Miles Alexander James MacLeod

Coupling simulation and experiment: The bimodal strategy in integrative systems biology

The importation of computational methods into biology is generating novel methodological strategies for managing complexity which philosophers are only just starting to explore and elaborate. This paper aims to enrich our understanding of methodology in integrative systems biology, which is developing novel epistemic and cognitive strategies for managing complex problem-solving tasks. We illustrate this through developing a case study of a bimodal researcher from our ethnographic investigation of two systems biology research labs. The researcher constructed models of metabolic and cell-signaling pathways by conducting her own wet-lab experimentation while building simulation models. We show how this coupling of experiment and simulation enabled her to build and validate her models and also triangulate and localize errors and uncertainties in them. This method can be contrasted with the unimodal modeling strategy in systems biology which relies more on mathematical or algorithmic methods to reduce complexity. We discuss the relative affordances and limitations of these strategies, which represent distinct opinions in the field about how to handle the investigation of complex biological systems.

Building Simulations from the Ground Up: Modeling and Theory in Systems Biology

In this article, we provide a case study examining how integrative systems biologists build simulation models in the absence of a theoretical base. Lacking theoretical starting points, integrative systems biology researchers rely cognitively on the model-building process to disentangle and understand complex biochemical systems. They build simulations from the ground up in a nest-like fashion, by pulling together information and techniques from a variety of possible sources and experimenting with different structures in order to discover a stable, robust result. Finally, we analyze the alternative role and meaning theory has in systems biology expressed as canonical template theories like Biochemical Systems Theory.

Strategies for coordinating experimentation and modeling in integrative systems biology.

In this paper, we provide a novel analysis of the affordances and trade-offs of different strategies for integrating model-building and experimentation in integrative systems biology. Unimodal strategies rely on collaboration between experimenters and modelers in distinct laboratories. In a bimodal strategy modelers perform their own experiments. Each option has advantages and challenges. In the case of the labs we studied, the choice of strategy often depends on preferences held by lab directors as to the strategy that best achieves certain philosophical objectives concerning what they see as the aims of modeling in systems biology and the epistemic standards to apply to it. We identify an important connection between philosophical divisions in systems biology and the structure of research in systems biology. Better knowledge of these strategies and their philosophical motivations provide insight behind the diversifying structure of the field, and can help lab directors understand the challenges their researchers face, as well as the training and lab organization options available.

What makes interdisciplinarity difficult? Some consequences of domain specificity in interdisciplinary practice

Research on interdisciplinary science has for the most part concentrated on the institutional obstacles that discourage or hamper interdisciplinary work, with the expectation that interdisciplinary interaction can be improved through institutional reform strategies such as through reform of peer review systems. However institutional obstacles are not the only ones that confront interdisciplinary work. The design of policy strategies would benefit from more detailed investigation into the particular cognitive constraints, including the methodological and conceptual barriers, which also confront attempts to work across disciplinary boundaries. Lessons from cognitive science and anthropological studies of labs in sociology of science suggest that scientific practices may be very domain specific, where domain specificity is an essential aspect of science that enables researchers to solve complex problems in a cognitively manageable way. The limit or extent of domain specificity in scientific practice, and how it constrains interdisciplinary research, is not yet fully understood, which attests to an important role for philosophers of science in the study of interdisciplinary science. This paper draws upon two cases of interdisciplinary collaboration; those between ecologists and economists, and those between molecular biologists and systems biologists, to illustrate some of the cognitive barriers which have contributed to failures and difficulties of interactions between these fields. Each exemplify some aspect of domain specificity in scientific practice and show how such specificity may constrain interdisciplinary work.

Model Coupling in Resource Economics: Conditions for Effective Interdisciplinary Collaboration

In this article we argue for the importance of studying interdisciplinary collaborations by focusing on the role that good choice and design of model-building frameworks and strategies can play overcoming the inherent difficulties of collaborative research. We provide an empirical study of particular collaborations between economists and ecologists in resource economics. We discuss various features of how models are put together for interdisciplinary collaboration in these cases and show how the use of a coupled-model framework in this case to coordinate and combine background models from ecology and economics provided particular collaborative affordances and clear collaborative gain.

Applying the Causal Theory of Reference to Intentional Concepts

We argue that many recent philosophical discussions about the reference of everyday concepts of intentional states have implicitly been predicated on descriptive theories of reference. To rectify this, we attempt to demonstrate how a causal theory can be applied to intentional concepts. Specifically, we argue that some phenomena in early social development (e.g., mimicry, gaze following, and emotional contagion) can serve as reference fixers that enable children to track others’ intentional states and, thus, to refer to those states. This allows intentional concepts to be anchored to their referents, even if folk psychological descriptions turn out to be false.

Prospects and problems for standardizing model validation in systems biology

There are currently no widely shared criteria by which to assess the validity of computational models in systems biology. Here we discuss the feasibility and desirability of implementing validation standards for modeling. Having such a standard would facilitate journal review, interdisciplinary collaboration, model exchange, and be especially relevant for applications close to medical practice. However, even though the production of predictively valid models is considered a central goal, in practice modeling in systems biology employs a variety of model structures and model-building practices. These serve a variety of purposes, many of which are heuristic and do not seem to require strict validation criteria and may even be restricted by them. Moreover, given the current situation in systems biology, implementing a validation standard would face serious technical obstacles mostly due to the quality of available empirical data. We advocate a cautious approach to standardization. However even though rigorous standardization seems premature at this point, raising the issue helps us develop better insights into the practices of systems biology and the technical problems modelers face validating models. Further it allows us to identify certain technical validation issues which hold regardless of modeling context and purpose. Informal guidelines could in fact play a role in the field by helping modelers handle these.

The Epistemology-Only Approach to Natural Kinds: A Reply to Thomas Reydon

What I’m going to comment on here mostly are the issues of natural kinds that Thomas Reydon raises with respect to his ‘epistemology-first’ strategy, rather than specifically the problem of the special sciences, although is clear that it is with respect to the special sciences and the problem of field demarcation that any notion of natural kind has its most pressing application. I’m going to suggest a perhaps rather pragmatic extension of this strategy which would attempt to provide a degree of philosophical usefulness in this regard but avoid the traditional problems which I don’t think we escape unless this strategy is pushed to the logical conclusion of an epistemology-onlyapproach.

What does interdisciplinarity look like in practice: Mapping interdisciplinarity and its limits in the environmental sciences

In this paper we take a close look at current interdisciplinary modeling practices in the environmental sciences, and suggest that closer attention needs to be paid to the nature of scientific practices when investigating and planning interdisciplinarity. While interdisciplinarity is often portrayed as a medium of novel and transformative methodological work, current modeling strategies in the environmental sciences are conservative, avoiding methodological conflict, while confining interdisciplinary interactions to a relatively small set of pre-existing modeling frameworks and strategies (a process we call crystallization). We argue that such practices can be rationalized as responses in part to cognitive constraints which restrict interdisciplinary work. We identify four salient integrative modeling strategies in environmental sciences, and argue that this crystallization, while contradicting somewhat the novel goals many have for interdisciplinarity, makes sense when considered in the light of common disciplinary practices and cognitive constraints. These results provide cause to rethink in more concrete methodological terms what interdisciplinarity amounts to, and what kinds of interdisciplinarity are obtainable in the environmental sciences and elsewhere.

Models and Simulations

In this chapter we present some of the central philosophical issues emerging from the increasingly expansive and sophisticated roles computational modeling is playing in the natural and social sciences. Many of these issues concern the adequacy of more traditional philosophical descriptions of scientific practice and accounts of justification for handling computational science, particularly the role of theory in the generation and justification of physical models. However, certain novel issues are also becoming increasingly prominent as a result of the spread of computational approaches, such as nontheory-driven simulations , computational methods of inference, and the important, but often ignored, role of cognitive processes in computational model building.