Measurement in biology is methodized by theory ⋆
Maël Montévila
a Institut
de Recherche et d’Innovation, Centre Pompidou 4 Rue Aubry le Boucher, 75004 Paris, France
Abstract
We characterize access to empirical objects in biology from a theoretical perspective. Unlike objects in current
physical theories, biological objects are the result of a history and their variations continue to generate a history. This
property is the starting point of our concept of measurement. We argue that biological measurement is relative to a
natural history which is shared by the different objects subjected to the measurement and is more or less constrained
by biologists. We call symmetrization the theoretical and often concrete operation which leads to considering
biological objects as equivalent in a measurement. Last, we use our notion of measurement to analyze research
strategies. Some strategies aim to bring biology closer to the epistemology of physical theories, by studying objects
as similar as possible, while others build on biological diversity.
Keywords: Biological measurement, experiments, evolution, systematics, strains, symmetry
5 Conclusion
Contents
1 Introduction
2 Measurement in physics
2.1 Classical measurement .
2.2 Quantum measurement
2.3 Reference frame . . . .
2.4 Conclusion . . . . . . .
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3 A theoretical account of biological measurement
3.1 Phylogenetic classification and nomenclature of biological objects . . . . . . . . . .
3.2 Observed and controlled genealogy . . .
3.3 Historical contexts . . . . . . . . . . . . .
3.4 Synchronic aspects of measurement . . .
3.4.1 Current context . . . . . . . . . . .
3.4.2 Choosing or eliminating individuals . . . . . . . . . . . . . . . . . .
3.4.3 Data acquisition . . . . . . . . . .
3.5 Irreducibility of biological variation . . .
3.6 Recapitulation . . . . . . . . . . . . . . .
4 Discussion
4.1 The radical materiality of biological phenomena . . . . . . . . . . . . . . . . . . .
4.2 Symmetry and symmetrisation . . . . . .
4.3 Measurement strategies . . . . . . . . . .
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appear as: Montévil, M. (2019) Measurement in biology is
methodized by theory. Biology and Philosophy http://dx.doi.org/10.
1007/s10539-019-9687-x.
Email address: mael.montevil@gmail.com (Maël Montévil)
URL: https://montevil.theobio.org (Maël Montévil)
Preprint submitted to Biology and Philosophy
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1. Introduction
Science and more specifically biology and medicine
are facing a crisis where systematic attempts to reproduce experiments published in reputable journals fail
in the majority of cases (Begley & Ellis, 2012; Baker,
2016). The management and organization of scientific
institutions have been investigated, and the pressure to
publish has been heavily criticized (Begley & Ioannidis,
2014; Lancet, 2018). In the case of experimental biology,
theoretical and philosophical analyses can also play a
role to understand and respond to this crisis (Nadin,
2017). There are aspects proper to biological experiments that should be analyzed systematically in light of
the current understanding of living beings. This discussion is also particularly relevant now that the scientific
focus on (Big) Data analyses bears the risk of forgetting
that data are generated in specific empirical conditions
(Leonelli, 2014). Data detached from these conditions
without proper justification do not carry a genuine scientific meaning.
A scientist cannot assume that her access to reality
is one of an omniscient daemon. Understanding what
it means to observe natural phenomena is fundamental.
This question is multi-faceted. Part of it pertains to the
complementary knowledge advocated by Chang (2004),
but part of the answer should be principled, in the relevant theoretical framework. We concur with Einstein’s
epistemological statement: “whether you can observe a
thing or not depends on the theory which you use. It is
the theory which decides what can be observed” (A. Einstein quoted in Salam, 1990). In physics, measurement
April 23, 2019
to accommodate the organism measured and its commensurability with other organisms. We will develop mostly
the latter idea since it has not been systematically analyzed and raises questions which are proper to biology.
To address the specificities of biological measurement
and conceptualize the commensurability of organisms,
we need theoretical insights on organisms.
We use the principles proposed recently for a theory
of organisms (Mossio et al., 2016; Montévil et al., 2016;
Soto et al., 2016a). This framework provides a conceptual continuity between the understanding of organisms
and evolution. In particular, it emphasizes historical
analyses both for phylogenesis and ontogenesis.
In this framework, biological objects are not defined
theoretically like objects in physical theories. The theoretical definition of objects is mathematical in physics.
Despite quantitative differences, the changes of a welldefined object are assumed to follow an underlying mathematical structure. Invariants and invariant preserving
transformations (symmetries) define these mathematical
structures (Van Fraassen, 1989; Longo & Montévil, 2014).
For example, a falling stone follows the same equation
during its fall despite its changes of position and velocity,
and a falling log would follow the same equation. As a
result, physicists can talk about the generic phenomenon
of falling bodies. Physical notions of measurement apply
to generic objects, and the reproducibility of physical
experiments is guaranteed, at least statistically, once the
same generic conditions apply.
By contrast, biological objects are historical in the
sense that their organizations stem from an evolutionary and individual history and continue to produce a
history. This idea has been developed theoretically and
called the principle of variation (Montévil et al., 2016).
To an extent, this principle is in line with earlier ideas, in
particular, the contingency thesis of Beatty (1995) and
the centrality of historicity defended by Gould (2002,
chap. 11) in a critical assessment of the work of D’Arcy
Thomson. For example, a falling tetrapod is not a purely
physical notion since “tetrapod” is a biological concept.
In the atmosphere, tetrapods do not just fall, some fly
and others are gliders. All these behaviors require different equations, and these changes of equation depend on
the underlying evolutionary history. This basic example
illustrates the general idea that biological objects should
not be conceived as generic and are prone to more profound changes than objects in physics, including the
appearance of new possibilities (Montévil, 2018). Moreover, biological objects are contextual in the sense that
their organizations depend on their past and current contexts. In other words, describing biological objects does
not just involve many quantities, but quantities which
are endowed with different biological meaning, and new
relevant quantities can appear over time.
In a nutshell, biologists manipulate objects which are
understood theoretically as the result of a history and
continue to produce a history: diachronic objects. With
is described in theories and is a fundamental part of their
formulation. The notion of measurement embedded
in theories provides a general link between the output
of measurement and the theoretical and mathematical
description of the objects of study. For example, measurements in classical mechanics provide approximate
results while they change objects in quantum mechanics. There are many other aspects of measurement which
are philosophically important; however, in this article,
we aim to ground widely shared practices on theoretical
principles.
Biologists often use physical concepts, and measurement is no exception. The notion of measurement of
classical mechanics is widely used in biology. Moreover,
Wagner (2010) and Houle et al. (2011) advocate the use
of measurement theory in biology. This setting leads us
to inquire whether biology requires a distinct notion of
measurement. In the literature, there is at least one such
account: following the informational metaphor, molecular biology often considers measurement as a classical
measurement applied to finite, entirely discrete features:
the sequences of nucleotides. A classical measurement
has a limited precision, but knowing finite, discrete structures with a sufficient finite precision means knowing
them exactly (Schrödinger, 1944). The same reasoning
applies mutadis mutandis to other discrete structures such
as the topology of networks (Huneman, 2018). This reasoning only applies to the discrete aspect of the objects,
and not the continuous ones such as position in physical
space.
This point of view is in contrast with experimental
methodologies which are very rich and sometimes subtle
(Weber, 2004; Kohler, 1994). In this paper, we argue
that general theoretical principles of biology leads to a
theoretical account of biological measurement which
clarifies several aspects of experimental methodologies.
Measurement requires commensurability. For example, measuring the length of an object requires to
identify the distance between its edges with the length of
another object such as a ruler. It also requires abstract
constructs: in this example not only a theory of space
(or space-time) but also assumptions on the object measured. These assumptions ensure that the measurement
has a meaning (Houle et al., 2011). For example, when
measuring a length, is the object solid, or flexible, does
it have well-defined boundaries, like a box, or not, like a
cloud. As a result, measurement is never only about a
single object (token). In biology, the measurement of
a part or an aspect of an organism may be performed
by the commensurability with a physical object, for example, the length (in meters) of this organism, here and
now, measured in physical units. However, this alone is
only sufficient to know if we can put it ”as is” in a box of
a given length. The biological meaning of a length and
the procedure to assess it are very different for a tree or a
snake. Therefore, we posit that biological measurement
is not only about the intended part or aspect, but also has
2
these ideas stemming from the theory of evolution in
mind, experimental reproducibility is not a straightforward notion. Biological objects tend spontaneously to
vary whereas perfect reproducibility, even statistically,
would require fixed physiology and development, at least
at an abstract level.
In section 2, we introduce how several physical theories define measurement and the epistemological and
theoretical roles this notion plays. Section 3 discusses
the theoretical nature of biological measurement. Biological measurements accommodate natural histories
and contexts, not just quantities. Section 4 explores several implications of our framework. In particular, we
classify different research strategies to handle biological
measurement.
that the state of the system becomes an eigenstate associated with the quantity obtained. The other eigenstates
in the initial superposition disappear irreversibly. Quantum measurement has an algebraic (and geometric) nature.
There is an internal coherence to this notion. Performing the same measurement twice in a row will lead
to the same result because the state of the object is already an eigenstate associated with this result: the result
obtained in the first measurement is the only possible
outcome in the second.
Different observables do not necessarily lead to the
same decomposition. An eigenstate which corresponds
to a specific position, for example, does not correspond
to a specific velocity and the other way around. Then,
measuring the position, measuring the velocity and measuring the position again will not necessarily lead to the
same position twice. Lastly, some authors argue that,
in an experiment, a measurement is needed to put the
system in a known initial state (Mugur-Schächter, 2002).
The typical theoretical structure of an experiment is then:
measurement, time evolution (Schrödinger equation typically), measurement.
2. Measurement in physics
In order to exemplify our aims in biology, we discuss
briefly how the main physical theories conceptualize measurement. We are interested in measurement considered
in principle in general theoretical frameworks and not in
specific experimental situations. For the theory, what
does “obtaining quantities” in experiments or observations means? These accounts are sufficiently general to
be valid for any practical situation in the corresponding
theory, and they have deep practical and theoretical consequences.
2.3. Reference frame
Experimenters choose space-time reference frames arbitrarily to represent concrete situations and describe features such as positions quantitatively. Relativity (Galilean,
special and general) states how the description of a situation in one reference frame can be transformed into the
description of the same situation in another reference
frame and ensures that these descriptions are coherent.
This concept overcomes the arbitrary choices of reference frames, and its mathematical nature is geometric.
2.1. Classical measurement
In classical mechanics, a system has a pointwise state
in the space of possible states. The empirical access to
this state is approximate: a measurement has a finite
precision, 𝜖, which can in principle be arbitrarily small.
Thus, the state of a system is a point, and the result of
the measurement is an interval. Classical measurement
is a metrical notion: it stems from the concept of distance.
Classical dynamics are deterministic, but measurements may or may not allow to predict the subsequent trajectory. Unpredictable dynamics such as chaotic dynamics are called sensitive to initial conditions. The notion
of measurement articulates determinism and randomness in the sense of theoretical impredictability (Gillies,
2012; Longo & Montévil, 2017). This example shows that
a simple notion of measurement can have far-reaching
conceptual consequences.
2.4. Conclusion
The concepts of physical measurement we described
are principles in their respective theory, and they are very
different. Their common point is that they all describe
the role of the experimenter and its instruments in an
abstract and very concise way.
3. A theoretical account of biological measurement
To describe our theoretical notion of biological measurement, we rely mainly on the principle of variation
(Montévil et al., 2016). This principle builds on evolutionary biology and states that biological objects can vary
in a stronger sense than objects described by physical
theories. The latter change, but physicists understand
their changes by underlying stable mathematical structures. Instead, biological variations in the strong sense
require changing mathematical structures. Biological
objects are formed by a cascade of such variations and
the notions of historicity and contextuality become fundamental. Figure 1 summarizes this perspective which
guides our analysis of biological measurement.
2.2. Quantum measurement
In quantum mechanics, measurement involves the
commensurability of a microscopic object and a macroscopic object. Quantum measurement changes the object and leads to quantum randomness. Informally, a
quantum state can be decomposed for a given measurement as the superposition (the sum) of different states
called eigenstates. Each of them corresponds to a single
obtainable result. Performing the measurement means
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establishes
time
variation
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Specific object
are a part of
History
Observable
features
(Constraints)
change
are a part of
establishes
Observable
features
(Constraints)
Context
Context
Figure 1: Theoretical structure of biological objects, after Montévil et al. (2016). In biology, organisms are not described theoretically by invariants and
invariant preserving transformations (symmetries) which would provide a generic meaning to the features observed. Instead, their regularities
are constraints that come from an history and collectively maintain each other in a given context. These constraints can change over time as the
objects continue to generate a history over physiologic, developmental and evolutive time scales. An account of biological measurement has to
accommodate simultaneously the measured aspects (constraints) and the rest of the organism which we describe as a specific object.
In physics, objects can be highly simplified and remain relevant for physics. For example, it is sound to
study a material composed only of iron. In biology, this
is not the case. For example, looking at one or several
molecules alone pertains to biochemistry, not biology.
In biology, the measured features of organisms or cells,
such as the concentration of molecules or the shape of
tissues, are measured in organisms or cells and are generally produced and maintained by them (Mossio et al.,
2016; Montévil & Mossio, 2015). Therefore, our discussion of biological measurement is not limited to the parts
observed per se. Instead, our approach of measurement
accommodates both the parts observed and the organisms associated. Both are reported carefully in empirical
studies, and we posit that they are elementary aspects of
biological measurement. This section may be seen as the
theorization of a typical “method section” in any experimental paper in biology.
In order to provide stability to the meaning of the
names used to describe living beings, systematics establish and follow strict rules to describe new species and
other clades (e.g., genus, family). Nomenclature codes
use the principle of typification. Typification means that
defining a name requires a type. For example, the definition of a name at the family level requires a genus-level
type, a genus-level name requires a species type, and describing a new species (or subspecies) requires referring
to one specimen (holotype) or several specimens (syntype) which are kept in a collection (CZN International,
1999; McNeill et al., 2012, art. 72.3 and 40 resp.). Typification ensures the stability of the definition of names
even if the classification changes. Name-bearing types
are required to be in a biologically inactive state and thus
are fixed reference objects (McNeill et al., 2012, art. 8.4).
Typification implies that the definition of biological
names ultimately depends on specific, static, material
objects (Grandcolas, 2017). This situation is in contrast
with the theoretical definitions in the International System of Unit based on physical theories. For example,
a meter is the distance traveled by light in vacuum in
1/299792458 seconds. This definition refers to matter but
does not need the conservation of a specific object. Instead, it uses the generic, theoretical object called “light
in the vacuum” which has an invariant velocity in both
special and general relativity1 .
Names associated with specific material objects (types)
are not sufficient for scientific practices. In order to endow names with a more general meaning, systematics
3.1. Phylogenetic classification and nomenclature of biological
objects
Reporting a biological measurement starts with describing the organisms observed and naming them. The
theoretical and philosophical underpinnings of these
names are an essential aspect of biological measurements.
The standard, general way to name organisms is to use
systematics. Biologists always use this method, even
though other methods can complement it, as discussed
in the following section.
We want to emphasize two aspects of this method
that impact the concept of measurement. The first is the
definition of the names themselves and the second is the
phylogenetic classification of living beings (de Queiroz,
1992; Lecointre & Le Guyader, 2006).
1 Historically, the definition of a meter has first been theoretical,
then it used a standard prototype. The current definition is again
theoretical.
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Evolution
Types
neti c
Phyloge
an al y s i s
Homo sapiens
Mus musculus
Rattus novergicus
Rodents
Mammals
Chordates
Xenopus laevis
Drosophila melanogaster
Figure 2: Principle of the phylogenetic classification. LEFT : a schematic representation of the genealogy of a few species over evolutionary timescales.
This genealogy is not observable as such. MIDDLE: the consequence of evolution is the presence of diverse life forms, some of which are used by
biologists as types. Name-bearing types formally define names. Names are then extended to the specimens of the same species. RIGHT: the
characters that the specimens share and do not share are used to assess their evolutionary proximity with a mathematical model of evolution.
Acceptable groups are defined as the descent of a theoretical common ancestor and lead to a classification.
uses the phylogenetic classification method (de Queiroz,
1992; Lecointre & Le Guyader, 2006). This method classifies living beings by estimating their genealogy. The
genealogy is a theoretical concept that stems from the
theory of evolution; however, the genealogy of current
organisms spans billions of years, and human observers
cannot access it directly. As a result, the phylogenetic
classification uses different concepts than a genealogical
tree. For example, it is impossible to determine whether
a fossil species is an ancestor of a current species, but it
is possible to establish that they are closely related genealogically. The phylogenetic method distinguishes a
theoretical level and an observable level which is reminiscent of the distinction between a state and what can
be observed in physics.
The phylogenetic classification assesses the evolutionary proximity between different organisms. Systematists
start with the characters characterizing the different organisms, including DNA sequences. These characters are
used by a computational method which provides a nested
hierarchy of groups, see figure 2. These methods typically assume that the most likely situation minimizes the
number of evolutionary changes, and in particular the
appearance of novelties. These analyses lead to classifications where acceptable groups, called monophyletic
groups or clades, are the descent of a common theoretical ancestor. The classification can then be used for taxonomic purposes. Of course, evolutionary reasonings
guide the choice of the characters and the computational
method used, and these choices are commonly debated.
Clades are defined by their estimated historical origin
and not by their current ecological status or physiology.
Since the definition of clades is based on a historical
analysis, it accommodates the diversity and diversification of living beings straightforwardly. For example,
the famous goat (Capra aegagrus hircus) discussed by
West-Eberhard (2003) is a paradigmatic example of de-
velopmental plasticity because it is bipedal: a significant
change occurred in a single specimen. Despite its peculiarities, this specimen is still part of the subspecies C.
aegagrus hircus because the subspecies is defined by its
historical origin, not by its properties.
Biological observations typically refer to a specific
clade, usually a species or subspecies. By definition of a
clade, this only ascertains a given shared theoretical ancestor. This common past involves similarities between
the specimens studied, but it does not guarantee that
the properties of interest in a given investigation will be
similar or even exist.
3.2. Observed and controlled genealogy
The design and description of typical biological experiments use genealogical elements that go beyond
what systematics can provide. Genealogical knowledge
is provided by the direct observation of the lineages leading to the specimens studied and can be more or less
comprehensive. Of course, direct genealogical knowledge is limited to the historical period where biologists
follow the appropriate methods, that is to say, about a
century at best.
Usually, direct genealogical knowledge goes with
more or less control over the genealogy. In the case
of organisms reproducing sexually, there are two main
strategies to control genealogies: establishing inbred
or outbred strains, see figure 3A. Inbred strains stem
from several generations of inbreeding. By enforcing
this behavior, biologists aim to obtain a genetically homogeneous population. Inbred strains still change over
time at least as a consequence of genetic drift. These
changes lead to the definition of substrains that have
biologically relevant differences and are not interchangeable (Simpson et al., 1997). By contrast, outbred strains
aim to maintain heterozygote populations while keeping
as much genetic homogeneity as possible. These strains
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are more genetically labile than inbred strains and are often considered more variable phenotypically (Chia et al.
2005; Festing 2014, however see Jensen et al. 2016).
A specific nomenclature for strains completes the
nomenclature deriving from systematics. For example,
a widespread strain in biomedical research is the inbred
mouse strain C57BL/6 (Black 6) (Festing, 2014). Naming strains to report an experiment includes the breeding
institution. For example, C57BL/6NCrl are Black 6 mice
from the National Institutes of Health (N) and which
are bred by Charles River Laboratory (Crl) (Sacca et al.,
2013).
The choice of strain can profoundly impact experimental results. For example, Black 6 mice have singular
features such as their nociception (sensation of pain)
(Mogil et al., 1999). Isaacs (1986) tested the incidence
of tumors in rats exposed to the carcinogen DMBA and
found that this incidence is 0%, 15%, 40% and above
90% depending on the strain used. The sensitivity to endocrine disruptors also depends on the strain (Spearow
et al., 1999).
In the case of cells, the situation is overall similar to
the case of animals. Cell lines and sub-lines are established, named, and exchanged between laboratories. For
example, the first laboratory immortal human cell line,
the HeLa cell line, originated from a single patient, Henrietta Lacks (who died in 1951) and thus HeLa cells have
a common origin. This cell line is widely used, and more
than 99000 references in PubMed mention it (08/2018).
Cell lines have two specificities (fig. 3B). First, a single
cell can originate a clonal population in common cases.
Second, the use of frozen samples enables biologist to
“stop” biological time. Biologists use these operations to
obtain populations of cells that are far closer genealogically to their common ancestor than cells which would
be proliferating with variations in culture.
Both animal strains and cell lines can be modified
for research purposes, either by artificial selection for a
specific trait or by genetic engineering, a subject extensively discussed by Kohler (1994) in the case of Drosophila
melanogaster. These modifications are not only aiming
for a specific new trait; they include ruling out animals
with spontaneous, problematic mutations.
It is standard practice to communicate live sample
between research laboratories or between breeding institutions (Kohler, 1994, chap. 5). Communicating live
samples is required for biologists to ensure that the specimens studied in different laboratories are close genealogically and carry the same spontaneous or artificial changes
if any. Commitment to perform these exchanges is required to publish in many journals. Replicating an experiment using specimens from a controlled genealogy
requires an exchange of matter, a point that we discuss
in section 4.1.
Genealogies are not limited to cell division and sexual
reproduction. Viruses lead to horizontal transfers, biologists use a diversity of manipulations, such as chimera
obtained by the fusion of different zygotes. Last, some
authors consider that microbiomes should be considered as parts of organisms which implies that several
lineages come together to form a holobiont (Gilbert,
2014). These examples are beyond the basic concept of
genealogy but fit a broader concept of genealogy sensu
the historical origins of specimens.
The use of controlled strains and cells lines is not universal in biological experiments. For example, cells may
come directly from recent human samples, and animals
may come from captures in the wild. However, the practice of using sometimes very tightly controlled genealogies is widespread, in particular in biomedical research.
The active control of genealogies, including modifications, leads to a situation where the natural history of
the specimens is entangled with the human history of
biological sciences (see Kohler, 1994, for a discussion in
the case of D. melanogaster).
The knowledge and control over part of the recent
genealogy of the specimens experimented upon is a supplement to the phylogenetic method of classification. It
ensures that the specimen studied have a recent shared
past. Even though this control is tighter than with the
classifications of systematics alone, the same theoretical
and philosophical limitations apply: the description is
historical and does not ensure that the specimens have
the very same organizations. Nevertheless, several methods provide partial control over biological organizations.
For example, inbred strains are (almost) homogeneous
genetically, and some aspects of animal phenotypes are
controlled regularly in breeding institutions. Thus, these
methods provide precise knowledge and control over the
historical origin of the specimens studied and limited
direct control over their organizations.
3.3. Historical contexts
Knowledge and control of the past of organisms and
cells used in an experiment are not limited to their genealogy. Their past contexts are also relevant. By context, we mean the environment, including the possible
interactions with other organisms. The control of past
contexts can go from the timescale of many generations
to the timescale of ontogenesis or even the shorter time
scales preceding the experiment.
In the case of cell culture, the control and knowledge
of the context stem first from the use of a standardized
medium, temperature, and protection from contaminations. Even the choice of supplies such as centrifugal
tubes used with the medium can have dramatic consequences on cellular behaviors (Soto et al., 1991). Another critical parameter is the density of cells. When
this density is too low, the lack of quorum effect can
change cellular behaviors. On the opposite, when the
density is too high, the cells constrain each other’s proliferation. Moreover, cells typically need time to adjust
to a change of conditions such as a change of medium
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Subculture
Wild specimens
Fr
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io n
E
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xc
an
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Experiment
Outbred
Experiment
No controlled shared ancestor
Inbred
A
Time
Shared ancestor
Time
B
Figure 3: Observed and controlled genealogies. A: A schematic representation of strain breeding. Biologists use wild or domesticated specimens to start
controlled strains. In the case of inbred strains, there is no crossing with specimens external to the strain. In the case of outbred animals, some
diversity is regularly introduced. Substrains may be defined, either because they are the result of genetic manipulations, selection in outbred
strains, or just as the result of genetic drift. B: Controlled genealogies in the case of cells. Doing a standard subculture is not enough to ensure that the
individuals of a population share a recent common ancestor. To ensure a recent common ancestor, biologists typically perform a highly diluted
subculture which isolates a single cell. This cell and its descent proliferate, and their proliferation leads to a new population. This population can
then be frozen in order to stop biological processes, and in particular to stop proliferation and the associated variations (Soto et al., 2016a).
Subsets of this frozen population can be used to perform experiments and be shared with other laboratories. The cells obtained using this method
share a known, recent common ancestor and are often used to reproduce experiments.
Longo & Montévil (2011b). All these factors are important since they determine the status of the cells subjected
to the experimentation. In order to perform controlled
experiments, experimenters choose an initial status that
can be obtained consistently in a cell population (homogeneity) and different replicates (reproducibility). The
most straightforward condition that can be obtained and
sustained consistently is unconstrained proliferation.
In the case of animals, the situation is similar to that
of cells. In laboratory conditions, the control of the context includes typically the temperature, light cycle, the
nature and quantity of food, avoiding pathogens, and
the number of animals per cage. For example, Heindel
et al. (2015, section 2.6) describe the context in which
animals are raised before and during a large scale experiment. However, their past context can be considered
problematic. This work aims to study the effects of the
endocrine-disrupting chemical bisphenol A (BPA). The
animal experimented upon are raised in BPA free cages,
but they originate from strains which are raised in polycarbonate cages by the animal provider, and polycarbonate leaks BPA. The exposure of pregnant females to BPA
have known effects spanning two generations (“grandmother effect”, Susiarjo et al., 2007) and there are other
known and probably also unknown epigenetic factors.
Understanding the importance of past contexts requires a short theoretical discussion on heredity. Under the assumption that DNA sequences are the only
form of heredity, contexts before an experiment are
relevant only during development. However, this assumption is not valid in general, and epigenetic inher-
itance is a widespread phenomenon (Jablonka & Raz,
2009; Jablonka et al., 2014; Danchin et al., In press). Let
us introduce a simple example that does not require recent advances in epigenetics. MMTV is a retrovirus
which can be inherited exogenously from the milk of
an infected host to another animal, usually its descent
(Dudley et al., 2016). If, say, inbred mice are fed milk
from contaminated mice of another strain, then these
mice will carry MMTV and transmit it to their descent.
A contaminated female will lead to a substrain which
is genetically identical to the original inbred strain (as
long as the retrovirus does not alter mice DNA) but has
critical immunological and oncologic differences.
Many strategies such as working with inbred strains
or clonal cell populations strive for genetic uniformity.
These strategies could be extended formally to known
forms of epigenetic heredity. However, the knowledge
and control of past contexts over several generations is
an indirect, partial way to control known and unknown
epigenetic heredity, in combination with the control of
genealogies. As a conclusion, past contexts over several
generations are relevant.
The context at the timescale of one generation is also
relevant, as advocated by the concept of ecological developmental biology (Gilbert & Epel, 2009). Even the
position of a fetus relative to its male and female siblings
in the uterus has a measurable impact (Ryan & Vandenbergh, 2002). The context matters at shorter timescales
too. For example, to measure heart rate or blood pressure on a rat, biologists need to take into account the
memory and anticipation associated with the procedure
7
(Longo & Montévil, 2011b; Nadin, 2017, for conceptual
frameworks). In this particular case, the stress induced
by the measurement impacts the heart rate and can be
limited by training the animal, that is changing its anticipations (Gross & Luft, 2003).
The context in which organisms and cells live before
the experiment matters from the timescale of several
generations to the timescales of development and physiology. The work on past contexts complements the one
on genealogies as a method to manage the past of the
specimens studied. It follows that the same epistemological limitations apply.
• The maximum metabolic rate (MMR) considers
the maximum level of sustainable activity. By focusing on the upper boundary of the metabolism,
only the determinants of this boundary are relevant and not the various characters involved in
biological activities (fig. 4E4).
3.4. Synchronic aspects of measurement
The aspects of measurement discussed above are mostly
diachronic: they pertain to the past of objects. By contrast, this section analyses aspects relevant during the
observation of intended features.
3.4.1. Current context
Overall, the discussion in the previous section applies also to the context during an experiment. The context contributes to the definition of the specimens and
quantities observed. This contribution is both practical
and theoretical. It is practical because it describes the
necessary operations required to perform the same measurement beyond using the same apparatus and reading
its results. It is theoretical because the meaning of the
results depends on these operations.
To illustrate the importance of the context, let us
consider the example of mammal metabolism observed
by the oxygen consumption rate. This rate seems to
be a simple empirical quantity; however, it depends on
the activity of the organism observed and its relevant
components. To compare the metabolism of different
organisms, biologists define different kinds of physiological activity. The target activities have to be meaningful
and achievable for all the organisms considered, which
may be difficult when measurement applies to the many
different species of a large clade. In all cases, the meaning of the results depends on the nature of the activity
chosen (fig. 4). Metabolic rates have several definitions
(Longo & Montévil, 2014, chap. 2 for a review):
• The field metabolic rate (FMR) corresponds to
the activity of organisms in an ecosystem, without
constraints from the observer (fig. 4E1).
By choosing different contexts, biologists co-determine
what is observed even when the same measurement apparatus is used to observe the same part. The BMR and
MMR show that it is even possible to choose observations that focus on properties shared by different species
by leveling down the weight of the organizational diversity stemming from history.
3.4.2. Choosing or eliminating individuals
Filtering of individuals is a method to control strains:
breeders disregard animals with deleterious mutations,
diseases, or other peculiarities. Sometimes, only minimal
control over the past context and genealogy is possible.
For example, in humans, most methods above would
be unethical. Choosing individuals having specific characteristics and eliminating individuals with unwanted
characteristics is an alternative method of control on the
organisms investigated.
Filtering of individuals is possible during experiments; however, it impacts the meaning of the results.
For example, in the case of a toxicological experiment,
unexpected variations should be reported since they may
be relevant to understand the effect of the chemical studied and may be investigated in other studies. However,
if we want to study the “normal” physiology of insulin
after long-term exposure to high-sugar diet, then it is
necessary to rule out diabetic animals. Last, the quantities of interest cannot be measured at the expected time
point in the case of individuals who meet an untimely
death, which is an uncontrolled filter.
Filtering of individuals by their properties is a complementary way to control biological objects. Performing this filtering enables biologists to discard specimens
which have gone through unwanted variations, or which
have not gone through expected variations. Criteria
can range from developmental anomalies, mutations,
pathologies to animals frightened during measurement.
3.4.3. Data acquisition
Biological measurements typically provide quantities, and this process has an anhistorical dimension that
is comparable to physics. The notion of measurement
of classical physics is relevant in biology. When measuring a continuous quantity such as the velocity, the
measurement is never exact and provides an interval instead of a single quantity (§ 2.1). Other physical notions
such as reference frames can also be relevant. Wagner
(2010) and Houle et al. (2011) fruitfully import concepts
of measurement theory in biology which are relevant for
• The basal metabolic rate (BMR) considers organisms at rest, that is to say, undisturbed, non-sleeping
organisms in a thermoneutral environment and
in a post-absorptive state. Evolution leads to a diversity in the activities of organisms and the BMR
levels down the impact of this diversity on the
metabolism (fig. 4E3). It is not always possible
to instantiate this definition; for example, ruminants are never in post-absorptive state (fig. 4E2).
8
S1
S1’
S2
S3
S1
S1’
S2
S3
S1
E1
S2
q
q
q
q
E3
S3
A
E2
B
q
q
q
q
q
q
q
q
E4
q
q
q
q
Figure 4: Different measurements of the same quantity. A: A schematic representation of the appearance and disappearance of relevant characters. Dotted
lines represent relations of homology. White shapes are characters which disappeared. B: Four different ways to measure a quantity 𝑞. S1 and S1’ are
two similar specimens. All represented characters impact 𝑞. The size of a symbol represents the impact of the corresponding character on 𝑞 in the
given context. E1: A measurement performed without specific care for the characters contributing to 𝑞, e.g., the field metabolic rate. E2: A
measurement performed in a standardized way for S1 but not for the other species. E3: The animal performs no specific activity which reduces
the weight of several characters, e.g., the basal metabolic rate. In this case, only homologous characters remain quantitatively relevant. E4: A
constraint dominates the determination of the measured quantity despite the diversity of relevant characters, e.g., the maximum metabolic rate.
the synchronic aspect of measurement. Since these aspects are not properly biological, we will not develop
them further here.
The principle of variation implies that an observed
feature can become ill-defined or acquire a different
meaning. Here, biology goes beyond standard measurement theory since the changes of biological objects
lead to a collapse of the original meaning of the quantities observed. For example, the heart rate is defined by
beat-to-beat intervals, but pathological situations such
as torsade de pointes escape the standard definition of
a heartbeat, and the notion of heart rate becomes illdefined. Similarly, the properties of the hind legs of the
bipedal goat discussed above have a different meaning
than in its quadruped counterparts.
Last, most experimental protocols in biology use control groups which are not subjected to the transformations investigated (Johnson & Besselsen, 2002). Control
groups enable experimenters to assess the organization
of specimens having the same historical origin and exposed to the same context than the organisms subjected
to a putative difference maker. Controls enable biologists to estimate whether the results stem from the context, spontaneous variations, or conditions tested. Biological objects are labile, and control groups are the
closest reference point possible to the objects tested.
(Bolland et al., 2016). When observing a given feature
among several specimens, biologists report “not applicable” (NA) for a specimen when qualitative variations are
too significant. For example, pathological heartbeats
that do not follow the same sequence of events that regular heartbeats lead to beat-to-beat intervals that do not
have the same meaning. This kind of departures appears
for theoretical reasons and not only as a result of experimental errors or as the result of the improper theoretical
definition of the target quantities.
Observable, qualitative variations can be shown experimentally even for clonal cells, for example as a result
of asymmetries in cellular division (Cai et al., 2006; Stewart et al., 2005; Lindner et al., 2008; Soto et al., 2016a)
or for dynamical reasons (Braun, 2015). Of course, the
development of multicellular organisms also leads to a
high level of variations. Variations occur even when comparing an individual with itself at another time point,
even in the case of close time points. For example, many
physiological time series are non-stationary (West, 2006;
Longo & Montévil, 2014). Stationary time series follow
the same distribution over time which implies that the
mean is a stable quantity. By contrast, non-stationarity
implies that assessing the average at different times will
not necessarily yield the same results. As a consequence,
it is not possible to characterize an organism by precise
values of physiologic quantities, and precise results are
only valid at a specific time point.
3.5. Irreducibility of biological variation
Despite the use of methodologies providing tight
control over biological objects, the principle of variation entails that there are always possible qualitative
variations. Variations can impact the observed features
directly, making them variable, changing their meaning or even possibly making them ill-defined. Populations which are too similar are evidence of malpractice
3.6. Recapitulation
To sum our theoretical approach up, biological measurement has to accommodate simultaneously the aspect
observed and the organism in which it takes place. We
propose the following principles :
9
ate
Esti m
s
Genealogy, context,
manipulation
Phylogenetic
classification
Protocol
Observed features
Type
B
A
Results
Gene transfer
C
Time
Long time scales
Time scales where biological work takes place
Experiment
Figure 5: Recapitulation of the diachronic elements used to define the objects of a typical experiment. The whole construct illustrated is required to describe
the measurement performed. A: The objects are the result of an evolutionary history, which is not directly accessible but can be estimated by the
phylogenetic method. B: Specimens of a given species can be used to breed a strain in controlled conditions. C: Elements of this strain are used
in an experiment to obtain data.
1. Measurement has a synchronic dimension for the
tures observed (§ 3.5).
aspect or part of interest (§ 3.4.3). Usually, the
concept of measurement from classical physics is
4. Discussion
relevant, that is to say, measurement as limited
precision. Concepts of measurement theory can
4.1. The radical materiality of biological phenomena
also be used (Houle et al., 2011).
The role of matter in experiments is critical to their
2. The measurement is relative to/constituted by the
epistemological analysis (Morgan, 2002). In physics, thehistory and contexts of the organisms of interest.
ories define objects mathematically, by invariants and
Historicity, here, means a cascade of context-dependent,invariant preserving transformations. This epistemologqualitative variations. A measurement includes a
ical structure justifies that the same theoretical object
specific way to manipulate and describe these concan be instantiated independently de novo. For example,
texts and natural histories, for example, referring
the speed of light in the vacuum can be assessed on two
to a theoretical or concrete common ancestor.
independent light beams: it is an invariant of the the(a) Genealogy handles an uncontrolled history that
ory. By contrast, biological objects stem from an history.
is shared by the different organisms studied.
It follows that empirical knowledge in biology cannot
Methods include the phylogenetic classificabe abstracted from concrete material objects (tokens)
tion (§ 3.1) and direct genealogical control in
materializing this history. In this perspective, biologithe case of strains and cell lines (§ 3.2).
cal phenomena display a radical materiality (Soto et al.,
(b) Past and current contexts (environment/interac2016b). Our discussion on biological measurement iltions) can be (partially) known in the field or
lustrates this idea. Biological names, in systematics, are
controlled in laboratories or breeding institunot defined by a theoretical construct, they are defined
tions. Relevant contexts include past contexts
by specific specimens called name-bearing types (§ 3.1).
over several generations, during the developThen, experimenting with individuals of a species associment or shortly before observations (§ 3.3),
ated with this name means experimenting on individuals
and current contexts, during the experiment
which descend from an ancestor shared by both the specand observations (§ 3.4.1).
imens experimented upon and the name-bearing type.
(c) Choosing or eliminating individuals can be used
These specimens possess a diachronic, material continuto observe or eliminate specific histories or
ity over time: the genealogy. The same reasoning applies
variations (pathological cases, unwanted beto the controlled strains and cell lines; the exchange
havior, ontogenetic or phylogenetic histories,
of living specimens between laboratories is the further
etc., § 3.4.2).
materialization of this philosophical idea.
3. Uncontrolled variations can always impact the measurement, including the very definition of the fea10
In general, we can distinguish between different categories of theoretical situations in the relationship between matter and theoretical definitions. The methods
to reproduce observations characterize them:
However, biological objects are the result of a history
and continue to generate a history. Interpreting this
notion in terms of symmetries leads to assert that when
time flows, describing the changes of biological objects
can require changes of symmetry that do not stem from
the description of the initial objects. These changes are
the core of the principle of variation (Longo & Montévil,
2011a; Montévil et al., 2016; Montévil, 2018). As hinted
to in the introduction, these variations conflict with the
aim to perform reproducible experiments. In biology
unlike in physics, the symmetries associated with reproducibility are not granted theoretically. Instead, they depend on the measurement as summarized in section 3.6.
Since biological regularities are more labile than physical ones, symmetries are not provided directly by the theory. Instead, they are co-established by the measurement
process and the biological objects used. We propose to
call this practical and theoretical operation “symmetrization”. Biologists typically work on specimens of the same
species or more generally specimens with a shared common past. In experiments, they assume a partial equivalence between these specimens and how they are organized. In other words, biologists posit an approximate
symmetry between the organization of different organisms and their response to experiments. Control over
past contexts is also a symmetrization of the specimens
studied and are often designed with this issue in mind.
For example, in section 3.3, we have discussed how biologists aim for cells in vitro to be in a consistent state
over time, that is to say, how biologists symmetrize cells.
The different methods described in section 3 should be
seen as different symmetrizations.
Different symmetrizations can be performed during
an experiment or even during data acquisition. Choosing a symmetrization or another endows the results with
entirely different biological meanings. Figure 4 illustrates this idea and shows different ways to make organisms equivalent. In figure 4E1, by being in the field, organisms express their historically (evolutionary) relevant
activities and these activities are diverse. In figure 4E3,
different organisms are mostly restrained to activities
that are common to them: the experimenter performs a
stronger symmetrization by limiting the characters involved in the determination of the observed quantity.
We can distinguish two kinds of symmetrization:
concrete and epistemic symmetrizations. Concrete symmetrizations involve the action of biologists on objects.
For example, establishing inbred strains is a concrete
symmetrization of genomes, and the symmetrizations
illustrated in figure 4 are also concrete symmetrizations.
By contrast, epistemic symmetrizations do not change
material objects and are limited to determining what
is considered equivalent, that is to say, symmetric by
permutation. For example, we mentioned that the position of a fetus in utero has measurable consequences.
Taking this aspect into account or not corresponds to
different epistemic symmetrizations. The concept of
1. The description of objects is generic, and the same
theoretical object can be instantiated empirically
twice without communication of matter, as discussed by Feynman & Gleick (1967, chap. 4). It is
the case in current physical theories.
2. The object’s behavior is the specific result of a history.
(a) Scientists use the permanence of the material
object studied. They may be fixed artificially.
For example, the name-bearing types used in
systematics serve as static references for future observations. They may also continue to
change over time, for example, in the case of
the biosphere.
(b) The objects reproduce. This property provides
an exponential amount of objects sharing a
common past. The study of living organisms
and cells falls typically in this category (case
studies such as types above are an exception).
4.2. Symmetry and symmetrisation
Symmetries play a central role in physics (Feynman
& Gleick, 1967; Van Fraassen, 1989; Bailly & Longo,
2011; Longo & Montévil, 2014) and will enable us to provide a more in-depth analysis of biological measurement.
Symmetries are transformations which do not change
the relevant aspects of a given object. For example, the
equation describing free fall does not change for an experiment performed one century ago or today. Time
translation is a transformation that does not change the
theoretical description of the object: a symmetry. Moreover, the same equation applies regardless of the nature
of the object which is another fundamental symmetry.
For example, concerning free fall, if experimenters replace an iron bead with another one, or a copper or wood
bead, the phenomenon remains the same: permuting
(interchanging) these objects is a symmetry. Symmetries
can be either exact, in the sense that they stem from fundamental principles, or approximate.
The concept of experimental reproducibility is a notion of symmetry. The reproducibility of an experiment
means that the same set of observations can be performed
by different observers, on different material objects, at
different times and places.
Moreover, in a given experiment, biologists typically
use different specimens exposed to the same conditions
in order to perform statistical tests. The tests assume that
these specimens follow the same probability distribution,
that is, the tests assume that behind the quantitative
variations observed there is a single abstract mathematical object (the probability distribution): this is again an
assumption of symmetry.
11
epistemic symmetrization is particularly relevant for statistical analyses and subsequent biological reasonings.
As a consequence of the principle of variation, the
concept of epistemic symmetrization is always relevant:
biologists have to symmetrize organisms which are not
genuinely symmetric. Concrete symmetrizations occur
in most experiments, but not in observations without
experiments such as the observation of specimens in
systematics. Performing concrete symmetrization constrains the kind of biological objects studied. For example, it is far easier to symmetrize cells in culture by
maintaining unconstrained proliferation.
In conclusion, biological measurement as summarized in 3.6 describes both the concrete and epistemic
symmetrizations performed to obtain experimental results and endow them with biological meaning.
which increase simultaneously the reproducibility, singularity, and alterations associated with the measurement.
In other words, these strategies lose generality and alter
the specimens in order to increase the reproducibility of
the measurement. At the limit, these strategies aim to
generate specimens which are as symmetrized as possible and would have the same status than the objects of
physics: these strategies aim the genericization of biological organizations and use many methods to reduce
diversity. For example, in the case of cells, samples are
frozen to prevent spontaneous variations between experiments. The focus on model organisms at the level of the
research community is also a collective strategy of genericization. In situations like clinical trials, on humans,
genericization methods are limited for ethical reasons.
In contrast to genericization strategies, other strategies
on the same axis aim to gain generality and coherence
with evolutionary history but at the cost of more variability in the results. It follows that these strategies face
more difficulties to obtain statistically significant results.
In order to face the reproducibility crisis and to obtain significant results with fewer animals, it is common
to promote strategies genericizing specimens (Festing,
2014; Chia et al., 2005). However, these strategies bear
the cost of studying singular organizations: the results
obtained may not even be representative of the species
studied, and we have seen that strains of the same species
have distinct properties. Another example is that the conditions of the laboratory reduce exposure to pathogens
in order to symmetrize the life history of animals studied, which is part of the alteration axis. However, this
situation leads to immunological functions that differ
from wild animals (Abolins et al., 2010).
The genericization of specimens aims, at the limit,
to study a single, reproducible organization and is thus
highly singular. Results may depend on the specificities
of these organizations and their contexts in unknown
ways. Therefore, these strategies are vulnerable to minor
departures from the genericization performed initially.
For example, performing measurement in different laboratories always involve a change of context despite the
explicit control of many factors. Genericizations aim
reproducibility in the sense of specimens that are very
similar, but the reproducibility of experiments is made
difficult by the lack of generality of the measurement.
In figure 6, there are only two cases which are far
from this axis. The first corresponds to measurements
like the basal metabolic rate, see figure 4E3. This measurement is reproducible and nevertheless general. Its
downside is that the organisms are put in a specific state
to level down the consequences of the diversity of the
characters impacting the measured quantity. Its strategy
is to symmetrize a shared aspect of organisms when the
genericizations discussed above symmetrize complete
organizations.
Case studies are the second strategy departing from
the main axis. Case studies focus on a single individual
4.3. Measurement strategies
Baxendale (2018) proposes to map scientific practices
on a continuum of strategies defined by their stances concerning reductionism. In this section, we apply a similar
approach to measurement strategies. Our concept of biological measurement leads to the notion that measurement depends on symmetrizations, but symmetrizations
can be performed more or less tightly and at different
levels. To represent these strategies, we propose to organize measurement strategies along three axes as illustrated in figure 6. In this section, we discuss only the
different measurement strategies, and we do not imply
that they necessarily succeed or that they prevent the
joint use of other strategies.
The first axis describes whether the measurement is
variable or on the opposite reproducible. Here, reproducibility means that the measurement generates data
consistently with different specimens. For example, using inbred strains generally leads to more reproducible
results than wild specimens.
The second axis describes whether the measurement
targets singular or general objects. Working on the
metabolic rate of mammals is more general than working on a single species by measuring wild specimens.
Both are more general than outbred strains and a fortiori
inbred strains, where the genotype is symmetrized. Reciprocally, inbred strains are more singular than outbred
strains and so on.
The last axis assesses whether the measurement defines objects coherent with their evolutionary past or
instead whether the objects are more or less profoundly
altered. For example, inbred strains are homozygotes
for all genes which is not the case of mammals in their
evolutionary history. Similarly, the basal metabolic rate
is far less representative of a species past evolution than
the field metabolic rate — but the latter depends on the
field.
A qualitative axis emerges in this three-dimensional
space, see figure 6. This axis is given by the strategies
12
General
General
Altering
history
Field metabolic
rate, E1
Basal metabolic rate, E3
Altering
history
Wild specimens
Variable
Reproducible
Coherent
with history
Singular
Variable
Symmetrization
of the genealogy
and past context:
genericization of
specimens
Coherent
with history
Case study
Outbred Reproducible
Inbred
In vitro
Singular
Figure 6: Different measurement strategies. The three axes correspond to measurements that are reproducible vs. variable, general vs. singular and
coherent with their evolutionary past vs. altered by experimenters. LEFT: axis where many strategies lie. On one end of the spectrum, these
strategies aim to produce specimens that are as close to each other as possible by controlling them tightly. On the other end, experimenters relax
this control and aim to make more general measurements. RIGHT: different cases are represented in the space describing measurement strategies.
Most measurement strategies are on the axis represented on the left, but departures from this axis are equally interesting since they represent
other ways to approach biological phenomena empirically, e.g., case studies.
and reproducibility is not a goal. For example, Patterson
& Linden (1981) study the intelligence of non-human
primates. To do so, the authors did not develop standardized conditions and protocols. Instead, they taught
sign language to several specimens and focused on a particularly gifted gorilla, Koko, who mastered up to 2000
symbols. Other examples are works on types in systematics, the study of the bipedal goat discussed by WestEberhard (2003) and the cloned sheep, Dolly, which is
one success among 277 attempts and remained the only
success for a long time (Wilmut et al., 1997). While
the study of types does not involve alterations, teaching
Koko or cloning a sheep do: case studies are diverse for
the third axis.
Case studies are sometimes neglected by experimenters
who strive to design reproducible experiments in order to study mechanisms. For example, the success of
cloning Dolly without reproducing this feat led to an
intense debate, especially when evidence accumulated
that Dolly was indeed cloned from an adult cell (Solter,
1998). However, in our conceptual framework, case studies have a specific epistemic role. They are sufficient
to prove the existence of a possibility in a theoretical
context where biological possibilities are not predefined
(Montévil, 2018). The bipedal goat shows the extent of
developmental plasticity and studying a type is sufficient
to defend the existence of a new species. Case studies can
be extensive; for example, the anatomy of the bipedal
goat has been described in details. In case studies, the
analysis of a single part in several organisms is typically
replaced by the analysis of many parts or aspects in a
13
single organism. Last, the study of types in systematics
plays a pivotal role in the general architecture of biological knowledge to name biological objects.
Representing different measurement strategies by the
symmetrizations performed is fruitful. These strategies
are different responses to the difficulties raised by the
historical and varying nature of biological objects.
5. Conclusion
Our theoretical notion of measurement accommodates how biologists manipulate immensely complex
objects, organisms and cells typically, which are the result of a history and continue to produce a history by
generating qualitative variations. The concept of biological measurement which we propose accommodates
simultaneously the organisms or cells and their part or
aspect of interest which may be quantified. In our framework, a measurement is relative to a history and context.
To develop reproducible experiments, biologists observe
specimens with a shared past. This shared past is ascertained by systematics and by direct knowledge and
control of both their genealogy and past contexts. In
the study of objects defined by their history, the objects
which can be considered equivalent are objects having a
shared past. In this context, we call symmetrization the
concrete and theoretical operations which establish and
posit the equivalence of different objects with more or
less tightly controlled shared pasts and contexts. Symmetrization also includes the operations performed during the observation which can constrain and structure
variability.
The notion of biological measurement is compatible
with different research strategies and leads to a framework to map them. In this framework, we find two polar
opposites. In one end, strategies strive to genericize biological organizations at the cost of studying singular
organizations and altering them. To implement these
strategies, biologists developed a plethora of methods.
They expose objects to similar contexts and ensure that
they have recent, controlled common ancestors. In some
cases, biologists freeze samples to prevent them from undergoing variations between experiments. On the other
end of the spectrum, the objects studied are more general (e.g., diverse genetically) and coherent with their
evolutionary history, but they are also more variable.
There are strategies which escape this opposition, for
example, case studies or methods to level down the diversity relevant for the part studied while the rest of the
organizations remain diverse.
Having a clear notion of what it means to access
biological objects empirically is critical for biological
knowledge. In this paper, we provide only an outline
of biological measurement, and this notion deserves further discussions, focusing on both general and specific
situations. Nevertheless, since our notion builds on
solid ground, namely the theory of evolution and extensions for organisms, we hope that our work will be of
use for further research. We have shown that biological
measurement has significant differences with the notions
of measurement in physics. Depending on the perspective, biological measurement may be seen as an extension of classical measurement in order to accommodate
the historicity and variability of biological objects, or
as a different concept altogether because the objects are
not described theoretically by underlying equations. In
all cases, acknowledging the specificities of biological
measurement should provide new systematic ways to approach biological observations critically and ultimately
to promote experimental reproducibility.
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Acknowledgments
I am grateful to Ana Soto, Giuseppe Longo, Carlos
Sonnenschein, Guillaume Lecointre, Matteo Mossio, Arnaud Pocheville and Véronique Thomas-Vaslin for their
comments on previous versions of this article and helpful
discussions. I also thank the two anonymous reviewers
and the editor for their candid comments.
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15
Measurement in biology is methodized by theory ⋆
Maël Montévila
a Institut
de Recherche et d’Innovation, Centre Pompidou 4 Rue Aubry le Boucher, 75004 Paris, France
Abstract
We characterize access to empirical objects in biology from a theoretical perspective. Unlike objects in current physical theories,
biological objects are the result of a history and their variations continue to generate a history. This property is the starting point of
our concept of measurement. We argue that biological measurement is relative to a natural history which is shared by the different
objects subjected to the measurement and is more or less constrained by biologists. We call symmetrization the theoretical and
often concrete operation which leads to considering biological objects as equivalent in a measurement. Last, we use our notion of
measurement to analyze research strategies. Some strategies aim to bring biology closer to the epistemology of physical theories, by
studying objects as similar as possible, while others build on biological diversity.
Keywords: Biological measurement, experiments, evolution, systematics, strains, symmetry
1. Introduction
Contents
1
Introduction
1
2
Measurement in physics
2.1 Classical measurement
2.2 Quantum measurement
2.3 Reference frame . . . .
2.4 Conclusion . . . . . .
3
A theoretical account of biological measurement
3.1 Phylogenetic classification and nomenclature of
biological objects . . . . . . . . . . . . . . . .
3.2 Observed and controlled genealogy . . . . . . .
3.3 Historical contexts . . . . . . . . . . . . . . .
3.4 Synchronic aspects of measurement . . . . . . .
3.4.1 Current context . . . . . . . . . . . . .
3.4.2 Choosing or eliminating individuals . .
3.4.3 Data acquisition . . . . . . . . . . . .
3.5 Irreducibility of biological variation . . . . . . .
3.6 Recapitulation . . . . . . . . . . . . . . . . . .
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Discussion
4.1 The radical materiality of biological phenomena
4.2 Symmetry and symmetrisation . . . . . . . . .
4.3 Measurement strategies . . . . . . . . . . . . .
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⋆ Published as: Montévil, M. (2019) Measurement in biology is methodized
by theory. Biology and Philosophy 34: 35. http://dx.doi.org/10.1007/
s10539-019-9687-x.
Email address: mael.montevil@gmail.com (Maël Montévil)
URL: https://montevil.theobio.org (Maël Montévil)
Preprint submitted to Biology and Philosophy
Science and more specifically biology and medicine are facing
a crisis where systematic attempts to reproduce experiments published in reputable journals fail in the majority of cases (Begley
& Ellis, 2012; Baker, 2016). The management and organization
of scientific institutions have been investigated, and the pressure
to publish has been heavily criticized (Begley & Ioannidis, 2014;
Lancet, 2018). In the case of experimental biology, theoretical
and philosophical analyses can also play a role to understand and
respond to this crisis (Nadin, 2017). There are aspects proper to
biological experiments that should be analyzed systematically
in light of the current understanding of living beings. This discussion is also particularly relevant now that the scientific focus
on (Big) Data analyses bears the risk of forgetting that data are
generated in specific empirical conditions (Leonelli, 2014). Data
detached from these conditions without proper justification do
not carry a genuine scientific meaning.
A scientist cannot assume that her access to reality is one
of an omniscient daemon. Understanding what it means to
observe natural phenomena is fundamental. This question is
multi-faceted. Part of it pertains to the complementary knowledge advocated by Chang (2004), but part of the answer should
be principled, in the relevant theoretical framework. We concur with Einstein’s epistemological statement: “whether you can
observe a thing or not depends on the theory which you use. It
is the theory which decides what can be observed” (A. Einstein
quoted in Salam, 1990). In physics, measurement is described
in theories and is a fundamental part of their formulation. The
notion of measurement embedded in theories provides a general link between the output of measurement and the theoretical
and mathematical description of the objects of study. For example, measurements in classical mechanics provide approximate
results while they change objects in quantum mechanics. There
are many other aspects of measurement which are philosophiApril 29, 2019
cally important; however, in this article, we aim to ground widely
shared practices on theoretical principles.
Biologists often use physical concepts, and measurement is
no exception. The notion of measurement of classical mechanics
is widely used in biology. Moreover, Wagner (2010) and Houle
et al. (2011) advocate the use of measurement theory in biology. This setting leads us to inquire whether biology requires
a distinct notion of measurement. In the literature, there is at
least one such account: following the informational metaphor,
molecular biology often considers measurement as a classical
measurement applied to finite, entirely discrete features: the sequences of nucleotides. A classical measurement has a limited
precision, but knowing finite, discrete structures with a sufficient
finite precision means knowing them exactly (Schrödinger, 1944).
The same reasoning applies mutadis mutandis to other discrete
structures such as the topology of networks (Huneman, 2018).
This reasoning only applies to the discrete aspect of the objects,
and not the continuous ones such as position in physical space.
This point of view is in contrast with experimental methodologies which are very rich and sometimes subtle (Weber, 2004;
Kohler, 1994). In this paper, we argue that general theoretical
principles of biology leads to a theoretical account of biological measurement which clarifies several aspects of experimental
methodologies.
Measurement requires commensurability. For example, measuring the length of an object requires to identify the distance
between its edges with the length of another object such as a
ruler. It also requires abstract constructs: in this example not
only a theory of space (or space-time) but also assumptions on
the object measured. These assumptions ensure that the measurement has a meaning (Houle et al., 2011). For example, when
measuring a length, is the object solid, or flexible, does it have
well-defined boundaries, like a box, or not, like a cloud. As a
result, measurement is never only about a single object (token).
In biology, the measurement of a part or an aspect of an organism may be performed by the commensurability with a physical
object, for example, the length (in meters) of this organism, here
and now, measured in physical units. However, this alone is only
sufficient to know if we can put it ”as is” in a box of a given
length. The biological meaning of a length and the procedure
to assess it are very different for a tree or a snake. Therefore, we
posit that biological measurement is not only about the intended
part or aspect, but also has to accommodate the organism measured and its commensurability with other organisms. We will
develop mostly the latter idea since it has not been systematically
analyzed and raises questions which are proper to biology. To
address the specificities of biological measurement and conceptualize the commensurability of organisms, we need theoretical
insights on organisms.
We use the principles proposed recently for a theory of organisms (Mossio et al., 2016; Montévil et al., 2016; Soto et al.,
2016a). This framework provides a conceptual continuity between the understanding of organisms and evolution. In particular, it emphasizes historical analyses both for phylogenesis and
ontogenesis.
In this framework, biological objects are not defined theoretically like objects in physical theories. The theoretical definition
of objects is mathematical in physics. Despite quantitative differences, the changes of a well-defined object are assumed to follow
an underlying mathematical structure. Invariants and invariant
preserving transformations (symmetries) define these mathematical structures (Van Fraassen, 1989; Longo & Montévil, 2014).
For example, a falling stone follows the same equation during
its fall despite its changes of position and velocity, and a falling
log would follow the same equation. As a result, physicists can
talk about the generic phenomenon of falling bodies. Physical notions of measurement apply to generic objects, and the
reproducibility of physical experiments is guaranteed, at least
statistically, once the same generic conditions apply.
By contrast, biological objects are historical in the sense that
their organizations stem from an evolutionary and individual history and continue to produce a history. This idea has been developed theoretically and called the principle of variation (Montévil
et al., 2016). To an extent, this principle is in line with earlier
ideas, in particular, the contingency thesis of Beatty (1995) and
the centrality of historicity defended by Gould (2002, chap. 11)
in a critical assessment of the work of D’Arcy Thomson. For
example, a falling tetrapod is not a purely physical notion since
“tetrapod” is a biological concept. In the atmosphere, tetrapods
do not just fall, some fly and others are gliders. All these behaviors require different equations, and these changes of equation
depend on the underlying evolutionary history. This basic example illustrates the general idea that biological objects should
not be conceived as generic and are prone to more profound
changes than objects in physics, including the appearance of new
possibilities (Montévil, 2018). Moreover, biological objects are
contextual in the sense that their organizations depend on their
past and current contexts. In other words, describing biological objects does not just involve many quantities, but quantities
which are endowed with different biological meaning, and new
relevant quantities can appear over time.
In a nutshell, biologists manipulate objects which are understood theoretically as the result of a history and continue to
produce a history: diachronic objects. With these ideas stemming from the theory of evolution in mind, experimental reproducibility is not a straightforward notion. Biological objects tend
spontaneously to vary whereas perfect reproducibility, even statistically, would require fixed physiology and development, at least
at an abstract level.
In section 2, we introduce how several physical theories define measurement and the epistemological and theoretical roles
this notion plays. Section 3 discusses the theoretical nature of
biological measurement. Biological measurements accommodate
natural histories and contexts, not just quantities. Section 4 explores several implications of our framework. In particular, we
classify different research strategies to handle biological measurement.
2. Measurement in physics
In order to exemplify our aims in biology, we discuss briefly
how the main physical theories conceptualize measurement. We
are interested in measurement considered in principle in general
2
theoretical frameworks and not in specific experimental situations. For the theory, what does “obtaining quantities” in experiments or observations means? These accounts are sufficiently
general to be valid for any practical situation in the corresponding theory, and they have deep practical and theoretical consequences.
states how the description of a situation in one reference frame
can be transformed into the description of the same situation
in another reference frame and ensures that these descriptions
are coherent. This concept overcomes the arbitrary choices of
reference frames, and its mathematical nature is geometric.
2.1. Classical measurement
In classical mechanics, a system has a pointwise state in the
space of possible states. The empirical access to this state is
approximate: a measurement has a finite precision, 𝜖, which can
in principle be arbitrarily small. Thus, the state of a system is a
point, and the result of the measurement is an interval. Classical
measurement is a metrical notion: it stems from the concept of
distance.
Classical dynamics are deterministic, but measurements may
or may not allow to predict the subsequent trajectory. Unpredictable dynamics such as chaotic dynamics are called sensitive
to initial conditions. The notion of measurement articulates
determinism and randomness in the sense of theoretical impredictability (Gillies, 2012; Longo & Montévil, 2017). This
example shows that a simple notion of measurement can have
far-reaching conceptual consequences.
The concepts of physical measurement we described are principles in their respective theory, and they are very different. Their
common point is that they all describe the role of the experimenter and its instruments in an abstract and very concise way.
2.4. Conclusion
3. A theoretical account of biological measurement
To describe our theoretical notion of biological measurement,
we rely mainly on the principle of variation (Montévil et al., 2016).
This principle builds on evolutionary biology and states that biological objects can vary in a stronger sense than objects described
by physical theories. The latter change, but physicists understand their changes by underlying stable mathematical structures.
Instead, biological variations in the strong sense require changing mathematical structures. Biological objects are formed by
a cascade of such variations and the notions of historicity and
contextuality become fundamental. Figure 1 summarizes this
perspective which guides our analysis of biological measurement.
In physics, objects can be highly simplified and remain relevant for physics. For example, it is sound to study a material
composed only of iron. In biology, this is not the case. For example, looking at one or several molecules alone pertains to
biochemistry, not biology. In biology, the measured features
of organisms or cells, such as the concentration of molecules
or the shape of tissues, are measured in organisms or cells and
are generally produced and maintained by them (Mossio et al.,
2016; Montévil & Mossio, 2015). Therefore, our discussion of
biological measurement is not limited to the parts observed per
se. Instead, our approach of measurement accommodates both
the parts observed and the organisms associated. Both are reported carefully in empirical studies, and we posit that they are
elementary aspects of biological measurement. This section may
be seen as the theorization of a typical “method section” in any
experimental paper in biology.
2.2. Quantum measurement
In quantum mechanics, measurement involves the commensurability of a microscopic object and a macroscopic object. Quantum measurement changes the object and leads to quantum
randomness. Informally, a quantum state can be decomposed
for a given measurement as the superposition (the sum) of different states called eigenstates. Each of them corresponds to a
single obtainable result. Performing the measurement means
that the state of the system becomes an eigenstate associated
with the quantity obtained. The other eigenstates in the initial
superposition disappear irreversibly. Quantum measurement has
an algebraic (and geometric) nature.
There is an internal coherence to this notion. Performing the
same measurement twice in a row will lead to the same result
because the state of the object is already an eigenstate associated
with this result: the result obtained in the first measurement is
the only possible outcome in the second.
Different observables do not necessarily lead to the same decomposition. An eigenstate which corresponds to a specific position, for example, does not correspond to a specific velocity and
the other way around. Then, measuring the position, measuring
the velocity and measuring the position again will not necessarily
lead to the same position twice. Lastly, some authors argue that,
in an experiment, a measurement is needed to put the system in
a known initial state (Mugur-Schächter, 2002). The typical theoretical structure of an experiment is then: measurement, time
evolution (Schrödinger equation typically), measurement.
3.1. Phylogenetic classification and nomenclature of biological objects
Reporting a biological measurement starts with describing
the organisms observed and naming them. The theoretical and
philosophical underpinnings of these names are an essential
aspect of biological measurements. The standard, general way
to name organisms is to use systematics. Biologists always use
this method, even though other methods can complement it, as
discussed in the following section.
We want to emphasize two aspects of this method that impact the concept of measurement. The first is the definition
of the names themselves and the second is the phylogenetic
classification of living beings (de Queiroz, 1992; Lecointre &
Le Guyader, 2006).
2.3. Reference frame
Experimenters choose space-time reference frames arbitrarily
to represent concrete situations and describe features such as
positions quantitatively. Relativity (Galilean, special and general)
3
determines
establishes
time
variation
de
ter
mi
ne
s
Specific object
de
ter
mi
ne
s
Specific object
are a part of
History
Observable
features
(Constraints)
change
are a part of
establishes
Observable
features
(Constraints)
Context
Context
Figure 1: Theoretical structure of biological objects, after Montévil et al. (2016). In biology, organisms are not described theoretically by invariants and invariant preserving
transformations (symmetries) which would provide a generic meaning to the features observed. Instead, their regularities are constraints that come from an history and
collectively maintain each other in a given context. These constraints can change over time as the objects continue to generate a history over physiologic, developmental
and evolutive time scales. An account of biological measurement has to accommodate simultaneously the measured aspects (constraints) and the rest of the organism
which we describe as a specific object.
In order to provide stability to the meaning of the names
used to describe living beings, systematics establish and follow
strict rules to describe new species and other clades (e.g., genus,
family). Nomenclature codes use the principle of typification.
Typification means that defining a name requires a type. For
example, the definition of a name at the family level requires a
genus-level type, a genus-level name requires a species type, and
describing a new species (or subspecies) requires referring to one
specimen (holotype) or several specimens (syntype) which are
kept in a collection (CZN International, 1999; McNeill et al.,
2012, art. 72.3 and 40 resp.). Typification ensures the stability of
the definition of names even if the classification changes. Namebearing types are required to be in a biologically inactive state
and thus are fixed reference objects (McNeill et al., 2012, art.
8.4).
Typification implies that the definition of biological names
ultimately depends on specific, static, material objects (Grandcolas, 2017). This situation is in contrast with the theoretical
definitions in the International System of Unit based on physical theories. For example, a meter is the distance traveled by
light in vacuum in 1/299792458 seconds. This definition refers
to matter but does not need the conservation of a specific object. Instead, it uses the generic, theoretical object called “light
in the vacuum” which has an invariant velocity in both special
and general relativity1 .
Names associated with specific material objects (types) are
not sufficient for scientific practices. In order to endow names
with a more general meaning, systematics uses the phylogenetic classification method (de Queiroz, 1992; Lecointre &
Le Guyader, 2006). This method classifies living beings by estimating their genealogy. The genealogy is a theoretical concept
that stems from the theory of evolution; however, the genealogy of current organisms spans billions of years, and human observers cannot access it directly. As a result, the phylogenetic
classification uses different concepts than a genealogical tree. For
example, it is impossible to determine whether a fossil species is
an ancestor of a current species, but it is possible to establish that
they are closely related genealogically. The phylogenetic method
distinguishes a theoretical level and an observable level which is
reminiscent of the distinction between a state and what can be
observed in physics.
The phylogenetic classification assesses the evolutionary proximity between different organisms. Systematists start with the
characters characterizing the different organisms, including dna
sequences. These characters are used by a computational method
which provides a nested hierarchy of groups, see figure 2. These
methods typically assume that the most likely situation minimizes the number of evolutionary changes, and in particular the
appearance of novelties. These analyses lead to classifications
where acceptable groups, called monophyletic groups or clades,
are the descent of a common theoretical ancestor. The classification can then be used for taxonomic purposes. Of course, evolutionary reasonings guide the choice of the characters and the
computational method used, and these choices are commonly
debated.
Clades are defined by their estimated historical origin and
not by their current ecological status or physiology. Since the
definition of clades is based on a historical analysis, it accommodates the diversity and diversification of living beings straightforwardly. For example, the famous goat (Capra aegagrus hircus)
discussed by West-Eberhard (2003) is a paradigmatic example
of developmental plasticity because it is bipedal: a significant
change occurred in a single specimen. Despite its peculiarities,
this specimen is still part of the subspecies C. aegagrus hircus because the subspecies is defined by its historical origin, not by its
1 Historically, the definition of a meter has first been theoretical, then it used
a standard prototype. The current definition is again theoretical.
4
Evolution
Types
neti c
Phyloge
an al y s i s
Homo sapiens
Mus musculus
Rattus novergicus
Rodents
Mammals
Chordates
Xenopus laevis
Drosophila melanogaster
Figure 2: Principle of the phylogenetic classification. Left : a schematic representation of the genealogy of a few species over evolutionary timescales. This genealogy is
not observable as such. Middle: the consequence of evolution is the presence of diverse life forms, some of which are used by biologists as types. Name-bearing types
formally define names. Names are then extended to the specimens of the same species. Right: the characters that the specimens share and do not share are used to
assess their evolutionary proximity with a mathematical model of evolution. Acceptable groups are defined as the descent of a theoretical common ancestor and lead to
a classification.
are Black 6 mice from the National Institutes of Health (N) and
which are bred by Charles River Laboratory (Crl) (Sacca et al.,
2013).
The choice of strain can profoundly impact experimental
results. For example, Black 6 mice have singular features such as
their nociception (sensation of pain) (Mogil et al., 1999). Isaacs
(1986) tested the incidence of tumors in rats exposed to the
carcinogen DMBA and found that this incidence is 0%, 15%,
40% and above 90% depending on the strain used. The sensitivity
to endocrine disruptors also depends on the strain (Spearow et al.,
1999).
In the case of cells, the situation is overall similar to the case
of animals. Cell lines and sub-lines are established, named, and
exchanged between laboratories. For example, the first laboratory immortal human cell line, the HeLa cell line, originated
from a single patient, Henrietta Lacks (who died in 1951) and
thus HeLa cells have a common origin. This cell line is widely
used, and more than 99000 references in PubMed mention it
(08/2018). Cell lines have two specificities (fig. 3B). First, a single
cell can originate a clonal population in common cases. Second,
the use of frozen samples enables biologist to “stop” biological
time. Biologists use these operations to obtain populations of
cells that are far closer genealogically to their common ancestor
than cells which would be proliferating with variations in culture.
Both animal strains and cell lines can be modified for research
purposes, either by artificial selection for a specific trait or by
genetic engineering, a subject extensively discussed by Kohler
(1994) in the case of Drosophila melanogaster. These modifications
are not only aiming for a specific new trait; they include ruling
out animals with spontaneous, problematic mutations.
It is standard practice to communicate live sample between
research laboratories or between breeding institutions (Kohler,
1994, chap. 5). Communicating live samples is required for biologists to ensure that the specimens studied in different laboratories
are close genealogically and carry the same spontaneous or artificial changes if any. Commitment to perform these exchanges
is required to publish in many journals. Replicating an experi-
properties.
Biological observations typically refer to a specific clade, usually a species or subspecies. By definition of a clade, this only
ascertains a given shared theoretical ancestor. This common past
involves similarities between the specimens studied, but it does
not guarantee that the properties of interest in a given investigation will be similar or even exist.
3.2. Observed and controlled genealogy
The design and description of typical biological experiments
use genealogical elements that go beyond what systematics can
provide. Genealogical knowledge is provided by the direct observation of the lineages leading to the specimens studied and can
be more or less comprehensive. Of course, direct genealogical
knowledge is limited to the historical period where biologists
follow the appropriate methods, that is to say, about a century at
best.
Usually, direct genealogical knowledge goes with more or less
control over the genealogy. In the case of organisms reproducing
sexually, there are two main strategies to control genealogies: establishing inbred or outbred strains, see figure 3A. Inbred strains
stem from several generations of inbreeding. By enforcing this
behavior, biologists aim to obtain a genetically homogeneous
population. Inbred strains still change over time at least as a consequence of genetic drift. These changes lead to the definition
of substrains that have biologically relevant differences and are
not interchangeable (Simpson et al., 1997). By contrast, outbred
strains aim to maintain heterozygote populations while keeping
as much genetic homogeneity as possible. These strains are more
genetically labile than inbred strains and are often considered
more variable phenotypically (Chia et al. 2005; Festing 2014,
however see Jensen et al. 2016).
A specific nomenclature for strains completes the nomenclature deriving from systematics. For example, a widespread
strain in biomedical research is the inbred mouse strain C57BL/6
(Black 6) (Festing, 2014). Naming strains to report an experiment
includes the breeding institution. For example, C57BL/6NCrl
5
Subculture
Wild specimens
Fr
o
n
ze
:n
o
va
t
ria
io n
E
h
xc
an
ge
Experiment
Outbred
Experiment
A
Shared ancestor
No controlled shared ancestor
Inbred
Time
Time
B
Figure 3: Observed and controlled genealogies. A: A schematic representation of strain breeding. Biologists use wild or domesticated specimens to start controlled strains. In
the case of inbred strains, there is no crossing with specimens external to the strain. In the case of outbred animals, some diversity is regularly introduced. Substrains
may be defined, either because they are the result of genetic manipulations, selection in outbred strains, or just as the result of genetic drift. B: Controlled genealogies in
the case of cells. Doing a standard subculture is not enough to ensure that the individuals of a population share a recent common ancestor. To ensure a recent common
ancestor, biologists typically perform a highly diluted subculture which isolates a single cell. This cell and its descent proliferate, and their proliferation leads to a new
population. This population can then be frozen in order to stop biological processes, and in particular to stop proliferation and the associated variations (Soto et al.,
2016a). Subsets of this frozen population can be used to perform experiments and be shared with other laboratories. The cells obtained using this method share a
known, recent common ancestor and are often used to reproduce experiments.
ment using specimens from a controlled genealogy requires an
exchange of matter, a point that we discuss in section 4.1.
Genealogies are not limited to cell division and sexual reproduction. Viruses lead to horizontal transfers, biologists use
a diversity of manipulations, such as chimera obtained by the
fusion of different zygotes. Last, some authors consider that
microbiomes should be considered as parts of organisms which
implies that several lineages come together to form a holobiont
(Gilbert, 2014). These examples are beyond the basic concept
of genealogy but fit a broader concept of genealogy sensu the
historical origins of specimens.
The use of controlled strains and cells lines is not universal
in biological experiments. For example, cells may come directly
from recent human samples, and animals may come from captures in the wild. However, the practice of using sometimes
very tightly controlled genealogies is widespread, in particular in
biomedical research. The active control of genealogies, including
modifications, leads to a situation where the natural history of
the specimens is entangled with the human history of biological sciences (see Kohler, 1994, for a discussion in the case of D.
melanogaster).
The knowledge and control over part of the recent genealogy of the specimens experimented upon is a supplement to the
phylogenetic method of classification. It ensures that the specimen studied have a recent shared past. Even though this control
is tighter than with the classifications of systematics alone, the
same theoretical and philosophical limitations apply: the description is historical and does not ensure that the specimens have
the very same organizations. Nevertheless, several methods provide partial control over biological organizations. For example,
inbred strains are (almost) homogeneous genetically, and some
aspects of animal phenotypes are controlled regularly in breeding
institutions. Thus, these methods provide precise knowledge and
control over the historical origin of the specimens studied and
limited direct control over their organizations.
3.3. Historical contexts
Knowledge and control of the past of organisms and cells
used in an experiment are not limited to their genealogy. Their
past contexts are also relevant. By context, we mean the environment, including the possible interactions with other organisms.
The control of past contexts can go from the timescale of many
generations to the timescale of ontogenesis or even the shorter
time scales preceding the experiment.
In the case of cell culture, the control and knowledge of
the context stem first from the use of a standardized medium,
temperature, and protection from contaminations. Even the
choice of supplies such as centrifugal tubes used with the medium
can have dramatic consequences on cellular behaviors (Soto et al.,
1991). Another critical parameter is the density of cells. When
this density is too low, the lack of quorum effect can change
cellular behaviors. On the opposite, when the density is too
high, the cells constrain each other’s proliferation. Moreover,
cells typically need time to adjust to a change of conditions such
as a change of medium (Longo & Montévil, 2011b). All these
factors are important since they determine the status of the cells
subjected to the experimentation. In order to perform controlled
experiments, experimenters choose an initial status that can be
obtained consistently in a cell population (homogeneity) and
different replicates (reproducibility). The most straightforward
condition that can be obtained and sustained consistently is
unconstrained proliferation.
The case of animals is similar to the case of cells. In laboratory conditions, the control of the context includes typically the
6
temperature, light cycle, the nature and quantity of food, avoiding pathogens, and the number of animals per cage. For example,
Heindel et al. (2015, section 2.6) describe the context in which
animals are raised before and during a large scale experiment.
However, their past context can be considered problematic. This
work aims to study the effects of the endocrine-disrupting chemical bisphenol A (BPA).The animal experimented upon are raised
in BPA free cages, but they originate from strains which are raised
in polycarbonate cages by the animal provider, and polycarbonate leaks BPA. The exposure of pregnant females to BPA have
known effects spanning two generations (“grandmother effect”,
Susiarjo et al., 2007) and there are other known and probably
also unknown epigenetic factors.
Understanding the importance of past contexts requires a
short theoretical discussion on heredity. Under the assumption
that dna sequences are the only form of heredity, contexts before
an experiment are relevant only during development. However,
this assumption is not valid in general, and epigenetic inheritance
is a widespread phenomenon (Jablonka & Raz, 2009; Jablonka
et al., 2014; Danchin et al., 2019). Let us introduce a simple
example that does not require recent advances in epigenetics.
MMTV is a retrovirus which can be inherited exogenously from
the milk of an infected host to another animal, usually its descent (Dudley et al., 2016). If, say, inbred mice are fed milk from
contaminated mice of another strain, then these mice will carry
MMTV and transmit it to their descent. A contaminated female
will lead to a substrain which is genetically identical to the original inbred strain (as long as the retrovirus does not alter mice
DNA) but has critical immunological and oncologic differences.
Many strategies such as working with inbred strains or clonal
cell populations strive for genetic uniformity. These strategies
could be extended formally to known forms of epigenetic heredity. However, the knowledge and control of past contexts over
several generations is an indirect, partial way to control known
and unknown epigenetic heredity, in combination with the control of genealogies. As a conclusion, past contexts over several
generations are relevant.
The context at the timescale of one generation is also relevant,
as advocated by the concept of ecological developmental biology
(Gilbert & Epel, 2009). Even the position of a fetus relative
to its male and female siblings in the uterus has a measurable
impact (Ryan & Vandenbergh, 2002). The context matters at
shorter timescales too. For example, to measure heart rate or
blood pressure on a rat, biologists need to take into account the
memory and anticipation associated with the procedure (Longo
& Montévil, 2011b; Nadin, 2017, for conceptual frameworks).
In this particular case, the stress induced by the measurement
impacts the heart rate and can be limited by training the animal,
that is, changing its anticipations (Gross & Luft, 2003).
The context in which organisms and cells live before the
experiment matters from the timescale of several generations to
the timescales of development and physiology. The work on past
contexts complements the one on genealogies as a method to
manage the past of the specimens studied. It follows that the
same epistemological limitations apply.
3.4. Synchronic aspects of measurement
The aspects of measurement discussed above are mostly diachronic: they pertain to the past of objects. By contrast, this
section analyses aspects relevant during the observation of intended features.
3.4.1. Current context
Overall, the discussion in the previous section applies also
to the context during an experiment. The context contributes
to the definition of the specimens and quantities observed. This
contribution is both practical and theoretical. It is practical
because it describes the necessary operations required to perform
the same measurement beyond using the same apparatus and
reading its results. It is theoretical because the meaning of the
results depends on these operations.
To illustrate the importance of the context, let us consider
the example of mammal metabolism observed by the oxygen consumption rate. This rate seems to be a simple empirical quantity;
however, it depends on the activity of the organism observed and
its relevant components. To compare the metabolism of different
organisms, biologists define different kinds of physiological activity. The target activities have to be meaningful and achievable for
all the organisms considered, which may be difficult when measurement applies to the many different species of a large clade.
In all cases, the meaning of the results depends on the nature of
the activity chosen (fig. 4). Metabolic rates have several definitions (Longo & Montévil, 2014, chap. 2 for a review):
• The field metabolic rate (FMR) corresponds to the activity
of organisms in an ecosystem, without constraints from
the observer (fig. 4E1).
• The basal metabolic rate (BMR) considers organisms at
rest, that is to say, undisturbed, non-sleeping organisms in a
thermoneutral environment and in a post-absorptive state.
Evolution leads to a diversity in the activities of organisms
and the BMR levels down the impact of this diversity
on the metabolism (fig. 4E3). It is not always possible
to instantiate this definition; for example, ruminants are
never in post-absorptive state (fig. 4E2).
• The maximum metabolic rate (MMR) considers the maximum level of sustainable activity. By focusing on the upper boundary of the metabolism, only the determinants of
this boundary are relevant and not the various characters
involved in biological activities (fig. 4E4).
By choosing different contexts, biologists co-determine what
is observed even when the same measurement apparatus is used
to observe the same part. The BMR and MMR show that it is
even possible to choose observations that focus on properties
shared by different species by leveling down the weight of the
organizational diversity stemming from history.
3.4.2. Choosing or eliminating individuals
Filtering of individuals is a method to control strains: breeders disregard animals with deleterious mutations, diseases, or
other peculiarities. Sometimes, only minimal control over the
7
S1
S1’
S2
S3
S1
S1’
S2
S3
S1
E1
S2
q
q
q
q
E3
S3
A
E2
B
q
q
q
q
q
q
q
q
E4
q
q
q
q
Figure 4: Different measurements of the same quantity. A: A schematic representation of the appearance and disappearance of relevant characters. Dotted lines represent
relations of homology. White shapes are characters which disappeared. B: Four different ways to measure a quantity 𝑞. S1 and S1’ are two similar specimens. All
represented characters impact 𝑞. The size of a symbol represents the impact of the corresponding character on 𝑞 in the given context. E1: A measurement performed
without specific care for the characters contributing to 𝑞, e.g., the field metabolic rate. E2: A measurement performed in a standardized way for S1 but not for the
other species. E3: The animal performs no specific activity which reduces the weight of several characters, e.g., the basal metabolic rate. In this case, only homologous
characters remain quantitatively relevant. E4: A constraint dominates the determination of the measured quantity despite the diversity of relevant characters, e.g., the
maximum metabolic rate.
past context and genealogy is possible. For example, in humans,
most methods above would be unethical. Choosing individuals
having specific characteristics and eliminating individuals with
unwanted characteristics is an alternative method of control on
the organisms investigated.
Filtering of individuals is possible during experiments; however, it impacts the meaning of the results. For example, in the
case of a toxicological experiment, unexpected variations should
be reported since they may be relevant to understand the effect
of the chemical studied and may be investigated in other studies. However, if we want to study the “normal” physiology of
insulin after long-term exposure to high-sugar diet, then it is
necessary to rule out diabetic animals. Last, the quantities of
interest cannot be measured at the expected time point in the
case of individuals who meet an untimely death, which is an uncontrolled filter.
Filtering of individuals by their properties is a complementary way to control biological objects. Performing this filtering
enables biologists to discard specimens which have gone through
unwanted variations, or which have not gone through expected
variations. Criteria can range from developmental anomalies,
mutations, pathologies to animals frightened during measurement.
The principle of variation implies that an observed feature can
become ill-defined or acquire a different meaning. Here, biology
goes beyond standard measurement theory since the changes of
biological objects lead to a collapse of the original meaning of
the quantities observed. For example, the heart rate is defined
by beat-to-beat intervals, but pathological situations such as
torsade de pointes escape the standard definition of a heartbeat,
and the notion of heart rate becomes ill-defined. Similarly, the
properties of the hind legs of the bipedal goat discussed above
have a different meaning than in its quadruped counterparts.
Last, most experimental protocols in biology use control
groups which are not subjected to the transformations investigated (Johnson & Besselsen, 2002). Control groups enable
experimenters to assess the organization of specimens having
the same historical origin and exposed to the same context than
the organisms subjected to a putative difference maker. Controls
enable biologists to estimate whether the results stem from the
context, spontaneous variations, or conditions tested. Biological
objects are labile, and control groups are the closest reference
point possible to the objects tested.
3.5. Irreducibility of biological variation
Despite the use of methodologies providing tight control over
biological objects, the principle of variation entails that there are
always possible qualitative variations. Variations can impact the
observed features directly, making them variable, changing their
meaning or even possibly making them ill-defined. Populations
which are too similar are evidence of malpractice (Bolland et al.,
2016). When observing a given feature among several specimens,
biologists report “not applicable” (NA) for a specimen when qualitative variations are too significant. For example, pathological
heartbeats that do not follow the same sequence of events that
regular heartbeats lead to beat-to-beat intervals that do not have
the same meaning. This kind of departures appears for theoretical reasons and not only as a result of experimental errors or
3.4.3. Data acquisition
Biological measurements typically provide quantities, and
this process has an anhistorical dimension that is comparable to
physics. The notion of measurement of classical physics is relevant
in biology. When measuring a continuous quantity such as the
velocity, the measurement is never exact and provides an interval
instead of a single quantity (§ 2.1). Other physical notions such
as reference frames can also be relevant. Wagner (2010) and
Houle et al. (2011) fruitfully import concepts of measurement
theory in biology which are relevant for the synchronic aspect
of measurement. Since these aspects are not properly biological,
we will not develop them further here.
8
4. Discussion
as the result of the improper theoretical definition of the target
quantities.
Observable, qualitative variations can be shown experimentally even for clonal cells, for example as a result of asymmetries
in cellular division (Cai et al., 2006; Stewart et al., 2005; Lindner
et al., 2008; Soto et al., 2016a) or for dynamical reasons (Braun,
2015). Of course, the development of multicellular organisms
also leads to a high level of variations. Variations occur even
when comparing an individual with itself at another time point,
even in the case of close time points. For example, many physiological time series are non-stationary (West, 2006; Longo &
Montévil, 2014). Stationary time series follow the same distribution over time which implies that the mean is a stable quantity.
By contrast, non-stationarity implies that assessing the average
at different times will not necessarily yield the same results. As
a consequence, it is not possible to characterize an organism by
precise values of physiologic quantities, and precise results are
only valid at a specific time point.
4.1. The radical materiality of biological phenomena
The role of matter in experiments is critical to their epistemological analysis (Morgan, 2002). In physics, theories define
objects mathematically, by invariants and invariant preserving
transformations. This epistemological structure justifies that the
same theoretical object can be instantiated independently de novo.
For example, the speed of light in the vacuum can be assessed on
two independent light beams: it is an invariant of the theory. By
contrast, biological objects stem from an history. It follows that
empirical knowledge in biology cannot be abstracted from concrete material objects (tokens) materializing this history. In this
perspective, biological phenomena display a radical materiality
(Soto et al., 2016b). Our discussion on biological measurement
illustrates this idea. Biological names, in systematics, are not
defined by a theoretical construct, they are defined by specific
specimens called name-bearing types (§ 3.1). Then, experimenting with individuals of a species associated with this name means
experimenting on individuals which descend from an ancestor
shared by both the specimens experimented upon and the namebearing type. These specimens possess a diachronic, material
continuity over time: the genealogy. The same reasoning applies
to the controlled strains and cell lines; the exchange of living
specimens between laboratories is the further materialization of
this philosophical idea.
In general, we can distinguish between different categories
of theoretical situations in the relationship between matter and
theoretical definitions. The methods to reproduce observations
characterize them:
3.6. Recapitulation
To sum our theoretical approach up, biological measurement
has to accommodate simultaneously the aspect observed and
the organism in which it takes place. We propose the following
principles :
1. Measurement has a synchronic dimension for the aspect
or part of interest (§ 3.4.3). Usually, the concept of measurement from classical physics is relevant, that is to say,
measurement as limited precision. Concepts of measurement theory can also be used (Houle et al., 2011).
2. The measurement is relative to/constituted by the history
and contexts of the organisms of interest. Historicity, here,
means a cascade of context-dependent, qualitative variations. A measurement includes a specific way to manipulate and describe these contexts and natural histories, for
example, referring to a theoretical or concrete common
ancestor.
(a) Genealogy handles an uncontrolled history that is
shared by the different organisms studied. Methods
include the phylogenetic classification (§ 3.1) and
direct genealogical control in the case of strains and
cell lines (§ 3.2).
(b) Past and current contexts (environment/interactions)
can be (partially) known in the field or controlled in
laboratories or breeding institutions. Relevant contexts include past contexts over several generations,
during the development or shortly before observations (§ 3.3), and current contexts, during the experiment and observations (§ 3.4.1).
(c) Choosing or eliminating individuals can be used to
observe or eliminate specific histories or variations
(pathological cases, unwanted behavior, ontogenetic
or phylogenetic histories, etc., § 3.4.2).
3. Uncontrolled variations can always impact the measurement, including the very definition of the features observed
(§ 3.5).
1. The description of objects is generic, and the same theoretical object can be instantiated empirically twice without communication of matter, as discussed by Feynman &
Gleick (1967, chap. 4). It is the case in current physical
theories.
2. The object’s behavior is the specific result of a history.
(a) Scientists use the permanence of the material object
studied. They may be fixed artificially. For example,
the name-bearing types used in systematics serve as
static references for future observations. They may
also continue to change over time, for example, in
the case of the biosphere.
(b) Scientists use the fact that the objects reproduce.
This property provides an exponential amount of
objects sharing a common past. The study of living
organisms and cells falls typically in this category
(case studies such as types above are an exception).
4.2. Symmetry and symmetrisation
Symmetries play a central role in physics (Feynman & Gleick, 1967; Van Fraassen, 1989; Bailly & Longo, 2011; Longo &
Montévil, 2014) and will enable us to provide a more in-depth
analysis of biological measurement. Symmetries are transformations which do not change the relevant aspects of a given object.
For example, the equation describing free fall does not change
for an experiment performed one century ago or today. Time
9
ate
Esti m
s
Genealogy, context,
manipulation
Phylogenetic
classification
Protocol
Observed features
Type
B
A
Results
Gene transfer
C
Time
Long time scales
Time scales where biological work takes place
Experiment
Figure 5: Recapitulation of the diachronic elements used to define the objects of a typical experiment. The whole construct illustrated is required to describe the measurement
performed. A: The objects are the result of an evolutionary history, which is not directly accessible but can be estimated by the phylogenetic method. B: Specimens of a
given species can be used to breed a strain in controlled conditions. C: Elements of this strain are used in an experiment to obtain data.
translation is a transformation that does not change the theoretical description of the object: a symmetry. Moreover, the same
equation applies regardless of the nature of the object which is
another fundamental symmetry. For example, concerning free
fall, if experimenters replace an iron bead with another one, or a
copper or wood bead, the phenomenon remains the same: permuting (interchanging) these objects is a symmetry. Symmetries
can be either exact, in the sense that they stem from fundamental
principles, or approximate.
The concept of experimental reproducibility is a notion of
symmetry. The reproducibility of an experiment means that the
same set of observations can be performed by different observers,
on different material objects, at different times and places.
Moreover, in a given experiment, biologists typically use
different specimens exposed to the same conditions in order to
perform statistical tests. The tests assume that these specimens
follow the same probability distribution, that is, the tests assume
that behind the quantitative variations observed there is a single
abstract mathematical object (the probability distribution): this
is again an assumption of symmetry.
However, biological objects are the result of a history and
continue to generate a history. Interpreting this notion in terms
of symmetries leads to assert that when time flows, describing the
changes of biological objects can require changes of symmetry
that do not stem from the description of the initial objects. These
changes are the core of the principle of variation (Longo &
Montévil, 2011a; Montévil et al., 2016; Montévil, 2018). As
hinted to in the introduction, these variations conflict with the
aim to perform reproducible experiments. In biology unlike in
physics, the symmetries associated with reproducibility are not
granted theoretically. Instead, they depend on the measurement
as summarized in section 3.6.
Since biological regularities are more labile than physical
ones, symmetries are not provided directly by the theory. In-
stead, they are co-established by the measurement process and
the biological objects used. We propose to call this practical and
theoretical operation “symmetrization”. Biologists typically work
on specimens of the same species or more generally specimens
with a shared common past. In experiments, they assume a partial
equivalence between these specimens and how they are organized.
In other words, biologists posit an approximate symmetry between the organization of different organisms and their response
to experiments. Control over past contexts is also a symmetrization of the specimens studied and are often designed with this
issue in mind. For example, in section 3.3, we have discussed
how biologists aim for cells in vitro to be in a consistent state
over time, that is to say, how biologists symmetrize cells. The different methods described in section 3 should be seen as different
symmetrizations.
Different symmetrizations can be performed during an experiment or even during data acquisition. Choosing a symmetrization or another endows the results with entirely different biological meanings. Figure 4 illustrates this idea and shows different
ways to make organisms equivalent. In figure 4E1, by being in the
field, organisms express their historically (evolutionary) relevant
activities and these activities are diverse. In figure 4E3, different
organisms are mostly restrained to activities that are common
to them: the experimenter performs a stronger symmetrization
by limiting the characters involved in the determination of the
observed quantity.
We can distinguish two kinds of symmetrization: concrete
and epistemic symmetrizations. Concrete symmetrizations involve the action of biologists on objects. For example, establishing inbred strains is a concrete symmetrization of genomes,
and the symmetrizations illustrated in figure 4 are also concrete
symmetrizations. By contrast, epistemic symmetrizations do not
change material objects and are limited to determining what is
considered equivalent, that is to say, symmetric by permutation.
10
For example, we mentioned that the position of a fetus in utero
has measurable consequences. Taking this aspect into account
or not corresponds to different epistemic symmetrizations. The
concept of epistemic symmetrization is particularly relevant for
statistical analyses and subsequent biological reasonings.
As a consequence of the principle of variation, the concept
of epistemic symmetrization is always relevant: biologists have
to symmetrize organisms which are not genuinely symmetric.
Concrete symmetrizations occur in most experiments, but not
in observations without experiments such as the observation of
specimens in systematics. Performing concrete symmetrization
constrains the kind of biological objects studied. For example,
it is far easier to symmetrize cells in culture by maintaining
unconstrained proliferation.
In conclusion, biological measurement as summarized in 3.6
describes both the concrete and epistemic symmetrizations performed to obtain experimental results and endow them with
biological meaning.
reproducibility of the measurement. At the limit, these strategies
aim to generate specimens which are as symmetrized as possible and would have the same status than the objects of physics:
these strategies aim the genericization of biological organizations and use many methods to reduce diversity. For example, in
the case of cells, samples are frozen to prevent spontaneous variations between experiments. The focus on model organisms at the
level of the research community is also a collective strategy of
genericization. In situations like clinical trials, on humans, genericization methods are limited for ethical reasons. In contrast to
genericization strategies, other strategies on the same axis aim
to gain generality and coherence with evolutionary history but
at the cost of more variability in the results. It follows that these
strategies face more difficulties to obtain statistically significant
results.
In order to face the reproducibility crisis and to obtain significant results with fewer animals, it is common to promote strategies genericizing specimens (Festing, 2014; Chia et al., 2005).
However, these strategies bear the cost of studying singular organizations: the results obtained may not even be representative
of the species studied, and we have seen that strains of the same
species have distinct properties. Another example is that the conditions of the laboratory reduce exposure to pathogens in order
to symmetrize the life history of animals studied, which is part
of the alteration axis. However, this situation leads to immunological functions that differ from wild animals (Abolins et al.,
2010).
The genericization of specimens aims, at the limit, to study
a single, reproducible organization and is thus highly singular.
Results may depend on the specificities of these organizations
and their contexts in unknown ways. Therefore, these strategies are vulnerable to minor departures from the genericization
performed initially. For example, performing measurement in
different laboratories always involve a change of context despite
the explicit control of many factors. Genericizations aim reproducibility in the sense of specimens that are very similar, but the
reproducibility of experiments is made difficult by the lack of
generality of the measurement.
In figure 6, there are only two cases which are far from
this axis. The first corresponds to measurements like the basal
metabolic rate, see figure 4E3. This measurement is reproducible
and nevertheless general. Its downside is that the organisms are
put in a specific state to level down the consequences of the diversity of the characters impacting the measured quantity. Its
strategy is to symmetrize a shared aspect of organisms when the
genericizations discussed above symmetrize complete organizations.
Case studies are the second strategy departing from the main
axis. Case studies focus on a single individual and reproducibility
is not a goal. For example, Patterson & Linden (1981) study
the intelligence of non-human primates. To do so, the authors
did not develop standardized conditions and protocols. Instead,
they taught sign language to several specimens and focused on
a particularly gifted gorilla, Koko, who mastered up to 2000
symbols. Other examples are works on types in systematics, the
study of the bipedal goat discussed by West-Eberhard (2003)
and the cloned sheep, Dolly, which is one success among 277
4.3. Measurement strategies
Baxendale (2018) proposes to map scientific practices on
a continuum of strategies defined by their stances concerning
reductionism. In this section, we apply a similar approach to
measurement strategies. Our concept of biological measurement
leads to the notion that measurement depends on symmetrizations, but symmetrizations can be performed more or less tightly
and at different levels. To represent these strategies, we propose
to organize measurement strategies along three axes as illustrated
in figure 6. In this section, we discuss only the different measurement strategies, and we do not imply that they necessarily
succeed or that they prevent the joint use of other strategies.
The first axis describes whether the measurement is variable
or on the opposite reproducible. Here, reproducibility means
that the measurement generates data consistently with different
specimens. For example, using inbred strains generally leads to
more reproducible results than wild specimens.
The second axis describes whether the measurement targets
singular or general objects. Working on the metabolic rate of
mammals is more general than working on a single species by
measuring wild specimens. Both are more general than outbred
strains and a fortiori inbred strains, where the genotype is symmetrized. Reciprocally, inbred strains are more singular than
outbred strains and so on.
The last axis assesses whether the measurement defines objects coherent with their evolutionary past or instead whether
the objects are more or less profoundly altered. For example, inbred strains are homozygotes for all genes which is not the case
of mammals in their evolutionary history. Similarly, the basal
metabolic rate is far less representative of a species past evolution
than the field metabolic rate — but the latter depends on the
field.
A qualitative axis emerges in this three-dimensional space,
see figure 6. This axis is given by the strategies which increase
simultaneously the reproducibility, singularity, and alterations associated with the measurement. In other words, these strategies
lose generality and alter the specimens in order to increase the
11
General
General
Altering
history
Field metabolic
rate, E1
Basal metabolic rate, E3
Altering
history
Wild specimens
Variable
Reproducible
Coherent
with history
Singular
Variable
Symmetrization
of the genealogy
and past context:
genericization of
specimens
Coherent
with history
Case study
Outbred Reproducible
Inbred
In vitro
Singular
Figure 6: Different measurement strategies. The three axes correspond to measurements that are reproducible vs. variable, general vs. singular and coherent with their
evolutionary past vs. altered by experimenters. Left: axis where many strategies lie. On one end of the spectrum, these strategies aim to produce specimens that are as
close to each other as possible by controlling them tightly. On the other end, experimenters relax this control and aim to make more general measurements. Right:
different cases are represented in the space describing measurement strategies. Most measurement strategies are on the axis represented on the left, but departures
from this axis are equally interesting since they represent other ways to approach biological phenomena empirically, e.g., case studies.
attempts and remained the only success for a long time (Wilmut
et al., 1997). While the study of types does not involve alterations,
teaching Koko or cloning a sheep do: case studies are diverse for
the third axis.
Case studies are sometimes neglected by experimenters who
strive to design reproducible experiments in order to study mechanisms. For example, the success of cloning Dolly without reproducing this feat led to an intense debate, especially when evidence
accumulated that Dolly was indeed cloned from an adult cell
(Solter, 1998). However, in our conceptual framework, case studies have a specific epistemic role. They are sufficient to prove the
existence of a possibility in a theoretical context where biological
possibilities are not predefined (Montévil, 2018). The bipedal
goat shows the extent of developmental plasticity and studying a
type is sufficient to defend the existence of a new species. Case
studies can be extensive; for example, the anatomy of the bipedal
goat has been described in details. In case studies, the analysis
of a single part in several organisms is typically replaced by the
analysis of many parts or aspects in a single organism. Last, the
study of types in systematics plays a pivotal role in the general
architecture of biological knowledge to name biological objects.
Representing different measurement strategies by the symmetrizations performed is fruitful. These strategies are different
responses to the difficulties raised by the historical and varying
nature of biological objects.
concept of biological measurement which we propose accommodates simultaneously the organisms or cells and their part or
aspect of interest which may be quantified. In our framework,
a measurement is relative to a history and context. To develop
reproducible experiments, biologists observe specimens with a
shared past. This shared past is ascertained by systematics and by
direct knowledge and control of both their genealogy and past
contexts. In the study of objects defined by their history, the objects which can be considered equivalent are objects having a
shared past. In this context, we call symmetrization the concrete
and theoretical operations which establish and posit the equivalence of different objects with more or less tightly controlled
shared pasts and contexts. Symmetrization also includes the operations performed during the observation which can constrain
and structure variability.
The notion of biological measurement is compatible with
different research strategies and leads to a framework to map
them. In this framework, we find two polar opposites. In one
end, strategies strive to genericize biological organizations at the
cost of studying singular organizations and altering them. To
implement these strategies, biologists developed a plethora of
methods. They expose objects to similar contexts and ensure that
they have recent, controlled common ancestors. In some cases,
biologists freeze samples to prevent them from undergoing variations between experiments. On the other end of the spectrum,
the objects studied are more general (e.g., diverse genetically)
and coherent with their evolutionary history, but they are also
more variable. There are strategies which escape this opposition,
for example, case studies or methods to level down the diversity
relevant for the part studied while the rest of the organizations
remain diverse.
Having a clear notion of what it means to access biological
5. Conclusion
Our theoretical notion of measurement accommodates how
biologists manipulate immensely complex objects, organisms
and cells typically, which are the result of a history and continue
to produce a history by generating qualitative variations. The
12
objects empirically is critical for biological knowledge. In this
paper, we provide only an outline of biological measurement,
and this notion deserves further discussions, focusing on both
general and specific situations. Nevertheless, since our notion
builds on solid ground, namely the theory of evolution and extensions for organisms, we hope that our work will be of use for
further research. We have shown that biological measurement
has significant differences with the notions of measurement in
physics. Depending on the perspective, biological measurement
may be seen as an extension of classical measurement in order to
accommodate the historicity and variability of biological objects,
or as a different concept altogether because the objects are not
described theoretically by underlying equations. In all cases, acknowledging the specificities of biological measurement should
provide new systematic ways to approach biological observations
critically and ultimately to promote experimental reproducibility.
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Acknowledgments
I am grateful to Ana Soto, Giuseppe Longo, Carlos Sonnenschein, Guillaume Lecointre, Matteo Mossio, Arnaud Pocheville
and Véronique Thomas-Vaslin for their comments on previous
versions of this article and helpful discussions. I also thank the
two anonymous reviewers and the editor for their candid comments.
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Maël Montévil