Since the early 20th century underdetermination has been one of the most contentious problems in the philosophy of science. In this article I relate the underdetermination problem to models in biology and defend two main lines of argument: First, the use of models in this discipline lends strong support to the underdetermination thesis. Second, models and theories in biology are not determined strictly by the logic of representation of the studied phenomena, but also by other (...) constraints such as research traditions, backgrounds of the scientists, aims of the research and available technology. Convincing evidence for the existence of underdetermination in biology, where models abound, comes both from the fact that for a natural phenomenon we can create a number of candidate models but also from the fact that we do not have a universal rule that would adjudicate among them. This all makes a strong case for the general validity of underdetermination thesis. (shrink)

Ever since the advent of molecular biology in the 1970s, mechanical models have become the dogma in the field, where a "true" understanding of any subject is equated to a mechanistic description. This has been to the detriment of the biomedical sciences, where, barring some exceptions, notable new feats of understanding have arguably not been achieved in normal and disease biology, including neurodegenerative disease and cancer pathobiology. I argue for a "mechanism-plus-X" paradigm, where mainstay elements of mechanistic (...) models such as hierarchy and correlation are combined with nomological principles such as general operative rules and generative principles. Depending on the question at hand and the nature of the inquiry, X could range from proven physical laws to speculative biological generalizations, such as the notional principle of cellular synchrony. I argue that the "mechanism-plus-X" approach should ultimately aim to move biological inquiries out of the deadlock of oft-encountered mechanistic pitfalls and reposition biology to its former capacity of illuminating fundamental truths about the world. (shrink)

The aim of this article is to investigate the relevance and implications of synthetic models for the study of the interactive dimension of minimal life and cognition, by taking into consideration how the use of artificial systems may contribute to an understanding of the way in which interactions may affect or even contribute to shape biological identities. To do so, this article analyzes experimental work in synthetic biology on different types of interactions between artificial and natural systems, more (...) specifically: between protocells and between biological living cells and protocells. It discusses how concepts such as control, cognition, communication can be used to characterize these interactions from a theoretical point of view, which criteria of relevance and evaluation of synthetic models can be applied to these cases, and what are their limits. (shrink)

A common view of how-possibly explanations in biology treats them as explanatorily incomplete. In addition to this interpretation of how-possibly explanation, I argue that there is another interpretation, one which features what I term “explanatory strategies.” This strategy-centered interpretation of how-possibly explanation centers on there being a different explanatory context within which how-possibly explanations are offered. I contend that, in conditions where this strategy context is recognized, how-possibly explanations can be understood as complete explanations. I defend this alternative interpretation (...) by analyzing the explanatory value of simple physical models the nineteenth century developmental biologist Wilhelm His constructed for animal development. (shrink)

Three metascientific concepts that have been object of philosophical analysis are the concepts oflaw, model and theory. The aim ofthis article is to present the explication of these concepts, and of their relationships, made within the framework of Sneedean or Metatheoretical Structuralism (Balzer et al. 1987), and of their application to a case from the realm of biology: Population Dynamics. The analysis carried out will make it possible to support, contrary to what some philosophers of science in general and (...) of biology in particular hold, the following claims: a) there are "laws" in biological sciences, b) many of the heterogeneous and different "models" of biology can be accommodated under some "theory", and c) this is exactly what confers great unifying power to biological theories. (shrink)

In this paper we present a new framework of idealization in biology. We characterize idealizations as a network of counterfactual and hypothetical conditionals that can exhibit different “degrees of contingency”. We use this idea to say that, in departing more or less from the actual world, idealizations can serve numerous epistemic, methodological or heuristic purposes within scientific research. We defend that, in part, this structure explains why idealizations, despite being deformations of reality, are so successful in scientific practice. For (...) illustrative purposes, we provide an example from population genetics, the Wright-Fisher Model. (shrink)

Biosemiotics is a growing fi eld that investigates semiotic processes in the living realm in an attempt to combine the fi ndings of the biological sciences and semiotics. Semiotic processes are more or less what biologists have typically referred to as “ signals, ” “ codes, ”and “ information processing ”in biosystems, but these processes are here understood under the more general notion of semiosis, that is, the production, action, and interpretation of signs. Thus, biosemiotics can be seen as (...) class='Hi'>biology interpreted as a study of living sign systems — which also means that semiosis or sign process can be seen as the very nature of life itself. In other words, biosemiotics is a field of research investigating semiotic processes (meaning, signification, communication, and habit formation in living systems) and the physicochemical preconditions for sign action and interpretation. -/- (...). (shrink)

The geosciences include a wide spectrum of disciplines ranging from paleontology to climate science, and involve studies of a vast range of spatial and temporal scales, from the deep-time history of microbial life to the future of a system no less immense and complex than the entire Earth. Modeling is thus a central and indispensable tool across the geosciences. Here, we review both the history and current state of model-based inquiry in the geosciences. Research in these fields makes use of (...) a wide variety of models, such as conceptual, physical, and numerical models, and more specifically cellular automata, artificial neural networks, agent-based models, coupled models, and hierarchical models. We note the increasing demands to incorporate biological and social systems into geoscience modeling, challenging the traditional boundaries of these fields. Understanding and articulating the many different sources of scientific uncertainty – and finding tools and methods to address them – has been at the forefront of most research in geoscience modeling. We discuss not only structuralmodel uncertainties, parameter uncertainties, and solution uncertainties, but also the diverse sources of uncertainty arising from the complex nature of geoscience systems themselves. Without an examination of the geosciences, our philosophies of science and our understanding of the nature of model-based science are incomplete. (shrink)

Historically embryogenesis has been among the most philosophically intriguing phenomena. In this paper I focus on one aspect of biological development that was particularly perplexing to the ancients: self-organisation. For many ancients, the fact that an organism determines the important features of its own development required a special model for understanding how this was possible. This was especially true for Aristotle, Alexander, and Simplicius, who all looked to contemporary technology to supply that model. However, they did not all agree on (...) what kind of device should be used. In this paper I explore the way these ancients made use of technology as a model for the developing embryo. I argue that their different choices of device reveal fundamental differences in the way each thinker understood the nature of biological development itself. In the final section of the paper I challenge the traditional view (dating back to Alexander's interpretation of Aristotle) that the use of automata in GA can simply be read off from their use in the de motu. (shrink)

Many biological processes and objects can be described by fractals. The paper uses a new type of objects – blinking fractals – that are not covered by traditional theories considering dynamics of self-similarity processes. It is shown that both traditional and blinking fractals can be successfully studied by a recent approach allowing one to work numerically with infinite and infinitesimal numbers. It is shown that blinking fractals can be applied for modeling complex processes of growth of biological systems including their (...) season changes. The new approach allows one to give various quantitative characteristics of the obtained blinking fractals models of biological systems. (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)

The INBIOSA project brings together a group of experts across many disciplines who believe that science requires a revolutionary transformative step in order to address many of the vexing challenges presented by the world. It is INBIOSA’s purpose to enable the focused collaboration of an interdisciplinary community of original thinkers. This paper sets out the case for support for this effort. The focus of the transformative research program proposal is biology-centric. We admit that biology to date has been (...) more fact-oriented and less theoretical than physics. However, the key leverageable idea is that careful extension of the science of living systems can be more effectively applied to some of our most vexing modern problems than the prevailing scheme, derived from abstractions in physics. While these have some universal application and demonstrate computational advantages, they are not theoretically mandated for the living. A new set of mathematical abstractions derived from biology can now be similarly extended. This is made possible by leveraging new formal tools to understand abstraction and enable computability. [The latter has a much expanded meaning in our context from the one known and used in computer science and biology today, that is "by rote algorithmic means", since it is not known if a living system is computable in this sense (Mossio et al., 2009).] Two major challenges constitute the effort. The first challenge is to design an original general system of abstractions within the biological domain. The initial issue is descriptive leading to the explanatory. There has not yet been a serious formal examination of the abstractions of the biological domain. What is used today is an amalgam; much is inherited from physics (via the bridging abstractions of chemistry) and there are many new abstractions from advances in mathematics (incentivized by the need for more capable computational analyses). Interspersed are abstractions, concepts and underlying assumptions “native” to biology and distinct from the mechanical language of physics and computation as we know them. A pressing agenda should be to single out the most concrete and at the same time the most fundamental process-units in biology and to recruit them into the descriptive domain. Therefore, the first challenge is to build a coherent formal system of abstractions and operations that is truly native to living systems. Nothing will be thrown away, but many common methods will be philosophically recast, just as in physics relativity subsumed and reinterpreted Newtonian mechanics. -/- This step is required because we need a comprehensible, formal system to apply in many domains. Emphasis should be placed on the distinction between multi-perspective analysis and synthesis and on what could be the basic terms or tools needed. The second challenge is relatively simple: the actual application of this set of biology-centric ways and means to cross-disciplinary problems. In its early stages, this will seem to be a “new science”. This White Paper sets out the case of continuing support of Information and Communication Technology (ICT) for transformative research in biology and information processing centered on paradigm changes in the epistemological, ontological, mathematical and computational bases of the science of living systems. Today, curiously, living systems cannot be said to be anything more than dissipative structures organized internally by genetic information. There is not anything substantially different from abiotic systems other than the empirical nature of their robustness. We believe that there are other new and unique properties and patterns comprehensible at this bio-logical level. The report lays out a fundamental set of approaches to articulate these properties and patterns, and is composed as follows. -/- Sections 1 through 4 (preamble, introduction, motivation and major biomathematical problems) are incipient. Section 5 describes the issues affecting Integral Biomathics and Section 6 -- the aspects of the Grand Challenge we face with this project. Section 7 contemplates the effort to formalize a General Theory of Living Systems (GTLS) from what we have today. The goal is to have a formal system, equivalent to that which exists in the physics community. Here we define how to perceive the role of time in biology. Section 8 describes the initial efforts to apply this general theory of living systems in many domains, with special emphasis on crossdisciplinary problems and multiple domains spanning both “hard” and “soft” sciences. The expected result is a coherent collection of integrated mathematical techniques. Section 9 discusses the first two test cases, project proposals, of our approach. They are designed to demonstrate the ability of our approach to address “wicked problems” which span across physics, chemistry, biology, societies and societal dynamics. The solutions require integrated measurable results at multiple levels known as “grand challenges” to existing methods. Finally, Section 10 adheres to an appeal for action, advocating the necessity for further long-term support of the INBIOSA program. -/- The report is concluded with preliminary non-exclusive list of challenging research themes to address, as well as required administrative actions. The efforts described in the ten sections of this White Paper will proceed concurrently. Collectively, they describe a program that can be managed and measured as it progresses. (shrink)

Philosophy can shed light on mathematical modeling and the juxtaposition of modeling and empirical data. This paper explores three philosophical traditions of the structure of scientific theory—Syntactic, Semantic, and Pragmatic—to show that each illuminates mathematical modeling. The Pragmatic View identifies four critical functions of mathematical modeling: (1) unification of both models and data, (2) model fitting to data, (3) mechanism identification accounting for observation, and (4) prediction of future observations. Such facets are explored using a recent exchange between two (...) groups of mathematical modelers in plant biology. Scientific debate can arise from different modeling philosophies. (shrink)

Are living organisms--as Descartes argued--just machines? Or is the nature of life such that it can never be fully explained by mechanistic models? In this thought-provoking and controversial book, eminent geophysicist Walter M. Elsasser argues that the behavior of living organisms cannot be reduced to physico-chemical causality. Suggesting that molecular biology today is at the same point as Newtonian physics on the eve of the quantum revolution, Elsasser lays the foundation for a theoretical biology that points the (...) way toward a natural philosophy of organic life. Explicitly repudiating "vitalism" (the notion that the laws of nature need to be modified when applied to living organisms), Elsasser argues instead that the structural complexity of even a single living cell is "transcomputational"--that is, beyond the power of any imaginable system to compute. Beginning from this insight, Elsasser leads the reader through a step-by-step process that ultimately arrives at the conclusion that living and non-living matter are separated by "a no-man's land of irrationality." Trained in Germany as a physicist, Elsasser first pondered the implications of quantum mechanics for biology as early as 1951. The more closely he studied the inherent complexity of life, the more skeptical he became of the reductionist view of organisms as tiny machines. "An organism," he concluded, "is a source of causal chains which cannot be traced beyond a terminal point because they are lost in the unfathomable complexity of the organism." Like the physicist who works within the bounds of an unfathomable universe, Elsasser argues, the biologist must seek answers within a system that is no less unfathomable. (shrink)

Many biologists and philosophers have worried that importing models of reasoning from the physical sciences obscures our understanding of reasoning in the life sciences. In this paper we discuss one example that partially validates this concern: part-whole reductive explanations. Biology and physics tend to incorporate different models of temporality in part-whole reductive explanations. This results from differential emphases on compositional and causal facets of reductive explanations, which have not been distinguished reliably in prior philosophical analyses. Keeping these (...) two facets distinct facilitates the identifi cation of two further aspects of reductive explanation: intrinsicality and fundamentality. Our account provides resources for discriminating between different types of reductive explanation and suggests a new approach to comprehending similarities and differences in the explanatory reasoning found in biology and physics. (shrink)

‘‘Theoretical biology’’ is a surprisingly heter- ogeneous field, partly because it encompasses ‘‘doing the- ory’’ across disciplines as diverse as molecular biology, systematics, ecology, and evolutionary biology. Moreover, it is done in a stunning variety of different ways, using anything from formal analytical models to computer sim- ulations, from graphic representations to verbal arguments. In this essay I survey a number of aspects of what it means to do theoretical biology, and how they compare with (...) the allegedly much more restricted sense of theory in the physical sciences. I also tackle a recent trend toward the presentation of all-encompassing theories in the biological sciences, from general theories of ecology to a recent attempt to provide a conceptual framework for the entire set of biological disciplines. Finally, I discuss the roles played by philosophers of science in criticizing and shap- ing biological theorizing. (shrink)

We advance an account that grounds cognition, specifically decision-making, in an activity all organisms as autonomous systems must perform to keep themselves viable—controlling their production mechanisms. Production mechanisms, as we characterize them, perform activities such as procuring resources from their environment, putting these resources to use to construct and repair the organism's body and moving through the environment. Given the variable nature of the environment and the continual degradation of the organism, these production mechanisms must be regulated by control mechanisms (...) that select when a production is required and how it should be carried out. To operate on production mechanisms, control mechanisms need to procure information through measurement processes and evaluate possible actions. They are making decisions. In all organisms, these decisions are made by multiple different control mechanisms that are organized not hierarchically but heterarchically. In many cases, they employ internal models of features of the environment with which the organism must deal. Cognition, in the form of decision-making, is thus fundamental to living systems which must control their production mechanisms. (shrink)

Delineating the framework for a fundamental model of long-range coherence in biological systems is said to rely on principles beyond parameters addressed by current physical science. Just as phenomena of quantum mechanics lay beyond tools of classical Newtonian mechanics we must now enter a 3rd regime of unified field, UF mechanics. In this paper we present a battery of nine empirical protocols for manipulating long-range coherence in complex self-organized living systems (SOLS) in a manner surmounting the Copenhagen Interpretation of quantum (...) uncertainty (space-quantization) thereby allowing empirical access to underlying coherent biophysical principles driving self-organization. Interestingly, while the UF is not indicative of a 5th fundamental force in the usual phenomenal sense of quantal transfer during field interactions; it does however provide an inherent ‘force of coherence’ in an energyless ontological sense by a process called ‘topological switching’ of higher dimensional (HD) brane dynamics. It is this putative inherent property that produces long-range coherence and leads to the possibility of its direct experimental mediation. (shrink)

We present a technique for the analysis of pattern formation by a class of models for the formation of ocular dominance stripes in the striate cortex of some mammals. The method, which employs the adiabatic approximation to derive a set of ordinary differential equations for patterning modes, has been successfully applied to reaction-diffusion models for striped patterns [1]. Models of ocular dominance stripes have been studied [2,3] by computation, or by linearization of the model equations. These techniques (...) do not provide a rationale for the origin of the stripes. We show here that stripe formation is a non-linear property of the models. Our analysis indicates that stripe selection is closely linked to a property in the dynamics of the models which arises from a symmetry between ipsilateral and contralateral synapses to the visual cortex of a given hemisphere. (shrink)

The book answers long-standing questions on scientific modeling and inference across multiple perspectives and disciplines, including logic, mathematics, physics and medicine. The different chapters cover a variety of issues, such as the role models play in scientific practice; the way science shapes our concept of models; ways of modeling the pursuit of scientific knowledge; the relationship between our concept of models and our concept of science. The book also discusses models and scientific explanations; models in (...) the semantic view of theories; the applicability of mathematical models to the real world and their effectiveness; the links between models and inferences; and models as a means for acquiring new knowledge. It analyzes different examples of models in physics, biology, mathematics and engineering. Written for researchers and graduate students, it provides a cross-disciplinary reference guide to the notion and the use of models and inferences in science. (shrink)

Using as case studies two early diagrams that represent mechanisms of the cell division cycle, we aim to extend prior philosophical analyses of the roles of diagrams in scientific reasoning, and specifically their role in biological reasoning. The diagrams we discuss are, in practice, integral and indispensible elements of reasoning from experimental data about the cell division cycle to mathematical models of the cycle’s molecular mechanisms. In accordance with prior analyses, the diagrams provide functional explanations of the cell cycle (...) and facilitate the construction of mathematical models of the cell cycle. But, extending beyond those analyses, we show how diagrams facilitate the construction of mathematical models, and we argue that the diagrams permit nomological explanations of the cell cycle. We further argue that what makes diagrams integral and indispensible for explanation and model construction is their nature as locality aids: they group together information that is to be used together in a way that sentential representations do not. (shrink)

Modern life sciences research is increasingly relying on artificial intelligence (AI) approaches to model biological systems, primarily centered around the use of machine learning (ML) models. Although ML is undeniably useful for identifying patterns in large, complex data sets, its widespread application in biological sciences represents a significant deviation from traditional methods of scientific inquiry. As such, the interplay between these models and scientific understanding in biology is a topic with important implications for the future of scientific (...) research, yet it is a subject that has received little attention. Here, we draw from an epistemological toolkit to contextualize recent applications of ML in biological sciences under modern philosophical theories of understanding, identifying general principles that can guide the design and application of ML systems to model biological phenomena and advance scientific knowledge. We propose that conceptions of scientific understanding as information compression, qualitative intelligibility, and dependency relation modelling provide a useful framework for interpreting ML-mediated understanding of biological systems. Through a detailed analysis of two key application areas of ML in modern biological research – protein structure prediction and single cell RNA-sequencing – we explore how these features have thus far enabled ML systems to advance scientific understanding of their target phenomena, how they may guide the development of future ML models, and the key obstacles that remain in preventing ML from achieving its potential as a tool for biological discovery. Consideration of the epistemological features of ML applications in biology will improve the prospects of these methods to solve important problems and advance scientific understanding of living systems. (shrink)

A formal model of the processes of digestion in a hypothetical cell is developed and discussed as a case study of how the threefold logic of Peircean semiotics works within Rosen’s paradigm of relational ontology. The formal model is used to demonstrate several fundamental differences between a relational description of biological processes and a mechanistic description. The formal model produces a logic of embodied generalization that is mediated and determined by the cell through its interactions with the environment. Specifically, the (...) synchronization of the functions of pattern recognition and semantic attribution results in an open and adaptive learning system that is stabilized by a hermeneutical circle. The relational principles of biosemiotics demonstrated through this case study are applicable to other biological systems, as well as to the relational ontology of systems theory, artificial intelligence systems, and relativistic quantum theory. (shrink)

This paper examines two parallel discussions of scientific modeling which have invoked experimentation in addressing the role of models in scientific inquiry. One side discusses the experimental character of models, whereas the other focuses on their exploratory uses. Although both relate modeling to experimentation, they do so differently. The former has considered the similarities and differences between models and experiments, addressing, in particular, the epistemic value of materiality. By contrast, the focus on exploratory modeling has highlighted the (...) various kinds of exploratory functions of models in the early stages of inquiry. These two perspectives on modeling are discussed through a case study in the field of synthetic biology. The research practice in question explores biological control by making use of an ensemble of different epistemic means: mathematical models and simulations, synthetic genetic circuits and intracellular measuring devices, and finally electronic circuits. We argue that the study of exploratory modeling should trace the ways different epistemic means, in different materialities, are being combined over time. Finally, the epistemic status of such novel investigative objects as synthetic genetic circuits is evaluated, with the conclusion that they can function as both experiments and models. (shrink)

According to the PubMed resource from the U.S. National Library of Medicine, over 750,000 scientific articles have been published in the ~5000 biomedical journals worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to (...) be able to assimilate and mine data from related investigations for purposes of meta-analysis. While computers have the potential for assisting investigators in the extraction, management and analysis of these data, information contained in the traditional journal publication is still largely unstructured, free-text descriptions of study design, experimental application and results interpretation, making it difficult for computers to gain access to the content of what is being conveyed without significant manual intervention. In order to circumvent these roadblocks and make the most of the output from the biomedical research enterprise, a variety of related standards in knowledge representation are being developed, proposed and adopted in the biomedical community. In this chapter, we will explore the current status of efforts to develop minimum information standards for the representation of a biomedical experiment, ontologies composed of shared vocabularies assembled into subsumption hierarchical structures, and extensible relational data models that link the information components together in a machine-readable and human-useable framework for data mining purposes. (shrink)

This paper adds to the philosophical literature on mechanistic explanation by elaborating two related explanatory functions of idealisation in mechanistic models. The first function involves explaining the presence of structural/organizational features of mechanisms by reference to their role as difference-makers for performance requirements. The second involves tracking counterfactual dependency relations between features of mechanisms and features of mechanistic explanandum phenomena. To make these functions salient, we relate our discussion to an exemplar from systems biological research on the mechanism for (...) countering heat shock—the heat shock response system—in Escherichia coli bacteria. This research also reinforces a more general lesson: ontic constraint accounts in the literature on mechanistic explanation provide insufficiently informative normative appraisals of mechanistic models. We close by outlining an alternative view on the explanatory norms governing mechanistic representation. (shrink)

The evolution of altruism in human societies has been intensively investigated in social and natural sciences. A widely acknowledged recent idea is the “parochial altruism model,” which suggests that inter- group hostility and intragroup altruism can coevolve through lethal intergroup conflicts. The current article critically examines this idea by reviewing research relevant to intergroup conflicts in human evolutionary history from evolutionary biology, psychology, cultural anthropology, and archaeology. After a brief intro- duction, section 2 illustrates the mathematical model of parochial (...) altruism and some critiques of the model and its interpretation, primarily from an evolutionary biology point of view. Section 3 delves into the archaeological evidence of prehistoric intergroup conflicts in the Japanese archipelago’s Jomon and Yayoi periods, Europe’s Mesolithic period, and North America’s Pacific period as counter examples of the paro- chial altruism model. In section 4, the ethnographies of intergroup relationships and conflicts reveal that intergroup relationships in many ethnic groups are not as simple as the assumption in the mathematical model of parochial altruism. In section 5, we outline psychological research on intergroup conflicts which suggest that intergroup hostility and ingroup altruism are not necessarily correlated. In conclusion, we argue that the assumption and parameter settings of the parochial altruism model are inconsistent with empirical data. (shrink)

The picture of synthetic biology as a kind of engineering science has largely created the public understanding of this novel field, covering both its promises and risks. In this paper, we will argue that the actual situation is more nuanced and complex. Synthetic biology is a highly interdisciplinary field of research located at the interface of physics, chemistry, biology, and computational science. All of these fields provide concepts, metaphors, mathematical tools, and models, which are typically utilized (...) by synthetic biologists by drawing analogies between the different fields of inquiry. We will study analogical reasoning in synthetic biology through the emergence of the functional meaning of noise, which marks an important shift in how engineering concepts are employed in this field. The notion of noise serves also to highlight the differences between the two branches of synthetic biology: the basic science-oriented branch and the engineering-oriented branch, which differ from each other in the way they draw analogies to various other fields of study. Moreover, we show that fixing the mapping between a source domain and the target domain seems not to be the goal of analogical reasoning in actual scientific practice. (shrink)

Abstract: Without directly taking sides in the design/anti-design debate, this paper defends the following position: the assertion that biological evolution “is” design-free presupposes the possibility to model biological evolution in a design-free way. Certainly, there are design-free models of evolution based on cumulative selection. But “to model” is a verb denoting “modeling” as the process leading to a model. So any modeling – trivially – needs “previous human design.” Nevertheless, contrary to other scientific activities which legitimately consider models (...) while ignoring the process leading to them, evolution theory must take into account the human activity of modeling required by its corresponding models. (shrink)

Cancer biology features the ascription of normal functions to parts of cancers. At least some ascriptions of function in cancer biology track local normality of parts within the global abnormality of the aberration to which those parts belong. That is, cancer biologists identify as functions activities that, in some sense, parts of cancers are supposed to perform, despite cancers themselves having no purpose. The present paper provides a theory to accommodate these normal function ascriptions—I call it the Modeling (...) Account of Normal Function (MA). MA comprises two claims. First, normal functions are activities whose performance by the function-bearing part contributes to the self-maintenance of the whole system and, thereby, results in the continued presence of that part. Second, MA holds that models of system-level activities that are (partly) constitutive of self-maintenance are improved by including a representation of the relevant function-bearing part and by making reference to the activity/activities that part performs which contribute(s) to those system-level activities. I contrast MA with two other accounts that seek to explicate the ascription of normal functions in biology, namely, the organizational account and the selected effects account. Both struggle to extend to cancer biology. However, I offer ecumenical readings which allow them to recover some ascriptions of normal function to parts of cancers. So, though I contend that MA excels in this respect, the purpose of this paper is served if it provides materials for bridging the gap between cancer biology, philosophy of cancer, and the literature on function. (shrink)

Synthetic biology is a field of research that concentrates on the design, construction, and modification of new biomolecular parts and metabolic pathways using engineering techniques and computational models. By employing knowledge of operational pathways from engineering and mathematics such as circuits, oscillators, and digital logic gates, it uses these to understand, model, rewire, and reprogram biological networks and modules. Standard biological parts with known functions are catalogued in a number of registries (e.g. Massachusetts Institute of Technology Registry of (...) Standard Biological Parts). Biological parts can then be selected from the catalogue and assembled in a variety of combinations to construct a system or pathway in a microbe. Through the innovative re-engineering of biological circuits and the optimization of certain metabolic pathways, biological modules can be designed to reprogram organisms to produce products or behaviors. Synthetic biology is what is known as a “platform technology”. That is, it generates highly transferrable theoretical models, engineering principles, and know-how that can be applied to create potential products in a wide variety of industries. Proponents suggest that applications of synthetic biology may be able to provide scientific and engineered solutions to a multitude of worldwide problems from health to energy. Synthetic biology research has already been successful in constructing microbial products which promise to offer cheaper pharmaceuticals such as the antimalarial synthetic drug artemisinin, engineered microbes capable of cleaning up oil spills, and the engineering of biosensors that can detect the presence of high concentrations of arsenic in drinking water. One of the potential benefits of synthetic biology research is in its application to biofuel production. It is this application which is the focus of this entry. The term “biofuel” has referred generally to all liquid fuels that are sourced from plant or plant byproducts and are used for energy necessary for transportation vehicles (Thompson 2012). Biofuels that are produced using synthetic biological techniques re-engineer microbes into biofuel factories are a subset of these. (shrink)

In The Biopsychosocial Model of Health and Disease, Derek Bolton and Grant Gillett argue that a defensible updated version of the biopsychosocial model requires a metaphysically adequate account of disease causation that can accommodate biological, psychological, and social factors. This present paper offers a philosophical critique of their account of biopsychosocial causation. I argue that their account relies on claims about the normativity and the semantic content of biological information that are metaphysically contentious. Moreover, I suggest that these claims are (...) unnecessary for a defence of biopsychosocial causation, as the roles of multiple and diverse factors in disease causation can be readily accommodated by a more widely accepted and less metaphysically contentious account of causation. I then raise the more general concern that they are misdiagnosing the problem with the traditional version of the biopsychosocial model. The challenge when developing an explanatorily valuable version of the biopsychosocial model, I argue, is not so much providing an adequate account of biopsychosocial causation, but providing an adequate account of causal selection. Finally, I consider how this problem may be solved to arrive at a more explanatorily valuable and clinically useful version of the biopsychosocial model. (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)

A cognitive ethnography of how bioengineering scientists create innovative modeling methods. In this first full-scale, long-term cognitive ethnography by a philosopher of science, Nancy J. Nersessian offers an account of how scientists at the interdisciplinary frontiers of bioengineering create novel problem-solving methods. Bioengineering scientists model complex dynamical biological systems using concepts, methods, materials, and other resources drawn primarily from engineering. They aim to understand these systems sufficiently to control or intervene in them. What Nersessian examines here is how cutting-edge bioengineering (...) scientists integrate the cognitive, social, material, and cultural dimensions of practice. Her findings and conclusions have broad implications for researchers in philosophy, science studies, cognitive science, and interdisciplinary studies, as well as scientists, educators, policy makers, and funding agencies. In studying the epistemic practices of scientists, Nersessian pushes the boundaries of the philosophy of science and cognitive science into areas not ventured before. She recounts a decades-long, wide-ranging, and richly detailed investigation of the innovative interdisciplinary modeling practices of bioengineering researchers in four university laboratories. She argues and demonstrates that the methods of cognitive ethnography and qualitative data analysis, placed in the framework of distributed cognition, provide the tools for a philosophical analysis of how scientific discoveries arise from complex systems in which the cognitive, social, material, and cultural dimensions of problem-solving are integrated into the epistemic practices of scientists. Specifically, she looks at how interdisciplinary environments shape problem-solving. Although Nersessian’s case material is drawn from the bioengineering sciences, her analytic framework and methodological approach are directly applicable to scientific research in a broader, more general sense, as well. (shrink)

An influential position in the philosophy of biology claims that there are no biological laws, since any apparently biological generalization is either too accidental, fact-like or contingent to be named a law, or is simply reducible to physical laws that regulate electrical and chemical interactions taking place between merely physical systems. In the following I will stress a neglected aspect of the debate that emerges directly from the growing importance of mathematical models of biological phenomena. My main aim (...) is to defend, as well as reinforce, the view that there are indeed laws also in biology, and that their difference in stability, contingency or resilience with respect to physical laws is one of degrees, and not of kind . (shrink)

The problem with reductionism in biology is not the reduction, but the implicit attitude of determinism that usually accompanies it. Methodological reductionism is supported by deterministic beliefs, but making such a connection is problematic when it is based on an idea of determinism as fixed predictability. Conflating determinism with predictability gives rise to inaccurate models that overlook the dynamic complexity of our world, as well as ignore our epistemic limitations when we try to model it. Furthermore, the assumption (...) of a strictly deterministic framework is unnecessarily hindering to biology. By removing the dogma of determinism, biological methods, including reductive methods, can be expanded to include stochastic models and probabilistic interpretations. Thus, the dogma of reductionism can be saved once its ties with determinism are severed. In this paper, I analyze two problems that have faced molecular biology for the last 50 years—protein folding and cancer. Both cases demonstrate the long influence of reductionism and determinism on molecular biology, as well as how abandoning determinism has opened the door to more probabilistic and unconstrained reductive methods in biology. (shrink)

It is now increasingly accepted that many existing biological and medical ontologies can be improved by adopting tools and methods that bring a greater degree of logical and ontological rigor. In this chapter we will focus on the merits of a logically sound approach to ontologies from a methodological point of view. As we shall see, one crucial feature of a logically sound approach is that we have clear and functional definitions of the relational expressions such as ‘is a’ and (...) ‘part of ’. (shrink)

[penultimate draft; prepared for publication in Aristotle’s Parts of Animals: A Critical Guide, ed. Sophia Connell – please cite final version] -/- Parts of Animals II.10 makes a new beginning in Aristotle’s study of animals. In it, Aristotle proposes to “now speak as if we are once more at an origin, beginning first with those things that are primary” (655b28-9). This is the start of his account of the non-uniform parts of blooded animals: parts such as eyes, noses, mouths, etc., (...) as opposed to uniform parts like blood and flesh. PA II.10 proposes a new strategy for studying these parts: “one ought to speak about the human kind first” (656a10). Beginning “first” with the “primary” things thus amounts to beginning with humans (655b28-9). -/- Why does Aristotle think this strategy is appropriate for his project? One answer is that it reflects a fundamental anthropocentrism. Lloyd, for instance, has raised the possibility that the basic assumptions that guide Aristotle’s biology may to some extent reflect the anthropocentrism of his culture, where the relation of humans to animals was “a preoccupation of popular beliefs”. In this paper, I develop an interpretation that both builds on and challenges this suggestion. I do so by investigating how Aristotle thinks this strategy works in theory and in practice: his justifications for adopting it and its interaction with his scientific commitments. I argue that Aristotle adopts it in part because he thinks that humans are such that his scientific concepts apply to them in a particularly clear way. (This too is a form of anthropocentrism: one that might be resisted by arguing that humans are not the best illustrations of these concepts—that Aristotle ought to look elsewhere for models with the features he desires.) -/- More specifically, Aristotle holds that starting with humans helps establish the causal explanations of the parts of other animals, particularly when they are recalcitrant. What makes humans suitable for this role is a special teleological relation between their parts and the ends they serve: in humans, he supposes, this connection is particularly tight (in a way I explain below). Consequently, Aristotle thinks he can use humans to illuminate which sorts of features tend to be for the sake of which ends, and then extend the results of this inquiry to the parts of the other animals. (shrink)

We argue that genuine biological autonomy, or described at human level as free will, requires taking into account quantum vacuum processes in the context of biological teleology. One faces at least three basic problems of genuine biological autonomy: (1) if biological autonomy is not physical, where does it come from? (2) Is there a room for biological causes? And (3) how to obtain a workable model of biological teleology? It is shown here that the solution of all these three problems (...) is related to the quantum vacuum. We present a short review of how this basic aspect of the fundamentals of quantum theory, although it had not been addressed for nearly 100 years, actually it was suggested by Bohr, Heisenberg, and others. Realizing that the quantum mechanical measurement problem associated with the “collapse” of the wave function is related, in the Copenhagen Interpretation of quantum mechanics, to a process between self-consciousness and the external physical environment, we are extending the issue for an explanation of the different processes occurring between living organisms and their internal environment. Definitions of genuine biological autonomy, biological aim, and biological spontaneity are presented. We propose to improve the popular two-stage model of decisions with a biological model suitable to obtain a deeper look at the nature of the mind-body problem. In the newly emerging picture biological autonomy emerges as a new, fundamental and inevitable element of the scientific worldview. (shrink)

One of the central aims of science is explanation: scientists seek to uncover why things happen the way they do. This chapter addresses what kinds of explanations are formulated in biology, how explanatory aims influence other features of the field of biology, and the implications of all of this for biology education. Philosophical treatments of scientific explanation have been both complicated and enriched by attention to explanatory strategies in biology. Most basically, whereas traditional philosophy of science (...) based explanation on derivation from scientific laws, there are many biological explanations in which laws play little or no role. Instead, the field of biology is a natural place to turn for support for the idea that causal information is explanatory. Biology has also been used to motivate mechanistic accounts of explanation, as well as criticisms of that approach. Ultimately, the most pressing issue about explanation in biology may be how to account for the wide range of explanatory styles encountered in the field. This issue is crucial, for the aims of biological explanation influence a variety of other features of the field of biology. Explanatory aims account for the continued neglect of some central causal factors, a neglect that would otherwise be mysterious. This is linked to the persistent use of models like evolutionary game theory and population genetic models, models that are simplified to the point of unreality. These explanatory aims also offer a way to interpret many biologists’ total commitment to one or another methodological approach, and the intense disagreements that result. In my view, such debates are better understood as arising not from different theoretical commitments, but commitments to different explanatory projects. Biology education would thus be enriched by attending to approaches to biological explanation, as well as the unexpected ways that these explanatory aims influence other features of biology. I suggest five lessons for teaching about explanation in biology that follow from the considerations of this chapter. (shrink)

Ecological neutral theory has been controversial as an alternative to niche theory for explaining community structure. Neutral theory, which explains community structure in terms of ecological drift, is frequently charged with being unrealistic, but commentators have usually not provided an account of theory or model realism. In this paper, I propose a framework for comparing the “realism” or accuracy of alternative theories within a domain with respect to the extent to which the theories abstract and idealize. Using this framework I (...) argue, contrary to most previous commentators, that neutral and niche theories are similarly realistic. Realism cannot provide a basis for accepting or rejecting either type of theory; instead, community ecologists should continue working with a plurality of models. While theoretical unification may become possible, we should treat a plurality of complementary, partial models as the expected situation within community ecology. (shrink)

In this essay I suggest that we view design principles in systems biology as minimal models, for a design principle usually exhibits universal behaviors that are common to a whole range of heterogeneous (living and nonliving) systems with different underlying mechanisms. A well-known design principle in systems biology, integral feedback control, is discussed, showing that it satisfies all the conditions for a model to be a minimal model. This approach has significant philosophical implications: it not only accounts (...) for how design principles explain, but also helps clarify one dispute over design principles, e.g., whether design principles provide mechanistic explanations or a distinct kind of explanations called design explanations. (shrink)

Both the irreducible complexity of biological phenomena and the aim of a universalized biology (life-as-it-could-be) have lead to a deep methodological shift in the study of life; represented by the appearance of ALife, with its claim that computational modelling is the main tool for studying the general principles of biological phenomenology. However this methodological shift implies important questions concerning the aesthetic, engineering and specially the epistemological status of computational models in scientific research: halfway between the well established categories (...) of theory and experiment. ALife models become powerful epistemic artefacts allowing the simulation of emergent phenomena, the interaction between different levels of organization and the integration of different causal factors in the very same manipulable object. The use of computational models in ALife can be classified in four main categories depending on their position between theoretical and empirical practices: generic, conceptual, functional and mechanistic. For each of these categories we analyse their epistemic value and select paradigmatic examples that illustrate how ALife models can be fruitfully inserted in the study of life. (shrink)

This paper critically revises the organisational account of teleology, which argues that living systems are first and foremost oriented towards a goal: maintaining their own conditions of existence. It points out some limitations of this account, mainly in the capability to account for the richness and complexity of biological systems and their purposeful behaviours. It identifies the reason of these limitations in the theoretical grounding of this account, specifically in the too narrow notion of closure of constraints, focused on self-production. (...) It proposes to ground an organisational account of biological teleology in the capability of living system not just to produce and replace their parts, but to control their own internal dynamics and behaviours in such a way as to maintain themselves. This theoretical framework has two advantages. It better captures the distinctive features of biological organisations and consequently the richness and active nature of their purposeful behaviours. By doing so, it makes it possible to apply this framework beyond minimal theoretical models to real biological cases. (shrink)

This selective review explores biologically inspired learning as a model for intelligent robot control and sensing technology on the basis of specific examples. Hebbian synaptic learning is discussed as a functionally relevant model for machine learning and intelligence, as explained on the basis of examples from the highly plastic biological neural networks of invertebrates and vertebrates. Its potential for adaptive learning and control without supervision, the generation of functional complexity, and control architectures based on self-organization is brought forward. Learning without (...) prior knowledge based on excitatory and inhibitory neural mechanisms accounts for the process through which survival-relevant or task-relevant representations are either reinforced or suppressed. The basic mechanisms of unsupervised biological learning drive synaptic plasticity and adaptation for behavioral success in living brains with different levels of complexity. The insights collected here point toward the Hebbian model as a choice solution for “intelligent” robotics and sensor systems. Keywords: Hebbian learning; synaptic plasticity; neural networks; self-organization; brain; reinforcement; sensory processing; robot control . (shrink)

Direct neurological and especially imaging-driven investigations into the structures essential to naturally occurring cognitive systems in their development and operation have motivated broadening interest in the potential for artificial consciousness modeled on these systems. This first paper in a series of three begins with a brief review of Boltuc’s (2009) “brain-based” thesis on the prospect of artificial consciousness, focusing on his formulation of h-consciousness. We then explore some of the implications of brain research on the structure of consciousness, finding limitations (...) in biological approaches to the study of consciousness. Looking past these limitations, we introduce research in artificial consciousness designed to test for the emergence of consciousness, a phenomenon beyond the purview of the study of existing biological systems. (shrink)

In 1926, Haldane published an essay titled 'On Being the Right Size' in which he argued that the structure, function, and behavior of an organism are strongly conditioned by the physical forces that exert the greatest impact at the scale at which it exists. This chapter puts Haldane’s insight to work in the context of contemporary cell and molecular biology. Owing to their minuscule size, cells and molecules are subject to very different forces than macroscopic organisms. In a sense, (...) macroscopic and microscopic entities inhabit different “worlds”: the former is ruled by gravity and inertia, whereas the latter is governed by Brownian motion. One implication is that we should be extremely skeptical of models and analogies that seek to explain properties of microscopic entities by appealing to properties of macroscopic ones. Unfortunately, this is precisely what the appeal to engineering metaphors in molecular biology attempts to do. Molecular biologists routinely resort to such metaphors because they are familiar and intuitively intelligible. But if our machines were the size of molecules it would be impossible for them to function the way they do. It follows that we should avoid distorting biological reality by construing it in engineering terms. In this chapter I examine four key metaphors in molecular biology – “genetic program,” “cellular circuitry,” “molecular machine,” and “molecular motor” – and I argue that their deficiencies derive from their neglect of scale. I also try to explain why many biologists today appear to have forgotten the importance of scale that Haldane drew attention to in his essay. I suggest that the reason has to do with the influence of Schrödinger’s argument in 'What is Life?' regarding the stability of the gene. (shrink)

I investigate how theoretical assumptions, pertinent to different perspectives and operative during the modeling process, are central in determining how nature is actually taken to be. I explore two different models by Michael Turelli and Steve Frank of the evolution of parasite-mediated cytoplasmic incompatility, guided, respectively, by Fisherian and Wrightian perspectives. Since the two models can be shown to be commensurable both with respect to mathematics and data, I argue that the differences between them in the (1) mathematical (...) presentation of the models, (2) explanations, and (3) objectified ontologies stem neither from differences in mathematical method nor the employed data, but from differences in the theoretical assumptions, especially regarding ontology, already present in the respective perspectives. I use my "set up, mathematically manipulate, explain, and objectify" (SMEO) account of the modeling process to track the model-mediated imposition of theoretical assumptions. I conclude with a discussion of the general implications of my analysis of these models for the controversy between Fisherian and Wrightian perspectives. (shrink)

To naturalize the concept of teleological causality in biology it is not enough to avoid assuming backward causation or positing the existence of an inscrutable te- leological essence like the élan vital. We must also specify how the causality of or- ganisms is distinct from the causality of designed artifacts like thermostats or asym- metrically oriented processes like the ubiquitous increase of entropy. Historically, the concept of teleological causality in biology has been based on an analogy to the (...) familiar experience of purposeful action. This is experienced by us as a disposition to achieve a general type of end that is represented in advance, and which regulates the selection of efficient means to achieve it. Inspired by this analogy, to bridge the gap between biology and human agency we describe a simple molecular pro- cess called autogenesis that shows how two linked complementary self-organizing processes can give rise to higher-order relations that resemble purposeful disposi- tions, though expressed in terms of constraints on molecular processes. Because the autogenic model is described in sufficient detail to be empirically realizable, it provides a proof of principle demonstrating a simple form of teleological causality. (shrink)

Philosophers of biology have worked extensively on how we ought best to interpret the probabilities which arise throughout evolutionary theory. In spite of this substantial work, however, much of the debate has remained persistently intractable. I offer the example of Bayesian models of divergence time estimation as a case study in how we might bring further resources from the biological literature to bear on these debates. These models offer us an example in which a number of different (...) sources of uncertainty are combined to produce an estimate for a complex, unobservable quantity. These models have been carefully analyzed in recent biological work, which has determined the relationship between these sources of uncertainty, both quantitatively and qualitatively. I suggest here that this case shows us the limitations of univocal analyses of probability in evolution, as well as the simple dichotomy between “subjective” and “objective” probabilities, and I conclude by gesturing toward ways in which we might introduce more sophisticated interpretive taxonomies of probability as a path toward advancing debates on probability in the life sciences. (shrink)