Diversifying the picture of explanations in biological sciences: ways of combining topology with mechanisms

Abstract

Besides mechanistic explanations of phenomena, which have been seriously investigated in the last decade, biology and ecology also include explanations that pinpoint specific mathematical properties as explanatory of the explanandum under focus. Among these structural explanations, one finds topological explanations, and recent science pervasively relies on them. This reliance is especially due to the necessity to model large sets of data with no practical possibility to track the proper activities of all the numerous entities. The paper first defines topological explanations and then explains why topological explanations and mechanisms are different in principle. Then it shows that they are pervasive both in the study of networks-whose importance has been increasingly acknowledged at each level of the biological hierarchy-and in contexts where the notion of selective neutrality is crucial; this allows me to capture the difference between mechanisms and topological explanations in terms of practical modelling practices. The rest of the paper investigates how in practice mechanisms and topologies are combined. They may be articulated in theoretical structures and explanatory strategies, first through a relation of constraint, second in interlevel theories (Sect. 3), or they may condition each other (Sect. 4). Finally, I explore how a particular model can integrate mechanistic informations, by focusing on the recent practice of merging networks in ecology and its consequences upon multiscale modelling (Sect. 5).

Causal Concepts in Biology: How Pathways Differ from Mechanisms and Why It Matters

“…The explanatorily relevant information in these pathways cases is similar to the "connectionist" information discussed by(Bechtel and Levy 2013) and the topological information discussed by(Huneman 2018), although there are clear differences. Comparing these accounts to the present analysis is an interesting topic for future work.…”

Minimal Structure Explanations, Scientific Understanding and Explanatory Depth

“…The latter ones I will call the minimal structure explanations. The best example of minimal structure explanation is topological explanation (Darasson 2018;Huneman 2010Huneman , 2015Kostic 2018a,b;Rathkopf 2015), but perhaps there could be other too, e.g. minimal model explanations (Batterman and Rice 2014), some accounts of mathematical explanations in science (Batterman 2010;Lange 2012), structural explanations (Huneman 2017).…”

“…In order to make my case, I discuss an example of scientific explanation that is pervasively used in biology (Levy and Bechtel 2013;Green et al 2016;Huneman 2010Huneman , 2015, medicine (Darasson 2018), complexity theory (Rathkopf 2018) and neuroscience (Craver 2016 3 ; Kostic 2018 a,b), i.e. the topological explanation.…”

The Philosophy of Biology

“…Most who adopt this position view abstract, noncompositional models as conceptually complimentary to their mechanistic counterparts: they are nonoverlapping magisteria, each offering unique explanatory virtues either to distinct types of phenomena, or else to distinct facets of the same phenomena (Brigandt 2013;Green et al 2014;Thery 2015;Mekios 2015). It may be, for instance, that the formal structures of these abstract models, constructed as they often are from mathematical-cum-topological relations, license an explanatory prowess of a distinctly stronger sort: instead of merely providing causal explanations of particular states of the systems they represent, they may also elucidate certain modal features of those systems which explain the constraints on all of their possible states (Lange 2013;Huneman 2015;Breidenmoser & Wolkenhauer 2015). Moreover, if the formal structures of these non-mechanistic models are capable of correctly capturing central features of the causal architecture of biological systems (as suggested above), the adoption and subsequent refinement of such models may even heuristically aid in the process of mechanism discovery and elucidation (Fagan 2012;Zednik 2015;Baetu 2015).…”