Recent philosophical analyses of the epistemic dimension of images in the sciences show a certain trend in acknowledging potential roles of these images beyond their merely decorative or pedagogical functions. We argue, however, that this new debate has yet paid little attention to a special type of pictures, we call ‘visual metaphor’, and its versatile heuristic potential in organizing data, supporting communication, and guiding research, modeling, and theory formation. Based on a case study of Conrad Hal Waddington’s epigenetic landscape images (...) in biology, we develop a descriptive framework applicable to heuristic roles of various visual metaphors in the sciences. (shrink)

Recent philosophical analyses of the epistemic dimension of images in the sciences show a certain trend in acknowledging potential roles of these images beyond their merely decorative or pedagogical functions. We argue, however, that this new debate has yet paid little attention to a special type of pictures, we call ‘visual metaphor’, and its versatile heuristic potential in organizing data, supporting communication, and guiding research, modeling, and theory formation. Based on a case study of Conrad Hal Waddington’s epigenetic landscape images (...) in biology, we develop a descriptive framework applicable to heuristic roles of various visual metaphors in the sciences. (shrink)

It seems that the reception of Conrad Hal Waddington’s work never really gathered speed in mainstream biology. This paper, offering a transdisciplinary survey of approaches using his epigenetic landscape images, argues that (i) Waddington’s legacy is much broader than is usually recognized—it is widespread across the life sciences (e.g. stem cell biology, developmental psychology and cultural anthropology). In addition, I will show that (ii) there exist as yet unrecognized heuristic roles, especially in model building and theory formation, which Waddington’s images (...) play within his work. These different methodological facets envisioned by Waddington are used as a natural framework to analyze and classify the manners of usage of epigenetic landscape images in post-Waddingtonian ‘landscape approaches’. This evaluation of Waddington’s pictorial legacy reveals that there are highly diverse lines of traditions in the life sciences, which are deeply rooted in Waddington’s methodological work. (shrink)

A crucial question for a process view of life is how to identify a process and how to follow it through time. The genidentity view can contribute decisively to this project. It says that the identity through time of an entity X is given by a well-identified series of continuous states of affairs. Genidentity helps address the problem of diachronic identity in the living world. This chapter describes the centrality of the concept of genidentity for David Hull and proposes an (...) extension of Hull’s view to the ubiquitous phenomenon of symbiosis. Finally, using immunology as a key example, it shows that the genidentity view suggests that the main interest of a process approach is epistemological rather than ontological and that its principal claim is one of priority, namely that processes precede and define things, and not vice versa. (shrink)

We address the question of whether and to what extent explanatory and modelling strategies in systems biology are mechanistic. After showing how dynamic mathematical models are actually required for mechanistic explanations of complex systems, we caution readers against expecting all systems biology to be about mechanistic explanations. Instead, the aim may be to generate topological explanations that are not standardly mechanistic, or to arrive at design principles that explain system organization and behaviour in general, but not specific mechanisms. These abstraction (...) strategies serve various aims, including prediction and control, that are central to understanding the epistemic diversity of systems biology. (shrink)

Individuation and organization in complex living multi-level ecosystem occurs as dynamical processes from early ontogeny. The notion of living “holon” displaying dynamic self-assertion and integration is used here to explain the ecosystems dynamic processes. The update of the living holon state according to the continuous change of the dynamic system allows for its viability. This is interpreted as adaptation, selection and organization by the human that observes the system a posteriori from its level. Our model concerns the complex dynamics of (...) the adaptive immune system, integrating holon-lymphocytes that collectively preserve the identity and integrity of the organism. Each lymphocyte individualizes as a dynamic holon-lymphocyte, with somatic gene individuation leading to an individual, singular antigen immunoreceptor type, promoting the self-assertion. In turn, the “Immunoception” allows for perception of the environmental antigenic context, thus integration of the holon in its environment. The self-assertion/integration of holon-lymphocyte starts from fetal stages and is influenced by mother Lamarckian acquired historicity transmissions, a requisite for the integrity of the holobiont-organism. We propose a dynamic model of the perception by holon-lymphocyte, and at the supra-clonal level of the immune system functions that sustain the identity and integrity of the holon-holobiont organism. (shrink)

ion is one of the important processes in scientific modeling. It has always been implied that abstraction is an agent-centric activity that involves the cognitive processes of scientists in model building. I contend that there is an autonomous aspect of abstraction in many modeling activities. I argue that the autonomous process of abstraction is continuous with the agent-centric abstraction but capable of evolving independently from the modeler’s abstraction activity.

Against the neo-Darwinian assumption that genetic factors are the principal source of variation upon which natural selection operates, a phenotype-first hypothesis strikes us as revolutionary because development would seem to constitute an independent source of variability. Richard Watson and his co-authors have argued that developmental memory constitutes one such variety of phenotypic variability. While this version of the phenotype-first hypothesis is especially well-suited for the late metazoan context, where animals have a sufficient history of selection from which to draw, appeals (...) to developmental memory seem less plausible in the evolutionary context of the early metazoans. I provide an interpretation of Stuart Newman’s account of deep metazoan phylogenesis that suggests that spandrels are, in addition to developmental memory, an important reservoir of phenotypic variability. I conclude by arguing that Gerd Müller’s “side-effect hypothesis” is an illuminating generalization of the proposed non-Watsonian version of the phenotype-first hypothesis. (shrink)

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 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. (shrink)

Evolutionary systems biology is an emerging hybrid approach that integrates methods, models, and data from evolutionary and systems biology. Drawing on themes that arose at a cross-disciplinary meeting on ESB in 2013, we discuss in detail some of the explanatory friction that arises in the interaction between evolutionary and systems biology. These tensions appear because of different modeling approaches, diverse explanatory aims and strategies, and divergent views about the scope of the evolutionary synthesis. We locate these discussions in the context (...) of long-running philosophical deliberations on explanation, modeling, and theoretical synthesis. We show how many of the issues central to ESB’s progress can be understood as general philosophical problems. The benefits of addressing these philosophical issues feed back into philosophy too, because ESB provides excellent examples of scientific practice for the development of philosophy of science and philosophy of biology. (shrink)

We present two case studies from contemporary biology in which we observe conflicts between established and emerging approaches. The first case study discusses the relation between molecular biology and systems biology regarding the explanation of cellular processes, while the second deals with phylogenetic systematics and the challenge posed by recent network approaches to established ideas of evolutionary processes. We show that the emergence of new fields is in both cases driven by the development of high-throughput data generation technologies and the (...) transfer of modeling techniques from other fields. New and emerging views are characterized by different philosophies of nature, i.e. by different ontological and methodological assumptions and epistemic values and virtues. This results in a kind of conflict we call “epistemic competition” that manifests in two ways: On the one hand, opponents engage in mutual critique and defense of their fundamental assumptions. On the other hand, they compete for the acceptance and integration of the knowledge they provide by a broader scientific community. Despite an initial rhetoric of replacement, the views as well as the respective audiences come to be seen as more clearly distinct during the course of the debate. Hence, we observe—contrary to many other accounts of scientific change—that conflict results in the formation of new niches of research, leading to co-existence and perceived complementarity of approaches. Our model thus contributes to the understanding of the pluralization of the scientific landscape. (shrink)

Evolutionary systems biology (ESB) aims to integrate methods from systems biology and evolutionary biology to go beyond the current limitations in both fields. This article clarifies some conceptual difficulties of this integration project, and shows how they can be overcome. The main challenge we consider involves the integration of evolutionary biology with developmental dynamics, illustrated with two examples. First, we examine historical tensions between efforts to define general evolutionary principles and articulation of detailed mechanistic explanations of specific traits. Next, these (...) tensions are further clarified by considering a recent case from another field focused on developmental dynamics: stem cell biology. In the stem cell case, incompatible explanatory aims block integration. Experimental approaches aim at mechanistic explanation while dynamical system models offer explanation in terms of general principles. We then discuss an ESB case in which integration succeeds: search for general attractors using a dynamical systems framework synergizes with the experimental search for detailed mechanisms. Contrasts between the positive and negative cases suggest general lessons for achieving an integrated understanding of developmental and evolutionary dynamics. The key integrative move is to acknowledge two complementary aims, both relevant to explanation: identifying the space of possible dynamic states and trajectories, and mechanistic understanding of causal interactions underlying a specific phenomenon of interest. These two aims can support one another in a joint project characterizing dynamic aspects of evolving lineages. This more inclusive project can lead to insights that cannot be reached by either approach in isolation. (shrink)

Life scientists increasingly rely upon abstraction-based modeling and reasoning strategies for understanding biological phenomena. We introduce the notion of constraint-based reasoning as a fruitful tool for conceptualizing some of these developments. One important role of mathematical abstractions is to impose formal constraints on a search space for possible hypotheses and thereby guide the search for plausible causal models. Formal constraints are, however, not only tools for biological explanations but can be explanatory by virtue of clarifying general dependency-relations and patterning between (...) functions and structures. We describe such situations as constraint-based explanations and argue that these differ from mechanistic strategies in important respects. While mechanistic explanations emphasize change-relating causal features, constraint-based explanations emphasize formal dependencies and generic organizational features that are relatively independent of lower-level changes in causal details. Our distinction between mechanistic and constraint-based explanations is pragmatically motivated by the wish to understand scientific practice. We contend that delineating the affordances and assumptions of different explanatory questions and strategies helps to clarify tensions between diverging scientific practices and the innovative potentials in their combination. Moreover, we show how constraint-based explanation integrates several features shared by otherwise different philosophical accounts of abstract explanatory strategies in biology. (shrink)

I propose a dynamic causal approach to characterizing the notion of a mechanism. Levy and Bechtel, among others, have pointed out several critical limitations of the new mechanical philosophy, and pointed in a new direction to extend this philosophy. Nevertheless, they have not fully fleshed out what that extended philosophy would look like. Based on a closer look at neuroscientific practice, I propose that a mechanism is a dynamic causal system that involves various components interacting, typically nonlinearly, with one another (...) to produce a phenomenon of interest. (shrink)

In this essay I propose that what design principles in systems biology and systems neuroscience do is to present abstract characterizations of mechanisms, and thereby facilitate mechanistic explanation. To show this, one design principle in systems neuroscience, i.e., the multilayer perceptron, is examined. However, Braillard contends that design principles provide a sort of non-mechanistic explanation due to two related reasons: they are very general and describe non-causal dependence relationships. In response to this, I argue that, on the one hand, all (...) mechanisms are more or less general, and on the other, many design principles are causal systems. (shrink)

This paper examines a case of failed interdisciplinary collaboration, between experimental stem cell research and theoretical systems biology. Recently, two groups of theoretical biologists have proposed dynamical systems models as a basis for understanding stem cells and their distinctive capacities. Experimental stem cell biologists, whose work focuses on manipulation of concrete cells, tissues and organisms, have largely ignored these proposals. I argue that ‘failure to communicate’ in this case is rooted in divergent views of explanation: the theoretically-inclined modelers are committed (...) to a version of the covering-law view, while experimental stem cell biologists aim at mechanistic explanations. I propose a way to reconcile these two explanatory approaches to cell development, and discuss the significance of this result for interdisciplinary collaboration in systems biology and beyond. (shrink)

Stem cells did not become a proper research object until the 1960 s. Yet the term and the basic mind-set—namely the conception of single undifferentiated cells, be they embryonic or adult, as the basic units responsible for a directed process of development, differentiation and increasing specialisation—were already in place at the end of the nineteenth century and then transmitted on a non-linear path in the form of tropes and diagrams. Ernst Haeckel and August Weismann played a special role in this (...) story. The first coined the term Stammzelle (stem cell), the second was the author of the first cellular stem-tree diagram. Even today, I shall argue, the understanding of stem cells, especially the popular perception, is to a large extent a Haeckelian–Weismannian one. After having demonstrated this, by analysing the terminology, in this essay I will focus on the use of cytogenetic tree diagrams between 1892 and 1925 and on the tacit understanding of stem cells that they transmit. (shrink)

Evolutionary developmental biology (evo-devo) is considered a ‘mechanistic science,’ in that it causally explains morphological evolution in terms of changes in developmental mechanisms. Evo-devo is also an interdisciplinary and integrative approach, as its explanations use contributions from many fields and pertain to different levels of organismal organization. Philosophical accounts of mechanistic explanation are currently highly prominent, and have been particularly able to capture the integrative nature of multifield and multilevel explanations. However, I argue that evo-devo demonstrates the need for a (...) broadened philosophical conception of mechanisms and mechanistic explanation. Mechanistic explanation (in terms of the qualitative interactions of the structural parts of a whole) has been developed as an alternative to the traditional idea of explanation as derivation from laws or quantitative principles. Against the picture promoted by Carl Craver, that mathematical models describe but usually do not explain, my discussion of cases from the strand of evo-devo which is concerned with developmental processes points to qualitative phenomena where quantitative mathematical models are an indispensable part of the explanation. While philosophical accounts have focused on the actual organization and operation of mechanisms, properties of developmental mechanisms that are about how a mechanism reacts to modifications are of major evolutionary significance, including robustness, phenotypic plasticity, and modularity. A philosophical conception of mechanisms is needed that takes into account quantitative changes, transient entities and the generation of novel types of entities, feedback loops and complex interaction networks, emergent properties, and, in particular, functional-dynamical aspects of mechanisms, including functional (as opposed to structural) organization and distributed, system-wide phenomena. I conclude with general remarks on philosophical accounts of explanation. (shrink)

The biological sciences have always proven a fertile ground for philosophical analysis, one from which has grown a rich tradition stemming from Aristotle and flowering with Darwin. And although contemporary philosophy is increasingly becoming conceptually entwined with the study of the empirical sciences with the data of the latter now being regularly utilised in the establishment and defence of the frameworks of the former, a practice especially prominent in the philosophy of physics, the development of that tradition hasn’t received the (...) wider attention it so thoroughly deserves. This review will briefly introduce some recent significant topics of debate within the philosophy of biology, focusing on those whose metaphysical themes (in everything from composition to causation) are likely to be of wide-reaching, cross-disciplinary interest. (shrink)