Explanations In Biology Research Articles

Why ask why? Toward coordinating knowledge of proximate and ultimate explanations in physiology.

In physiology education, students must learn to recognize and construct causal explanations. This challenges students, in part, because causal explanations in biology manifest in different varieties. Unlike other natural sciences, causal mechanisms in physiology support physiological functions and reflect biological adaptations. Therefore, students must distinguish between questions that prompt a proximate or an ultimate explanation. In the present investigation, we aimed to determine how these different varieties of student knowledge coordinate within students' written explanations. Prior research in science education demonstrates that students present specific challenges when distinguishing between proximate and ultimate explanations: students appear to conflate the two or construct other nonmechanistic explanations. This investigation, however, demonstrates that analytic frameworks can distinguish between students' proximate and ultimate explanations when they are provided explanatory scaffolds that contextualize questions. Moreover, these scaffolds and prompts help students distinguish between physiological functions and the cellular and molecular mechanisms that underpin them. Together, these findings deliver insight into the context-sensitive nature of student knowledge in physiology education and offer an analytic framework for identifying and characterizing student knowledge in physiology.NEW & NOTEWORTHY Why ask why? How questions posed in physiology task students with developing their mechanistic reasoning. Why questions sometimes undermine this reasoning. Prior research, however, also illustrates that framing the context of a question explicitly supports students in distinguishing between question types. We further illustrate how providing such context in the form of explanatory scaffolds and prompts allows students to tap different and useful varieties of knowledge when constructing written explanations.

Causal‐mechanical explanations in biology: Applying automated assessment for personalized learning in the science classroom

AbstractOne of the core practices of science is constructing scientific explanations. However, numerous studies have shown that constructing scientific explanations poses significant challenges to students. Proper assessment of scientific explanations is costly and time‐consuming, and teachers often do not have a clear definition of the educational goals for formulating scientific explanations. Consequently, teachers struggle to support their students in this process. It is hoped that recent advances in machine learning (ML) and its application to educational technologies can assist teachers and learners in analyzing student responses and providing automated formative feedback according to well‐defined pedagogical criteria. In this study, we present a method to automate the entire assessment‐feedback process. First, we developed a causal‐mechanical (CM)‐based grading rubric and applied it to student responses to two open‐ended items. Second, we used unsupervised ML tools to identify patterns in student responses. Those patterns enable the definition of “meta‐categories” of explanation types and the design of personalized feedback adapted to each category. Third, we designed an in‐class intervention with personalized formative feedback that matches the response patterns. We used natural language processing and ML algorithms to assess students' explanations and provide feedback. Findings from a controlled experiment demonstrated that a CM‐based grading scheme can be used to identify meaningful patterns and inform the design of formative feedback that promotes student ability to construct explanations in biology. We discuss possible implications for automated assessment and personalized teaching and learning of scientific writing in K‐12 science education.

Phylogenetically distant animals sleep: why do sleep researchers care?

Philosophers examining mechanistic explanations in biology have identified heuristic strategies scientists use in discovering mechanisms. This paper examines the heuristic strategy of investigating phylogenetically distant model organisms, using research on sleep in fruit flies as an example. At the time sleep was discovered in flies in 2000 next to nothing was known about mechanisms regulating sleep in flies and what they could reveal about those in us. One relatively straightforward line of research focused on homologous genes in flies and humans, using those in flies to understand what roles their homologs played in controlling sleep in us. But other research focused on a higher level of organization—the neural networks involved in homeostatic and circadian control of sleep. This raises a puzzle—given that fly and vertebrate brains are organized very differently, how could sleep regulation in flies serve as an informative model of vertebrate sleep? I argue that the basic design of mechanisms such as those regulating sleep can be conserved even as the composition of the mechanism changes and that researchers can hope to use the designs deciphered in flies as heuristic models for understanding sleep in humans.

Neuroethology in The Age of Gene Editing: New Tools And Novel Insights into The Molecular And Neural Basis of Behavior

Synopsis Fifty years ago, animal models studied in the lab were highly diverse, and biological insights were derived from experiments in many species. However, the pursuit of mechanistic explanations in organismal biology led to a shift in the species most commonly studied. The advent of genetic manipulations and economies of scale promoted the consolidation of research into fewer species (eg, Drosophila melanogaster, Mus musculus, Danio rerio, Caenorhabditis elegans). As a result, the tremendous variety of evolutionary adaptations across species provided insights into ultimate causes of evolution, but their proximate mechanisms have been understudied in recent decades. Within the last decade, developments in genome modifications have enabled functional genetic studies in a wide variety of species. This special issue combines papers derived from a symposium organized at the Society for Integrative and Comparative Biology conference in January 2023 in Austin, Texas. The symposium entitled “Neuroethology in the age of gene editing: New tools and novel insights into the molecular and neural basis of behavior” was convened to catalyze the transfer of knowledge and skills from researchers who have applied genome modification technologies in new model organisms to scientists who would like to bring these approaches to their own research programs. We highlight this work here, and suggest how the future of biological knowledge will be informed by these powerful experiments.

Using enactive robotics to think outside of the problem-solving box: How sensorimotor contingencies constrain the forms of emergent autononomous habits.

We suggest that the influence of biology in 'biologically inspired robotics' can be embraced at a deeper level than is typical, if we adopt an enactive approach that moves the focus of interest from how problems are solved to how problems emerge in the first place. In addition to being inspired by mechanisms found in natural systems or by evolutionary design principles directed at solving problems posited by the environment, we can take inspiration from the precarious, self-maintaining organization of living systems to investigate forms of cognition that are also precarious and self-maintaining and that thus also, like life, have their own problems that must be be addressed if they are to persist. In this vein, we use a simulation to explore precarious, self-reinforcing sensorimotor habits as a building block for a robot's behavior. Our simulations of simple robots controlled by an Iterative Deformable Sensorimotor Medium demonstrate the spontaneous emergence of different habits, their re-enactment and the organization of an ecology of habits within each agent. The form of the emergent habits is constrained by the sensory modality of the robot such that habits formed under one modality (vision) are more similar to each other than they are to habits formed under another (audition). We discuss these results in the wider context of: (a) enactive approaches to life and mind, (b) sensorimotor contingency theory, (c) adaptationist vs. structuralist explanations in biology, and (d) the limits of functionalist problem-solving approaches to (artificial) intelligence.

Two kinds of historical explanation in Evolutionary Biology

Historical explanations in evolutionary biology are commonly characterized as narrative explanations. Examples include explanations of the evolution of particular traits and explanations of macroevolutionary transitions. In this paper I present two case studies of explanations in accounts of pathogen evolution and host-pathogen coevolution, respectively, and argue that one of them is captured well by established accounts of time-sequenced narrative explanation. The other one differs from narrative explanations in important respects, even though it shares some characteristics with them as it is also a population-level historical explanation. I thus argue that the second case represents a different kind of explanation that I call historical explanation of type phenomena. The main difference between the two kinds of explanation is the conceptualization of the explanandum phenomena as particulars or type phenomena, respectively. Narrative explanations explain particulars but also deal with generalization, regularities and type phenomena. Historical explanations of type phenomena, on the other hand, explain multiply realizable phenomena but also deal with particulars. The two kinds of explanation complement each other because they explain different aspects of evolution.

Revisiting abstraction and idealization: how not to criticize mechanistic explanation in molecular biology

Abstraction and idealization are the two notions that are most often discussed in the context of assumptions employed in the process of model building. These notions are also routinely used in philosophical debates such as that on the mechanistic account of explanation. Indeed, an objection to the mechanistic account has recently been formulated precisely on these grounds: mechanists cannot account for the common practice of idealizing difference-making factors in models in molecular biology. In this paper I revisit the debate and I argue that the objection does not stand up to scrutiny. This is because it is riddled with a number of conceptual inconsistencies. By attempting to resolve the tensions, I also draw several general lessons regarding the difficulties of applying abstraction and idealization in scientific practice. Finally, I argue that more care is needed only when speaking of abstraction and idealization in a context in which these concepts play an important role in an argument, such as that on mechanistic explanation.

The logic of explanation in molecular biology: historical-processual and logical-procedural aspects

This work addresses biological explanations and aims to provide a philosophical account which brings together logical-procedural and historical-processual aspects when considering molecular pathways. It is argued that, having molecular features as explananda, a particular non-classical logical language – Zsyntax – can be used to formally represent, in terms of logical theorems, types of molecular processes (pathways), and to grasp how we get from one molecular interaction to another, hence explaining why a given outcome occurs. Expressing types of molecular biology processes in terms of the Zsyntax language allows us to represent causal interactions by taking into account their context-sensitivity, and amounts to partly reviving the spirit of the so-called received view of explanation – which aimed to capture scientific explanatory accounts in terms of their logical structure and their appealing to nomological relations. Such a partial revival is pursued by invoking here non-classical deductions and empirical generalisations, which are called to provide the epistemic norms to explain the behavior of molecular pathways.

Mathematical Explanations in Evolutionary Biology or Naturalism? A Challenge for the Statisticalist

This article presents a challenge that those philosophers who deny the causal interpretation of explanations provided by population genetics might have to address. Indeed, some philosophers, known as statisticalists, claim that the concept of natural selection is statistical in character and cannot be construed in causal terms. On the contrary, other philosophers, known as causalists, argue against the statistical view and support the causal interpretation of natural selection. The problem I am concerned with here arises for the statisticalists because the debate on the nature of natural selection intersects the debate on whether mathematical explanations of empirical facts are genuine scientific explanations. I argue that if the explanations provided by population genetics are regarded by the statisticalists as non-causal explanations of that kind, then statisticalism risks being incompatible with a naturalist stance. The statisticalist faces a dilemma: either she maintains statisticalism but has to renounce naturalism; or she maintains naturalism but has to content herself with an account of the explanations provided by population genetics that she deems unsatisfactory. This challenge is relevant to the statisticalists because many of them see themselves as naturalists.

Causal and non-causal explanations in code biology

In the philosophy of science, we can consider debates about the nature of non-causal explanations in general (e.g. Reutlinger, Saatsi 2018; Lange 2017) and then especially those in the life sciences (e.g. Huneman, 2018; Kostić 2020). These debates are accompanied by the development of a new mechanism that is becoming the major response to the nature of scientific explanation in the life sciences (e.g. Craver, Darden 2013; Craver 2006); and also by the development of a design explanation (e.g. Eck, Mennes 2016) that represents a modern variant of a functional explanation.In this paper, we will methodically:1. evaluate the plurality of explanatory strategies in contemporary science (chapter 2).2. describe the mechanical philosophy and mechanistic explanation (Glennan 2016; Craver, Darden 2013, etc.) (chapter 3).3. explicate the role of mechanisms in code biology (Barbieri 2015, 2002, etc.) and its relation to the new mechanism (chapter 4).4. fulfill the main goal of the paper – to apply mechanistic explanations in code biology (Barbieri 2019, etc.) and to apply their suitability for this scientific domain (chapter 5).

The Structure of Causal Explanations in Population Biology.

The scope of this paper can be clarified by means of a well-known phenomenon that is usually called 'industrial melanism': the fact that the melanic form of the peppered moth became dominant in industrial areas in England in the second half of the nineteenth century. Such changes in relative phenotype frequencies are important explananda for population biologists. Apart from trying to explain such changes over time, population biologists also often try to explain differences between populations, e.g. why yellow shell colour is dominant in certain colonies of land snails and almost absent in other colonies. The causal explanations that are given to address such explananda are the objects of analysis in this paper. Our primary aim is to explicate their structure: we want to capture the typical ingredients of causal explanations in population biology, and their organisation. Based on this explication, we discuss how natural selection fits into recent mechanical philosophy of science, and engage in the debate on the nature of evolutionary theory.

Are topological explanations really free of mechanisms?

Topological explanations in biology have been largely assumed to be free of mechanisms. However, by examining two classic topological explanations in the philosophical literature, this article has identified mechanisms in the corrected and complete formulations of both explanations. This constitutes the major work of this article. The minor work of this article is to address a follow-up question: given that these two topological explanations contain mechanisms, would this significantly blur the widely assumed boundary between topological and mechanistic explanations? My answer to this question is negative and the argument I have developed is that although these two topological explanations contain mechanisms, these mechanisms are explanatorily irrelevant to the target properties, which is in stark contrast to the situation in mechanistic explanations.

Aristotelian Explanation and Homology in Biology

In his account of epistēmē, the highest level of understanding attainable in philosophical inquiry, Aristotle articulates standards for the ideal explanations that confer this level of understanding. I argue that Aristotle's key standard for epistēmē is of central importance for the biological homology concept. The explanatory shortcoming that results from violating this standard has been vaguely articulated in recent literature on homology; Aristotle's account offers a more neutral and precise formulation of the shortcoming and its antidote. Further, the risk for this shortcoming has heightened with recent accounts of homology grounded in genetics, increasing the contemporary relevance of Aristotelian epistēmē.

Why did the stegosaurus have plates, or is biology second-rate because it thinks in therms of ends?

There is something distinctively different about explanation in the biological sciences, as opposed to explanation in the physical sciences. In the former one has functional arguments, arguments making reference to what Aristotle called “final causes.” As in: “The function of the plates on the back of the Stegosaurus was to keep the body at a constant temperature.” Since the Scientific Revolution, such explanations have been forbidden in the physical sciences. Does this then mean that biology is second rate, as is suggested by many including Immanuel Kant? It is argued that the Darwinian mechanism of natural selection explains why there is need of functional explanation in biology and that once this point is grasped, there is no reason to judge biology second rate.

Un enfoque inferencial y dinámico de la modelización y de la comprensión en biología

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.

Revisiting darwinian teleology: A case for inclusive fitness as design explanation

This paper elaborates a general framework to make sense of teleological explanations in Darwinian evolutionary biology. It relies on an attempt to tie natural selection to a sense of optimization. First, after assessing the objections made by any attempt to view selection as a maximising process within population genetics, it understands Grafen's Formal Darwinism (FD) as a conceptual link established between population genetics and behavioral ecology's adaptationist framework (without any empirical commitments). Thus I suggest that this provides a way to make sense of teleological explanations in biology under their various modes. Then the paper criticizes two major ways of accounting for teleology: a Darwinian one, the etiological view of biological functions, and a non-Darwinian one, here labeled “intrinsic teleology” view, which covers several subtypes of accounts, including plasticity-oriented conceptions of evolution or organizational views of function. The former is centered on traits while the latter is centered on organisms; this is shown to imply that both accounts are unable to provide a systematic understanding of biological teleology. Finally the paper argues that viewing teleology as maximization of inclusive fitness along the FD lines as understood here allows one to make sense of both the design of organisms and the individual traits as adaptions. Such notion is thereby claimed to be the proper meaning of teleology in evolutionary biology, since it avoids the opposed pitfalls of etiological views and intrinsic-teleology view, while accounting for the same features as they do.

Explaining the behaviour of random ecological networks: the stability of the microbiome as a case of integrative pluralism

Explaining the behaviour of ecosystems is one of the key challenges for the biological sciences. Since 2000, new-mechanism has been the main model to account for the nature of scientific explanation in biology. The universality of the new-mechanist view in biology has been however put into question due to the existence of explanations that account for some biological phenomena in terms of their mathematical properties (mathematical explanations). Supporters of mathematical explanation have argued that the explanation of the behaviour of ecosystems is usually provided in terms of their mathematical properties, and not in mechanistic terms. They have intensively studied the explanation of the properties of ecosystems that behave following the rules of a non-random network. However, no attention has been devoted to the study of the nature of the explanation in those that form a random network. In this paper, we cover that gap by analysing the explanation of the stability behaviour of the microbiome recently elaborated by Coyte and colleagues, to determine whether it fits with the model of explanation suggested by the new-mechanists or by the defenders of mathematical explanation. Our analysis of this case study supports three theses: (1) that the explanation is not given solely in terms of mechanisms, as the new-mechanists understand the concept; (2) that the mathematical properties that describe the system play an essential explanatory role, but they do not exhaust the explanation; (3) that a non-previously identified appeal to the type of interactions that the entities in the network can exhibit, as well as their abundance, is also necessary for Coyte and colleagues’ account to be fully explanatory. From the combination of these three theses we argue for the necessity of an integrative pluralist view of the nature of behaviour explanation when this is given by appealing to the existence of a random network.

The Complexity-based Explanatory Strategy, Biological Levels, and the Origin of Life

A long-standing debate on the causality of levels in biological explanations has divided philosophers into two camps. The reductionist camp insists on the causal primacy of lower, molecular levels, while the critics point out the inescapable shifting, reciprocity, and circularity of levels across biological explanations. We argue, however, that many explanations in biology do not exclusively draw their explanatory power from detailed insights into inter-level interactions; they predominantly require identifying the adequate levels of biological complexity to be explained. Moreover, the main explanatory strategies grounding both theoretical and experimental approaches to one of the central debates in contemporary biology, i.e., on the origin of life, are primarily and sometimes exclusively driven by issues concerning the levels of biochemical complexity, and these only subsequently frame more substantial and detailed accounts of inter-level biochemical interactions.

Neutral Spaces and Topological Explanations in Evolutionary Biology: Lessons from Some Landscapes and Mappings

AbstractI consider recent uses of the notion of neutrality in evolutionary biology and ecology, questioning their relevance to the kind of explanation recently labeled ‘topological explanation’. Focusing on fitness landscapes and genotype-phenotype maps, I explore the explanatory uses of neutral subspaces, as modeled in two perspectives: hyperdimensional fitness landscapes and RNA sequence-structure maps. I argue that topological properties of such spaces account for features of evolutionary systems: respectively, capacity for adaptive evolution toward global optima and mutational robustness of genotypes. Thus many models appealing to “neutral” manifolds provide topological alternatives to hypothetical mechanisms.

What and How Do Cancer Systems Biologists Explain?

AbstractIn this article, we argue, first, that there are very different research projects that fall under the heading of “systems biology of cancer.” While they share some general features, they differ in their aims and theoretical commitments. Second, we argue that some explanations in systems biology of cancer are concerned with properties of signaling networks (such as robustness or fragility) and how they may play an important causal role in patterns of vulnerability to cancer. Further, some systems biological explanations are compelling illustrations of how “top-down” and “bottom-up” approaches to the same phenomena may be integrated.