The Spectrum of Mechanism-oriented Models for Explanations of Biological Phenomena

TL;DR

This paper categorizes and clarifies the various types of mechanism-oriented models used in biological explanations, aiming to improve semantic clarity and scientific communication in life sciences modeling workflows.

Contribution

It introduces a structured spectrum of seven workflow-related model types for biological explanations, enhancing understanding and fostering an ontology of mechanism-based simulation research.

Findings

Seven distinct model types identified and illustrated.

Workflow stages linked to different explanatory models.

Enhanced semantic clarity in biological modeling workflows.

Abstract

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.