Diagrams as Vehicles for Scientific Reasoning

We argue that diagrams are not just a communicative tool but play important roles in the reasoning of biologists: in characterizing the phenomenon to be explained, identifying explanatory relations, and developing an account of the responsible mechanism. In the first two tasks diagrams facilitate applying visual processing to the detection of patterns that constitute phenomena or explanatory relations. Diagrams of a mechanism serve to guide reasoning about what parts and operations are needed and how potential parts of the mechanism are related to each other. Further they guide the development of computational models used to determine how the mechanism will behave. We illustrate each of these uses of diagrams with examples from research on circadian rhythm

Being Emergence vs. Pattern Emergence: Complexity, Control, and Goal-Directedness in Biological Systems

Emergence is much discussed by both philosophers and scientists. But, as noted by Mitchell (2012), there is a significant gulf; philosophers and scientists talk past each other. We contend that this is because philosophers and scientists typically mean different things by emergence, leading us to distinguish being emergence and pattern emergence. While related to distinctions offered by others between, for example, strong/weak emergence or epistemic/ontological emergence (Clayton, 2004, pp. 9–11), we argue that the being vs. pattern distinction better captures what the two groups are addressing. In identifying pattern emergence as the central concern of scientists, however, we do not mean that pattern emergence is of no interest to philosophers. Rather, we argue that philosophers should attend to, and even contribute to, discussions of pattern emergence. But it is important that this discussion be distinguished, not conflated, with discussions of being emergence. In the following section we explicate the notion of being emergence and show how it has been the focus of many philosophical discussions, historical and contemporary. In section 3 we turn to pattern emergence, briefly presenting a few of the ways it figures in the discussions of scientists (and philosophers of science who contribute to these discussions in science). Finally, in sections 4 and 5, we consider the relevance of pattern emergence to several central topics in philosophy of biology: the emergence of complexity, of control, and of goal-directedness in biological systems

Explaining features of fine-grained phenomena using abstract analyses of phenomena and mechanisms: two examples from chronobiology

Explanations of biological phenomena such as cell division, protein synthesis or circadian rhythms commonly take the form of models of the responsible mechanisms. Recently philosophers of science have attempted to analyze this practice, presenting mechanisms as organized collections of parts performing operations that together produce the phenomenon. But in some cases what researchers seek to explain is not a general phenomenon, but a specific feature of a more fine-grained phenomenon. In some of these cases, it is not the model of the mechanism that performs the explanatory work. I consider a case in which the investigator offered an abstract representation of a fine-grained phenomenon to show why in had the feature in question. I consider a second case in which a researcher abstracted from the mechanism to identify a design principle that explains why the functioning mechanism exhibits a specific feature

Analyzing network models to make discoveries about biological mechanisms

Systems biology provides alternatives to the strategies to developing mechanistic explanations traditionally pursued in cell and molecular biology and much discussed in accounts of mechanistic explanation. Rather than starting by identifying a mechanism for a given phenomenon and decomposing it, systems biologists often start by developing cell-wide networks of detected connections between proteins or genes and construe clusters of highly interactive components as potential mechanisms. Using inference strategies such as ‘guilt-by-association’, researchers advance hypotheses about functions performed of these mechanisms. I examine several examples of research on budding yeast, first on what are taken to be enduring networks and subsequently on networks that change as cells perform different activities or respond to different external condition

Using the hierarchy of biological ontologies to identify mechanisms in flat networks

Systems biology has provided new resources for discovering and reasoning about mechanisms. In addition to generating databases of large bodies of data, systems biologists have introduced platforms such as Cytoscape to represent protein–protein interactions, gene interactions, and other data in networks. Networks are inherently flat structures. One can identify clusters of highly connected nodes, but network representations do not represent these clusters as at a higher level than their constituents. Mechanisms, however, are hierarchically organized: they can be decomposed into their parts and their activities can be decomposed into component operations. A potent bridge between flat networks and hierarchical mechanisms is provided by biological ontologies, both those curated by hand such as Gene Ontology (GO) and those extracted directly from databases such as Network Extracted Ontology (NeXO). I examine several examples in which by applying ontologies to networks, systems biologists generate new hypotheses about mechanisms and characterize these novel strategies for developing mechanistic explanations

Analyzing network models to make discoveries about biological mechanisms

Systems biology provides alternatives to the strategies to developing mechanistic explanations traditionally pursued in cell and molecular biology and much discussed in accounts of mechanistic explanation. Rather than starting by identifying a mechanism for a given phenomenon and decomposing it, systems biologists often start by developing cell-wide networks of detected connections between proteins or genes and construe clusters of highly interactive components as potential mechanisms. Using inference strategies such as ‘guilt-by-association’, researchers advance hypotheses about functions performed of these mechanisms. I examine several examples of research on budding yeast, first on what are taken to be enduring networks and subsequently on networks that change as cells perform different activities or respond to different external condition

The Importance of Constraints and Control in Biological Mechanisms: Insights from Cancer Research

Research on diseases such as cancer reveals that primary mechanisms, which have been the focus of study by the new mechanists in philosophy of science, are often subject to control by other mechanisms. Cancer cells employ the same primary mechanisms as healthy cells, but control them differently. I use cancer research to highlight just how widespread control is in individual cells. To provide a framework for understanding control, I reconceptualize mechanisms as imposing constraints on flows of free energy, with control mechanisms operating on flexible constraints in primary mechanisms. Control mechanisms themselves often form complex, integrated networks

From Parts to Mechanisms: Research Heuristics for Addressing Heterogeneity in Cancer Genetics

A major approach to cancer research in the late 20th century was to search for genes that, when altered, initiated the development of a cell into a cancerous state (oncogenes) or failed to stop this development (tumor suppressor genes). But as researchers acquired the capacity to sequence tumors and incorporated the resulting data into databases, it became apparent that for many tumors no genes were frequently altered and that the genes altered in different tumors in the same tissue type were often distinct. To address this heterogeneity problem, many researchers looked to a higher level of organization—to mechanisms in which gene products (proteins) participated. They proposed to reduce heterogeneity by recognizing that multiple gene alterations affect the same mechanism and that it is the altered mechanism that is responsible for the cell developing one or more hallmarks of cancer. I examine how mechanisms figure in this research and focus on two heuristics researchers use to integrate proteins into mechanisms, one focusing on pathways and one focusing on clusters in networks