  Within the diverse interdisciplinary life sciences domains, semantic,
workflow, and methodological ambiguities can prevent the appreciation of
explanations of phenomena, handicap the use of computational models, and hamper
communication among scientists, engineers, and the public. Members of the life
sciences community commonly, and too often loosely, draw on "mechanistic model"
and similar phrases when referring to the processes of discovering and
establishing causal explanations of biological phenomena. Ambiguities in
modeling and simulation terminology and methods diminish clarity, credibility,
and the perceived significance of research findings. To encourage improved
semantic and methodological clarity, we describe the spectrum of
Mechanism-oriented Models being used to develop explanations of biological
phenomena. We cluster them into three broad groups. We then expand the three
groups into a total of seven workflow-related model types having clearly
distinguishable features. We name each type and illustrate with diverse
examples drawn from the literature. These model types are intended to
contribute to the foundation of an ontology of mechanism-based simulation
research in the life sciences. We show that it is within the model-development
workflows that the different model types manifest and exert their scientific
usefulness by enhancing and extending different forms and degrees of
explanation. The process starts with knowledge about the phenomenon and
continues with explanatory and mathematical descriptions. Those descriptions
are transformed into software and used to perform experimental explorations by
running and examining simulation output. The credibility of inferences is thus
linked to having easy access to the scientific and technical provenance from
each workflow stage.
