Nicholaos Jones

Network Analyses in Systems Biology: New Strategies for Dealing with Biological Complexity

The increasing application of network models to interpret biological systems raises a number of important methodological and epistemological questions. What novel insights can network analysis provide in biology? Are network approaches an extension of or in conflict with mechanistic research strategies? When and how can network and mechanistic approaches interact in productive ways? In this paper we address these questions by focusing on how biological networks are represented and analyzed in a diverse class of case studies. Our examples span from the investigation of organizational properties of biological networks using tools from graph theory to the application of dynamical systems theory to understand the behavior of complex biological systems. We show how network approaches support and extend traditional mechanistic strategies but also offer novel strategies for dealing with biological complexity.

Inference to the More Robust Explanation

There is a new argument form within theoretical biology. This form takes as input competing explanatory models; it yields as output the conclusion that one of these models is more plausible than the others. The driving force for this argument form is an analysis showing that one model exhibits more parametric robustness than its competitors. This article examines these inferences to the more robust explanation, analysing them as variants of inference to the best explanation. The article defines parametric robustness and distinguishes it from more familiar kinds of robustness. The article also argues that parametric robustness is an explanatory virtue not subsumed by more familiar explanatory virtues, and that the plausibility verdicts in the conclusions of inferences to the more robust explanations are best interpreted as guidance for research activity, rather than claims about likely truth. 1. Introducing Inference to the More Robust Explanation2. Inference to the More Robust Explanation in the Study of Apoptosis 2.1. Regulating apoptosis2.2. Competing models and evidential indecision2.3. Measuring robustness2.4. Robustness as a guide to plausibility2.5. Varieties of robustness3. Inference to the More Robust Explanation as Inference to the Best Explanation 3.1. The structure of inference to the best explanation3.2. Parametric robustness as an explanatory virtue rather than an explanandum3.3. Relation of parametric robustness to other explanatory virtues4. Epistemological Significance of Inference to the More Robust Explanation 4.1. Plausibility in practice4.2. Plausibility in principle5. Conclusion Introducing Inference to the More Robust Explanation Inference to the More Robust Explanation in the Study of Apoptosis 2.1. Regulating apoptosis2.2. Competing models and evidential indecision2.3. Measuring robustness2.4. Robustness as a guide to plausibility2.5. Varieties of robustness Regulating apoptosis Competing models and evidential indecision Measuring robustness Robustness as a guide to plausibility Varieties of robustness Inference to the More Robust Explanation as Inference to the Best Explanation 3.1. The structure of inference to the best explanation3.2. Parametric robustness as an explanatory virtue rather than an explanandum3.3. Relation of parametric robustness to other explanatory virtues The structure of inference to the best explanation Parametric robustness as an explanatory virtue rather than an explanandum Relation of parametric robustness to other explanatory virtues Epistemological Significance of Inference to the More Robust Explanation 4.1. Plausibility in practice4.2. Plausibility in principle Plausibility in practice Plausibility in principle Conclusion

Constraint‐Based Reasoning for Search and Explanation: Strategies for Understanding Variation and Patterns in Biology

Life scientists increasingly rely upon abstraction-based modeling and reasoning strategies for understanding biological phenomena. We introduce the notion of constraint-based reasoning as a fruitful tool for conceptualizing some of these developments. One important role of mathematical abstractions is to impose formal constraints on a search space for possible hypotheses and thereby guide the search for plausible causal models. Formal constraints are, however, not only tools for biological explanations but can be explanatory by virtue of clarifying general dependency-relations and patterning between functions and structures. We describe such situations as constraint-based explanations and argue that these differ from mechanistic strategies in important respects. While mechanistic explanations emphasize change-relating causal features, constraint-based explanations emphasize formal dependencies and generic organizational features that are relatively independent of lower-level changes in causal details.. Our distinction between mechanistic and constraint-based explanations is pragmatically motivated by the wish to understand scientific practice. We contend that delineating the affordances and assumptions of different explanatory questions and strategies helps to clarify tensions between diverging scientific practices and the innovative potentials in their combination. Moreover, we show how constraint-based explanation integrate several features shared by otherwise different philosophical accounts of abstract explanatory strategies in biology

Mereological Heuristics for Huayan Buddhism

This is an attempt to explain, in a way familiar to contemporary ways of thinking about mereology, why someone might accept some prima facie puzzling remarks by Fazang, such as his claims that the eye of a lion is its ear and that a rafter of a building is identical to the building itself. These claims are corollaries of the Huayan Buddhist thesis that everything is part of everything else, and it is intended here to show that there is a rational basis for this thesis that involves a nonstandard notion of parthood and, importantly, that does not violate the principle of noncontradiction.

Fazang's Total Power Mereology: An Interpretive Analytic Reconstruction

In his Treatise on the Golden Lion, Fazang says that wholes are in each of their parts and that each part of a whole is every other part of the whole. In this paper, I offer an interpretation of these remarks according to which they are not obviously false, and I use this interpretation in order to rigorously reconstruct Fazangs arguments for his claims. On the interpretation I favor, Fazang means that the presence of a wholes part suffices for the presence of the whole and that the presence of any such part is both necessary and sufficient for the presence of any other part. I also argue that this interpretation is more plausible than its extant competitors.

Strategies of Explanatory Abstraction in Molecular Systems Biology

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 details; generalizations, which simplify by generalizing details; and structurations, which simplify by contextualizing details. Using a common explanandum as a reference point—namely, the robust perfect adaptation of chemotaxis in Escherichia coli—I argue that each strategy targets various abstraction types to different mechanistic details.

Extracting Phenomena, Integrating Explanations, and Styling Representations: Some Frontiers for Philosophizing About Biology

“A neglected topic, of particular interest to me, refers to the benefits that different representation formats, and especially visual formats in contrast to sentential/linguistic ones, provide for epistemically oriented cognitive activities such as hypothesis generation and discovery, data aggregation and organization, model construction and simulation, and explanation of various kinds. Visual representations are so prominent and relevant for biology – and especially, I think, for systems biology approaches – that they are often objects of special interest… Our understanding of the role of visuals in biological practice, from both philosophy and cognitive science, has not kept pace with these developments. This is, I think, an underappreciated area for philosophical inquiry – and an attractive area too, if you enjoy putting visuals into papers and presentations.”

A principles-based model of ethical considerations in military decision making

When comparing alternative courses of action, modern military decision makers often must consider both the military effectiveness and the ethical consequences of the available alternatives. The basis, design, calibration, and performance of a principles-based computational model of ethical considerations in military decision making are reported in this article. The relative ethical violation (REV) model comparatively evaluates alternative military actions based upon the degree to which they violate contextually relevant ethical principles. It is based on a set of specific ethical principles deemed by philosophers and ethicists to be relevant to military courses of action. A survey of expert and non-expert human decision makers regarding the relative ethical violation of alternative actions for a set of specially designed calibration scenarios was conducted to collect data that was used to calibrate the REV model. Perhaps unsurprisingly, the survey showed that people, even experts, disagreed greatly amongst themselves regarding the scenarios’ ethical considerations. Despite this disagreement, two significant results emerged. First, after calibration the REV model performed very well in terms of replicating the ethical assessments of human experts for the calibration scenarios. The REV model outperformed an earlier model that was based on tangible consequences rather than ethical principles, that earlier model performed comparably to human experts, the experts outperformed human non-experts, and the non-experts outperformed random selection of actions. All of these performance comparisons were measured quantitatively and confirmed with suitable statistical tests. Second, although humans tended to value some principles over others, none of the ethical principles involved—even the principle of not harming civilians—completely overshadowed all of the other principles.