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)

Much of biological and economic theorizing takes place by modeling, the indirect study of real-world phenomena by the construction and examination of models. Books and articles about biological and economic theory are often books and articles about models, many of which are highly idealized and chosen for their explanatory power and analytical convenience rather than for their fit with known data sets. Philosophers of science have recognized these facts and have developed literatures about the nature of models, modeling, (...) idealization, as well as testing of models and explanation by models, for both biology and economics. The impetus for this special issue came from our recognition that there is remarkably little overlap between the “modeling in biology” and “modeling in economics” literatures, despite many of the same themes appearing in these literatures. (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)

Understanding modeling in biology requires understanding how biology is organized as a discipline and how this organization influences the research practices of biologists. Biology includes a wide range of sub-disciplines, such as cell biology, population biology, evolutionary biology, molecular biology, and systems biology among others. Biologists in sub-disciplines such as cell, molecular, and systems biology believe that the use of a few experimental models allows them to discover biological universals, whereas (...) biologists in sub-disciplines such as ecology and evolutionary biology believe that the use of many different experimental and mathematical models is necessary in order to do this. Many practitioners of both approaches misunderstand best practices of modeling, especially those related to model testing. We stress the need for biologists to better engage with best practices and for philosophers of biology providing normative guidance for biologists to better engage with current developments in biology. This is especially important as biology transitions from a “data-poor” to a “data-rich” discipline. If 21st century biology is going to capitalize on the unprecedented availability of ecological, evolutionary, and molecular data, of computational resources, and of mathematical and statistical tools, biologists will need a better understanding of what modeling is and can be. (shrink)

Molecular systems orchestrating the biology of the cell typically involve a complex web of interactions among various components and span a vast range of spatial and temporal scales. Computational methods have advanced our understanding of the behavior of molecular systems by enabling us to test assumptions and hypotheses, explore the effect of different parameters on the outcome, and eventually guide experiments. While several different mathematical and computational methods are developed to study molecular systems at different spatiotemporal scales, there is (...) still a need for methods that bridge the gap between spatially‐detailed and computationally‐efficient approaches. In this review, we summarize the capabilities of agent‐based modeling (ABM) as an emerging molecular systems biology technique that provides researchers with a new tool in exploring the dynamics of molecular systems/pathways in health and disease. (shrink)

Recent controversy over the existence of biological laws raises questions about the cognitive aims of theoretical modeling in that science. If there are no laws for successful theoretical models to approximate, then what is it that successful theories do? One response is to regard theoretical models as tools. But this instrumental reading cannot accommodate the explanatory role that theories are supposed to play. Yet accommodating the explanatory function, as articulated by Brandon and Sober for example, seems to involve us (...) once again in a reliance on laws. The paper concludes that we must rethink both the nature of laws and theoretical explanation in biology. (shrink)

Prediction and control sufficient for reliable medical and other interventions are prominent aims of modeling in systems biology. The short-term attainment of these goals has played a strong role in projecting the importance and value of the field. In this paper I identify the standard models must meet to achieve these objectives as predictive robustness—predictive reliability over large domains. Drawing on the results of an ethnographic investigation and various studies in the systems biology literature, I explore four (...) current obstacles to achieving predictive robustness; data constraints, parameter uncertainty, collaborative constraints and system-scale requirements. I use a case study and the commentary of systems biologists themselves to show that current practices in the field, rather than pursuing these goals, frequently use models heuristically to investigate and build understanding of biological systems that do not meet standards of predictive robustness but are nonetheless effective uses of computation. A more heuristic conception of modeling allows us to interpret current practices as ways that manage these obstacles more effectively, particularly collaborative constraints, such that modelers can in the long-run at least work towards prediction and control. (shrink)

In the first part of this article we survey general similarities and differences between biological and social macroevolution. In the second (and main) part, we consider a concrete mathematical model capable of describing important features of both biological and social macroevolution. In mathematical models of historical macrodynamics, a hyperbolic pattern of world population growth arises from non-linear, second-order positive feedback between demographic growth and technological development. Based on diverse paleontological data and an analogy with macrosociological models, we suggest that the (...) hyperbolic character of biodiversity growth can be similarly accounted for by non-linear, second-order positive feedback between diversity growth and the complexity of community structure. We discuss how such positive feedback mechanisms can be modelled mathematically. (shrink)

The fate of optimality modeling is typically linked to that of adaptationism: the two are thought to stand or fall together (Gould and Lewontin, Proc Relig Soc Lond 205:581–598, 1979; Orzack and Sober, Am Nat 143(3):361–380, 1994). I argue here that this is mistaken. The debate over adaptationism has tended to focus on one particular use of optimality models, which I refer to here as their strong use. The strong use of an optimality model involves the claim that selection (...) is the only important influence on the evolutionary outcome in question and is thus linked to adaptationism. However, biologists seldom intend this strong use of optimality models. One common alternative that I term the weak use simply involves the claim that an optimality model accurately represents the role of selection in bringing about the outcome. This and other weaker uses of optimality models insulate the optimality approach from criticisms of adaptationism, and they account for the prominence of optimality modeling (broadly construed) in population biology. The centrality of these uses of optimality models ensures a continuing role for the optimality approach, regardless of the fate of adaptationism. (shrink)

Systems biologists often distance themselves from reductionist approaches and formulate their aim as understanding living systems “as a whole.” Yet, it is often unclear what kind of reductionism they have in mind, and in what sense their methodologies would offer a superior approach. To address these questions, we distinguish between two types of reductionism which we call “modular reductionism” and “bottom-up reductionism.” Much knowledge in molecular biology has been gained by decomposing living systems into functional modules or through detailed (...) studies of molecular processes. We ask whether systems biology provides novel ways to recompose these findings in the context of the system as a whole via computational simulations. As an example of computational integration of modules, we analyze the first whole-cell model of the bacterium M. genitalium. Secondly, we examine the attempt to recompose processes across different spatial scales via multi-scale cardiac models. Although these models rely on a number of idealizations and simplifying assumptions as well, we argue that they provide insight into the limitations of reductionist approaches. Whole-cell models can be used to discover properties arising at the interfaces of dynamically coupled processes within a biological system, thereby making more apparent what is lost through decomposition. Similarly, multi-scale modeling highlights the relevance of macroscale parameters and models and challenges the view that living systems can be understood “bottom-up.” Specifically, we point out that system-level properties constrain lower-scale processes. Thus, large-scale modeling reveals how living systems at the same time are more and less than the sum of the parts. (shrink)

This open access book assesses the prospects of (re)adopting organization as a pivotal concept in biology. It shows how organization can nourish biological thinking and practice, by reconnecting with the idea of biology as the science of organized systems. The book provides a comprehensive state-of-the-art picture of the characterizations and uses of the concept of organization in both biological science and philosophy of biology. It also deals with a variety of themes – including evolution, organogenesis, heredity, cognition (...) and ecology – with respect to which the concept of organization can guide the elaboration of original models and new experimental protocols. It will be of interest to biologists and scholars working in philosophy of science alike. (shrink)

This paper aims to propose an inferential and dynamic approach to understanding with models in biology. Understanding plays a central role in the practice of modeling. From its links with the other two central elements of scientific research, experimentation, and explanation, we show its epistemic relevance to the case of explanation in biology. Furthermore, by including the notion of understanding, we propose a non-referentialist perspective on scientific models, which is determined by their use.

This paper aims to propose an inferential and dynamic approach to understanding with models in biology. Understanding plays a central role in the practice of modeling. From its links with the other two central elements of scientific research, experimentation, and explanation, we show its epistemic relevance to the case of explanation in biology. Furthermore, by including the notion of understanding, we propose a non-referentialist perspective on scientific models, which is determined by their use.

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)

The processes of wound healing and bone regeneration and problems in tissue engineering have been an active area for mathematical modeling in the last decade. Here we review a selection of recent models which aim at deriving strategies for improved healing. In wound healing, the models have particularly focused on the inflammatory response in order to improve the healing of chronic wound. For bone regeneration, the mathematical models have been applied to design optimal and new treatment strategies for normal (...) and specific cases of impaired fracture healing. For the field of tissue engineering, we focus on mathematical models that analyze the interplay between cells and their biochemical cues within the scaffold to ensure optimal nutrient transport and maximal tissue production. Finally, we briefly comment on numerical issues arising from simulations of these mathematical models. (shrink)

In the first part of this article we survey general similarities and differences between biological and social macroevolution. In the second (and main) part, we consider a concrete mathematical model capable of describing important features of both biological and social macroevolution. In mathematical models of historical macrodynamics, a hyperbolic pattern of world population growth arises from non-linear, second-order positive feedback between demographic growth and technological development. Based on diverse paleontological data and an analogy with macrosociological models, we suggest that the (...) hyperbolic character of biodiversity growth can be similarly accounted for by non-linear, second-order positive feedback between diversity growth and the complexity of community structure. We discuss how such positive feedback mechanisms can be modelled mathematically. (shrink)

This chapter contains sections titled: Why Use Mathematical Models? Theory‐based Models Data‐based Modeling Computational Approaches Conclusions Notes.

In recent years, there has much attention given by philosophers to the ubiquitous role of models and modeling in the biological sciences. Philosophical debates has focused on several areas of discussion. First, what are models in the biological sciences? The term ‘model’ is applied to mathematical structures, graphical displays, computer simulations, and even concrete organisms. Is there an account which unifies these disparate structures? Second, scientists routinely distinguish between theories and models; however, this distinction is more difficult to draw (...) in the biological sciences since biologists often only have a variety of models and rarely have something like a fundamental theory. What then is a theory in biology? Third, how are models related to empirical or “target” systems? (shrink)

Accounts of the relation between theories and models in biology concentrate on mathematical models. In this paper I consider the dual role of models as representations of natural systems and as a material basis for theorizing. In order to explicate the dual role, I develop the concept of a remnant model, a material entity made from parts of the natural system(s) under study. I present a case study of an important but neglected naturalist, Joseph Grinnell, to illustrate the extent (...) to which mundane practices in a museum setting constitute theorizing. I speculate that historical and sociological analyses of institutions can play a specific role in the philosophical analysis of model-building strategies. (shrink)

Modern integrative systems biology defines itself by the complexity of the problems it takes on through computational modeling and simulation. However in integrative systems biology computers do not solve problems alone. Problem solving depends as ever on human cognitive resources. Current philosophical accounts hint at their importance, but it remains to be understood what roles human cognition plays in computational modeling. In this paper we focus on practices through which modelers in systems biology use computational (...) simulation and other tools to handle the cognitive complexity of their modeling problems so as to be able to make significant contributions to understanding, intervening in, and controlling complex biological systems. We thus show how cognition, especially processes of simulative mental modeling, is implicated centrally in processes of model-building. At the same time we suggest how the representational choices of what to model in systems biology are limited or constrained as a result. Such constraints help us both understand and rationalize the restricted form that problem solving takes in the field and why its results do not always measure up to expectations. (shrink)

This chapter focuses on a novel class of models used in frontier research in the bioengineering sciences – in vitro simulation models – that provide the basis for biological experimentation. These bioengineered models are hybrid constructions, composed of living tissues or cells and engineered materials. Specifically, it discusses the processes through which in vitro models were built, experimented with, and justified in a tissue engineering lab. It examines processes of design, construction, experimentation, evaluation, and redesign of in vitro simulation models, (...) in general, as instances of building the source analogy (as distinguished from retrieving an analogy), which figures prominently in creative frontier scientific research. Building the analogical source is a bootstrapping process, which furthers the articulation, as well as the solution of the problem. -/- . (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)

In which respects do modeling and experimenting resemble or differ from each other? We explore this question through studying in detail the combinatorial strategy in synthetic biology whereby scientists triangulate experimentation on model organisms, mathematical modeling, and synthetic modeling. We argue that this combinatorial strategy is due to the characteristic constraints of the three epistemic activities. Moreover, our case study shows that in some cases materiality clearly matters, in fact it provides the very rationale of synthetic (...) modeling. We will show how the materialities of the different kinds of models – biological components versus mathematical symbols – in combination with their different structures – the complexity of biological organisms versus the isolated network structure and its mathematical dynamics - define the spectrum of epistemic possibilities in synthetic biology. Furthermore, our case shows that from the perspective of scientific practice the question of whether or not simulations are like or unlike experiments is often beside the point, since they are used to accomplish different kinds of things. (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)

Mesoscale modeling is often considered merely as a practical strategy used when information on lower-scale details is lacking, or when there is a need to make models cognitively or computationally tractable. Without dismissing the importance of practical constraints for modeling choices, we argue that mesoscale models should not just be considered as abbreviations or placeholders for more “complete” models. Because many systems exhibit different behaviors at various spatial and temporal scales, bottom-up approaches are almost always doomed to fail. (...) Mesoscale models capture aspects of multi-scale systems that cannot be parameterized by simple averaging of lower-scale details. To understand the behavior of multi-scale systems, it is essential to identify mesoscale parameters that “code for” lower-scale details in a way that relate phenomena intermediate between microscopic and macroscopic features. We illustrate this point using examples of modeling of multi-scale systems in materials science and biology, where identification of material parameters such as stiffness or strain is a central step. The examples illustrate important aspects of a so-called “middle-out” modeling strategy. Rather than attempting to model the system bottom-up, one starts at intermediate scales where systems exhibit behaviors distinct from those at the atomic and continuum scales. One then seeks to upscale and downscale to gain a more complete understanding of the multi-scale system. The cases highlight how parameterization of lower-scale details not only enables tractable modeling but is also central to understanding functional and organizational features of multi-scale systems. (shrink)

Ideological language is widespread in theoretical biology. Evolutionary game theory has been defended as a worldview and a leap of faith, and sexual selection theory has been criticized for what it posits as basic to biological nature. Views such as these encourage the impression of ideological rifts in the field. I advocate an alternative interpretation, whereby many disagreements between different camps of biologists merely reflect methodological differences. This interpretation provides a more accurate and more optimistic account of the state (...) of play in the field of biology. It also helps account for biologists' tendency to embrace ideological positions. (shrink)

Background on probability and evolution -- Laying the foundation. Population-environment systems ; Causal probability and empirical practice ; Irrelevance of fitness as a causal property of token organisms ; Roles of environmental variation in selection -- Reconstructing evolution and chance. Populations in biological practice: Pragmatic yet real ; Real causation in pragmatic population-environment systems ; Fitness concepts in measurement and modeling ; Chance in population-environment systems ; The input measure problem for MM-CCS chance -- Conclusion.

This essay provides an introduction to the terminology, concepts, methods, and challenges of image‐based modeling in biology. Image‐based modeling and simulation aims at using systematic, quantitative image data to build predictive models of biological systems that can be simulated with a computer. This allows one to disentangle molecular mechanisms from effects of shape and geometry. Questions like “what is the functional role of shape” or “how are biological shapes generated and regulated” can be addressed in the framework (...) of image‐based systems biology. The combination of image quantification, model building, and computer simulation is illustrated here using the example of diffusion in the endoplasmic reticulum. (shrink)

Estimation of the repartition of asynchronous cells in the cell cycle can be explained by two hypotheses:– - the cells are supposed to be distributed into three groups: cells with a 2c DNA content (G0/1 phase), cells with a 4c DNA content (G2+M phase) and cells with a DNA content ranging from 2c to 4c (S phase); – - there is a linear relationship between the amount of fluorescence emitted by the fluorescent probe which reveals the DNA and the DNA (...) content. According to these hypotheses, the cell cycle can be represented by the following equation. (shrink)

A common reductionist assumption is that macro-scale behaviors can be described "bottom-up" if only sufficient details about lower-scale processes are available. The view that an "ideal" or "fundamental" physics would be sufficient to explain all macro-scale phenomena has been met with criticism from philosophers of biology. Specifically, scholars have pointed to the impossibility of deducing biological explanations from physical ones, and to the irreducible nature of distinctively biological processes such as gene regulation and evolution. This paper takes a step (...) back in asking whether bottom-up modeling is feasible even when modeling simple physical systems across scales. By comparing examples of multi-scale modeling in physics and biology, we argue that the “tyranny of scales” problem presents a challenge to reductive explanations in both physics and biology. The problem refers to the scale-dependency of physical and biological behaviors that forces researchers to combine different models relying on different scale-specific mathematical strategies and boundary conditions. Analyzing the ways in which different models are combined in multi-scale modeling also has implications for the relation between physics and biology. Contrary to the assumption that physical science approaches provide reductive explanations in biology, we exemplify how inputs from physics often reveal the importance of macro-scale models and explanations. We illustrate this through an examination of the role of biomechanics modeling in developmental biology. In such contexts, the relation between models at different scales and from different disciplines is neither reductive nor completely autonomous, but interdependent. (shrink)

In previous work, I have looked in detail at the capacity and the limits of the linguistics model as applied to gene expression. The recent use of a primitive applied linguistic model in Apple's SIRI system allows further analysis. In particular, the failings of this system resemble those of the HGP; the model used also helps point out the shortcomings of the concept of the "gene". This is particularly urgent as we are entering an era of applied biology in (...) the absence of theory, and indeed an era with a near-epidemic of retracted papers. There are a few workarounds proposed. One is to add to the nascent field of biosemiotics a more explicit concern with syntax. At the time of writing, Apple is being sued for false advertising of its iphone 4s, with the associated claim that apple had solved many of the problems of natural language processing by computer . The system was bought by Apple from a company called SIRI, and in turn was based on the notion, trumpeted by the prior art in a company called Dejima, that nlpbc could be done by keywords alone. Yet the hype resembles nothing so much as the misrepresentation of the Human Genome Project fed to the media in the glory days at the beginning of this millennium, and it says a lot for the status of scientists in society that they have avoided Apple's fate. In this paper, a short review of several current themes in theoretical and applied biology is first proposed. Then the tensions implicit in the notion that the "gene" is simultaneously to be identified as a unit of inheritance and spatially located over spatially well-defined nucleotides is explored and the notion is found to be incoherent. An expanded notion of inheritance is proposed in the context of a focus on inheritance as necessarily involving species, population and organism over time. While it is premature to talk about a paradigm shift, it is certainly arguable that biology urgently needs a sophisticated theory of how symbols work substantially more sophisticated than that implicit in the HGP; Biosemiotics affords a framework in which this might be tried. Indeed, as this paper concludes, there may yet be room for a "Bionoetics", a perspective in which biological explanation can be extended to include cognition in all its forms. Finally, a working sketch of a modeling environment written in LISP, one that shows promise in reflecting the complexities discussed in the paper, is included. Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}. (shrink)

Wright & Liley provide an advance in addressing the interaction of multiple scales of processing in the brain. It should address in more detail the biological evidence that underlies the models it proposes to replace.

Two computer simulations are investigated that model interaction of cultural evolution of language and biological evolution of adaptations to language. Both are agent‐based models in which a population of agents imitates each other using realistic vowels. The agents evolve under selective pressure for good imitation. In one model, the evolution of the vocal tract is modeled; in the other, a cognitive mechanism for perceiving speech accurately is modeled. In both cases, biological adaptations to using and learning speech evolve, even though (...) the system of speech sounds itself changes at a more rapid time scale than biological evolution. However, the fact that the available acoustic space is used maximally (a self‐organized result of cultural evolution) is constant, and therefore biological evolution does have a stable target. This work shows that when cultural and biological traits are continuous, their co‐evolution may lead to cognitive adaptations that are strong enough to detect empirically. (shrink)

The general structure of Steels & Belpaeme's (S&B's) central premise is appealing. Theoretical stances that focus on one type of mechanism miss the fact that multiple mechanisms acting in concert can provide convergent constraints for a more robust capacity than any individual mechanism might achieve acting in isolation. However, highlighting the significance of complex constraint interactions raises the possibility that some of the relevant constraints may have been left out of S&B's own models. Although abstract modeling can help clarify (...) issues, it also runs the risk of oversimplification and misframing. A more subtle implication of the significance of interacting constraints is that it calls for a close relationship between theoretical and empirical research. (shrink)

In his famous article “The Unreasonable Effectiveness of Mathematics in the Natural Sciences” Eugen Wigner argues for a unique tie between mathematics and physics, invoking even religious language: “The miracle of the appropriateness of the language of mathematics for the formulation of the laws of physics is a wonderful gift which we neither understand nor deserve”. The possible existence of such a unique match between mathematics and physics has been extensively discussed by philosophers and historians of mathematics. Whatever the merits (...) of this claim are, a further question can be posed with regard to mathematization in science more generally: What happens when we leave the area of theories and laws of physics and move over to the realm of mathematical modeling in interdisciplinary contexts? Namely, in modeling the phenomena specific to biology or economics, for instance, scientists often use methods that have their origin in physics. How is this kind of mathematical modeling justified? (shrink)

Two computer simulations are investigated that model interaction of cultural evolution of language and biological evolution of adaptations to language. Both are agent-based models in which a population of agents imitates each other using realistic vowels. The agents evolve under selective pressure for good imitation. In one model, the evolution of the vocal tract is modeled; in the other, a cognitive mechanism for perceiving speech accurately is modeled. In both cases, biological adaptations to using and learning speech evolve, even though (...) the system of speech sounds itself changes at a more rapid time scale than biological evolution. However, the fact that the available acoustic space is used maximally is constant, and therefore biological evolution does have a stable target. This work shows that when cultural and biological traits are continuous, their co-evolution may lead to cognitive adaptations that are strong enough to detect empirically. (shrink)

Unlike inert objects, organisms and their cells have the ability to initiate activity by themselves and thus change their properties or states even in the absence of an external cause. This crucial difference led us to search for principles suitable for the study organisms. We propose that cells follow the default state of proliferation with variation and motility, a principle of biological inertia. This means that in the presence of sufficient nutrients, cells will express their default state. We also propose (...) a principle of variation that addresses two central features of organisms, variation and historicity. To address interdependence between parts, we use a third principle, the principle of organization, more specifically, the notion of the closure of constraints. Within this theoretical framework, constraints are specific theoretical entities defined by their relative stability with respect to the processes they constrain. Constraints are mutually dependent in an organized system and act on the default state.Here we discuss the application and articulation of these principles for mathematical modeling of morphogenesis in a specific case, that of mammary ductal morphogenesis, with an emphasis on the default state. Our model has both a biological component, the cells, and a physical component, the matrix that contains collagen fibers. Cells are agents that move and proliferate unless constrained; they exert mechanical forces that act (i) on collagen fibers and (ii) on other cells. As fibers are organized, they constrain the cells’ ability to move and to proliferate. This model exhibits a circularity that can be interpreted in terms of the closure of constraints. Implementing our mathematical model shows that constraints to the default state are sufficient to explain the formation of mammary epithelial structures. Finally, the success of this modeling effort suggests a stepwise approach whereby additional constraints imposed by the tissue and the organism can be examined in silico and rigorously tested by in vitro and in vivo experiments, in accordance with the organicist perspective we embrace. (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)

The aim of this paper is to describe and analyze the epistemological justification of a proposal initially made by the biomathematician Robert Rosen in 1958. In this theoretical proposal, Rosen suggests using the mathematical concept of “category” and the correlative concept of “natural equivalence” in mathematical modeling applied to living beings. Our questions are the following: According to Rosen, to what extent does the mathematical notion of category give access to more “natural” formalisms in the modeling of living (...) beings? Is the so -called “naturalness” of some kinds of equivalences (which the mathematical notion of category makes it possible to generalize and to put at the forefront) analogous to the naturalness of living systems? Rosen appears to answer “yes” and to ground this transfer of the concept of “natural equivalence” in biology on such an analogy. But this hypothesis, although fertile, remains debatable. Finally, this paper makes a brief account of the later evolution of Rosen’s arguments about this topic. In particular, it sheds light on the new role played by the notion of “category” in his more recent objections to the computational models that have pervaded almost every domain of biology since the 1990s. (shrink)

This paper argues that scale-dependence of physical and biological processes offers resistance to reductionism and has implications that support a specific kind of downward causation. I demonstrate how insights from multiscale modeling can provide a concrete mathematical interpretation of downward causation as boundary conditions for models used to represent processes at lower scales. The autonomy and role of macroscale parameters and higher-level constraints are illustrated through examples of multiscale modeling in physics, developmental biology, and systems biology. (...) Drawing on these examples, I defend the explanatory importance of constraining relations for understanding the behavior of biological systems. (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 (...) class='Hi'>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)

The extended reduced Kies distribution, which is an asymmetric flexible extension of the reduced Kies distribution, is the subject of this research. Some of its most basic mathematical properties are deduced from its formal definitions. We computed the ExRKD parameters using eight well-known methods. A full simulation analysis was done that allows the study of these estimators’ asymptotic behavior. The efficiency and applicability of the ExRKD are investigated via the modeling of COVID-19 and milk data sets, which demonstrates that (...) the ExRKD delivers a better match to the data sets when compared to competing models. (shrink)

Though only established as a discipline since the 1970s, philosophy of biology has already triggered investigations about its own history The Oxford handbook of philosophy of biology, Oxford University Press, New York, pp 11–33, 2008). When it comes to assessing the road since travelled—the research questions that have been pursued—manuals and ontologies also offer specific viewpoints, highlighting dedicated domains of inquiry and select work. In this article, we propose to approach the history of the philosophy of biology (...) with a complementary data-driven perspective that makes use of statistical algorithms applied to the complete full-text corpus of one major journal of the field—Biology and Philosophy—from its launch in 1986 up until 2017. By running text-mining and topic-modeling algorithms, we identified 67 key research topics that span across these 32 years. We also investigated the evolution of these topics over time and their fluctuating significance in the journal articles. Our results concur with known episodes or traits of the discipline—for instance, the significance of evolution-related topics or the decrease of articles with a marked historical dimension—but also highlight a diversity of topics that is much richer than what is usually acknowledged. (shrink)