Philippe Huneman has recently questioned the widespread application of mechanistic models of scientific explanation based on the existence of structural explanations, i.e. explanations that account for the phenomenon to be explained in virtue of the mathematical properties of the system where the phenomenon obtains, rather than in terms of the mechanisms that causally produce the phenomenon. Structural explanations are very diverse, including cases like explanations in terms of bowtie structures, in terms of the topological properties of the system, or in (...) terms of equilibrium. The role of mathematics in bowtie structured systems and in topologically constrained systems has recently been examined in different papers. However, the specific role that mathematical properties play in equilibrium explanations requires further examination, as different authors defend different interpretations, some of them closer to the new-mechanistic approach than to the structural model advocated by Huneman. In this paper, we cover this gap by investigating the explanatory role that mathematics play in Blaser and Kirschner’s nested equilibrium model of the stability of persistent long-term human-microbe associations. We argue that their model is explanatory because: i) it provides a mathematical structure in the form of a set of differential equations that together satisfy an ESS; ii) that the nested nature of the ESSs makes the explanation of host-microbe persistent associations robust to any perturbation; iii) that this is so because the properties of the ESS directly mirror the properties of the biological system in a non-causal way. The combination of these three theses make equilibrium explanations look more similar to structural explanations than to causal-mechanistic explanation. (shrink)

I consider three explanatory strategies from recent systems biology that are driven by mathematics as much as mechanistic detail. Analysis of differential equations drives the first strategy; topological analysis of network motifs drives the second; mathematical theorems from control engineering drive the third. I also distinguish three abstraction types: aggregations, which simplify by condensing information; generalizations, which simplify by generalizing information; and structurations, which simplify by contextualizing information. Using a common explanandum as reference point—namely, the robust perfect adaptation of chemotaxis (...) in Escherichia coli—I argue that each strategy invokes a different combination of abstraction types and that each targets its abstractions to different mechanistic details. (shrink)

Due to the variation, contingency and complexity of living systems, biology is often taken to be a science without fundamental theories, laws or general principles. I revisit this question in light of the quest for design principles in systems biology and show that different views can be reconciled if we distinguish between different types of generality. The philosophical literature has primarily focused on generality of specific models or explanations, or on the heuristic role of abstraction. This paper takes a different (...) approach in emphasizing a theory-constituting role of general principles. Design principles signify general dependencyrelations between structures and functions, given a set of formally defined constraints. I contend that design principles increase our understanding of living systems by relating specific models to general types. The categorization of types is based on a delineation of the scope of biological possibilities, which serves to identify and define the generic features of classes of systems. To characterize the basis for general principles through generic abstraction and reasoning about possibility spaces, I coin the term constraint-based generality. I show that constraintbased generality is distinct from other types of generality in biology, and argue that general principles play a unifying role that does not entail theory reduction. (shrink)

Cell biologists, including those seeking molecular mechanistic explanations of cellular phenomena, frequently rely on experimental strategies focused on identifying the cellular context relevant to their investigations. We suggest that such practices can be understood as a guided decomposition strategy, where molecular explanations of phenomena are defined in relation to natural contextual (cell) boundaries. This “top-down” strategy contrasts with “bottom-up” reductionist approaches where well-defined molecular structures and activities are orphaned by their displacement from actual biological functions. We focus on the central (...) role of microscopic imaging in cell biology to uncover possible constraints on the system. We show how identified constraints are used heuristically to limit possible mechanistic explanations to those that are biologically meaningful. Historical examples of this process described here include discovery of the mechanism of oxidative phosphorylation in mitochondria, molecular explanation of the first steps in protein secretion, and identification of molecular motors. We suggest that these instances are examples of a form of downward causation or, more specifically, constraining relations, where higher-level structures and variables delimit and enable lower-level system states. The guided decomposition strategy in our historical cases illustrates the irreducibility of experimentally identified constraints in explaining biological activities of cells. Rather than viewing decomposition and recomposition as separate epistemic activities, we contend that they need to be iteratively integrated to account for the ontological complexity of multi-level systems. (shrink)

Scientists regularly make possibility claims. While philosophers of science are well aware of the distinction between epistemic and objective notions of possibility, we believe that they often fail to apply this distinction in their analyses of scientific practices that employ modal concepts. We argue that heeding this distinction will help further progress in current debates in the philosophy of science, as it shows that the debaters talk about different things, rather than disagree on the same issue. We first discuss how (...) the two notions differ with respect to their epistemology and show that these differences are sometimes ignored in the philosophy of science. We then revisit four current philosophy of science debates about modelling, that are framed in modal terms, to showcase how the distinction significantly clarifies these debates and thereby and helps advance them. (shrink)

Many biologists appeal to the so-called Krogh principle when justifying their choice of experimental organisms. The principle states that “for a large number of problems there will be some animal of choice, or a few such animals, on which it can be most conveniently studied”. Despite its popularity, the principle is often critiqued for implying unwarranted generalizations from optimal models. We argue that the Krogh principle should be interpreted in relation to the historical and scientific contexts in which it has (...) been developed and used. We interpret the Krogh Principle as a heuristic, i.e., as a recommendation to approach biological problems through organisms where a specific trait or physiological mechanism is expected to be most distinctively displayed or most experimentally accessible. We designate these organisms “Krogh organisms.” We clarify the differences between uses of model organisms and non-standard Krogh organisms. Among these is the use of Krogh organisms as “negative models” in biomedical research, where organisms are chosen for their dissimilarity to human physiology. Importantly, the representational scope of Krogh organisms and the generalizability of their characteristics are not fixed or assumed but explored through experimental studies. Research on Krogh organisms is steeped in the comparative method characteristic of zoology and comparative physiology, in which studies of biological variation produce insights into general physiological constraints. Accordingly, we conclude that the Krogh principle exemplifies the advantages of studying biological variation as a strategy to produce generalizable insights. (shrink)

Despite the importance of network analysis in biological practice, dominant models of scientific explanation do not account satisfactorily for how this family of explanations gain their explanatory power in every specific application. This insufficiency is particularly salient in the study of the ecology of the microbiome. Drawing on Coyte et al. (2015) study of the ecology of the microbiome, Deulofeu et al. (2021) argue that these explanations are neither mechanistic, nor purely mathematical, yet they are substantially empirical. Building on their (...) criticisms, in the present work we make a step further elucidating this kind of explanations with a general analytical framework according to which scientific explanations are ampliative, specialized embeddings (ASE), which has recently been successfully applied to other biological subfields. We use ASE to reconstruct in detail the Coyte et al.’s case study and on its basis, we claim that network explanations of the ecology of the microbiome, and other similar explanations in ecology, gain their epistemological force in virtue of their capacity to embed biological phenomena in non-accidental generalizations that are simultaneously ampliative and specialized. (shrink)

This paper offers a contribution to debates around integrative aspects of systems biology and engages with issues related to the circumstances under which physicists look at biological problems. We use oral history as one of the methodological tools to gather the empirical material, conducting interviews with physicists working in systems biology. The interviews were conducted at several institutions in Brazil, Germany, Israel and the U.S. Biological research has been increasingly dependent on computational methods, high-throughput technologies, and multidisciplinary skills. Quantitative scientists (...) are joining biological departments and collaborations between physicists and biologists are particularly vigorous. This state of affairs raises a number of questions, such as: What are the circumstances under which physicists approach biological problems in systems biology? What kind of interdisciplinary challenges must be tackled? The paper suggests that, concerning physicists’ move to work on biological systems, there are common reasons to move, the transition must be understood in terms of degrees, physicists have a rationale for simplifying systems, and distinct conceptions of model and modeling strategies are recurrent. We identified problems regarding linguistic clarity and integration of epistemological aims. We conclude that cultural unconformities within the systems biology community have important consequences to the flow of scientific knowledge. (shrink)