Abstract
In developing mechanistic explanations for biological phenomena, researchers have their choice of several different types of diagrams. First, a mechanism diagram spatially represents a proposed mechanism, typically using simple shapes for its parts and arrows for their operations. Beyond this representational role, such diagrams can provide a platform for further reasoning. Published diagrams in circadian biology show how question marks support reasoning about the proposed molecular mechanisms by flagging where there are knowledge gaps or uncertainties. Second, an annotated mechanism diagram can support computational modeling of the dynamics of a proposed mechanism. Each variable and parameter needed for the model is added to the diagram adjacent to the appropriate part or operation. Anchoring the model in this way helps with its construction, revision, and interpretation. Third, a network diagram fosters a different approach to mechanistic reasoning. Layout algorithms are applied to data generated by high-throughput experiments to reveal modules that correspond to mechanisms. We present examples in which network diagrams enable viewers to advance hypotheses about previously unknown mechanisms or unknown parts and operations of known mechanisms as well as to develop new understanding about how a given mechanism is situated in a larger environment.
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We gratefully acknowledge the support of NSF Grant 1127640.
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Bechtel, W., Abrahamsen, A., Sheredos, B. (2018). Using Diagrams to Reason About Biological Mechanisms. In: Chapman, P., Stapleton, G., Moktefi, A., Perez-Kriz, S., Bellucci, F. (eds) Diagrammatic Representation and Inference. Diagrams 2018. Lecture Notes in Computer Science(), vol 10871. Springer, Cham. https://doi.org/10.1007/978-3-319-91376-6_26
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