Processes 2018, 6,56
perspectives. (3) The extent to which a particular set of mathematical expressions or software
engineering methods resonates with a simulation researcher will likely have a significant impact
on that person’s determination of whether a particular computational mechanism-oriented model
is sufficiently mechanistic or not, which, in turn, may impact that person’s assessment of credibility.
There are, of course, other influences and even larger issues to consider. For example, the interpretation
of what is happening within all the above workflows is part of the philosophy of science. We put these
important influences aside for now as they are beyond the scope of this overview.
Increasing complexity in pursuit of mechanism-oriented models that improve explanatory
credibility is an explicit strategy within biology simulation research (e.g., see [26,44,46]). For the
larger community of biologists, a priority is achieving deeper, more useful explanations of phenomena
that facilitate advancing both science and health. The scientific usefulness of biology simulation
as a discipline will become more evident to the larger community as credible multi-phenomena
explanations become available. Achieving credible multi-phenomena explanations requires moving
rightward on all spectra in Figures 1 and 5. But doing so requires increasing support from the larger
biology community. Improving clarity, semantic and otherwise, is a necessary and essential small step
to achieving that increased support.
By characterizing I–III and IV–VII we demonstrate how semantic clarity can be improved even
as the complexity of those models of explanation increases. These categories of types of models and
simulations may serve as a foundation for a clear ontology of mechanism-oriented simulation research
in biology.
In summary, “mechanistic model” is used specifically and as an umbrella term within the
computational biology community. Unclear, vague labeling of a computational model as “mechanistic”
risks providing readers an ungrounded perception of its credibility, intentionally or unintentionally.
We provide clear descriptions and illustrations of broad categories of explanatory models. We suggest
terminology and language that modelers can use to more accurately explain how diverse
mechanism-oriented computational models are—or are not—”mechanistic.” The language is also
intended to enable the audience of those models, which can be rather diverse, to more easily understand
what it is about the model that is mechanistic.
Supplementary Materials: The following is available online at http://www.mdpi.com/2227-9717/6/5/56/s1,
Supporting text: Role of the Committee on Credible Practice of Modeling and Simulation in Healthcare.
Author Contributions: C.A.H. managed manuscript preparation, content organization, development and editing;
A.E., W.W.L., F.M.G, E.A.S., M.K.T., L.M. and C.A.H. contributed to the development of the presented ideas;
C.A.H. created the Figures.
Acknowledgments: We thank Glen E.P. Ropella for providing content suggestions; Andrew Smith and Ryan
Kennedy for constructive criticism during manuscript development; and Mitzi Baker editorial input. This work
was supported in part by the National Institutes of Health: R01GM104139 (A.E.), R01HL101200 (F.M.G.),
R01EB022903 (W.W.L.); The National Science Foundation: NSF:CAREER 1452728 (E.A.S.); InSilico Labs LLC
(L.M.); and the UCSF BioSystems group (C.A.H.).
Conflicts of Interest: The authors declare no conflict of interest. InSilico Labs LLC supported L.M.’s effort but
imposed no commercial restrictions or constraints. The funding sponsors had no role in the design of the study; in
the collection, analyses, or interpretation of data; in the writing of the manuscript and in the decision to publish
the results.
References
1.
Craver, C.; Tabery, J. Mechanisms in Science. In The Stanford Encyclopedia of Philosophy; Springer:
Berlin/Heidelberg, Germany, 2017.
2.
Braillard, P.A.; Malaterre, C. Explanation in Biology: An Enquiry into the Diversity of Explanatory Patterns in the
Life Sciences; Braillard, P.A., Malaterre, C., Eds.; Springer: Dordrecht, The Netherland, 2015.
3.
Kaplan, D.M.; Craver, C.F. The explanatory force of dynamical and mathematical models in neuroscience:
A mechanistic perspective. Philos. Sci. 2011, 78, 601–627. [CrossRef]
200
perspectives. (3) The extent to which a particular set of mathematical expressions or software
engineering methods resonates with a simulation researcher will likely have a significant impact
on that person’s determination of whether a particular computational mechanism-oriented model
is sufficiently mechanistic or not, which, in turn, may impact that person’s assessment of credibility.
There are, of course, other influences and even larger issues to consider. For example, the interpretation
of what is happening within all the above workflows is part of the philosophy of science. We put these
important influences aside for now as they are beyond the scope of this overview.
Increasing complexity in pursuit of mechanism-oriented models that improve explanatory
credibility is an explicit strategy within biology simulation research (e.g., see [26,44,46]). For the
larger community of biologists, a priority is achieving deeper, more useful explanations of phenomena
that facilitate advancing both science and health. The scientific usefulness of biology simulation
as a discipline will become more evident to the larger community as credible multi-phenomena
explanations become available. Achieving credible multi-phenomena explanations requires moving
rightward on all spectra in Figures 1 and 5. But doing so requires increasing support from the larger
biology community. Improving clarity, semantic and otherwise, is a necessary and essential small step
to achieving that increased support.
By characterizing I–III and IV–VII we demonstrate how semantic clarity can be improved even
as the complexity of those models of explanation increases. These categories of types of models and
simulations may serve as a foundation for a clear ontology of mechanism-oriented simulation research
in biology.
In summary, “mechanistic model” is used specifically and as an umbrella term within the
computational biology community. Unclear, vague labeling of a computational model as “mechanistic”
risks providing readers an ungrounded perception of its credibility, intentionally or unintentionally.
We provide clear descriptions and illustrations of broad categories of explanatory models. We suggest
terminology and language that modelers can use to more accurately explain how diverse
mechanism-oriented computational models are—or are not—”mechanistic.” The language is also
intended to enable the audience of those models, which can be rather diverse, to more easily understand
what it is about the model that is mechanistic.
Supplementary Materials: The following is available online at http://www.mdpi.com/2227-9717/6/5/56/s1,
Supporting text: Role of the Committee on Credible Practice of Modeling and Simulation in Healthcare.
Author Contributions: C.A.H. managed manuscript preparation, content organization, development and editing;
A.E., W.W.L., F.M.G, E.A.S., M.K.T., L.M. and C.A.H. contributed to the development of the presented ideas;
C.A.H. created the Figures.
Acknowledgments: We thank Glen E.P. Ropella for providing content suggestions; Andrew Smith and Ryan
Kennedy for constructive criticism during manuscript development; and Mitzi Baker editorial input. This work
was supported in part by the National Institutes of Health: R01GM104139 (A.E.), R01HL101200 (F.M.G.),
R01EB022903 (W.W.L.); The National Science Foundation: NSF:CAREER 1452728 (E.A.S.); InSilico Labs LLC
(L.M.); and the UCSF BioSystems group (C.A.H.).
Conflicts of Interest: The authors declare no conflict of interest. InSilico Labs LLC supported L.M.’s effort but
imposed no commercial restrictions or constraints. The funding sponsors had no role in the design of the study; in
the collection, analyses, or interpretation of data; in the writing of the manuscript and in the decision to publish
the results.
References
1.
Craver, C.; Tabery, J. Mechanisms in Science. In The Stanford Encyclopedia of Philosophy; Springer:
Berlin/Heidelberg, Germany, 2017.
2.
Braillard, P.A.; Malaterre, C. Explanation in Biology: An Enquiry into the Diversity of Explanatory Patterns in the
Life Sciences; Braillard, P.A., Malaterre, C., Eds.; Springer: Dordrecht, The Netherland, 2015.
3.
Kaplan, D.M.; Craver, C.F. The explanatory force of dynamical and mathematical models in neuroscience:
A mechanistic perspective. Philos. Sci. 2011, 78, 601–627. [CrossRef]
200