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Updated: Jun 15, 2025

A Multilayer Microfluidic Platform for the Conduction of Prolonged Cell-Free Gene Expression
Published on: October 6, 2019
Constraint-based reasoning in cell biology: on the explanatory role of context
Karl S Matlin1,2, Sara Green3
1Division of Biological Sciences, Committee on Conceptual, Historical, and Social Studies of Science, University of Chicago, Chicago, IL, USA. kmatlin@uchicago.edu.
Abstract:
Cell biologists, including those seeking molecular mechanistic explanations of cellular phenomena, frequently rely on experimental strategies focused on identifying the cellular context relevant to their investigations. We suggest that such practices can be understood as a guided decomposition strategy, where molecular explanations of phenomena are defined in relation to natural contextual (cell) boundaries. This "top-down" strategy contrasts with "bottom-up" reductionist approaches where well-defined molecular structures and activities are orphaned by their displacement from actual biological functions. We focus on the central role of microscopic imaging in cell biology to uncover possible constraints on the system. We show how identified constraints are used heuristically to limit possible mechanistic explanations to those that are biologically meaningful. Historical examples of this process described here include discovery of the mechanism of oxidative phosphorylation in mitochondria, molecular explanation of the first steps in protein secretion, and identification of molecular motors. We suggest that these instances are examples of a form of downward causation or, more specifically, constraining relations, where higher-level structures and variables delimit and enable lower-level system states. The guided decomposition strategy in our historical cases illustrates the irreducibility of experimentally identified constraints in explaining biological activities of cells. Rather than viewing decomposition and recomposition as separate epistemic activities, we contend that they need to be iteratively integrated to account for the ontological complexity of multi-level systems.
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