A high profile context in which physics and biology meet today is in the new field of systems biology. Systems biology is a fascinating subject for sociological investigation because the demands of interdisciplinary collaboration have brought epistemological issues and debates front and centre in discussions amongst systems biologists in conference settings, in publications, and in laboratory coffee rooms. One could argue that systems biologists are conducting their own philosophy of science. This paper explores the epistemic aspirations of the field by (...) drawing on interviews with scientists working in systems biology, attendance at systems biology conferences and workshops, and visits to systems biology laboratories. It examines the discourses of systems biologists, looking at how they position their work in relation to previous types of biological inquiry, particularly molecular biology. For example, they raise the issue of reductionism to distinguish systems biology from molecular biology. This comparison with molecular biology leads to discussions about the goals and aspirations of systems biology, including epistemic commitments to quantification, rigor and predictability. Some systems biologists aspire to make biology more similar to physics and engineering by making living systems calculable, modelable and ultimately predictable—a research programme that is perhaps taken to its most extreme form in systems biology’s sister discipline: synthetic biology. Other systems biologists, however, do not think that the standards of the physical sciences are the standards by which we should measure the achievements of systems biology, and doubt whether such standards will ever be applicable to ‘dirty, unruly living systems’. This paper explores these epistemic tensions and reflects on their sociological dimensions and their consequences for future work in the life sciences. (shrink)

Systems biology is the rapidly growing and heavily funded successor science to genomics. Its mission is to integrate extensive bodies of molecular data into a detailed mathematical understanding of all life processes, with an ultimate view to their prediction and control. Despite its high profile and widespread practice, there has so far been almost no bioethical attention paid to systems biology and its potential social consequences. We outline some of systems biology's most important socioethical issues by contrasting the concept of (...) systems as dynamic processes against the common static interpretation of genomes. New issues arise around systems biology's capacities for in silico testing, changing cultural understandings of life, synthetic biology, and commercialization. We advocate an interdisciplinary and interactive approach that integrates social and philosophical analysis and engages closely with the science. Overall, we argue that systems biology socioethics could stimulate new ways of thinking about socioethical studies of life sciences. (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)

There are a variety of topics in the philosophy of science that need to be rethought, in varying degrees, after one pays careful attention to the ways in which computer simulations are used in the sciences. There are a number of conceptual issues internal to the practice of computer simulation that can benefit from the attention of philosophers. This essay surveys some of the recent literature on simulation from the perspective of the philosophy of science and argues that philosophers have (...) a lot to learn by paying closer attention to the practice of simulation. (shrink)

Integrative systems biology is among the most innovative fields of contemporary science, bringing together scientists from a range of diverse backgrounds and disciplines to tackle biological complexity through computational and mathematical modeling. The result is a plethora of problem-solving techniques, theoretical perspectives, lab-structures and organizations, and identity labels that have made it difficult for commentators to pin down precisely what systems biology is, philosophically or sociologically. In this paper, through the ethnographic investigation of two ISB laboratories, we explore the particular (...) structural features of ISB thinking and organization and its relations to other disciplines that necessitate cognitive innovation at all levels from lab PI’s to individual researchers. We find that systems biologists face numerous constraints that make the production of models far from straight-forward, while at the same time they inhabit largely unstructured task environments in comparison to other fields. We refer to these environments as adaptive problem spaces. These environments they handle by relying substantially on the flexibility and affordances of model-based reasoning to integrate these various constraints and find novel adaptive solutions. Ultimately what is driving this innovation is a determination to construct new cognitive niches in the form of functional model building frameworks that integrate systems biology within the biological sciences. The result is an industry of diverse and different innovative practices and solutions to the problem of modeling complex, large-scale biological systems. (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)

Since the 1980s, several studies of visual perception have persuasively argued that important aspects of human vision are best accounted for not by recourse to inner mental representations but rather through socially observable actions and behaviors (e.g. Lynch 1985, Latour 1986, Lynch 1990, Goodwin 1994, Goodwin 1997, Sharrock & Coulter 1998). While there are clearly physiological mechanisms required for vision, psychological accounts of perception in terms of inner mental representations have been dislodged from their position as the basic term in (...) the interface between human beings and their environment and replaced with terms such as "social practice," and "vernacular intelligibility." The focus for these .. (shrink)

This chapter contains sections titled: Introduction Early Species Concepts—Linnaeus Biological Species Concepts Phylogenetic Species Concepts Species Concepts and Speciation Conclusions Postscript: Counterpoint References.

In the context of scientists' reflections on genomics, we examine some fundamental issues in the emerging postgenomic discipline of systems biology. Systems biology is best understood as consisting of two streams. One, which we shall call ‘pragmatic systems biology’, emphasises large‐scale molecular interactions; the other, which we shall refer to as ‘systems‐theoretic biology’, emphasises system principles. Both are committed to mathematical modelling, and both lack a clear account of what biological systems are. We discuss the underlying issues in identifying systems (...) and how causality operates at different levels of organisation. We suggest that resolving such basic problems is a key task for successful systems biology, and that philosophers could contribute to its realisation. We conclude with an argument for more sociologically informed collaboration between scientists and philosophers. BioEssays 27:1270–1276, 2005. © 2005 Wiley Periodicals, Inc. (shrink)

The paper discusses how systems biology is working toward complex accounts that integrate explanation in terms of mechanisms and explanation by mathematical models—which some philosophers have viewed as rival models of explanation. Systems biology is an integrative approach, and it strongly relies on mathematical modeling. Philosophical accounts of mechanisms capture integrative in the sense of multilevel and multifield explanations, yet accounts of mechanistic explanation have failed to address how a mathematical model could contribute to such explanations. I discuss how mathematical (...) equations can be explanatorily relevant. Several cases from systems biology are discussed to illustrate the interplay between mechanistic research and mathematical modeling, and I point to questions about qualitative phenomena, where quantitative models are still indispensable to the explanation. Systems biology shows that a broader philosophical conception of mechanisms is needed, which takes into account functional-dynamical aspects, interaction in complex networks with feedback loops, system-wide functional properties such as distributed functionality and robustness, and a mechanism’s ability to respond to perturbations. I offer general conclusions for philosophical accounts of explanation. (shrink)

Explaining the complex dynamics exhibited in many biological mechanisms requires extending the recent philosophical treatment of mechanisms that emphasizes sequences of operations. To understand how nonsequentially organized mechanisms will behave, scientists often advance what we call dynamic mechanistic explanations. These begin with a decomposition of the mechanism into component parts and operations, using a variety of laboratory-based strategies. Crucially, the mechanism is then recomposed by means of computational models in which variables or terms in differential equations correspond to properties of (...) its parts and operations. We provide two illustrations drawn from research on circadian rhythms. Once biologists identified some of the components of the molecular mechanism thought to be responsible for circadian rhythms, computational models were used to determine whether the proposed mechanisms could generate sustained oscillations. Modeling has become even more important as researchers have recognized that the oscillations generated in individual neurons are synchronized within networks; we describe models being employed to assess how different possible network architectures could produce the observed synchronized activity. (shrink)

A number of recent discussions comparing computer simulation and traditional experimentation have focused on the significance of “materiality.” I challenge several claims emerging from this work and suggest that computer simulation studies are material experiments in a straightforward sense. After discussing some of the implications of this material status for the epistemology of computer simulation, I consider the extent to which materiality (in a particular sense) is important when it comes to making justified inferences about target systems on the basis (...) of experimental results. (shrink)

Simulations (both digital and analog) and experiments share many features. But what essential features distinguish them? I discuss two proposals in the literature. On one proposal, experiments investigate nature directly, while simulations merely investigate models. On another proposal, simulations differ from experiments in that simulationists manipulate objects that bear only a formal (rather than material) similarity to the targets of their investigations. Both of these proposals are rejected. I argue that simulations fundamentally differ from experiments with regard to the background (...) knowledge that is invoked to argue for the “external validity” of the investigation. (shrink)

The importation of computational methods into biology is generating novel methodological strategies for managing complexity which philosophers are only just starting to explore and elaborate. This paper aims to enrich our understanding of methodology in integrative systems biology, which is developing novel epistemic and cognitive strategies for managing complex problem-solving tasks. We illustrate this through developing a case study of a bimodal researcher from our ethnographic investigation of two systems biology research labs. The researcher constructed models of metabolic and cell-signaling (...) pathways by conducting her own wet-lab experimentation while building simulation models. We show how this coupling of experiment and simulation enabled her to build and validate her models and also triangulate and localize errors and uncertainties in them. This method can be contrasted with the unimodal modeling strategy in systems biology which relies more on mathematical or algorithmic methods to reduce complexity. We discuss the relative affordances and limitations of these strategies, which represent distinct opinions in the field about how to handle the investigation of complex biological systems. (shrink)

Chronobiology, especially the study of circadian rhythms, provides a model scientific field in which philosophers can study how investigators from a variety of disciplines working at different levels of organization are each contributing to a multi-level account of the responsible mechanism. I focus on how the framework of mechanistic explanation integrates research designed to decompose the mechanism with efforts directed at recomposition that relies especially on computation models. I also examine how recently the integration has extended beyond basic research to (...) the processes through which the disruption of circadian rhythms contributes to disease, including various forms of cancer. Understanding these linkages has been facilitated by discoveries about how circadian mechanisms interact with mechanisms involved in other physiological processes, including the cell cycle and the immune system. (shrink)

- A recent debate has touched upon the question of whether diseases can be understood as dysfunctional mechanisms or whether there are "pathological" mechanisms that deserve to be investigated and explained independently (Nervi 2010; Moghaddam Taaheri 2011). Here I suggest that both views tell us something important about disease but that in many instances only a systemic view can shed light on the relationship between physiology and pathology. I provide examples from the literature in systems biology in support of my (...) position. As a result of my analysis, I conclude that a perspective narrowly focusing on mechanisms is insufficient if the goal is to get a comprehensive picture of disease. (shrink)

In recent years, the philosophical focus of the modeling literature has shifted from descriptions of general properties of models to an interest in different model functions. It has been argued that the diversity of models and their correspondingly different epistemic goals are important for developing intelligible scientific theories. However, more knowledge is needed on how a combination of different epistemic means can generate and stabilize new entities in science. This paper will draw on Rheinberger’s practice-oriented account of knowledge production. The (...) conceptual repertoire of Rheinberger’s historical epistemology offers important insights for an analysis of the modelling practice. I illustrate this with a case study on network modeling in systems biology where engineering approaches are applied to the study of biological systems. I shall argue that the use of multiple means of representations is an essential part of the dynamic of knowledge generation. It is because of – rather than in spite of – the diversity of constraints of different models that the interlocking use of different epistemic means creates a potential for knowledge production. (shrink)