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)

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)

We give in this paper indications about the dynamical impact coming from the main sources of perturbation in biological regulatory networks. First, we define the boundary of the interaction graph expressing the regulations between the main elements of the network . Then, we search what changes in the state values on the boundary could cause some changes of states in the core of the system . After, we analyse the role of the mode of updating on the asymptotics (...) of the network, essentially on the occurrence of limit cycles . Finally, we show the influence of some topological changes on the dynamical behaviour of the system. (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)

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)

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)

Computational approaches in systems biology have become a powerful tool for understanding the fundamental mechanisms of cellular metabolism and regulation. However, the interplay between the regulatory and the metabolic system is still poorly understood. In particular, there is a need for formal mathematical frameworks that allow analyzing metabolism together with dynamic enzyme resources and regulatory events. Here, we introduce a metabolic-regulatory network model that allows integrating metabolism with transcriptional regulation, macromolecule production and enzyme resources. Using this model, (...) we show that the dynamic interplay between these different cellular processes can be formalized by a hybrid automaton, combining continuous dynamics and discrete control. (shrink)

Graphical AbstractDevelopment, life cycle evolution and immunity were among the topics discussed at a recent meeting in Tutzing dedicated to the biology of the ‘basal’ metazoan taxa Porifera, Ctenophora, Placozoa and Cnidaria.

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.

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)

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)

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 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)

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)

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)

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)

It will be settled out for the open problem of designing an r-order finite-time state observer for reaction-diffusion genetic regulatory networks with time-varying delays. By assuming the Dirichlet boundary conditions, aiming to estimate the mRNA and protein concentrations via available network measurements. Firstly, sufficient F-T stability conditions for the filtering error system have been investigated via constructing an appropriate Lyapunov–Krasovskii functional and using several integral inequalities and convex technique simultaneously. These conditions are delay-dependent and reaction-diffusion-dependent and can be (...) checked by MATLAB toolbox. Furthermore, a method is proposed to design an r-order F-T state observer, and the explicit expressions of observer gains are given. Finally, a numerical example is presented to illustrate the effectiveness of the proposed method. (shrink)

An incoherent feed‐forward loop (FFL) is one of the most‐frequently observed motifs in biomolecular regulatory networks. It has been thought that the incoherent FFL is designed simply to induce a transient response shaped by a ‘fast activation and delayed inhibition’. We find that the dynamics of various incoherent FFLs can be further classified into two types: time‐dependent biphasic responses and dose‐dependent biphasic responses. Why do the structurally identical incoherent FFLs play such different dynamical roles? Through computational studies, we (...) show that the dynamics of the two types of incoherent FFLs are mutually exclusive. Following from further computational results and experimental observations, we hypothesize that incoherent FFLs have been optimally designed to achieve distinct biological function arising from different cellular contexts. Additional Supporting Information may be found in the online version of the article. BioEssays 30:1204–1211, 2008. © 2008 Wiley Periodicals, Inc. (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)

The problem of finite-time stability of switched genetic regulatory networks with time-varying delays via Wirtinger’s integral inequality is addressed in this study. A novel Lyapunov–Krasovskii functional is proposed to capture the dynamical characteristic of GRNs. Using Wirtinger’s integral inequality, reciprocally convex combination technique and the average dwell time method conditions in the form of linear matrix inequalities are established for finite-time stability of switched GRNs. The applicability of the developed finite-time stability conditions is validated by numerical results.

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)

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)

Mucoidy and cytotoxicity arise from two independent modifications of the phenotype of the bacterium Pseudomonas aeruginosa that contribute to the mortality and morbidity of cystic fibrosis. We show that, even though the transcriptional regulatory networks controlling both processes are quite different from a molecular or mechanistic point of view, they may be identical from a dynamic point of view: epigenesis may in both cases be the cause of the acquisition of these new phenotypes. This was highlighted by the (...) identity of formal graphs modelling these networks. A mathematical framework based on formal methods from computer science was defined and implemented with a software environment. It allows an easy and rigorous validation and certification of these models and of the experimental methods that can be proposed to falsify or validate the underlying hypothesis. (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)

We deal with the design problem of nonfragile state estimator for discrete-time genetic regulatory networks with time-varying delays and randomly occurring uncertainties. In particular, the norm-bounded uncertainties enter into the GRNs in random ways in order to reflect the characteristic of the modelling errors, and the so-called randomly occurring uncertainties are characterized by certain mutually independent random variables obeying the Bernoulli distribution. The focus of the paper is on developing a new nonfragile state estimation method to estimate the (...) concentrations of the mRNA and the protein for considered uncertain delayed GRNs, where the randomly occurring estimator gain perturbations are allowed. By constructing a Lyapunov-Krasovskii functional, a delay-dependent criterion is obtained in terms of linear matrix inequalities by properly using the discrete-time Wirtinger-based inequality and reciprocally convex combination approach as well as the free-weighting matrix method. It is shown that the proposed method ensures that the estimation error dynamics is globally asymptotically stable and the desired estimator parameter is designed via the solutions to certain LMIs. Finally, we provide two numerical examples to illustrate the feasibility and validity of the proposed estimation results. (shrink)

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

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)

Research shows that trained mentors achieve better results than untrained ones. Their training should particularly address their expectations for their future mentoring. Our study involved 190 preservice teachers, potential mentors of ongoing school mentoring for primary and secondary school students of all grades. They were randomly assigned to one of four conditions in a 2-x-2 between-subjects design of mentoring type (traditional mentoring versus e-mentoring) and mentoring context (non-pandemic versus COVID-19 pandemic). Participants assessed mentoring conducted under these four conditions in terms (...) of its appropriateness for achieving four mentoring program targets: learning, key skills, social targets, and problem coping. Participants were also asked to assess the resources available to achieve each program target. Overall, the potential mentors considered the various conditions to be suitable for achieving the four program targets. They were particularly favorable in their assessment of the possibility for the realization of learning targets. Likewise, they assumed that sufficient resources were available to achieve the targets. However, a repeated-measures MANOVA showed that the potential mentors considered more ambitious targets to be possible in traditional mentoring than in e-mentoring and normal (i.e., pre-pandemic) contextual conditions than during the COVID-19 pandemic. In contrast, they estimated the resources available to achieve the targets to be about the same in the four conditions. This indicates a decoupling of mentoring targets from the consideration of the resources needed to achieve them. This assumption was confirmed in correlation analyses and has implications for mentor training. (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)

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)

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)

Regulating technologies, innovations and risks is an activity that, as much as scientific research needs proofs and evidence. It is the site of development of a distinct kind of science, regulatory science. This special issue addresses the question of the standards of knowledge governing how we test, assess and monitor technologies and their effects. This topic is relevant and timely in the light of problematics of regulation of innovation, regulatory failure and capture. Given the enormous decisions and stakes (...) regulatory science commends, it becomes crucial to ask where its standards come from and gain credibility, but also what valuations of technology and appreciations of their risks or benefits do they embed, and who controls them? This paper introduces the four contributions comprising the special issue, and outlines a perspective from which to question the construction of regulatory science or, in the terminology adopted here, the authorization and standardization of regulatory knowledge, particularly the role of networks of scientific experts therein. (shrink)

This Article examines the regulatory state through the lens of evolving political economy, arguing that a significant reconstruction is now underway. The ongoing shift from an industrial mode of development to an informational one has created existential challenges for regulatory models and constructs developed in the context of the industrial economy. Contemporary contests over the substance of regulatory mandates and the shape of regulatory institutions are most usefully understood as moves within a larger struggle to chart (...) a new direction for the regulatory state in the era of informational capitalism. A regulatory state optimized for the information economy must develop rubrics for responding to three problems that have confounded existing regulatory regimes: platform power — the power to link facially separate markets and/or to constrain participation in markets by using technical protocols; infoglut — unmanageably voluminous, mediated information flows that create information overload; and systemic threat — nascent, probabilistically-defined harm to be realized at some point in the future. Additionally, it must develop institutions capable of exercising effective oversight of informationera activities. The information-era regulatory models that have begun to emerge are procedurally informal, mediated by networks of professional and technical expertise that define relevant standards, and financialized. Such models, however, also have tended to be both opaque to external observation and highly prone to capture. New institutional forms that might ensure their legal and political accountability have been slow to develop. (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)

Building a meaningful model of biological regulatory network is usually done by specifying the components and their interactions, by guessing the values of parameters, by comparing the predicted behaviors to the observed ones, and by modifying in a trial-error process both architecture and parameters in order to reach an optimal fitness. We propose here a different approach to construct and analyze biological models avoiding the trial-error part, where structure and dynamics are represented as formal constraints. We apply the method (...) to Hopfield-like networks, a formalism often used in both neural and regulatory networks modeling. The aim is to characterize automatically the set of all models consistent with all the available knowledge. The available knowledge is formalized into formal constraints. The latter are compiled into Boolean formula in conjunctive normal form and then submitted to a Boolean satisfiability solver. This approach allows to formulate a wide range of queries, expressed in a high level language, and possibly integrating formalized intuitions. In order to explore its potential, we use it to find cycles for 3-nodes networks and to determine the flower morphogenesis regulatory network of Arabidopsis thaliana. Applications of this technique are numerous and concern the building of models from data as well as the design of biological networks possessing specified behaviors. (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)

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)

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)

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)

Biochemical networks are often called upon to illustrate emergent properties of living systems. In this contribution, I question such emergentist claims by means of theoretical work on genetic regulatory models and random Boolean networks. If the existence of a critical connectivity Kc of such networks has often been coined “emergent” or “irreducible”, I propose on the contrary that the existence of a critical connectivity Kc is indeed mathematically explainable in network theory. This conclusion also applies to (...) many other types of formal networks and weakens the emergentist claim attached to bio-molecular networks, and by extension to living systems. (shrink)

The generation of patterns and the diversity of cell types in a multicellular organism require differential gene regulation. At the heart of this process are enhancers or cis‐regulatory modules (CRMs), genomic regions that are bound by transcription factors (TFs) that control spatio‐temporal gene expression in developmental networks. To date, only a few CRMs have been studied in detail and the underlying cis‐regulatory code is not well understood. Here, we review recent progress on the genome‐wide identification of CRMs (...) with chromatin immunoprecipitation of TF‐DNA complexes followed by microarrays (ChIP‐on‐chip). We focus on two computational approaches that have succeeded in predicting the expression pattern driven by a CRM either based on TF binding site preferences and their expression levels, or quantitative analysis of CRM occupancy by key TFs. We also discuss the current limits of these methods and highlight some of the key problems that have to be solved to gain a more complete understanding of the structure and function of CRMs. (shrink)

Interspecific comparisons reveal remarkable diversity in patterns of gene expression, even among closely related species. Combinatorial regulatory mechanisms could facilitate the evolution of this diversity. However, the high degree of interdependence characteristic of combinatorial networks would represent a major constraint on evolution and might generate many features that have no direct adaptive value.