Processes 2018, 6,56
Moving rightward in Figure 1 on spectra 1b and 1c involves incorporating deeper (validated)
insight into an expanding variety of interconnected biological processes and phenomena.
Mechanism-oriented models that are developing deeper (validated) insight into an expanding
variety of phenomena will be moving rightward on the Figure 5 spectra. As a consequence,
implementations must change during each move to the right. During those changes, information that
can influence—bias—simulation output can be lost and/or added. Documenting those influences
enhances credibility. The absence of such documentation risks creating a barrier to credibility, thus
limiting scientific usefulness.
7. Workflow, Provenance and Hybrid Models
Most biological scientists and clinicians have a general appreciation for and understanding of, the
workflow, the systems utilized and methods employed in wet-lab research. When they read a research
article reporting results of experiments, that knowledge influences their assessment of credibility.
Biological scientists and clinicians outside of the simulation field may be drawn to (and may consider
reading) a simulation-focused research report due to the prospect for improved explanatory insight
or practical utility. However, they do not have a corresponding appreciation for, or understanding
of, the workflow, the systems utilized, or the methods employed. Thus, there is a significant risk
that missing information and lack of clarity will erode the reader’s assessment of the credibility of
arguments presented and of simulation approaches in general.
The credibility of inferences about a phenomenon based on results of wet-lab experiments
depends on having easy access to the experiment’s provenance [45], i.e., the full context of the
experiment along with adequate descriptions of methods, materials and other important workflow
details. Removing or distancing observations and/or data from the experiment’s provenance abstracts
away both information and knowledge, thus weakening justifications for their application or use
elsewhere. By analogy, the credibility of explanations provided by simulations for how a phenomenon
may be generated depends on use context and includes having easy access to the provenance of
IV–VII [46]. Provenance begins with I–III and includes the full context of the simulation activities.
Also, by analogy, unlinking an element (e.g., mathematical descriptions or software implementation
details) from the information and knowledge provided by the original use context and provenance for
application or reuse elsewhere can weaken or eliminate justifications for the intended application or
reuse, thus eroding credibility and limiting scientific usefulness.
It is now common to encounter biology simulation research reports that seek merged explanations
of two or more phenomena or a description of phenomena across multiple biological levels or scales.
The software instantiations, commonly referred to as hybrid models, require means for the different,
originally separate and independent mechanism-oriented models to interact during execution.
Those means include adding software features and making changes to the previously independent
implementations. Describing the product of that process as hybrid alerts readers to expect the merged
system to behave in new ways. Some behaviors will be intended but others may be unintended.
The situation is somewhat analogous to combining two reagents during a wet-lab protocol when,
under some conditions, doing so risks an adverse interaction. The importance of providing clear
details is obvious.
8. Concluding Remarks
Although credibility and clarity are often correlated, other factors can have an even greater
influence on explanatory credibility. Each element in the I–VII characterizations will “resonate”
differently with different scientists, clinicians, and stakeholders. Here are three examples:
(1) The evidence selected to support a description of an Analogous-mechanism Model (II) may
resonate well with engineers and system biologists but less so with oncologists. (2) For a particular
characterization, the interpretations offered by authors in the context of selected simulation results
will likely resonate differently with scientists approaching the problem from basic science and clinical
199
Moving rightward in Figure 1 on spectra 1b and 1c involves incorporating deeper (validated)
insight into an expanding variety of interconnected biological processes and phenomena.
Mechanism-oriented models that are developing deeper (validated) insight into an expanding
variety of phenomena will be moving rightward on the Figure 5 spectra. As a consequence,
implementations must change during each move to the right. During those changes, information that
can influence—bias—simulation output can be lost and/or added. Documenting those influences
enhances credibility. The absence of such documentation risks creating a barrier to credibility, thus
limiting scientific usefulness.
7. Workflow, Provenance and Hybrid Models
Most biological scientists and clinicians have a general appreciation for and understanding of, the
workflow, the systems utilized and methods employed in wet-lab research. When they read a research
article reporting results of experiments, that knowledge influences their assessment of credibility.
Biological scientists and clinicians outside of the simulation field may be drawn to (and may consider
reading) a simulation-focused research report due to the prospect for improved explanatory insight
or practical utility. However, they do not have a corresponding appreciation for, or understanding
of, the workflow, the systems utilized, or the methods employed. Thus, there is a significant risk
that missing information and lack of clarity will erode the reader’s assessment of the credibility of
arguments presented and of simulation approaches in general.
The credibility of inferences about a phenomenon based on results of wet-lab experiments
depends on having easy access to the experiment’s provenance [45], i.e., the full context of the
experiment along with adequate descriptions of methods, materials and other important workflow
details. Removing or distancing observations and/or data from the experiment’s provenance abstracts
away both information and knowledge, thus weakening justifications for their application or use
elsewhere. By analogy, the credibility of explanations provided by simulations for how a phenomenon
may be generated depends on use context and includes having easy access to the provenance of
IV–VII [46]. Provenance begins with I–III and includes the full context of the simulation activities.
Also, by analogy, unlinking an element (e.g., mathematical descriptions or software implementation
details) from the information and knowledge provided by the original use context and provenance for
application or reuse elsewhere can weaken or eliminate justifications for the intended application or
reuse, thus eroding credibility and limiting scientific usefulness.
It is now common to encounter biology simulation research reports that seek merged explanations
of two or more phenomena or a description of phenomena across multiple biological levels or scales.
The software instantiations, commonly referred to as hybrid models, require means for the different,
originally separate and independent mechanism-oriented models to interact during execution.
Those means include adding software features and making changes to the previously independent
implementations. Describing the product of that process as hybrid alerts readers to expect the merged
system to behave in new ways. Some behaviors will be intended but others may be unintended.
The situation is somewhat analogous to combining two reagents during a wet-lab protocol when,
under some conditions, doing so risks an adverse interaction. The importance of providing clear
details is obvious.
8. Concluding Remarks
Although credibility and clarity are often correlated, other factors can have an even greater
influence on explanatory credibility. Each element in the I–VII characterizations will “resonate”
differently with different scientists, clinicians, and stakeholders. Here are three examples:
(1) The evidence selected to support a description of an Analogous-mechanism Model (II) may
resonate well with engineers and system biologists but less so with oncologists. (2) For a particular
characterization, the interpretations offered by authors in the context of selected simulation results
will likely resonate differently with scientists approaching the problem from basic science and clinical
199