are not sufficiently similar. Differences between laboratory mice and humans, for example, can hinder extrapolation in medical research. Mice that carry a partial or complete human physiological system, known as humanized mice, are supposed to make extrapolation more reliable by simulating a variety of human diseases. But what justifies our belief that these mice are similar enough to their human counterparts to (...) simulate human disease? I argue that, unless three requirements are met in the process of humanizing mice, very little does. My requirements are not meant to provide necessary and sufficient conditions that guarantee a particular outcome. Instead, they serve as a heuristic for guiding scientific judgments involving extrapolation. In developing each requirement, I engage with philosophical issues concerning the nature of model-based science and the mechanistic approach (and its limits) to making generalizations in the life sciences. (shrink)

_Brute Science_ investigates whether biomedical research using animals is, in fact, scientifically justified. Hugh LaFollette and Niall Shanks examine the issues in scientific terms using the models that scientists themselves use. They argue that we need to reassess our use of animals and, indeed, rethink the standard positions in the debate.

What do biologists want? If, unlike their counterparts in physics, biologists are generally wary of a grand, overarching theory, at what kinds of explanation do biologists aim? A history of the diverse and changing nature of biological explanation in a particularly charged field, "Making Sense of Life" draws our attention to the temporal, disciplinary, and cultural components of what biologists mean, and what they understand, when they propose to explain life.

Few ethical issues create as much controversy as invasive experiments on animals. Some scientists claim they are essential for combating major human disease, or detecting human toxins. Others claim the contrary, backed by thousands of patients harmed by pharmaceuticals developed using animal tests. Some claim all experiments are conducted humanely, to high scientific standards. Yet, a wealth of studies have recently revealed that laboratory animals suffer significant stress, which may distort experimental results. -Where, then, does the truth lie? -How useful (...) are such experiments in advancing human healthcare? -How much do animals suffer as a result? -And do students really need to dissect or experiment on animals? -What are the effects on their attitudes towards them? Bioethicist and veterinarian Andrew Knight presents more than a decade of ground-breaking scientific research, analysis and experience to provide evidence-based answers to a key question: is animal experimentation ethically justifiable? (shrink)

Model organisms became an indispensable part of experimental systems in molecular developmental and cell biology, constructed to investigate physiological and pathological processes. They are thought to play a crucial role for the elucidation of gene function, complementing the sequencing of the genomes of humans and other organisms. Accordingly, historians and philosophers paid considerable attention to various issues concerning this aspect of experimental biology. With respect to the representational features of model organisms, that is, their status as models, the main focus (...) was on generalization of phenomena investigated in such experimental systems. Model organisms have been said to be models for other organisms or a higher taxon. This, however, presupposes a representation of the phenomenon in question. I will argue that prior to generalization, model organisms allow researchers to built generative material models of phenomena – structures, processes or the mechanisms that explain them – through their integration in experimental set-ups that carve out the phenomena from the whole organism and thus represent them. I will use the history of zebrafish biology to show how model organism systems, from around 1970 on, were developed to construct material models of molecular mechanisms explaining developmental or physiological processes. (shrink)

Two decades of critique have sensitized historians and philosophers of science to the inadequacies of conventional dichotomies between theory and practice, thereby prompting the search for new ways of writing about science that are less beholden than the old ways to the epistemological mores of theoretical physics, and more faithful to the actual practices not only of physics but of all the natural sciences. The need for alternative descriptions seems particularly urgent if one is to understand the place of theory (...) (and, in parallel, the role of modeling) in contemporary molecular biology, a science where, until now, no division between theory and experiment has obtained, and where distinctions between representing and intervening, and more generally, between basic and applied science, are daily becoming more blurred. Indeed, the very division between theory and experiment threatens to slight the extensive and sophisticated theoretical analyses (and even modeling) on which experimental work in contemporary molecular biology so often depends. My aim in this paper is to find a way of talking about theoretical practices in biology that is directly rooted in the mix of conceptual and material work that biologists do. As an example of such theoretical practices, I choose for the focus of my analysis the development of a model for gene regulation out of the experimental work of Eric Davidson and his colleagues at Cal Tech. (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)

What has been called the new mechanistic philosophy conceives of mechanisms as the main providers of biological explanation. We draw on the characterization of the p53 gene in molecular oncology, to show that explaining a biological phenomenon implies instead a dynamic interaction between the mechanistic level—rendered at the appropriate degree of ontological resolution—and far more general explanatory tools that perform a fundamental epistemic role in the provision of biological explanations. We call such tools “explanatory frameworks”. They are called frameworks to (...) stress their higher level of generality with respect to bare mechanisms; on the other hand, they are called explanatory because, as we show in this paper, their importance in explaining biological phenomena is not secondary with respect to mechanisms. We illustrate how explanatory frameworks establish selective and local criteria of causal relevance that drive the search for, characterisation and usage of biological mechanisms. Furthermore, we show that explanatory frameworks allow for changes of scientific perspective on the causal relevance of mechanisms going beyond the account provided by the new mechanistic philosophy. (shrink)