Processes 2018, 6,56
2. Background
Framing the Context: Mechanisms as Explanations of Phenomena
A prerequisite for discussing mechanism-oriented biological models is adopting a definition for
“mechanism.” Over the past two decades, within the philosophy of science literature, mechanism has
emerged as a framework for thinking about fundamental issues in biology [1,2].
Braillard and Malaterre recently defined a biological mechanism [2]: “A mechanism can be thought
of as being composed of parts that interact causally (usually through chemical and mechanical interactions) and
that are organized in a specific way. This organization determines largely the behavior of the mechanism and
hence the phenomena that it produces. ... Mechanisms can be formalized in different ways, including with
the help of diagrams and schemas and are usually supplemented by causal narratives that describe how the
mechanisms produce the very phenomena to be accounted for.”
Authors often augment their diagrams, schemas and causal narratives with a computational
“narrative” (algorithm and implementation) that enables explicit predictions. We use the definitions
listed under Working Definitions (Box 1) and specify that a mechanism is a real thing; it is concrete.
A description is required for the term “mechanistic model.” Kaplan and Craver state [3]: “[That] the
line that demarcates [mechanistic] explanations from merely empirically adequate models seems to correspond to
whether the model describes the relevant causal structures that produce, underlie, or maintain the explanandum
phenomenon. This demarcation line is especially significant as it also corresponds to whether the model in
question reveals (however dimly) knobs and levers that potentially afford control over whether and precisely how
the phenomenon manifests.”
Thus, we see that there is a difference between a model that reproduces a phenomenon and a
model that does so using a mechanism that recapitulates the actual underlying mechanism.
Box 1. Working Definitions.
•
mechanism: (1) a structure, system (e.g., biological, mechanical, chemical, electrical and so on), or process
performing a function in virtue of its component parts, component operations and their organization
(adapted from [4]), where the function is responsible for the phenomenon to be explained; (2) entities and
activities organized in such a way that they are responsible for the phenomenon to be explained (adapted
from [5,6])
•
phenomenon: (1) an observable fact or event: an item of experience or reality; (2) a fact or event of scientific
interest susceptible of scientific description and explanation [7]
•
mechanistic: (1) determined by, for example, a mechanical, chemical, and/or electrical mechanism, or
executing software; (2) like, for example, a mechanical, chemical, or electrical mechanism in one or more
ways; (3) of or relating to using a mechanism as an approach to explaining a biological phenomenon;
(4) mechanism-oriented
Craver posits that mechanistic models are explanatory, but he notes [8]: “Some models sketch
explanations but leave crucial details unspecified or hidden behind filler terms. Some models are used to
conjecture a how-possibly explanation without regard to whether it is a how-actually explanation.”
The increasing variety and sophistication of published mechanism-oriented and mechanism-based
explanatory models reflect that biological mechanisms exhibit features that are not expressed in the
above definition of a mechanism. Darden discusses how features of mechanisms often become
necessary parts of adequate descriptions and representations of a mechanism [9]. She identifies five
features of biological mechanism, listed in Table 1, that often characterize mechanisms that adequately
explain biological phenomena. These features will be useful in broadly distinguishing among model
types and may provide a basis for further developing an ontology to support mechanism-oriented
simulation research. The phenomenon to be explained is the first feature because the search for a
mechanism-based model of explanation requires that the phenomenon is identified. Also, in biology,
it is often the case that phenomena at a finer biological scale constitute the explanatory mechanism of
183
2. Background
Framing the Context: Mechanisms as Explanations of Phenomena
A prerequisite for discussing mechanism-oriented biological models is adopting a definition for
“mechanism.” Over the past two decades, within the philosophy of science literature, mechanism has
emerged as a framework for thinking about fundamental issues in biology [1,2].
Braillard and Malaterre recently defined a biological mechanism [2]: “A mechanism can be thought
of as being composed of parts that interact causally (usually through chemical and mechanical interactions) and
that are organized in a specific way. This organization determines largely the behavior of the mechanism and
hence the phenomena that it produces. ... Mechanisms can be formalized in different ways, including with
the help of diagrams and schemas and are usually supplemented by causal narratives that describe how the
mechanisms produce the very phenomena to be accounted for.”
Authors often augment their diagrams, schemas and causal narratives with a computational
“narrative” (algorithm and implementation) that enables explicit predictions. We use the definitions
listed under Working Definitions (Box 1) and specify that a mechanism is a real thing; it is concrete.
A description is required for the term “mechanistic model.” Kaplan and Craver state [3]: “[That] the
line that demarcates [mechanistic] explanations from merely empirically adequate models seems to correspond to
whether the model describes the relevant causal structures that produce, underlie, or maintain the explanandum
phenomenon. This demarcation line is especially significant as it also corresponds to whether the model in
question reveals (however dimly) knobs and levers that potentially afford control over whether and precisely how
the phenomenon manifests.”
Thus, we see that there is a difference between a model that reproduces a phenomenon and a
model that does so using a mechanism that recapitulates the actual underlying mechanism.
Box 1. Working Definitions.
•
mechanism: (1) a structure, system (e.g., biological, mechanical, chemical, electrical and so on), or process
performing a function in virtue of its component parts, component operations and their organization
(adapted from [4]), where the function is responsible for the phenomenon to be explained; (2) entities and
activities organized in such a way that they are responsible for the phenomenon to be explained (adapted
from [5,6])
•
phenomenon: (1) an observable fact or event: an item of experience or reality; (2) a fact or event of scientific
interest susceptible of scientific description and explanation [7]
•
mechanistic: (1) determined by, for example, a mechanical, chemical, and/or electrical mechanism, or
executing software; (2) like, for example, a mechanical, chemical, or electrical mechanism in one or more
ways; (3) of or relating to using a mechanism as an approach to explaining a biological phenomenon;
(4) mechanism-oriented
Craver posits that mechanistic models are explanatory, but he notes [8]: “Some models sketch
explanations but leave crucial details unspecified or hidden behind filler terms. Some models are used to
conjecture a how-possibly explanation without regard to whether it is a how-actually explanation.”
The increasing variety and sophistication of published mechanism-oriented and mechanism-based
explanatory models reflect that biological mechanisms exhibit features that are not expressed in the
above definition of a mechanism. Darden discusses how features of mechanisms often become
necessary parts of adequate descriptions and representations of a mechanism [9]. She identifies five
features of biological mechanism, listed in Table 1, that often characterize mechanisms that adequately
explain biological phenomena. These features will be useful in broadly distinguishing among model
types and may provide a basis for further developing an ontology to support mechanism-oriented
simulation research. The phenomenon to be explained is the first feature because the search for a
mechanism-based model of explanation requires that the phenomenon is identified. Also, in biology,
it is often the case that phenomena at a finer biological scale constitute the explanatory mechanism of
183