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.

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.

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.

Cultural Niche Construction as a Framework for Reorienting Human-Environment Relations.

In this paper, we argue that the theory of cultural niche construction provides a cogent and fruitful framework for studying and managing human-environment relationships, including our conceptualizations of them. We first review the development of the ideas of niche construction from evolutionary to social contexts. We then discuss how various human cognitive and affective goals are achieved through our engagement and interaction with the environment, as cognitive and affective niche construction. We extend this analysis to the built environment, as urban niche construction, and provide two examples of urban design for which niche construction provides useful theoretical and practical insights. We also discuss how different urban policy initiatives are related through the lens of cultural niche construction.

Mesoscopic modeling as a cognitive strategy for handling complex biological systems.

In this paper we aim to give an analysis and cognitive rationalization of a common practice or strategy of modeling in systems biology known as a middle-out modeling strategy. The strategy in the cases we look at is facilitated through the construction of what can be called mesoscopic models. Many models built in computational systems biology are mesoscopic (midsize) in scale. Such models lack the sufficient fidelity to serve as robust predictors of the behaviors of complex biological systems, one of the signature goals of the field. This puts some pressure on the field to provide reasons for why and how these practices are warranted despite not meeting the stated goals of the field. Using the results of ethnographic study of problem-solving practices in systems biology, we aim to examine the middle-out strategy and mesoscopic modeling in detail and to show that these practices are rational responses to complex problem solving tasks on cognitive grounds in particular. However making this claim requires us to update the standard notion of bounded rationality to take account of how human cognition is coupled to computation in these contexts. Our account fleshes out the idea that has been raised by some philosophers on the "hybrid" nature of computational modeling and simulation. What we call "coupling" both extends modelers' capacities to handle complex systems, but also produces various cognitive and computational constraints which need to be taken into account in any computational problem solving strategy seeking to maintain insight and control over the models produced.

Modeling complexity: cognitive constraints and computational model-building in integrative systems biology.

Modern integrative systems biology defines itself by the complexity of the problems it takes on through computational modeling and simulation. However in integrative systems biology computers do not solve problems alone. Problem solving depends as ever on human cognitive resources. Current philosophical accounts hint at their importance, but it remains to be understood what roles human cognition plays in computational modeling. In this paper we focus on practices through which modelers in systems biology use computational simulation and other tools to handle the cognitive complexity of their modeling problems so as to be able to make significant contributions to understanding, intervening in, and controlling complex biological systems. We thus show how cognition, especially processes of simulative mental modeling, is implicated centrally in processes of model-building. At the same time we suggest how the representational choices of what to model in systems biology are limited or constrained as a result. Such constraints help us both understand and rationalize the restricted form that problem solving takes in the field and why its results do not always measure up to expectations.

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.

Modeling systems-level dynamics: Understanding without mechanistic explanation in integrative systems biology.

In this paper we draw upon rich ethnographic data of two systems biology labs to explore the roles of explanation and understanding in large-scale systems modeling. We illustrate practices that depart from the goal of dynamic mechanistic explanation for the sake of more limited modeling goals. These processes use abstract mathematical formulations of bio-molecular interactions and data fitting techniques which we call top-down abstraction to trade away accurate mechanistic accounts of large-scale systems for specific information about aspects of those systems. We characterize these practices as pragmatic responses to the constraints many modelers of large-scale systems face, which in turn generate more limited pragmatic non-mechanistic forms of understanding of systems. These forms aim at knowledge of how to predict system responses in order to manipulate and control some aspects of them. We propose that this analysis of understanding provides a way to interpret what many systems biologists are aiming for in practice when they talk about the objective of a "systems-level understanding."