Mechanisms, Pathways, and Networks: On Their Description and Explanation

Abstract Philosophical discussions of the biomedical sciences have attracted considerable interest over the past few decades, with much of the debate focusing on causation, complex systems, and explanatory levels, each understood in different ways. Although a wide range of views has emerged, a small number of notions have played a prominent and cross-cutting role in understanding how biological systems function. These notions, i.e., mechanism, pathway, and network, are especially central in molecular biology, the field we focus on in this contribution. More specifically, we aim to provide a comprehensive analysis of the relations among these notions as they are employed in molecular biology, shedding light on the relationship between description and explanation, which we treat as two distinct epistemic activities. Similar content being viewed by others Notes We assume, at least for our focus limited to molecular biology, that “pathway” and “process” can be taken as synonyms in that context. From now on, we will use only the first term even. The term used by the quoted author is kept in quotations. Our interest is here devoted to epistemological issues. For some reflections on the relations between, e.g., a mechanistic ontology and pathways which refer to both neo-mechanism and Ross’s view, see Glennan (2025). This is clarified in what follows, where we discuss the “p53 degradation mechanism” or “p53 degradation pathway". Bechtel (2020b, p. 1) has recently challenged the idea that “network representations are flat while mechanisms are organized into a hierarchy of levels” suggesting that the lesson to be drawn from the ways in which large-scale networks are analyzed in systems biology is different. Networks can be conceived as “clusters of nodes that operate as modules or mechanisms” (p. 1), stressing connectivity patterns holding between entities, and “bio-ontologies such as gene ontology (GO) [can be used] to annotate nodes with information about where entities appear in cells and the biological functions in which they participate” (p.1). This is taken to both support a mechanistic interpretation of networks and to be heuristically fruitful, providing new mechanistic knowledge on the parts and operations of the systems at stake. Rather than being opposed, mechanistic and network approaches are often used together, with networks too being interpreted as representing hierarchies of levels. Furthermore, processes enter the picture as well and are totally compatible with it: “nodes that cluster in the network and are annotated with the same biological process can be viewed as parts of mechanisms involved in those processes” (Bechtel 2020b, p. 6). A different view is held by Philippe Huneman (2025), who contrasts network analysis with a mechanistic methodology, while arguing that network analyses can also be explanatory. This example is taken from Boniolo and Lanfrancone (2016). In this case, following the nomenclature indicated by the HUGO Gene Nomenclature Committee; (www.genenames.org) MITF is the Microphthalmia associated transcription factor; SRC is the sarcoma viral oncogene homolog; SHCA is the SRC homology 2 domain containing, transforming protein A; Grb2 is the growth factor receptor-bound protein 2; SOS is the Son of Sevenless; GTP is the guanosine triphosphate; ATP is the adenosine triphosphate; PI3K is the phosphatidylinositol-4,5-bisphosphate 3-kinase; AKT is the serine/threonine-specific protein kinase; PIP2 is the phosphatidylinositol bisphosphate; PIP3 is the phosphatidylinositol trisphosphate; PDK1 is the phosphoinositide-dependent kinase-1; MAPK is the mitogen-activated protein kinases, originally called ERK (Extracellular signal-regulated kinases); MEK is the mitogen-activated protein kinase kinase; RAS is the Rat Sarcoma protein; RAF is a serine/threonine-protein kinase; GSK is the glycogen synthase kinase. 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