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Explanation in terms of gene regulatory networks (GRNs) has become standard practice in evolutionary developmental biology (evo-devo). In this paper, we argue that GRNs fail to provide a robust, mechanistic, and dynamic understanding of the developmental processes underlying the genotype-phenotype map. Explanations based on GRNs are limited by three main problems: (1) the problem of genetic determinism, (2) the problem of correspondence between network structure and function, and (3) the problem of diachronicity, as in the unfolding of causal interactions over time. Overcoming these problems requires dynamic mechanistic explanations, which rely not only on mechanistic decomposition, but also on dynamic modeling to reconstitute the causal chain of events underlying the process of development. We illustrate the power and potential of this type of explanation with a number of biological case studies that integrate empirical investigations with mathematical modeling and analysis. We conclude with general considerations on the relation between mechanism and process in evo-devo.
The gap gene network of dipteran insects (flies). (A) During early development, the blastoderm-stage embryo (shown as oval shapes, with the anterior to the left) gets subdivided into distinct territories of differential gene expression by the segmentation gene network. This network has a hierarchical structure, with protein gradients encoded by maternal co-ordinate genes at the top (see graph). They provide the regulatory input to the zygotic gap gene system, which forms the top-most hierarchical layer of the network. Gap and maternal co-ordinate genes then activate the pair-rule genes in a periodic pattern of seven stripes. Finally, a frequency-doubling event occurs and segment-polarity genes become expressed in 14 stripes, which form a molecular prepattern for the body segments that form later in development. Arrows indicate interactions between hierarchical layers and cross-repression within each layer. (B) Regulatory structure of the gap gene system. The position of gap gene expression domains is indicated as boxes along the antero-posterior axis. Cross-repressive interactions among gap genes are shown as T-bars. The dashed line indicates a bifurcation boundary between stationary and shifting domains. (C) Subcircuits of the gap gene system active in the anterior (AC/DC1), center (AC/DC2), and the posterior (AC/DC3) of the embryo. Flashes indicate which subcircuit is in a critical state in different dipteran species (Drosophila melanogaster and Megaselia abdita). Gap genes: hunchback (hb), Krüppel (Kr), knirps (kni), and giant (gt). See text and Jaeger (2018) for further details.
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Beyond Networks: Mechanism and Process in Evo-Devo
James DiFrisco & Johannes Jaeger
Abstract: Explanation in terms of gene regulatory networks (GRNs) has become
standard practice in evolutionary developmental biology (evo-devo). In this paper,
we argue that GRNs fail to provide a robust, mechanistic, and dynamic
understanding of the developmental processes underlying the genotype-phenotype
map. Explanations based on GRNs are limited by three main problems: (1) the
problem of genetic determinism, (2) the problem of correspondence between
network structure and function, and (3) the problem of diachronicity, as in the
unfolding of causal interactions over time. Overcoming these problems requires
dynamic mechanistic explanations, which rely not only on mechanistic
decomposition, but also on dynamic modeling to reconstitute the causal chain of
events underlying the process of development. We illustrate the power and potential
of this type of explanation with a number of biological case studies that integrate
empirical investigations with mathematical modeling and analysis. We conclude
with general considerations on the relation between mechanism and process in evo-
Keywords: evo-devo; genotype-phenotype map; dynamical systems; mechanistic
explanation; gene regulatory networks; homology
Our ignorance of the laws of variation is profound.
Darwin (1859, 105)
“The real goal of evo-devo is to explain evolution as the modification of
developmental processes, not merely to demonstrate that evolution has proceeded by
modifying development. Although genes are important aspects of the developmental
processes, they are not the processes themselves.
Amundson (2005, 247)
Introduction: the genotype-phenotype map
How does genetic variation map onto phenotypic variation? This question is crucial
for understanding the linkage between the two processes at the core of biological
evolution: the production of heritable phenotypic variation, and its sorting by natural
selection. Remarkably, in spite of the major advances in biology since the time of
Darwin, we still lack a systematic causal understanding of the genotype-phenotype
map (Burns 1970; Alberch 1991; Pigliucci 2010).
The “genotype-phenotype map” refers to the process of biological development,
in which genes causally contribute to the formation of complex and differentiated
phenotypic end-states. Since evolutionary processes such as selection operate on
phenotypes, causal explanations of evolutionary change require causal explanation
of how genotypes get mapped onto phenotypes through development. As Horder
(1989, 340) states, “[i]n order to achieve a modification of adult form, evolution
must modify the embryological processes responsible for that form.” Amundson
(2005) calls this idea the “causal completeness principle,” and localizes its origin
within the tradition of classical embryology.
Despite the straightforward reasoning behind the causal completeness principle,
many evolutionary biologists of the 20th century thought that the lack of a causal
understanding of the genotype-phenotype map did not create any significant
problems for evolutionary theory (e.g., Mayr 1961; Wallace 1986). It is rather like a
lack of extraneous causal detail that, while forming a necessary part of a complete
causal story, can be safely abstracted from for purposes of evolutionary (population-
level) explanations (see Scholl and Pigliucci 2014). The idea that development can
be safely ignored is based on the assumption that the genotype-phenotype map is
linear and deterministic, or that this is at least a valid approximation. It allows one
to effectively collapse variation at the phenotypic level onto the genetic level, in
order to describe and explain evolutionary dynamics purely in terms of genetic
variation. This is the central idealization strategy of population genetics.
However, we have known for a long time that the assumption of linearity and
determinism is false. The genotype-phenotype map is causally complex and non-
linear. It is also degenerate in the sense that the same genotype can generate different
phenotypes due to developmental plasticity (West-Eberhard 2003; Gilbert and Epel
2009; Moczek et al. 2011), while different genotypes often produce the same
phenotype due to the robustness of development (Waddington 1942, 1957;
Schmalhausen 1949; Kitano 2004; Wagner 2005, 2011; Masel and Siegal 2009).
Taken together, these properties of the genotype-phenotype map prevent many of
the explanatory goals of evolutionary biology from being realized using traditional
statistical-correlational methods.
Many biologists look to evolutionary developmental biology (evo-devo) to
provide the missing causal understanding of the genotype-phenotype map. This is
because evo-devo is supposed to go beyond the statistical association of genes with
phenotypes by investigating the developmental causes connecting genotype and
phenotype. Since evo-devo explains evolutionary change as change in
developmental mechanisms, it can be considered a “mechanistic” approach to
evolutionary biology (Wagner et al. 2000). As Brigandt (2015, 136) writes: evo-devo
“does not just lay out phylogenetic transformation sequences of morphological
characters, but offers a causal explanation of how those character transformations
occurred by means of changes in developmental mechanisms.” This sort of
explanation is what Calcott (2009) has called a “lineage explanation” in evolutionary
biology, which is complementary to traditional population-level explanations.
Change in developmental mechanisms is expected not only to explain actual
evolutionary sequences, but also to generate a “map of the possible” for evolution
(Alberch 1991), which determines the evolutionary potential, or evolvability, of
characters (Wagner and Altenberg 1996).
Over the past few decades, evo-devo has made significant progress in probing
and disentangling genotype-phenotype maps in different evolutionary lineages. The
major conceptual innovation behind its progress has been a shift away from the study
of individual gene-trait co-variations and towards network thinking, an approach in
which interactions between genes are seen as forming functional modules of gene
regulatory networks (GRNs) whose structure explains how development occurs.
This has led to better models of the complex regulatory architecture underlying
development and developmental evolution. In addition, it has enabled novel
questions and approaches in comparative evo-devo. However, even though network
thinking makes valuable contributions to our understanding of the structure of the
genotype, its capacity to provide mechanistic understanding of the genotype-
phenotype map is suspect.
In this paper, we argue that the robust model of the genotype-phenotype map
demanded by contemporary evo-devo requires us to go “beyond networks” in the
direction of dynamic mechanistic explanations of development. We will show how
GRN-centered explanations of development are limited by the complex degeneracy
of the genotype-phenotype map. Progress on this problem requires taking into
account not only network structure but also dynamics. Sections 2 and 3 introduce the
main ideas of network thinking and GRN-centered theories of development and
evolution. Section 4 examines how static networks fail to provide mechanistic
explanations of development. In Section 5, we show how processual models based
on dynamical systems theory can overcome this deficiency, and close in Section 6
by considering the relations between mechanism and process in the context of evo-
devo theory.
From gene-trait atomism to gene regulatory networks
In order to understand what gene regulatory networks are, and how they have come
to occupy the role of explaining development and its evolution, we have to take a
brief look the history leading up to the concept.
Among early evolutionary geneticists, it was common to view the association of
genes and traits as an aggregate of linear covariations. Let us call this view gene-
trait atomism. This view is taken to the extreme in Ronald Fisher’s (1930)
infinitesimal model, which postulates that traits depend on an infinite number of
genetic loci, each having an infinitesimally small effect on the trait (Orr 2000). In
the limit of infinite loci with infinitesimal effects, the influence of non-linear
interactions between genes vanishes completely.
Gene-trait atomism has always been in tension with phenomena that were known
early on in genetics, in particular epistasis (Bateson 1909), which refers to the
dependence of mutational effects on genetic background (Hansen 2013). However,
prominent work by T. H. Morgan and his colleagues attenuated this tension, in part
by promoting a difference-making view of genetic causation in contrast to the
productivist view common among embryologists. For Morgan, to say that a
Mendelian factor (gene) causes a character “does not assume that any one factor
produces a particular character directly and by itself, but only that a character in one
organism may differ from a character in another because the sets of factors in the
two organisms have one difference” (Morgan et al. 1915, 212, emphasis added). In
other words, if changing one gene correlates with a change in eye color, then we can
justifiably call that gene a cause of eye color, even though “the character is the
product of a number of genetic factors and of environmental conditions” (Morgan et
al. 1915, 210; see Waters 2007). Morgan’s distinction between genetic and
embryological causes of characters was enormously influential for separating the
problem of heredity from the complexities of development, and establishing the
modern concepts of genotype and phenotype (see Amundson 2005, 148 ff.).
Morgan’s distinction enabled a productive research program in genetics, but it
had a number of important limitations. One major problem is that Morgan’s methods
could only detect the genes underlying traits for which there is non-lethal variation
in the population (e.g., eye color and bristle number). Another major limitation was
the lack of an explanation for spatial differentiation in development. This issue was
only resolved much later with the operon model of gene regulation (Jacob and
Monod, 1959), which showed how structural (effector) genes could be turned on and
off by proteins encoded by regulatory genes in response to signals from the
Although the operon model was designed for a physiological response network
in a bacterium, which is characterized by fast and reversible processes, a central and
general explanatory role for gene regulation was immediately postulated (Monod
and Jacob 1961), and soon explicitly applied to the slower, irreversible processes of
eukaryotic multicellular development and differentiation (Morange 2014). This
forms the basis for the concept of gene regulatory networks (GRNs) that govern the
dynamics of embryogenesis (e.g., Britten and Davidson 1969). In parallel,
researchers formulated the hypothesis that regulatory differences in gene-gene
interactions could account for most of the phenotypic differences between
evolutionary lineages (e.g., King and Wilson 1975; Davidson and Erwin 2006).
GRNs are composed of regulatory genes, which encode transcription factors and
signaling proteins, and the target genes they regulate through cis-regulatory elements
in the genome (Figure 1) (e.g., Davidson et al. 2002). Signaling proteins form
cascades that transduce signals from the cellular environment to affect gene
expression in the nuclei of receiving cells. This is achieved through transcription
factors that bind to DNA and typically either activate or repress the expression of
their target genes. Cis-regulatory elements are the non-coding regions of DNA that
transcription factors bind to, lying upstream, downstream, or sometimes even within
the RNA-producing sequence of the regulated genes. These components can be
visually depicted in a network diagram, where nodes represent genes (and their
protein products) with associated cis-regulatory elements, and edges represent
regulatory interactions such as activation or repression (see Figure 1).
Figure 1. Gene regulatory networks consist of genes that encode transcription factors (circles)
and their mutual interactions, which can be activating (arrows) or repressive (T-bars, in the
abstract depiction on the left). Their activity is modulated by external inputs from the intra- or
extracellular environment. Each interaction between a regulator and its target is mediated by
transcription factors binding to cis-regulatory sequences (shown on the right) thereby inducing or
inhibiting expression of the target.
Despite the advent of GRNs in the 1960s, the implicit assumption of gene-trait
atomism survived for decades in the fields of developmental genetics and evo-devo.
It was at the heart of much of the work on homeotic genes that boosted the rise of
both of those fields in the 1980s and 90s (e.g., Carroll 1995, Holland 1999). In the
early 2000s, a renewed focus on GRNs led to a decisive shift from this gene-trait
atomism toward the more holistic “extended genotype” of gene-gene interactions
(compare, for example, Carroll 1995 and Carroll 2008). As a consequence, some
proponents of evo-devo began to claim that changes in cis-regulatory elements are
more important for phenotypic evolution than changes in the protein-coding
sequences of the genes themselves (e.g., Wray 2007; Stern and Orgogozo 2008;
Carroll 2008).
As the explanatory role of GRNs expanded over the past decades, Morgan’s
organizing distinction between evolutionary and developmental genetic causes came
to be reconfigured. Morgan’s distinction was made necessary by the fact that the
mechanistic causal linkage between genes and characters remained obscure. Once
the focus shifted towards the role of gene regulatory activity in development, genes
could be re-conceptualized not only as causes of heredity but also, through their
contextualization in GRNs, as causes of development. Organized into regulatory
networks, they produce phenotypic characters by means of molecular mechanisms.
The new theory of developmental gene regulation has thus been seen as offering an
integral theory of development and its evolution (Wagner et al. 2000). As Davidson
(2010, 918) puts it: “Evolution and development emerge as twin outputs of the same
mechanistic domain of regulatory system genomics.”
In the next section, we examine the main features of two exemplary bodies of
work, to understand the explanatory role of GRNs in current evo-devo theory: the
research program of Eric Davidson and colleagues, as well as Günter Wagner’s
genetic model of homology.
GRNs and ChINs
Over the last 50 years, Eric Davidson and his research group at Caltech have carried
out one of the most determined and influential efforts to characterize a
developmental GRN. Davidson and colleagues rely on engineering metaphors in
which a network is pictured as a “wiring diagram” composed of “circuits” and “sub-
circuits,” with certain connections “hard-wired” into the genome. In their most well-
known classification scheme (Davidson and Erwin 2006), GRN components are
arranged into the following categories: conserved kernels and more taxon- and
tissue-specific plug-ins, with input/output devices (or switches) connecting them,
which ultimately activate different sets of downstream differentiation gene batteries.
Network components that are downstream in the regulatory hierarchy are expected
to be more evolutionarily labile.
The most important aspect of Davidson’s theory in the present context is its claim
that GRNs suffice to explain all of development. This idea can be found in various
forms throughout Davidson’s recent body of work.
For example: “Once it includes
all or almost all specifically expressed regulatory genes, a GRN constitutes an
explanation of why the events of development occur” (Oliveri et al. 2008, 5961);
“The spatial causes of developmental events after the earliest stages of dependence
on egg cytoarchitecture are essentially all programmed in the genomic control
system” (Oliveri et al. 2008, 5961); “Development and evolution of the body plan,
and execution of physiological responses, devolve causally from the regulatory
genome” (Davidson 2010, 918). In essence, GRNs implement the genetic program
of development.
Such claims would seem to be prima facie overly ambitious in light of the known
degeneracy of the genotype-phenotype map. What is new about this form of genetic
determinism, however, is that we no longer treat genotypes as sets of individual
genes with dissociable effects on phenotypes, but as genetic networks that are
organized in such a way as to control specific, modular developmental processes.
It is worth noting that the initial formulation was much more modest, and the proposed research
program was more precisely circumscribed: “Undoubtedly important regulatory processes occur at
all levels of biological organization. We emphasize that this theory is restricted to processes of cell
regulation at the level of genomic transcription” (Britten and Davidson, 1969, 349).
The explanatory role of the genotype is shifted to GRNs at a higher level of
organization than individual genes. In Davidson’s theory, the nature of a particular
gene is less important than its position and connections to other genes in the network.
Thus, the claim of sufficiency for genetic explanations of development is not exactly
a claim about the genotype-phenotype map per se, but about a “GRN-phenotype”
For this sufficiency claim to be tenable, it is essential that the behavior or
functioning of a network can be inferred from its regulatory structure. As Davidson
states explicitly: “[a] given sub-circuit structure implies a given function […] what
the circuit can do depends directly on its structure” (Davidson 2010, 911); or
“[s]everal types of network subcircuits have been identified so far, each associated
with specific regulatory functions” (Peter and Davidson, 2017, 5862). This is what
we contest, and what makes it necessary to supplement research on the structure of
GRNs with dynamical modeling of network behavior.
Before proceeding to our argument, we introduce another theory of
developmental evolution that heavily relies on GRNs: Günter Wagner’s genetic
theory of homology (2007; 2014). Wagner’s framework differs from Davidson’s in
that, rather than attempting to construct a genetic theory of metazoan development
and evolution, the goal is to develop an account of homology for morphological
characters. Homology has many crucial roles in evolutionary biology and evo-devo
in particular (see DiFrisco 2019). While there is widespread agreement that
homology is something like sameness or similarity due to common descent (DiFrisco
2019; Hall 1994), a more sophisticated approach is needed for determining which
morphological characters are homologous with which. Wagner (2014) argues that
we cannot successfully individuate character homologies simply in terms of
morphological resemblance, physical distinctness, or embryological origin.
Characters must have evolutionary “individuality”—they must behave as variational
modules in evolutionary processes. In order for a character that we demarcate to
possess this sort of individuality, that character must be underwritten by an
appropriately modular genotype-phenotype map (Wagner and Altenberg 1996;
Jaeger and Monk, 2019). Invoking Riedl (1978), Wagner (2014, 44) formulates this
requirement as follows:
The individuality of body parts, required for homology to make biological
sense, requires specific genetic and developmental mechanisms to cause the
distinctness of the body part during the life of an individual and continuity of
distinctness in the course of evolution.
As Wagner (2014, 90) points out, the trouble is that the required sort of underlying
genetic and developmental mechanisms might not exist: “some level of variation in
the developmental mechanisms of homologous characters is the rule rather than the
The solution Wagner proposes for this problem involves distinguishing between
character identity and character state. Characters are modular body parts such as
forelimbs or hearts. Their identity is defined by their position and other structural
factors (Wagner 2014, 54). Character states are the more determinate properties that
differ across different instances of a character, such as its size, shape, and color
(Wagner 2014). Wagner maintains that it is character identity that provides the right
sort of individuality and evolutionary stability to ground morphological homology
(Wagner 2014, 80).
With this qualification in place, Wagner introduces the central postulate of his
genetic theory of homology: “The distinction between character identity and
character states […] is reflected in the genetic architecture of development in which
character identity has a different genetic substrate than character states” (Wagner
2014, 94). The genetic substrate that uniquely attaches to a character’s identity is a
subset of the GRN that generates it, which he calls Character Identity Network
(ChIN). ChINs are the phylogenetically conservative parts of regulatory networks
that give characters their modularity and evolutionary stability, and that must be
modified in order for evolutionary novelties to appear.
The basic mode of character specification in Wagner’s model is depicted
schematically in Figure 2. During embryogenesis, inductive signals or initial
patterning cascades provide positional information to a specific region of the
embryo. ChINs then “interpret the positional information signals and activate
position-specific developmental programs” (Wagner 2014, 97), thus translating
continuous positional information into discontinuous, individualized characters.
Finally, “realizer genes” controlled by the ChIN produce specific character states,
which vary widely across species sharing the same characters.
Figure 2. Character Identity Networks (ChINs) determine the identity, but not the state, of a
character. They receive inputs from general positional information signals, and induce the
expression of specific sets of realizer genes. In the example depicted here, the forewing and
hindwing are character identities common to all flying insects, but they exist in different states
in different sub-groups. In Drosophila the forewing is a flying wing and the hindwing a haltere,
which is used for balance during flight. By contrast, in Tribolium the forewing is a hardened
cuticular structure (the elytron) that covers the hindwing, which is used for flying.
Wagner is careful not to elevate ChINs to the status of a strict definition that
would be conceptually connected to character homology. Instead, it is meant to
function as an “idealized image,” or model, which may admit of exceptions but
which is nonetheless intended to be useful for orienting research in evo-devo
(Wagner 2014, 118). His characterization of ChINs includes the following main
features. ChINs are historically coextensive with the characters they specify, thus
grounding relations of homology (Wagner 2014, 118). Members of ChINs sustain
each other’s expression and jointly repress the development of alternative character
identities (Wagner 2014, 117). Finally, genes in a ChIN are jointly necessary and
often also individually sufficient to trigger the differentiation of a character (Wagner
2014, 118). These features highlight that ChIN components are not simply arrayed
in a linear chain of causation (recall Morgan’s genes), but “form a functional unit in
which developmental causality is realized at the level of the network rather than at
the level of the single gene” (Wagner 2014, 117).
In ascribing to GRNs the primary causal responsibility in the development of
phenotypic characters, Wagner’s genetic theory of homology is quite close to
Davidson’s framework. There are, of course, important differences between the two
besides their different theoretical aims. For example, Wagner’s framework is not
attached to genetic determinism as a general thesis about development. But both
frameworks are similar enough in ascribing a privileged causal role to GRNs that
they face some of the same difficulties and limitations, which we explore in the
following section.
GRNs do not adequately explain development
There are three main problems with GRNs as explanations of developmental
processes and morphological characters. The first is the problem of genetic
determinism, the second the problem of correspondence, and the third the problem
of diachronicity. The latter two problems are closely related to each other through
the degeneracy of the genotype-phenotype map (see Introduction), and the fact that
network structure only loosely correlates with network dynamics and function.
The problem of genetic determinism
In Davidson’s work, the claim that GRNs provide a sufficient explanation for
development gets supported by the following type of argument. There is a special
resemblance between parents and offspring: “frogs beget frogs and dogs beget dogs,
and never does one sort of animal produce an embryo that develops into another”
(Peter and Davidson 2015, 2). This resemblance cannot be explained by the
environment or by “magic hormones” (Peter and Davidson 2015, 2), but requires a
heritable genomic program containing the instructions to build organisms of a certain
type. “Such programs must exist; they must be identically replicated, hence genomic;
and they must suffice to control the nature of developmental events independently
and similarly in each organism” (Peter and Davidson 2015, 2).
The causal sufficiency of the genetic program hinges on two premises, the first
being that the genome is the sole source of heritability in organisms, the second that
the genomic program contains and processes all the instructions required to construct
a phenotype. Both premises are highly problematic.
First, different forms of non-genetic inheritance are well-established phenomena
by now (for recent reviews, see Danchin et al. 2011; Kronholm 2017; Bonduriansky
and Day 2018). Moreover, the genome does not simply copy itself from generation
to generation. Genetic transmission requires a continuity of cell state as well as
organismic integrity and activity (Griesemer 2000, 2006; Jaeger et al. 2012; Walsh
2015). This continuity is essential for the maintenance, replication, and ordered
segregation of the genome among offspring. The genome depends on processes that
are, in turn, dependent on the genome. For this reason, it is more accurate to say that
the genome, the organization of the cell, and concurrent regulatory dynamics are all
propagated across generations (Jaeger et al. 2012).
Second, regulatory processes occur at all levels of organization, not only at the
level of GRNs (cf. Britten and Davidson 1969, 349), and so we should not think that
the complete “instructions” for developmental construction lie in a genetic program.
The idea that the genome is a program is a metaphor, but its metaphorical status is
rarely acknowledged. The program metaphor has become reified, its existence
inferred from the robust reproducibility of development (Nijhout 1990). This sort of
inference a type of inference to the best explanation is only warranted if there are
no alternative explanations, or if the alternative explanations are evidently inferior.
That is not the case here: there are many different ways to generate reproducible
behavior. For example, attractors of dynamical systems provide a powerful
alternative explanation that is just as consistent with the reproducibility of
developmental outcomes as a genetic program (Thom 1976; Goodwin 1982; Oster
and Alberch 1982; Webster and Goodwin 1996; Jaeger et al. 2012; Jaeger and Monk,
2014; Green et al. 2015). Moreover, it does not require questionable assumptions
native to the program metaphor that are difficult to map onto biological reality, such
as algorithmic sets of instructions, or a hardware-software distinction.
The program metaphor quickly shows its limits in the context of biological
systems because the instructions of the program and the substrate it is running on are
one and the same thing. The components of a GRN (transcription factor proteins and
cis-regulatory sequences) are produced by the organism, which is in turn generated
by GRNs. A self-referential dynamic system of this kind is very different from what
is normally understood as a program, which consists of pre-coded and pre-scheduled
algorithmic sequences of instructions. In contrast, the structure of developmental
regulatory systems is constantly modified during development through inductive
signaling events and environmental cues (Jaeger 2019). If there were instructions to
be discovered, they would be continually rewriting themselves.
In summary, the genome is not the only source of organismic heritability, genes
and their interactions are not the only major causes of development, and the notion
of a genetic program today has limited metaphorical use at best, and is potentially
very misleading. Davidson’s view, despite being focused on network connections at
the systems level, amounts to a strong form of reductionist preformationism, and in
this respect is no different from the classical genetic determinism that preceded it.
Because cellular and environmental context is crucial for both genetic inheritance
and gene expression, genetic determinism is untenable. GRNs may be necessary, but
they are not sufficient to explain organismic development.
Here one could object that, even if the GRN is not causally sufficient for
explaining development, it contains all the most important difference-making causes
of development. So, even if factors like cell state, cell environment, and dynamics
would need to be part of an ideally complete causal story of how development
occurs, explanations of development can safely abstract from these factors without
much loss of explanatory power or specificity (cf. Waters 2007). This could be a
legitimate objection if there were one-to-one correspondences between network
structure, cellular dynamics, and phenotypic outcomes. However, these
correspondences frequently do not obtain in real biological systems, as we now
The problem of correspondence
The problem of correspondence affects the ChIN model of homology most
directly, but also undermines genetic determinism interpreted as an abstraction
strategy. The problem is succinctly stated by von Dassow and Munro (1999, 315):
“there is no a priori reason to believe that the same instantiation of a developmental
mechanism underlies a conserved developmental process in even closely related
organisms.” Phenotypic evolution and evolution of GRNs is to a large extent
dissociable: evolutionary changes at both levels show a marked degree of
independence from each other. This is because developmental processes and
phenotypic outcomes are underdetermined by the composition of their trait-
generating mechanism. Equivalent dynamics and homologous morphological traits
can be generated by a wide variety of regulatory mechanisms. Thus, there is no
guarantee that regulatory mechanisms in different individuals or lineages resemble
each other even if the resulting character is strongly conserved (von Dassow and
Munro, 1999). This diversity of mechanisms is due to network drift or
developmental system drift,” caused by mutations and polymorphisms in regulatory
network interactions that do not affect the robust dynamics or outcome of a
developmental process (True and Haag 2001; Haag and True 2018).
The converse of the correspondence assumption is the expectation that different
(non-homologous) traits are generated by distinct networks. This expectation does
not hold up either: the genotype-phenotype (or GRN-phenotype) map is degenerate
in both directions. Not only can many networks generate the same (homologous)
phenotype, but the same GRN can produce different phenotypes depending on
environmental and organismic context. More specifically, almost any given network
is able to generate some range of distinct dynamic behaviors (its dynamic
repertoire), depending on the precise parameter values of the system (such as
regulatory interaction strengths, production rates or the stability of network
components, for example), and its regulatory and environmental context (given by
its initial and boundary conditions) (Figure 3; Jaeger et al. 2012; Jaeger and Monk,
2019, and references therein). Hence, one and the same GRN can produce different
phenotypic outcomes in different situations. The subsystems driving these distinct
behaviours usually overlap in the sense that they are not cleanly separable in terms
of modular network structure (Jiménez et al. 2017; Jaeger 2018, Verd et al. 2019,
Jaeger and Monk 2019). Typically, there is no unique and exclusive set of genes and
interactions that defines a specific behaviour. Instead, network components tend to
contribute (in different ways) to different behaviours under different circumstances.
As a consequence, we will frequently fail to find specific structural differences
between networks underlying distinct developmental and morphological traits.
Figure 3. Dynamical repertoires of simple gene regulatory (sub)circuits. The structure of two
closely related circuits is shown to the left; their dynamical repertoire (consisting of the indicated
dynamical regimes) on the right. Top: the repressilator (Elowitz and Leibler, 2000), bottom: the
Independently, Weiss and Fullerton (2000) coined the term “phenogenetic drift” for the same
evolutionary process (see also Weiss 2005). This type of phenomenon was first discussed (but not
explicitly named) by Schmalhausen (1949).
AC/DC circuit (Panovska-Griffiths et al. 2013). The specific regime a circuit implements depends
on parameter values (e.g., strengths of regulatory interactions) and boundary conditions.
Another aspect of the correspondence problem concerns which genes and
interactions to include in a mechanism and which ones to leave out. Many GRNs
exhibit robust dynamics, either due to redundancy in subsystems, or compensatory
regulatory capacities of structurally unrelated subsystems (functional multiplexing,
Wimsatt 2007; or distributed robustness, Wagner 2008). It is not evident how to
assign functions to components of such networks, since perturbation often has no
consequences. Other perturbation effects depend heavily on the intra-organismic,
genetic, and environmental context of the network. There are many ambiguities and
context-dependencies in delimiting the boundaries of a GRN, and so it is often
unclear how to establish the” correspondence base of a specific developmental
The evolutionary dissociability of GRNs and homologous phenotypes blocks the
construction of lineage explanations that generalize across species. One way of
addressing the problem would be to abandon homology as the criterion of phenotypic
sameness, and instead to simply individuate traits in terms of their underlying GRNs.
In this approach, traits that have undergone developmental system drift would no
longer count as the same trait. However, even assuming that this strategy could be
successfully implemented, abandoning homology in order to re-establish
correspondence across levels would create other problems for a mechanistic theory
of developmental evolution. The homology constraint is necessary to preserve the
connection between developmental mechanisms and actual trait phylogenies, which
is what is targeted by lineage explanations in evo-devo in the first place. A similar
concern pertains to the way in which mechanisms are classified as the same or
different in assessing correspondence. Later, in Section 5, we explore how dynamic
approaches to developmental mechanism attenuate but do not completely resolve the
problem. Non-correspondence between developmental mechanisms and phenotypic
traits remains as an ultimate obstacle to the mechanistic research agenda of evo-
The problem of diachronicity
The third issue with GRN-based explanations is conceptual: the problem of
diachronicity. GRNs defined by genetic and molecular experimental approaches
consist of sets of genes and qualitative interactions represented by network graphs.
A graph is a static depiction (a “snapshot”) of the regulatory mechanism
Probably the most iconic network graph in current evo-devo is the representation of the sea urchin
endomesoderm specification network first presented (in parts) in Davidson et al. 2002. This graph is
not purely static, given that developmental timing of activation of specific sub-circuits is noted.
However, coarse-grained markers of developmental timing are far from capturing the dynamical
behaviors of the activated GRNs. An always up-to-date, interactive version of this graph can be
found at:
implemented by the GRN. To explain a time-extended chain of events, however, a
causal mechanism cannot be defined solely by structural properties at a time, but
must also include time-extended elements. It must account for the way in which the
system proceeds from its initial to its final state. Static graph representations do not
do this. The regulatory structure of a network constrains, but does not specify, what
the network does. In fact, the connection between structure and dynamics is often
rather loose. As mentioned earlier, even the simplest sub-circuits of a complex
network can exhibit a more or less extended repertoire of different possible
dynamical behaviors (see Figure 3). It is therefore incorrect to claim that network
sub-circuits “imply” specific behaviors or regulatory functions (Peter and Davidson
2017), no matter how simple those sub-circuits are. Network dynamics crucially
depend on the strength of regulatory interactions, and the initial and boundary
conditions of the system, i.e. the context a sub-circuit is embedded in. Adding or
removing a single regulatory interaction can often radically change the dynamical
repertoire of a sub-circuit.
Even if parameters and boundary conditions can be determined accurately by
experimental means, mental simulation of the behavior of a GRN is rarely possible.
As soon as more than two or three simultaneous non-linear interactions are involved
in a regulatory process, it quickly becomes impossible to infer system dynamics from
the graph of a network alone. A mechanistic understanding of more complex systems
must therefore rely on computational models of network dynamics to get any traction
at all on the system’s behavior.
Dynamical models and diachronic mechanism in evo-devo
It is a central concern of evo-devo to produce a causal account of the developmental
processes that constitute the genotype-phenotype map (see Introduction). We have
shown in the previous section that static depictions of GRNs frequently fail to
provide the right kind of causal-mechanistic explanation. What is missing is a way
to understand how a developmental process, in its particular intra- and extra-cellular
context, unfolds through time from its initial to its final state: mechanisms sufficient
to explain what the system does.
Philosophical analysis of mechanistic explanation in biology has tended to focus
on decomposition, the identification of system parts, and functional localization, the
attribution of specific operations or activities to individual components (Bechtel and
Abrahamsen 2005). These activities lie at the heart of what counts as an explanation
of development, as their orchestrated operation generates the phenotypic outcome of
a regulatory process (Bechtel and Abrahamsen 2010; Bechtel 2011, 2012; Brigandt
2015). In practice, mechanistic localization of activities largely relies on methods
from genetics and molecular biology, which involve perturbing specific components
of a system and then inferring their activity by interpreting the effects caused by the
perturbation. These kind of methods have an important limitation: they can only
identify components and activities that are necessary for a given developmental
process to occur, but they cannot show that the postulated mechanism is also
sufficient to produce the observed phenomenon.
The reason for this shortcoming is that the structure of developmental systems is
complex, typically consisting of more than two or three regulatory factors and their
non-linear interactions. Non-linear systems above a very basic level of complexity
cannot be decomposed into parts that can be studied separately and recomposed
additively to yield a faithful representation of system behavior. It is because of this
insufficiency of experimental decomposition and localization that the category of
“dynamic system” or, more generally, “process” acquires a special status, as it
cannot simply be collapsed down to activities of mechanistic components considered
separately. Currently, the best way we have of capturing the dynamics of complex
systems requires the use of computational and mathematical models (Bechtel and
Abrahamsen 2005, 2010; Bechtel 2011, 2012; Brigandt 2013, 2015).
Although they are an essential aspect of dynamic mechanistic explanation, these
models do not themselves have to be mechanistic in the stereotypical sense of being
constructed bottom-up from basic biomolecular components and measured
biophysical parameters representing their activities. A model of a mechanism is
deemed explanatory as long as it accurately captures the relevant aspects of its
operation, that is, the causal relations between components at an appropriate level of
description (Bechtel and Abrahamsen 2005; Brigandt 2013, 2015). This implies that
coarse-grained phenomenological models, or models fitted to data, can support
mechanistic explanation if they help interpret the systems-level behavior of the
mechanism under study (Jaeger and Crombach 2012; Jaeger et al. 2012; Jaeger and
Monk 2014; Jaeger and Sharpe 2014; Green et al. 2015). Although such models may
not provide mechanistic explanations by themselves, they become an integral part of
mechanistic explanation for complex non-linear networks by enabling the
recomposition or reconstitution of the overall operation of the system from its
decomposed components and localized functions (Bechtel and Abrahamsen 2010;
Bechtel 2011, 2012; Brigandt 2013, 2015).
As an example of dynamic mechanistic explanation, let us consider the
developmental and evolutionary dynamics of the gap gene network in dipteran
insects (flies and midges) (Figure 4) (see Jaeger 2018, for a recent review). This
GRN is involved in pattern formation and determination of body segments during
early embryogenesis (Jaeger 2011). It was functionally decomposed in the vinegar
fly Drosophila melanogaster through genetic and molecular perturbation assays
yielding a complete set of necessary components (transcription-factor encoding
genes) and their individual regulatory connections (Nüsslein-Volhard and
Wieschaus 1980; Nüsslein-Volhard et al. 1987; Akam 1987; Ingham 1988). This
corresponds to a static GRN-type explanation as discussed in the previous sections.
Figure 4. The gap gene network of dipteran insects (flies). (A) During early development, the
blastoderm-stage embryo (shown as oval shapes, with the anterior to the left) gets subdivided into
distinct territories of differential gene expression by the segmentation gene network. This network
has a hierarchical structure, with protein gradients encoded by maternal co-ordinate genes at the
top (see graph). They provide the regulatory input to the zygotic gap gene system, which forms
the top-most hierarchical layer of the network. Gap and maternal co-ordinate genes then activate
the pair-rule genes in a periodic pattern of seven stripes. Finally, a frequency-doubling event
occurs and segment-polarity genes become expressed in 14 stripes, which form a molecular pre-
pattern for the body segments that form later in development. Arrows indicate interactions
between hierarchical layers and cross-repression within each layer. (B) Regulatory structure of the
gap gene system. The position of gap gene expression domains is indicated as boxes along the
antero-posterior axis. Cross-repressive interactions among gap genes are shown as T-bars. The
dashed line indicates a bifurcation boundary between stationary and shifting domains.
(C) Subcircuits of the gap gene system active in the anterior (AC/DC1), center (AC/DC2), and
the posterior (AC/DC3) of the embryo. Flashes indicate which subcircuit is in a critical state in
different dipteran species (Drosophila melanogaster and Megaselia abdita). Gap genes:
hunchback (hb), Krüppel (Kr), knirps (kni), and giant (gt). See text and Jaeger (2018) for further
However, the sufficiency of components and activities to account for the overall
dynamics of the system was only established much later, through a modeling effort
that reverse-engineered the gap gene network by fitting dynamical computational
models to quantitative gene expression data (Jaeger et al. 2004a,b; see also
Crombach and Jaeger 2012; Green et al. 2015; Jaeger 2018). The resulting models
showed that although the core structure of interactions among gap genes remains the
same throughout the relevant developmental stage, inputs to the system from
maternal morphogen gradients change over time, rendering the system time-variable
(Verd et al. 2017). Analysis of these models revealed switch-like and oscillatory
regulatory dynamics in the anterior versus the posterior region of the embryo (Manu
et al. 2009; Verd et al. 2018) and mapped those different behaviors back onto specific
subsystems (dynamical modules) of the network (Jaeger 2018, Verd et al. 2019,
Jaeger and Monk 2019). Surprisingly, it turns out that all of these subsystems share
a common regulatory structure despite being composed of different (yet overlapping)
sets of components (gap genes), and despite producing qualitatively different
behavior depending on their spatial and network context (Figure 4).
One of the idealization strategies used here is to formulate the model at the level
of dynamical behaviors, without direct reference to (molecular) model components
and their interactions. Instead, model dynamics are characterized by the geometry of
configuration space or, more precisely, the type and arrangement of the system’s
trajectories and attractors (Jaeger and Crombach 2012; Jaeger and Monk 2014;
Jaeger 2018). The connection between this abstract level of analysis and specific
components and activities within the network is far from trivial, and needs to be
carefully established (Verd et al. 2019). In this case, the mechanistic nature of the
explanation arises from a tight integration of experimental and modeling
approachesnot from analysis of the model alone, nor from experimental
perturbations aimed at identifying the relevant components.
This type of approach not only sheds light on the developmental dynamics of the
gap gene network, but also on its evolution. A comparative analysis between
D. melanogaster and another dipteran species, the scuttle fly Megaselia abdita,
reveals a plausible (‘how-possibly’) scenario for the evolutionary trajectory of the
system (Figure 4) (Wotton et al. 2015; Crombach et al. 2016, Verd et al., 2019).
Comparison reveals that remarkably small and localized changes in the strength of
regulatory interactions (the activities of specific transcriptional regulators) can
account for the observed qualitative differences in gene expression dynamics
between the two species. These changes are required to compensate for differences
in the input into the gap gene system from upstream maternal gradients, such that
the resulting output of the system is equivalent in both flies (Figure 4). This type of
compensatory evolution affecting the strength of regulatory interactions in a network
is called quantitative developmental system drift (Wotton et al. 2015; Crombach et
al. 2016), and is probably a very widespread mode of network evolution.
Finally, differences in gene expression dynamics between Drosophila and
Megaselia can be explained in terms of the behavior of the subsystems (dynamical
modules) of the network (Verd et al. 2019). In each species there is a subsystem that
is highly sensitive to alterations in the strength of regulatory interactions. Such
subsystems are in a critical state, poised around a bifurcation boundary where the
dynamics of the system become altered in abrupt and qualitative ways (Jaeger and
Monk 2014, 2019; Verd et al. 2019). Differences in expression dynamics are caused
by different subsystems being critical in Drosophila compared to Megaselia (Figure
4). This provides a mechanistic explanation for the evolvability of the gap gene
network (Verd et al. 2019; Jaeger and Monk 2019): it reveals that some features of
gene expression are much more sensitive to evolutionary change than others.
It is important to note that the kind of mechanistic explanation we are endorsing
here does not necessarily include any molecular details of gene regulation. It is not
necessarily a molecular mechanism, and does not have to “bottom-out” in maximal
detail. The higher- or multi-level aspect of this type of mechanistic explanation is
even more strongly highlighted by another example: the process of vertebrate
segmentation or somitogenesis (reviewed in Oates et al. 2012; Hubaud and Pourquié
In contrast to segment determination in flies, vertebrate embryos add their body
segments (called somites) one by one during the posterior extension and growth of
the paraxial mesoderm (Figure 5). Confirming an earlier theoretical prediction
(Cooke and Zeeman 1976), experimental studies showed that this process involves
repeating kinematic waves of gene expression traveling anteriorly through the tissue
(a “clock”), in combination with a mechanism to slow down and stop these periodic
waves (a “wavefront”) (Palmeirim et al. 1997; Cooke 1998; Dale and Pourquié 2000;
Masamizu et al. 2006).
Figure 5. Vertebrate somitogenesis: conserved dynamics and characters despite divergent clock
mechanisms. (A) The vertebral column as well as the segmentation process that produces it is
conserved in vertebrates from fish (left), to birds (center), to mammals (right). (B) During this
process, the U-shaped paraxial mesoderm extends in posterior (P) direction, while a segmentation
clock drives waves of gene expression towards the anterior (A). Simultaneously, a wavefront of
cell specification advances posteriorly. Wherever a wave of gene expression meets the wavefront,
a new symmetric pair of somites (segments) is formed. (C) Clock mechanisms differ between
species, as indicated by different network structures. Note that the real clock mechanisms are far
more complicated that the simplified ones shown here.
The general principles of this patterning process are conserved among vertebrate
species, and they are reasonably well understood via dynamical models together with
diverse sources of experimental evidence. In contrast to the conserved high-level
generative principles, interestingly, the molecular details of the clock mechanism
differ markedly between species (Figure 5) (Dequéant et al. 2006; Krol et al. 2011).
Although most cyclic genes belong to three conserved signaling pathways known to
be involved in somitogenesis, very different sets of individual genes exhibit
oscillatory gene expression in different vertebrate groups. In other words, dynamic
behavior at the process level is conserved, while the underlying molecular details
have radically diverged in different lineages. Wotton et al. (2015) call the exchange
of components and interactions in a network during evolution, despite conservation
of the dynamics and patterning output, qualitative developmental system drift.
Given that both the molecular details and the dynamical model are fairly well-
established, the somitogenesis example provides an occasion to revisit the problem
of correspondence from the perspective of dynamic mechanistic explanation. In
somitogenesis, different molecular mechanisms underlie a homologous character. At
the same time, the different molecular mechanisms produce equivalent dynamics, or
invariant sets with respect to their pattern-forming potential (Goodwin 1982;
Webster and Goodwin 1996). Thus, while there is no one-to-one correspondence
between the character and the specific molecular mechanisms, there is a one-to-one
correspondence between the character and the developmental process as described
in the clock and wavefront model. Molecular differences that have accumulated due
to developmental system drift are screened off by phenotypic robustness at the
process level. This raises the intriguing possibility that correspondence might be re-
gained in other cases of developmental system drift by shifting the correspondence
base to a higher level than the molecular-genetic components.
Two factors caution against interpreting this as a complete solution to the
correspondence problem, however. First, there is no guarantee that equivalent
dynamics can be found in all cases of non-correspondence. Vertebrate digit identity
may present a more difficult case than somitogenesis, for example. Second, the clock
and wavefront model is only able to identify equivalent pattern-forming mechanisms
by abstracting from specific components. One can meaningfully raise the question
of whether the model by itself is genuinely mechanistic if it does not identify specific
components, molecular or otherwise. The relation between correspondence and
abstraction in dynamical models is an unexplored conceptual issue that we leave for
future investigation.
Although modeling may turn out to have important limitations when it comes to
correspondence, dynamical modeling of developmental processes like
somitogenesis nonetheless contributes significantly to our understanding of the
genotype-phenotype map. The dissociability of molecular mechanisms and
characters implies that we cannot understand the plasticity and robustness of
development, or the probability of phenotypic transitions, using evidence at the
molecular level alone. To be a truly mechanistic science, evo-devo will need to
embrace the dynamics of development as orchestrated patterning activity across
levels, from molecules and genes to whole networks, tissues, and organisms.
Many of the central aims of evo-devo are premised on its being a mechanistic
science (Wagner et al. 2000). A causal-mechanistic understanding of the genotype-
phenotype map is necessary for understanding how the production of heritable
variation at multiple organismic levels is causally connected to the sorting of traits
by population-level processes like selection. It is necessary for explaining, rather
than just describing, developmental phenomena such as phenotypic plasticity and
robustness. It is also necessary for understanding the variational properties and
evolvability of biological characters, and for illuminating the possibilities and
probabilities of evolutionary change (Alberch 1991; Wagner and Altenberg 1996).
The explanatory mode that predominates in contemporary research in evo-devo
is based on gene regulatory networks, as exemplified by Davidson’s hierarchical
GRN model of development and Wagner’s genetic model of homology. We have
argued that, although “network thinking” of this sort represents a major improvement
over classical gene-trait atomism, it still falls short of fulfilling the mechanistic
research agenda of evo-devo. This is due to problems with genetic determinism,
correspondence across levels, and diachronicity. Fundamentally, these problems
arise from the fact that GRNs are static structures, whereas much of the difference-
making action in development lies in the complex activities and non-linear
interactions of system components (Bechtel and Abrahamsen 2005, 2010; Bechtel
2011, 2012; Jaeger and Sharpe 2014; Brigandt 2015; Green et al. 2015).
The proposed alternative is to integrate dynamical modeling of developmental
processes into empirical practice alongside the identification of system components
and their structural relations. This is currently the only realistic way to go beyond
mechanistic decomposition and functional localizationoperations that identify
causally necessary components and their interactionsand towards the
reconstitution of system-level behaviors that are causally sufficient to produce
phenotypic outcomes. Dynamic mechanistic explanation resolves the problem of
diachronicity by introducing dynamics, it attenuates (but does not eliminate) the
problem of correspondence by causally connecting networks with phenotypes while
also describing conserved dynamics of divergent molecular mechanisms, and it
enables us to avoid the problematic assumptions of gene determinism by including
non-genetic regulatory factors and environmental influences in our models. Without
the extra step of modeling network dynamics, researchers will frequently and
systematically miss out on key aspects of the causal structure of the genotype-
phenotype map. In this sense, the way for evo-devo to become adequately
“mechanistic” is for it to become “processual.”
In making the above argument, we have picked up some general insights about
mechanistic explanation along the way, which we will briefly summarize here. It is
common for biologists to conflate mechanisms with molecular mechanisms, and to
discount explanations that are not entirely based on molecular components as not
being mechanistic and/or genuinely explanatory. This conflation is problematic, and
not only for the reason that mechanistic explanations can be based on non-molecular
components (e.g., cells, tissues, organs, or environmental factors). The
correspondence problem, and developmental system drift in particular, provides
empirical reasons why explanations of development should not always bottom-out
in molecular details. The fine structure of cyclical gene networks in zebrafish will
not explain why birds or mammals have somites, and why they develop the way they
do. By contrast, higher-level dynamics, as described by mechanisms based on phase
shifts between oscillators, have more explanatory power. There is a degree of
generality in the these higher-level models that permits addressing mechanistic
questions about genotype-phenotype mapping across wider taxonomic ranges than
would be possible with molecular mechanistic explanations. The challenge is, of
course, to determine which level and amount of detail is best for a given
investigation. The response to this challenge is likely to be heavily question- and
A further implication of the somitogenesis case relates to the construction of
phylogenetic transformation series, and specifically to what Calcott (2009) calls
“lineage explanations.” To explain why the underlying molecular mechanisms
diverged in spite of conservation at the level of the oscillatory dynamics and the
resulting trait of the morphological somites, we need a working understanding of
robustness at the process level. Dynamical models can provide such an
understanding, whereas it is difficult to imagine how this could be achieved with a
purely bottom-up inventory of molecular components and interactions. This is even
more evident in the case of insect segmentation and the gap gene network, where
one and the same critical subsystem produces different dynamics depending on
spatio-temporal and network context (Verd et al. 2019; Jaeger and Monk 2019). In
both cases, a descriptive phylogenetic series of static network representations
correlated to phenotypic traits would miss out on essential causal information. It
would be limited to recording the change without explaining it, while also omitting
key variational properties that arise from the dynamics of the system. Without the
requisite dynamical mechanisms, lineage explanations based on gene networks
remain “just-so” stories rather than “how-possibly” explanations.
Finally, we have assumed that categories of mechanism and process are
complementary, despite their being sometimes pitted against one another (e.g.,
Austin 2016). In the context of causal explanation for genotype-phenotype
mappings, there is no tension between these two categories as long as mechanistic
explanation is understood in the suitably broad sense of “dynamic mechanistic
explanation” (Bechtel and Abrahamsen 2005, 2010; Bechtel 2011, 2012). Claims to
the effect that one category is ontologically more fundamental than the other may be
philosophically interesting, but they are underdetermined by the forms of
explanation considered in this paper. The ontology of organisms cannot be simply
read off from an examination of existing practices of scientific explanation.
We thank two anonymous reviewers and an editor of this journal for insightful
comments. Thanks also to audiences at ISHPSSB 2019, the 2019 Venice Summer
School in Evo-Devo, the 2019 Summer School in Philosophy of the Life Sciences at
University of Rijeka, Institut Monod in Paris, and the EvoDevo Seminar Series in
Cambridge for feedback and vigorous discussion. JD thanks the Research
Foundation Flanders (FWO) and the Konrad Lorenz Institute for Evolution and
Cognition Research for financial support.
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... Selection acts at the level of the phenotype, not the genotype, and these 2 hierarchical levels are evolutionarily dissociable [16,17,[137][138][139]. Therefore, molecular processes can undergo dramatic shifts in their qualitative (and quantitative) composition, without necessarily altering the phenotypic outcome, an event also predicted by population genetic studies [24,137,138,140,141]. Various names have been coined to address this phenomenon, the most common terminologies being "developmental system drift" [142,143], "phenogenetic drift" [137], or "qualitative and quantitative system drift" [138,140]. ...
... Selection acts at the level of the phenotype, not the genotype, and these 2 hierarchical levels are evolutionarily dissociable [16,17,[137][138][139]. Therefore, molecular processes can undergo dramatic shifts in their qualitative (and quantitative) composition, without necessarily altering the phenotypic outcome, an event also predicted by population genetic studies [24,137,138,140,141]. Various names have been coined to address this phenomenon, the most common terminologies being "developmental system drift" [142,143], "phenogenetic drift" [137], or "qualitative and quantitative system drift" [138,140]. ...
... Selection acts at the level of the phenotype, not the genotype, and these 2 hierarchical levels are evolutionarily dissociable [16,17,[137][138][139]. Therefore, molecular processes can undergo dramatic shifts in their qualitative (and quantitative) composition, without necessarily altering the phenotypic outcome, an event also predicted by population genetic studies [24,137,138,140,141]. Various names have been coined to address this phenomenon, the most common terminologies being "developmental system drift" [142,143], "phenogenetic drift" [137], or "qualitative and quantitative system drift" [138,140]. ...
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The origin of RNA interference (RNAi) is usually explained by a defense-based hypothesis, in which RNAi evolved as a defense against transposable elements (TEs) and RNA viruses and was already present in the last eukaryotic common ancestor (LECA). However, since RNA antisense regulation and double-stranded RNAs (dsRNAs) are ancient and widespread phenomena, the origin of defensive RNAi should have occurred in parallel with its regulative functions to avoid imbalances in gene regulation. Thus, we propose a neutral evolutionary hypothesis for the origin of RNAi in which qualitative system drift from a prokaryotic antisense RNA (asRNA) gene regulation mechanism leads to the formation of RNAi through constructive neutral evolution (CNE). We argue that RNAi was already present in the ancestor of LECA before the need for a new defense system arose and that its presence helped to shape eukaryotic genomic architecture and stability.
... Conceptual issues troubling current explanations of developmental processes and morphological characters in terms of GRNs have been actively discussed by recent papers, e.g., [119,120]. One of the problems debated by James DiFrisco and Johannes Jaeger [120] is the disregard of the fact that phenotypic evolution is to a large extent dissociable from the evolution of GRNs (even strongly conserved characters may result from different developmental processes in different lineages). ...
... Conceptual issues troubling current explanations of developmental processes and morphological characters in terms of GRNs have been actively discussed by recent papers, e.g., [119,120]. One of the problems debated by James DiFrisco and Johannes Jaeger [120] is the disregard of the fact that phenotypic evolution is to a large extent dissociable from the evolution of GRNs (even strongly conserved characters may result from different developmental processes in different lineages). This "network drift" or "developmental system drift," caused by mutations and polymorphisms in regulatory network interactions, does not seem to affect the outcome of a developmental process [121,122]. ...
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Irrespective of the heuristic value of interpretations of developmental processes in terms of gene regulatory networks (GRNs), larger-angle views often suffer from: (i) an inadequate understanding of the relationship between genotype and phenotype; (ii) a predominantly zoocentric vision; and (iii) overconfidence in a putatively hierarchical organization of animal body plans. Here, we constructively criticize these assumptions. First, developmental biology is pervaded by adultocentrism, but development is not necessarily egg to adult. Second, during development, many unicells undergo transcriptomic profile transitions that are comparable to those recorded in pluricellular organisms; thus, their study should not be neglected from the GRN perspective. Third, the putatively hierarchical nature of the animal body is mirrored in the GRN logic, but in relating genotype to phenotype, independent assessments of the dynamics of the regulatory machinery and the animal’s architecture are required, better served by a combinatorial than by a hierarchical approach. The trade-offs between spatial and temporal aspects of regulation, as well as their evolutionary consequences, are also discussed. Multicellularity may derive from a unicell’s sequential phenotypes turned into different but coexisting, spatially arranged cell types. In turn, polyphenism may have been a crucial mechanism involved in the origin of complex life cycles.
... The evolutionary history of life provides exciting examples of how natural selection can specialize organs to diverse functions and morphologies 1,2 . However, the connection between evolutionary stabilized phenotype, the responsible genes, and their molecular function remains mostly enigmatic 3 . The evolution of specialized organs is driven by precise amino acid modifications in proteins, which sustain organ unique functions. ...
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Highly specialized enamel matrix proteins (EMPs) are predominantly expressed in odontogenic tissues and diverged from common ancestral gene. They are crucial for the maturation of enamel and its extreme complexity in multiple independent lineages. However, divergence of EMPs occured already before the true enamel evolved and their conservancy in toothless species suggests that non-canonical functions are still under natural selection. To elucidate this hypothesis, we carried out an unbiased, comprehensive phenotyping and employed data from the International Mouse Phenotyping Consortium to show functional pleiotropy of amelogenin, ameloblastin, amelotin, and enamelin, genes, i.e. in sensory function, skeletal morphology, cardiovascular function, metabolism, immune system screen, behavior, reproduction, and respiratory function. Mice in all KO mutant lines, i.e. amelogenin KO, ameloblastin KO, amelotin KO, and enamelin KO, as well as mice from the lineage with monomeric form of ameloblastin were affected in multiple physiological systems. Evolutionary conserved motifs and functional pleiotropy support the hypothesis of role of EMPs as general physiological regulators. These findings illustrate how their non-canonical function can still effect the fitness of modern species by an example of influence of amelogenin and ameloblastin on the bone physiology.
... However, currently, process ontology is undergoing a renaissance in the philosophy of biology, as scholars are beginning to think that it is a more appropriate framework to think about and to describe living objects (Meincke, 2018;DiFrisco and Jaeger, 2019). For instance, Nicholson and Dupré (2018, p: 2) wrote: ...
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Developing precise definitions and fine categories is an important part of the scientific endeavour, enabling fidelity of transfers of knowledge and the progress of science. Currently, as a result of research on symbiotic microorganisms, science has been flooded with discoveries which appear to undermine many commonly accepted concepts and to introduce new ones that often require updated conceptualisations. One question currently being debated concerns whether or not a holobiont can be considered an organism. Based on which concept, physiology or evolutionary, of the organism is chosen, the verdict differs. We attempt here to show how a change in perspective, from that of substance ontology into that of process ontology, is capable of reconciling opposing positions within the existing discussion and enabling the implementation of conceptual pluralism.
... Such transformative potential is often attributed to GRN topology, [38,65] but topology alone is not sufficient. [66,67] First, for GRN dynamics to unfold, the system needs "initial conditions"; that is, some initial phenotype to be transformed. [68,69] In models of metabolic phenotypes (whose traits are the concentration levels of some gene products), these initial conditions are simply the gene product concentrations that are present within the cell at the onset of GRN dynamics. ...
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Evolutionary biology is paying increasing attention to the mechanisms that enable phenotypic plasticity, evolvability, and extra‐genetic inheritance. Yet, there is a concern that these phenomena remain insufficiently integrated within evolutionary theory. Understanding their evolutionary implications would require focusing on phenotypes and their variation, but this does not always fit well with the prevalent genetic representation of evolution that screens off developmental mechanisms. Here, we instead use development as a starting point, and represent it in a way that allows genetic, environmental and epigenetic sources of phenotypic variation to be independent. We show why this representation helps to understand the evolutionary consequences of both genetic and non‐genetic phenotype determinants, and discuss how this approach can instigate future areas of empirical and theoretical research. It remains challenging to integrate plasticity, developmental biases, and non‐genetic inheritance in evolutionary theory, without treating them as genetically controlled phenomena. As an aid— inspired by the genotype‐phenotype map—we develop the concept of a 3P‐map: a representation where genetic, environmental and epigenetic factors are equally indispensable for phenotypic variation.
... 2 2 The ideas developed in Sections 3.1 and 3.2 are offered as a new foil to autonomism; they are not intended to systematize earlier criticisms of autonomism. Indeed, apart from a few mechanists discussed in Section 4.1, and Craver (2016), whom we discuss at length in Section 6, other challenges to autonomism are orthogonal to the position we develop below (Bechtel 2020, Green et al. 2018, DiFrisco and Jaeger 2019, Matthiessen 2017, Ross 2020). ...
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Recent debates concern the question of whether topological or “network” explanations are a species of mechanistic explanation. In this paper, we provide a more principled framework for the purposes of advancing these discussions. Our innovations on this front are threefold. First, we more precisely characterize the requirement that all topological explanations are mechanistic explanations, and show scientific practice to belie such a requirement. Second, we provide an account that unifies mechanistic and non-mechanistic topological explanations, thereby enriching both the mechanist and autonomist programs by highlighting when and where topological explanations are mechanistic. Third, we defend this view against some powerful mechanist objections. We conclude from this that topological explanations are autonomous from their mechanistic counterparts.
... First, Craver (2016), Craver and Povich (Craver and Povich 2017), and Povich (Povich 2021; have argued that topological explanations do not model the right kind of stuff, and that without some ontic backing they are not really explanatory. The second mechanist objection is that networks are insufficiently distinct from mechanisms, especially in terms of organization, and thus if anything they are not a distinct kind of topological model, but merely a very abstract kind of mechanistic model (Bechtel 2020;DiFrisco and Jaeger 2019;Glennan 2017;Levy and Bechtel 2013;Matthiessen 2017). Given this, if they do provide any kind of explanation, it is a mechanistic one. ...
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This chapter provides a systematic overview of topological explanations in the philosophy of science literature. It does so by presenting an account of topological explanation that I (Kostić and Khalifa 2021; Kostić 2020a; 2020b; 2018) have developed in other publications and then comparing this account to other accounts of topological explanation. Finally, this appraisal is opinionated because it highlights some problems in alternative accounts of topological explanations, and also it outlines responses to some of the main criticisms raised by the so-called new mechanists.
Phylogenetics emerged in the second half of the nineteenth century as a speculative storytelling discipline dedicated to providing narrative explanations for the evolution of taxa and their traits. It coincided with lineage thinking, a process that mentally traces character evolution along lineages of hypothetical ancestors. Ancestors in Evolutionary Biology traces the history of narrative phylogenetics and lineage thinking to the present day, drawing on perspectives from the history of science, philosophy of science, and contemporary scientific debates. It shows how the power of phylogenetic hypotheses to explain evolution resides in the precursor traits of hypothetical ancestors. This book provides a comprehensive exploration of the topic of ancestors, which is central to modern biology, and is therefore of interest to graduate students, researchers, and academics in evolutionary biology, palaeontology, philosophy of science, and the history of science.
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Insect segmentation is a well-studied and tractable system with which to investigate the genetic regulation of development. Though insects segment their germband using a variety of methods, modelling work implies that a single gene regulatory network can underpin the two main types of insect segmentation. This means limited genetic changes are required to explain significant differences in segmentation mode between different insects. This idea needs to be tested in a wider variety of species, and the nature of the gene regulatory network (GRN) underlying this model has not been tested. Some insects, for example Nasonia vitripennis and Apis mellifera segment progressively, a pattern not examined in previous studies of this segmentation model, producing stripes at different times progressively through the embryo, but not from a segment addition zone. Here we aim to understand the GRNs patterning Nasonia using a simulation-based approach. We found that an existing model of Drosophila segmentation (Clark, 2017) can be used to recapitulate Nasonia’s progressive segmentation, if provided with altered inputs in the form of expression of the timer genes Nv-caudal and Nv-odd paired. We predict limited topological changes to the pair rule network and show by RNAi knockdown, that Nv-odd paired is required for morphological segmentation. Together this implies that very limited changes to the Drosophila network are required to simulate Nasonia segmentation, despite significant differences in segmentation modes, implying that Nasonia use a very similar version of an ancestral GRN used by Drosophila, which must therefore have been conserved for at least 300 million years.
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This manuscript is a chapter in the book "Evolutionary Systems Biology: Advances, Questions, and Opportunities" to be published with Springer-Nature.
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The existence of discrete phenotypic traits suggests that the complex regulatory processes which produce them are functionally modular. These processes are usually represented by networks. Only modular networks can be partitioned into intelligible subcircuits able to evolve relatively independently. Traditionally, functional modularity is approximated by detection of modularity in network structure. However, the correlation between structure and function is loose. Many regulatory networks exhibit modular behaviour without structural modularity. Here we partition an experimentally tractable regulatory network-the gap gene system of dipteran insects-using an alternative approach. We show that this system, although not structurally modular, is composed of dynamical modules driving different aspects of whole-network behaviour. All these subcircuits share the same regulatory structure, but differ in components and sensitivity to regulatory interactions. Some subcircuits are in a state of criticality, while others are not, which explains the observed differential evolvability of the various expression features in the system.
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Homology is the fundamental determinant of the sameness of biological characters or traits. When two characters stand in a relation of homology, they belong to the same character kind. For example, the eyes of humans and birds are homologous as vertebrate eyes-that is, they are the same kind of character: vertebrate eyes. Although the concept of homology originated in pre-Darwinian comparative anatomy, it was subsequently revealed to be an evolutionary phenomenon caused by common descent. Contemporary investigators work roughly within the following generic evolutionary conception of homology: Homology: Two characters in distinct organisms or taxa are homologous if they are genealogically connected by continuous descent from a common ancestor that had the same character.
An organism’s phenotype can be thought of as consisting of a set of discrete traits, able to evolve relatively independently of each other. This implies that the developmental processes generating these traits—the underlying genotype-phenotype map—must also be functionally organised in a modular manner. The genotype-phenotype map lies at the heart of evolutionary systems biology. Recently, it has become popular to define developmental modules in terms of the structure of gene regulatory networks. This approach is inherently limited: gene networks often do not have structural modularity. More generally, the connection between structure and function is quite loose. In this chapter, we discuss an alternative approach based on the concept of dynamical modularity, which overcomes many of the limitations of structural modules. A dynamical module consists of the activities of a set of genes and their interactions that generate a specific dynamic behaviour. These modules can be identified and characterised by phase space analysis of data-driven models. We showcase the power and the promise of this new approach using several case studies. Dynamical modularity forms an important component of a general theory of the evolution of regulatory systems and the genotype-phenotype map they define.
The first comprehensive synthesis on development and evolution: it applies to all aspects of development, at all levels of organization and in all organisms, taking advantage of modern findings on behavior, genetics, endocrinology, molecular biology, evolutionary theory and phylogenetics to show the connections between developmental mechanisms and evolutionary change. This book solves key problems that have impeded a definitive synthesis in the past. It uses new concepts and specific examples to show how to relate environmentally sensitive development to the genetic theory of adaptive evolution and to explain major patterns of change. In this book development includes not only embryology and the ontogeny of morphology, sometimes portrayed inadequately as governed by "regulatory genes," but also behavioral development and physiological adaptation, where plasticity is mediated by genetically complex mechanisms like hormones and learning. The book shows how the universal qualities of phenotypes--modular organization and plasticity--facilitate both integration and change. Here you will learn why it is wrong to describe organisms as genetically programmed; why environmental induction is likely to be more important in evolution than random mutation; and why it is crucial to consider both selection and developmental mechanism in explanations of adaptive evolution. This book satisfies the need for a truly general book on development, plasticity and evolution that applies to living organisms in all of their life stages and environments. Using an immense compendium of examples on many kinds of organisms, from viruses and bacteria to higher plants and animals, it shows how the phenotype is reorganized during evolution to produce novelties, and how alternative phenotypes occupy a pivotal role as a phase of evolution that fosters diversification and speeds change. The arguments of this book call for a new view of the major themes of evolutionary biology, as shown in chapters on gradualism, homology, environmental induction, speciation, radiation, macroevolution, punctuation, and the maintenance of sex. No other treatment of development and evolution since Darwin's offers such a comprehensive and critical discussion of the relevant issues. Developmental Plasticity and Evolution is designed for biologists interested in the development and evolution of behavior, life-history patterns, ecology, physiology, morphology and speciation. It will also appeal to evolutionary paleontologists, anthropologists, psychologists, and teachers of general biology.
Homology—a similar trait shared by different species and derived from common ancestry, such as a seal's fin and a bird's wing—is one of the most fundamental yet challenging concepts in evolutionary biology. This book provides the first mechanistically based theory of what homology is and how it arises in evolution. The book argues that homology, or character identity, can be explained through the historical continuity of character identity networks—that is, the gene regulatory networks that enable differential gene expression. It shows how character identity is independent of the form and function of the character itself because the same network can activate different effector genes and thus control the development of different shapes, sizes, and qualities of the character. Demonstrating how this theoretical model can provide a foundation for understanding the evolutionary origin of novel characters, the book applies it to the origin and evolution of specific systems, such as cell types; skin, hair, and feathers; limbs and digits; and flowers. The first major synthesis of homology to be published in decades, this book reveals how a mechanistically based theory can serve as a unifying concept for any branch of science concerned with the structure and development of organisms, and how it can help explain major transitions in evolution and broad patterns of biological diversity.