Example: Biological Mechanisms

This page presents an overview of Biological mechanisms, then discusses how Ontiki might be used to capture essential details.

Note: Much of the material in this page is adapted or quoted from Craver and Darden's very readable and thought-provoking (if chewy :-) book, In Search of Mechanisms: Discoveries across the Life Sciences.

Biological Mechanisms

Biological systems are extremely complex, with subtle interactions. In an effort to make sense of observable and/or deduced phenomena, many scientists have settled (possibly without realizing it) on the approach of characterizing the entities, activities, and organization of the underlying biological mechanisms:

Mechanisms are entities and activities organized such that they are productive
of regular changes from start or set-up to finish or termination conditions.
-- In Search of Mechanisms

Teasing this apart a bit, we find that:

  • A mechanism produces one or more changes, which affect
    (e.g., produce, underlie, maintain) one or more phenomena.

  • Mechanisms have activities, each of which involves
    (e.g., consumes, creates, requires) one or more entities.

  • Activities may have enabling conditions and/or triggers;
    some activities cycle as long as they are enabled.

  • Activities have scope limitations (e.g., location, timing).

Neo4j Representation

Here's an early attempt at representing mechanisms by means of nodes and relationships, as used by Neo4j:

Neo4j's node and relationship properties can be used to handle some of the details, eg:

  • Entities have chemical and physical properties
    (e.g., charge, mass, size, shape, position, orientation)
    which affect their behavior in activities.

  • Activities have quantifiable temporal properties
    (e.g., order, rate, duration).


Mechanism schemas contain the sort of information described above. They can be used in many ways (e.g., to describe, explain, explore, organize, predict, and control phenomena). However, a given schema may not be suitable for every task. Specifically, schemas vary along four independent dimensions:

  • Completeness: black and gray box sketches to glass box schemas

  • Detail: abstract (e.g., "DNA → RNA → Protein") to specific

  • Support: how-possibly to how-plausibly to how-actually

  • Scope: narrow domain to wide applicability

When a particular schema is being studied or explained, related schemas may be referenced or included (i.e., as modules). The characteristics of these related schemas may vary markedly, as hinted by this Sidney Harris cartoon:


It should be possible to use Ontiki as a way to record the rough outline of known schemas, relate schemas to each other (and the underlying entities and activities), etc. This might (eventually) support a graph-based "map" of biological mechanisms.

The Blood Circulation page presents a hand-coded start at a representative reading list and schema outline.

Further Reading

This wiki page is maintained by Rich Morin, an independent consultant specializing in software design, development, and documentation. Please feel free to email comments, inquiries, suggestions, etc!

Topic revision: r5 - 15 Dec 2014, RichMorin
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