Co‐option of the eye developmental gene regulatory network may have led to the appearance of novel functional traits on the wings of flies and butterflies. The first trait is a recently described wing organ in a species of extinct midge resembling the outer layers of the midge's own compound eye. The second trait is red pigment patches on Heliconius butterfly wings connected to the expression of an eye selector gene, optix. These examples, as well as others, (...) are discussed regarding the type of empirical evidence and burden of proof that have been used to infer gene network co‐option underlying the origin of novel traits. A conceptual framework describing increasing confidence in inference of network co‐option is proposed. Novel research directions to facilitate inference of network co‐option are also highlighted, especially in cases where the pre‐existent and novel traits do not resemble each other. (shrink)

Co‐option of the eye developmental gene regulatory network may have led to the appearance of novel functional traits on the wings of flies and butterflies. The first trait is a recently described wing organ in a species of extinct midge resembling the outer layers of the midge's own compound eye. The second trait is red pigment patches on Heliconius butterfly wings connected to the expression of an eye selector gene, optix. These examples, as well as others, (...) are discussed regarding the type of empirical evidence and burden of proof that have been used to infer gene network co‐option underlying the origin of novel traits. A conceptual framework describing increasing confidence in inference of network co‐option is proposed. Novel research directions to facilitate inference of network co‐option are also highlighted, especially in cases where the pre‐existent and novel traits do not resemble each other. (shrink)

Hindbrain development is orchestrated by a vertebrate gene regulatory network that generates segmental patterning along the anterior–posterior axis via Hox genes. Here, we review analyses of vertebrate and invertebrate chordate models that inform upon the evolutionary origin and diversification of this network. Evidence from the sea lamprey reveals that the hindbrain regulatory network generates rhombomeric compartments with segmental Hox expression and an underlying Hox code. We infer that this basal feature was present in ancestral (...) vertebrates and, as an evolutionarily constrained developmental state, is fundamentally important for patterning of the vertebrate hindbrain across diverse lineages. Despite the common ground plan, vertebrates exhibit neuroanatomical diversity in lineage‐specific patterns, with different vertebrates revealing variations of Hox expression in the hindbrain that could underlie this diversification. Invertebrate chordates lack hindbrain segmentation but exhibit some conserved aspects of this network, with retinoic acid signaling playing a role in establishing nested domains of Hox expression. (shrink)

Recent work on the evolution of signaling systems provides a novel way of thinking about genetic information, where information is passed between genes in a regulatory network. I use examples from evolutionary developmental biology to show how information can be created in these networks and how it can be reused to produce rapid phenotypic change.

Different sources of data on the evolution of segmentation lead to very different conclusions. Molecular similarities in the developmental pathways generating a segmented body plan tend to suggest a segmented common ancestor for all bilaterally symmetrical animals. Data from paleontology and comparative morphology suggest that this is unlikely. A possible solution to this conundrum is that throughout evolution there was a parallel co‐option of gene regulatory networks that had conserved ancestral roles in determining body axes and in elongating (...) the anterior‐posterior axis. Inherent properties in some of these networks made them easily recruitable for generating repeated patterns and for determining segmental boundaries. Phyla where this process happened are among the most successful in the animal kingdom, as the modular nature of the segmental body organization allowed them to diverge and radiate into a bewildering array of variations on a common theme. (shrink)

Models of gene regulatory networks have proven useful for understanding many aspects of the highly complex behavior of biological control networks. Randomly generated non-Boolean networks were used in experimental simulations to generate data on dynamic phenotypes as a function of several genotypic parameters. We found that predictive relationships between some phenotypes and quantitative genotypic parameters such as number of network genes, interaction density, and initial condition could be derived depending on the strength of the topological genotype on (...) specific phenotypes. We quantitated the strength of the topological genotype effect on a number of phenotypes in multi-gene networks. For phenotypes with a low influence of topological genotype, derived and empirical relationships using quantitative genotype parameters were accurate in phenotypic outcomes. We found a number of dynamic network properties, including oscillation behaviors, that were largely dependent on genotype topology, and for which no such general quantitative relationships were determinable. It remains to be determined if these results are applicable to biological gene regulatory networks. (shrink)

Cascades of transcriptional regulation are the common source of the forward drive in all developmental systems. Increases in complexity and specificity of gene expression at successive stages are based on the collaboration of varied combinations of transcription factors already expressed in the cells to turn on new genes, and the logical relationships between the transcription factors acting and becoming newly expressed from stage to stage are best visualized as gene regulatory networks. However, gene regulatory networks (...) used in different developmental contexts underlie processes that actually operate through different sets of rules, which affect the kinetics, synchronicity, and logical properties of individual network nodes. Contrasting early embryonic development in flies and sea urchins with adult mammalian hematopoietic development from stem cells, major differences are seen in transcription factor dosage dependence, the silencing or damping impacts of repression, and the impact of cellular regulatory history on the parts of the genome that are accessible to transcription factor action in a given cell type. These different features not only affect the kinds of models that can illuminate developmental mechanisms in the respective biological systems, but also reflect the evolutionary needs of these biological systems to optimize different aspects of development. (shrink)

Various algorithms have been proposed for learning (partial) genetic regulatory networks through systematic measurements of differential expression in wild type versus strains in which expression of specific genes has been suppressed or enhanced, as well as for determining the most informative next experiment in a sequence. While the behavior of these algorithms has been investigated for toy examples, the full computational complexity of the problem has not received sufficient attention. We show that finding the true regulatory network (...) requires (in the worst-case) exponentially many experiments (in the number of genes). Perhaps more importantly, we provide an algorithm for determining the set of regulatory networks consistent with the observed data. We then show that this algorithm is infeasible for realistic data (specifically, nine genes and ten experiments). This infeasibility is not due to an algorithmic flaw, but rather to the fact that there are far too many networks consistent with the data (10 18 in the provided example). We conclude that gene perturbation experiments are useful in confirming regulatory network models discovered by other techniques, but not a feasible search strategy. (shrink)

Developmental processes in complex animals are directed by a hardwired genomic regulatory code, the ultimate function of which is to set up a progression of transcriptional regulatory states in space and time. The code specifies the gene regulatory networks (GRNs) that underlie all major developmental events. Models of GRNs are required for analysis, for experimental manipulation and, most fundamentally, for comprehension of how GRNs work. To model GRNs requires knowledge of both their overall structure, which depends (...) upon linkage amongst regulatory genes, and the modular building blocks of which GRNs are heirarchically constructed. The building blocks consist of basic transcriptional control processes executed by one or a few functionally linked genes. We show how the functions of several such building blocks can be considered in mathematical terms, and discuss resolution of GRNs by both “top down” and “bottom up” approaches. BioEssays 24:1118–1129, 2002. © 2002 Wiley‐Periodicals, Inc. (shrink)

This paper deals with the problem of reconstruction of the intergenic interaction graph from the raw data of genetic co-expression coming with new technologies of bio-arrays (DMA-arrays, protein-arrays, etc.). These new imaging devices in general only give information about the asymptotical part (fixed configurations of co-expression or limit cycles of such configurations) of the dynamical evolution of the regulatory networks (genetic and/or proteic) underlying the functioning of living systems. Extracting the casual structure and interaction coefficients of a gene (...) interaction network from the observed configurations is a complex problem. But if all the fixed configurations are supposedly observed and if they are factorizable into two or more subsets of values, then the interaction graph possesses as many connected components as the number of factors and the solution is obtained in polynomial time. This new result allows us for example to partly solve the topology of the genetic regulatory network ruling the flowering in Arabidopsis thaliana. (shrink)

Our primary interest is in determining how many gene perturbation experiments are required to determine the Various algorithms have been proposed for learning..

Consolidation of long-term memory is a highly and precisely regulated multistep process. The transcription regulator cAMP response element-binding protein (CREB) plays a key role in initiating memory consolidation. With time processing, first the cofactors are changed and, secondly, CREB gets dispensable. This ultimately changes the expressed gene program to genes required to maintain the memory. Regulation of memory consolidation also requires epigenetic mechanisms and control at the RNA level. At the neuronal circuit level, oscillation in the activity of CREB (...) and downstream factor define engram cells. Together the combination of all regulation mechanisms allows correct memory processing while keeping the process dynamic and flexible to adjust to different contexts. Also see the video abstract here https://youtu.be/BhSCSmorpEc. (shrink)

Evolution has shaped all living organisms on Earth, although many details of this process are shrouded in time. However, it is possible to see, with one's own eyes, evolution as it happens by performing experiments in defined laboratory conditions with microbes that have suitably fast generations. The longest‐running microbial evolution experiment was started in 1988, at which time twelve populations were founded by the same strain ofEscherichia coli. Since then, the populations have been serially propagated and have evolved for tens (...) of thousands of generations in the same environment. The populations show numerous parallel phenotypic changes, and such parallelism is a hallmark of adaptive evolution. Many genetic targets of natural selection have been identified, revealing a high level of genetic parallelism as well. Beneficial mutations affect all levels of gene regulation in the cells including individual genes and operons all the way to global regulatory networks. Of particular interest, two highly interconnected networks—governing DNA superhelicity and the stringent response—have been demonstrated to be deeply involved in the phenotypic and genetic adaptation of these experimental populations. BioEssays 29:846–860, 2007. © 2007 Wiley Periodicals, Inc. (shrink)

Transposable elements are no longer considered to be “junk” DNA. Here, we review how TEs can impact gene regulation systematically. TEs encode various regulatory elements that enables them to regulate gene expression. RJ Britten and EH Davidson hypothesized that TEs can integrate the function of various transcriptional regulators into gene regulatory networks. Uniquely TEs can deposit regulatory sites across the genome when they transpose, and thereby bring multiple genes under control of the same (...) class='Hi'>regulatory logic. Several studies together have robustly established that TEs participate in embryonic development and oncogenesis. We discuss the regulatory characteristics of TEs in context of evolution to understand the extent of their impact on gene networks. Understanding these features of TEs is central to future investigations of TEs in cellular processes and phenotypic presentations, which are applicable to development and disease studies. We re-visit the Britten–Davidson “gene-battery” model and understand the genetic and transcriptional impact of TEs in innovating gene regulatory networks. Transposable elements are exogenous sequences that have either been co-opted or repurposed for host functions. The vast amounts of TE sequence in the genome provides raw material for gene expression regulation, and still remains to be fully understood; the “gene-battery” model provides the basis for understanding this. (shrink)

After reviewing theoretical reasons for doubting that machine learning methods can accurately infer gene regulatory networks from microarray data, we test 10 algorithms on simulated data from the sea urchin network, and on microarray data for yeast compared with recent experimental determinations of the regulatory network in the same yeast species. Our results agree with the theoretical arguments: most algorithms are at chance for determining the existence of a regulatory connection between gene pairs, (...) and the algorithms that perform better than chance are nonetheless so errorprone as to be of little practical use in these applications. (shrink)

Metazoan genomes contain thousands of protein‐coding and non‐coding RNA genes, most of which are differentially expressed, i.e., at different locations, at different times during development, or in response to environmental signals. Differential gene expression is achieved through complex regulatory networks that are controlled in part by two types of trans‐regulators: transcription factors (TFs) and microRNAs (miRNAs). TFs bind to cis‐regulatory DNA elements that are often located in or near their target genes, while miRNAs hybridize to cis‐regulatory (...) RNA elements mostly located in the 3′ untranslated region of their target mRNAs. Here, we describe how these trans‐regulators interact with each other in the context of gene regulatory networks to coordinate gene expression at the genome‐scale level, and discuss future challenges of integrating these networks with other types of functional networks. (shrink)

At the interface of developmental biology and evolutionary biology, the very criteria of scientific knowledge are up for grabs. A central issue is the status of evolutionary genetics models, which some argue cannot coherently be used with complex gene regulatory network (GRN) models to explain the same evolutionary phenomena. Despite those claims, many researchers use evolutionary genetics models jointly with GRN models to study evolutionary phenomena. This dissertation compares two recent research projects in which researchers jointly use (...) the two kinds of models. I use methods from the empirical social sciences, such as digital corpus analysis, content analysis, and structured case analysis. I infer three primary conclusions about projects of the kind studied. First, they employ an implicit concept of gene that enables the joint use of both kinds of models. Second, they pursue more epistemic aims besides mechanistic explanation of phenomena. Third, they don’t work to create and export broad synthesized theories. Rather, they focus on phenomena too complex to be understood by a common general theory, they distinguish parts of the phenomena, and they apply models from different theories to the different parts. For such projects, seemingly incompatible models are synthesized largely through mediated representations of complex phenomena. (shrink)

Studies on the conservation of the inferred transcriptional regulatory network of prokaryotes have suggested that specific transcription factors are less‐widely conserved in comparison to their target genes. This observation implied that, at large evolutionary distances, the turnover of specific transcription factors through loss and non‐orthologous displacement might be a major factor in the adaptive radiation of prokaryotes. However, the recent work of Hershberg and Margalit1 suggests that, at shorter phylogenetic scales, the evolutionary dynamics of the bacterial transcriptional (...) class='Hi'>regulatory network might exhibit distinct patterns. The authors find previously unnoticed relationships between the regulatory mode (activation or repression), the number of regulatory interactions and their conservation patterns in γ‐proteobacteria. These relationships might be shaped by the differences in the adaptive value and mode of operation of different regulatory interactions. BioEssays 29:625–629, 2007. © 2007 Wiley Periodicals, Inc. (shrink)

The identification of network motifs has been widely considered as a significant step towards uncovering the design principles of biomolecular regulatory networks. To date, time‐invariant networks have been considered. However, such approaches cannot be used to reveal time‐specific biological traits due to the dynamic nature of biological systems, and hence may not be applicable to development, where temporal regulation of gene expression is an indispensable characteristic. We propose a concept of a “temporal sequence of network motifs”, (...) a sequence of network motifs in active sub‐networks constructed over time, and investigate significant network motifs in the active temporal sub‐networks of Drosophila melanogaster. Based on this concept, we find a temporal sequence of network motifs which changes according to developmental stages and thereby cannot be identified from the whole static network. Moreover, we show that the temporal sequence of network motifs corresponding to each developmental stage can be used to describe pivotal developmental events. (shrink)

Changes in the abundance of protein and RNA molecules can impair the formation of complexes in the cell leading to toxicity and death. Here we exploit the information contained in protein, RNA and DNA interaction networks to provide a comprehensive view of the regulation layers controlling the concentration‐dependent formation of assemblies in the cell. We present the emerging concept that RNAs can act as scaffolds to promote the formation ribonucleoprotein complexes and coordinate the post‐transcriptional layer of gene regulation. We (...) describe the structural and interaction network properties that characterize the ability of protein and RNA molecules to interact and phase separate in liquid‐like compartments. Finally, we show that presence of structurally disordered regions in proteins correlate with the propensity to undergo liquid‐to‐solid phase transitions and cause human diseases. Also see the video abstract here https://youtu.be/kfpqibsNfS0. (shrink)

Insulators play a central role in subdividing the chromosome into a series of discrete topologically independent domains and in ensuring that enhancers and silencers contact their appropriate target genes. In this review we first discuss the general characteristics of insulator elements and their associated protein factors. A growing collection of insulator proteins have been identified including a family of proteins whose expression is developmentally regulated. We next consider several unexpected discoveries that require us to completely rethink how insulators function (and (...) how they can best be assayed). These discoveries also require a reevaluation of how insulators might restrict or orchestrate (by preventing or promoting) interactions between regulatory elements and their target genes. We conclude by connecting these new insights into the mechanisms of insulator action to dynamic changes in the three‐dimensional topology of the chromatin fiber and the generation of specific patterns of gene activity during development and differentiation. (shrink)

In this post‐sequencing era, geneticists can focus on functional genomics on a much larger scale than ever before. One goal is the discovery and elucidation of the intricate genetic networks that co‐ordinate transcriptional activation in different regulatory circuitries. High‐throughput gene expression measurement using DNA arrays has thus become routine strategy. This approach, however, does not directly identify gene loci that belong to the same regulatory group; e.g., those that are bound by a common (set of) transcription (...) factor(s). Working in yeast, two groups have recently published an elegant method that could circumvent this problem, by combining chromatin immunoprecipitation and DNA microarrays.(1,2) The method is likely to provide a powerful tool for the dissection of global regulatory networks in eukaryotic cells. BioEssays 23:473–476, 2001. © 2001 John Wiley & Sons, Inc. (shrink)

Through their transcript products genes regulate the rates at which an immense variety of transcripts and subsequent proteins occur. Understanding the mechanisms that determine which genes are expressed, and when they are expressed, is one of the keys to genetic manipulation for many purposes, including the development of new treatments for disease. Viewing each gene in a genome as a distinct variable that is either on or off, or more realistically as a continuous variable, the values of some of (...) these variables influence the values of others through the regulatory proteins they express, including, of course, the possibility that the rate of expression of a gene at one time may, in various circumstances, influence the rate of expression of that same gene at a later time. If we imagine an arrow drawn from each gene expression variable at a given time to a gene variable whose expression it influences a short while after, the result is a network, technically a directed acyclic graph. For example, the DAG in Figure 1 is a representation of a system in which the expression level of gene G1 at time 1 ) causes the expression level of G2, which in turn causes the expression level of G3. The arrows in Figure 1 which do not have a variable at their tails are “error terms” which represent all of the causes of a variable other than the ones explicitly represented in the DAG. The DAG describes more than associations—it describes causal connections among gene expression rates. A shock to a cell—by mutation, heating, chemical treatment, etc. may alter the DAG describing the relations among gene expressions, for example by activating a gene that was otherwise not expressed, producing a cascade of new expression effects. Although “knockout” experiments can reveal some of the underlying causal network of gene expression levels, unless guided by information from other sources, such experiments are limited in how much of the network structure they can reveal, due to the sheer number of possible combinations of experimental manipulations of genes necessary to reveal the complete causal network. Recent developments have made it possible to compare quantitatively the expression of tens of thousands of genes in cells from different sources in a single experiment, and to trace gene expression over time in thousands of genes simultaneously. cDNA microarrays are already producing extensive data, much of it available on the web. Thus there are calls for analytic software that can be applied to microarray and other data to help infer regulatory networks. In this paper we will review current techniques that are available for searching for the causal relations between variables, describe algorithmic and data gathering obstacles to applying these techniques to gene expression levels, and describe the prospects for overcoming these obstacles. (shrink)

Network motifs are small patterns of connections, found over‐represented in gene regulatory networks. An example is the negative feedback loop (e.g. factor A represses itself). This opposes its own state so that when ‘on’ it tends towards ‘off’ – and vice versa. Here, we argue that such self‐opposition, if considered dimensionlessly, is analogous to the liar paradox: ‘This statement is false’. When ‘true’ it implies ‘false’ – and vice versa. Such logical constructs have provided philosophical consternation for (...) over 2000 years. Extending the analogy, other network topologies give strikingly varying outputs over different dimensions. For example, the motif ‘A activates B and A. B inhibits A’ can give switches or oscillators with time only, or can lead to Turing‐type patterns with both space and time (spots, stripes or waves). It is argued here that the dimensionless form reduces to a variant of ‘The following statement is true. The preceding statement is false’. Thus, merely having a static topological description of a gene network can lead to a liar paradox. Network diagrams are only snapshots of dynamic biological processes and apparent paradoxes can reveal important biological mechanisms that are far from paradoxical when considered explicitly in time and space. (shrink)

Numerous liver specific genes are transcriptionally activated by the binding to their promoter or enhancer of Hepatic Nuclear Factor 1 (HNF1). HNF1 contains a variant homeo‐domain and binds to DNA as either a homod‐imer or a heterodimer with the vHNF1 protein. Surprisingly, HNF1 is not restricted to hepatocytes but is expressed in epithelial cells of several endoderm derived organs and in mesoderm derived kidney tubules. Hence, HNF1 alone can not account for the differentiated state of the hepatic cells. In fact, (...) several other liver‐enriched transcription factors have been cloned. The hepatic phenotype could result from the combinatorial. expression of these regulators. Possible involvement of these trans‐acting factors in liver organogenesis and hepatic differentiation is discussed. (shrink)

Cover Photograph: Resolving developmental genetics in the fourth dimension: an illustration (by Kwang‐Hyun Cho himself) of the principle of dynamic network motifs in Drosophila development. Hitherto largely considered in terms of time‐invariant networks, drosophila development is viewed in the article by Man‐Sun Kim, Jeong‐Rae Kim, and Kwang‐Hyun Cho as the result of networks of gene interactions that change during the course of development. Using this paradigm, pivotal developmental events can be correlated with particular changes from one constellation of (...) gene interactions to a different one, and the method allows the identification of certain network motifs that are not identifiable using static approaches. Features article: “Dynamic network rewiring determines temporal regulatory functions in the Drosophila melanogaster development processes”, see pages 505 – 513. (shrink)

A network of gene regulation organized in a hierarchical and combinatorial manner is crucially involved in the development of the neural network, and has to be considered one of the main substrates of genetic change in its evolution. Though qualitative features may emerge by way of the accumulation of rather unspecific quantitative changes, it is reasonable to assume that at least in some cases specific combinations of regulatory parts of the genome initiated new directions of evolution, (...) leading to novel capabilities of the brain. These notions are applied, in this paper, to the evolution of the capability of cognition-based human empa­thy. It is suggested that it has evolved as a secondary effect of the evolution of strategic thought. Development of strategies depends on abstract representations of one’s own pos­sible future states in one’s own brain to allow assessment of their emotional desirability, but also on the representation and emotional evaluation of possible states of others, allowing anticipation of their behaviour. This is best achieved if representations of others are con­nected to one’s own emotional centres in a manner similar to self-representations. For this reason, the evolution of the human brain is assumed to have established representations with such linkages. No group selection is involved, because the quality of strategic thought affects the fitness of the individual. A secondary effect of this linkage is that both the actual states and the future perspectives of others elicit vicarious emotions, which may contribute to the motivations of altruistic behaviour. (shrink)

Because a large number of molecular mechanisms involved in gene regulation have been described during the last decades, it is now becoming possible to address questions about the global structure of gene regulatory networks, at least in the case of some of the best-characterized organisms.This paper presents a global characterization of the transcriptional regulation in Escherichiacoli on the basis of the current data. The connectivity of the corresponding network was evaluated by analyzing the distribution of the (...) number of genes regulated by a given regulatory protein, and the distribution of the number of regulatory genes regulating a given regulated gene. The mean connectivity found (between 2 and 3) shows a rather loosely interconnected structure. Special emphasis is given to circular sequences of interactions (“circuits”) because of their critical dynamical properties. Only one-element circuits were found, in which negative autoregulation is the dominant architecture. These global properties are discussed in light of several pertinent theoretical approaches, as well as in terms of physiological and evolutionary considerations. BioEssays 20:433–440, 1998. © 1998 John Wiley & Sons Inc. (shrink)

Because a large number of molecular mechanisms involved in gene regulation have been described during the last decades, it is now becoming possible to address questions about the global structure of gene regulatory networks, at least in the case of some of the best-characterized organisms.This paper presents a global characterization of the transcriptional regulation in Escherichiacoli on the basis of the current data. The connectivity of the corresponding network was evaluated by analyzing the distribution of the (...) number of genes regulated by a given regulatory protein, and the distribution of the number of regulatory genes regulating a given regulated gene. The mean connectivity found (between 2 and 3) shows a rather loosely interconnected structure. Special emphasis is given to circular sequences of interactions (“circuits”) because of their critical dynamical properties. Only one-element circuits were found, in which negative autoregulation is the dominant architecture. These global properties are discussed in light of several pertinent theoretical approaches, as well as in terms of physiological and evolutionary considerations. BioEssays 20:433–440, 1998. © 1998 John Wiley & Sons Inc. (shrink)

Because a large number of molecular mechanisms involved in gene regulation have been described during the last decades, it is now becoming possible to address questions about the global structure of gene regulatory networks, at least in the case of some of the best-characterized organisms.This paper presents a global characterization of the transcriptional regulation in Escherichiacoli on the basis of the current data. The connectivity of the corresponding network was evaluated by analyzing the distribution of the (...) number of genes regulated by a given regulatory protein, and the distribution of the number of regulatory genes regulating a given regulated gene. The mean connectivity found (between 2 and 3) shows a rather loosely interconnected structure. Special emphasis is given to circular sequences of interactions (“circuits”) because of their critical dynamical properties. Only one-element circuits were found, in which negative autoregulation is the dominant architecture. These global properties are discussed in light of several pertinent theoretical approaches, as well as in terms of physiological and evolutionary considerations. BioEssays 20:433–440, 1998. © 1998 John Wiley & Sons Inc. (shrink)

Langerhans cells (LCs), residing in the epidermis, are able to induce potent immunogenic responses and also to mediate immune tolerance. We propose that tolerogenic and immunogenic responses of LCs are directed by signaling from the epidermis and involve counter‐acting gene circuits that are coupled to a core maturation gene module. We base our analysis on recent genetic and genomic findings facilitating the understanding of the molecular mechanisms controlling these divergent immune functions. Comparing gene regulatory network (...) (GRN) analyses of various types of dendritic cells (DCs) including LCs we integrate signaling‐dependent (TGFβ, EpCAM, β‐Catenin) and transcription factor (IRF4, IRF1, NFκB) regulated gene circuits that appear to orchestrate the distinctive LC functional states. Our model proposes, that while epidermal signaling in the steady‐state promotes LC tolerogenic function, the disruption of cell‐cell contacts coupled with inflammatory signaling induces LC immunogenic programing. The conceptual framework emphasizes the sensing of discrete epidermal and inflammatory cues by resident LCs in dictating their genomic programing and cell state dynamics. (shrink)

Explanation in terms of gene regulatory networks has become standard practice in evolutionary developmental biology. 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: the problem of genetic determinism, the problem of correspondence between network structure and function, and 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. (shrink)

This paper concerns periodic solutions of a class of equations that model gene regulatory networks. Unlike the vast majority of previous studies, it is not assumed that all decay rates are identical. To handle this more general situation, we rely on monotonicity properties of these systems. Under an alternative assumption, it is shown that a classical fixed point theorem for monotone, concave operators can be applied to these systems. The required assumption is expressed in geometrical terms as an (...) alignment condition on so-called focal points . As an application, we show the existence and uniqueness of a stable periodic orbit for negative feedback loop systems in dimension 3 or more, and of a unique stable equilibrium point in dimension 2. This extends a theorem of Snoussi, which showed the existence of these orbits only. (shrink)

Embryonic development combines paradoxical properties: it has great precision, it is usually conducted at breakneck speed and it is flexible on relatively short evolutionary time scales, particularly at early stages. While these features appear mutually exclusive, we consider how they may be reconciled by the properties of key early regulatory networks. We illustrate these ideas with the network that controls development of endoderm progenitors. We argue that this network enables precision because of its intrinsic stability, self propagation (...) and dependence on signalling. The network enables high developmental speed because it is rapidly established by maternal inputs at multiple points. In turn these properties confer flexibility on an evolutionary time scale because they can be initiated in many ways, while buffering essential progenitor cell populations against changes in their embryonic environment on both evolutionary and developmental time scales. Although stable, these networks must be capable of rapid dissolution as cell differentiation progresses. While we focus on the core early endodermal network of vertebrates, we argue that these properties are likely to be general in early embryonic stem cell populations, such as mammalian ES cells. BioEssays 30:757–765, 2008. © 2008 Wiley Periodicals, Inc. (shrink)

Gene regulatory networks are intensively studied in biology. One of the main aims of these studies is to gain an understanding of how the structure of genetic networks relates to specific functions such as chemotaxis and the circadian clock. Scientists have examined this question by using model organisms such as Drosophila and mathematical models. In the last years, synthetic models—engineered genetic networks—have become more and more important in the exploration of gene regulation. What is the potential of (...) this new approach in the investigation of gene network structures? How do synthetic models relate to model organisms and mathematical models? (shrink)

The logic of genetic discovery has changed little over time, but the focus of biology is shifting from simple genotype–phenotype relationships to complex metabolic, physiological, developmental, and behavioral traits. In light of this, the traditional reductionist view of individual genes as privileged difference‐making causes of phenotypes is re‐examined. The scope and nature of genetic effects in complex regulatory systems, in which dynamics are driven by regulatory feedback and hierarchical interactions across levels of organization are considered. This review argues (...) that it is appropriate to treat genes as specific actual difference‐makers for the molecular regulation of gene expression. However, they are often neither stable, proportional, nor specific as causes of the overall dynamic behavior of regulatory networks. Dynamical models, properly formulated and validated, provide the tools to probe cause‐and‐effect relationships in complex biological systems, allowing to go beyond the limitations of genetic reductionism to gain an integrative understanding of the causal processes underlying complex phenotypes. (shrink)

Eric Davidson was a legend both in his science and his personality. He inspired and challenged a new generation of developmental biologists and I was lucky to be one of them. He changed the way we think about biological interactions by synthesizing a large scale, almost incomprehensible set of data into a causal model of a gene regulatory network. While his death leaves a big hole in our lives, his contribution to the conceptualization of regulatory biology (...) will inspire developmental and evolutionary biologists for decades to come. (shrink)

Although the interdisciplinary nature of contemporary biological sciences has been addressed by philosophers, historians, and sociologists of science, the different ways in which engineering concepts and methods have been applied in biology have been somewhat neglected. We examine - using the mechanistic philosophy of science as an analytic springboard - the transfer of network methods from engineering to biology through the cases of two biology laboratories operating at the California Institute of Technology. The two laboratories study gene (...) class='Hi'>regulatory networks, but in remarkably different ways. The research strategy of the Davidson lab fits squarely into the traditional mechanist philosophy in its aim to decompose and reconstruct, in detail, gene regulatory networks of a chosen model organism. In contrast, the Elowitz lab constructs minimal models that do not attempt to represent any particular naturally evolved genetic circuits. Instead, it studies the principles of gene regulation through a template-based approach that is applicable to any kinds of networks, whether biological or not. We call for the mechanists to consider whether the latter approach can be accommodated by the mechanistic approach, and what kinds of modifications it would imply for the mechanistic paradigm of explanation, if it were to address modelling more generally. (shrink)

Recent studies have revealed an astonishing diversity of sex chromosomes in many vertebrate lineages, prompting questions about the mechanisms of sex chromosome turnover. While there is considerable population genetic theory about the evolutionary forces promoting sex chromosome replacement, this theory has not yet been integrated with our understanding of the molecular and developmental genetics of sex determination. Here, we review recent data to examine four questions about how the structure of gene networks influences the evolution of sex determination. We (...) argue that patterns of epistasis, arising from the structure of genetic networks, may play an important role in regulating the rates and patterns of sex chromosome replacement. (shrink)

Large protein assemblies are usually the effectors of major cellular processes. The intricate cell homeostasis network is divided into numerous interconnected pathways, each controlled by a set of protein machines. One of these master regulators is the CCR4‐NOT complex, which ultimately controls protein expression levels. This multisubunit complex assembles around a scaffold platform, which enables a wide variety of well‐studied functions from mRNA synthesis to transcript decay, as well as other tasks still being identified. Solving the structure of the (...) entire CCR4‐NOT complex will help to define the distribution of its functions. The recently published three‐dimensional reconstruction of the complex, in combination with the known crystal structures of some of the components, has begun to address this. Methodological improvements in structural biology, especially in cryoelectron microscopy, encourage further structural and protein‐protein interaction studies, which will advance our comprehension of the gene expression machinery. (shrink)

Mutational robustness facilitates evolutionary innovations. Gene duplications are unique kinds of mutations, in that they generally increase such robustness. The frequent association of gene duplications in regulatory networks with evolutionary innovation is thus a special case of a general mechanism linking innovation to robustness. The potential power of this mechanism to promote evolutionary innovations on large time scales is illustrated here with several examples. These include the role of gene duplications in the vertebrate radiation, flowering plant (...) evolution and heart development, which encompass some of the most striking innovations in the evolution of life. BioEssays 30:367–373, 2008. © 2008 Wiley Periodicals, Inc. (shrink)

Somatic complementation by fusion of two mutant cells and mixing of their cytoplasms occurs when the genetic defect of one fusion partner is cured by the functional gene product provided by the other. We have found that complementation of mutational defects in the network mediating stimulus‐induced commitment and sporulation of Physarum polycephalum may reflect time‐dependent changes in the signaling state of its molecular building blocks. Network perturbation by fusion of mutant plasmodial cells in different states of activation, (...) and the time‐resolved analysis of somatic complementation effects can be used to systematically probe network structure and dynamics. Time‐resolved somatic complementation quantitatively detects regulatory interactions between the functional modules of a network, independent of their biochemical composition or subcellular localization, and without being limited to direct physical interactions. BioEssays 25:950–960, 2003. © 2003 Wiley Periodicals, Inc. (shrink)

Sex‐specific transcriptional and epigenomic profiles are detectable in the embryo very soon after fertilization. I propose that in male (XY) and female (XX) pre‐implantation embryos sex chromosomes establish sexually dimorphic interactions with the autosomes, before overt differences become apparent and long before gonadogenesis. Lineage determination restricts expression biases between the sexes, but the epigenetic differences are less constrained and can be perpetuated, accounting for dimorphisms that arise later in life. In this way, sexual identity is registered in the epigenome very (...) early in development. As development progresses, sex‐specific regulatory modules are harbored within shared transcriptional networks that delineate common traits. In reviewing this field, I propose that analyzing the mechanisms for sexual dimorphisms at the molecular and biochemical level and incorporating developmental and environmental factors will lead to a greater understanding of sex differences in health and disease. (shrink)

Günter Wagner’s Homology, Genes, and Evolutionary Innovation collects and synthesizes a vast array of empirical data, theoretical models, and conceptual analysis to set out a progressive research program with a central theoretical commitment: the genetic theory of homology. This research program diverges from standard approaches in evolutionary biology, provides sharpened contours to explanations of the origin of novelty, and expands the conceptual repertoire of evolutionary developmental biology. I concentrate on four aspects of the book in this essay review: the genetic (...) theory of homology and character identity networks; the implications for how we explain evolutionary novelties; the expanded set of concepts surrounding homology, and the epistemological conflicts between Wagner’s viewpoint and functionally-oriented evolutionary biology, as well as differences with other Evo–devo researchers. Together these have ramifications for how we interpret different explanatory approaches to evolutionary phenomena and understand relationships between the usefulness of concepts and the reality they represent. (shrink)