Work in progress

Robustness refers to the ability of a system to maintain characteristic behaviours in the face of internal or external changes. Robustness has become a major research topic in the life sciences, particularly in systems biology where researchers draw on engineering frameworks to explore organizational or design principles that might support robust properties in biological systems. Based on a comparative analysis of three models from systems biology, we argue that sometimes the robustness of biological properties or behaviours is best explained in causal-structural terms. In elaborating the notion of causal-structural explanations we focus on how a “thin” notion of design is used to conceptualize a robust biological behaviour, namely the perfect adaptation of bacterial chemotaxis. We conclude by showing how our account relates to the mechanistic framework.

This paper focuses on biological robustness understood as functional stability in the face of external or internal perturbations. Recent engineering-based modeling efforts in the field of systems biology suggest that the robustness of some characteristic behaviours or properties depends on the internal organization of biological systems. This implies that abstract representations of patterns of organization or design principles are sometimes the main explanatory categories used for understanding the robustness of certain biological properties. We characterize such model-based explanations as structural causal explanations and situate our account in the broader philosophical debate about explanation in biology.

One reason for the popularity of Craver's mutual manipulability account of constitutive relevance (MM) is that it seems to make good sense of the experimental practices and constitutive reasoning in the life sciences. Two recent papers (Baumgartner and Gebharter2016 & Baumgartner and Casini2017) propose a theoretical revision of (MM) in light of several important conceptual objections. Their alternative approach, the No De-Coupling account (NDC) conceives of constitution as a dependence relation which, once postulated, provides the best explanation of the impossibility of breaking the common cause coupling of a macro-level mechanism and its micro-level components. This entails an abductive view of constitutive inference. Proponents of the NDC account recognise that their discussion leaves open a big question concerning the practical dimension of the notion of constitutive relevance: Is it possible to faithfully reconstruct constitutional reasoning in science in terms of NDC? Focusing on the field of memory and LTP research, this paper argues that NDC provides a more adequate description of inter-level experiments in neuroscience. We also suggest that NDC highlights some significant practical recommendations of how to interpret the findings of interlevel experiments.

Biological modules are standardly conceived as internally organized structural and functional units which are relatively independent from one another. Proteins, macromolecules, cells, biochemical circuits, organs and whole organisms may fit in this characterization. Still, not any biological part counts as a module, or does it? Conceptualizing different biological structures and processes as modular boosts their similarity to more or less sophisticated man-made machines. This paper explores contemporary notions of biological modularity in order to gain further philosophical insight into the scope of the machine analogy in biology. Current bioscientific research uses networks-based methodologies to further investigate and develop the notion of biological modularity. One converging proposal is that biological systems are organized in nested hierarchies of structurally and functionally well-defined units. Drawing on two recent studies from metabolic and brain research, we show how graph theoretical representations of complex biological systems can contribute to the development of the biological concept of hierarchical modularity. More specifically, we argue that networks-based models can play the following epistemic roles: enable the visualization of the hierarchical organization of biological modules, mediate the construction of quantitative measures of hierarchical modularity, and corroborate theoretical accounts of hierarchical modularity as a gradual property of biological systems.

How common are emergent phenomena in the biological realm? The rise of the mechanistic outlook challenges the idea that biological phenomena are emergent in any substantive sense. Mechanism has a complicated relationship to emergence. Philosophical mechanism stresses the non-reductionist and hierarchical character of mechanistic models and explanations of biological phenomena. This maps onto the characterization of emergence, but the causal-constitutive character of mechanistic explanations seems to be at odds with the positive features of emergence: the novelty and independence of macrolevel phenomena. Current and past discussions of the relation between mechanism and emergence have overlooked the problem of biological robustness. Some kinds of robustness are not amenable to a mechanistic treatment. I claim that they hold the key to strengthening the positive characterization of emergent biological phenomena. Focusing on a case of multiscale kinetic modeling, I show how the investigation of biological robustness can alleviate the tension between mechanism and emergence.

Piccinini's mechanistic account of physical computation is currently one of the most well-developed non-reductionist views of the modeling and explanatory strategies used in computational neuroscience. The key idea of the account is that aligning the notion of physical computation and mechanism helps elucidate the distinctive character of computational models developed in the field and provides insight into the norms for evaluating their explanatory value. This paper explores two problems for the mechanistic account of physical computation: the non-decompositional features of some computational explanatory models used in cognitive neuroscience and the non-mechanistic character of the distinctive features of computational systems.