Mammalian synthetic biology holds the promise of providing novel therapeutic strategies, and the first success stories are beginning to be reported. Here we focus on the latest generation of mammalian transgene control devices, highlight state‐of‐the‐art synthetic gene network design, and cover prototype therapeutic circuits. These will have an impact on future gene‐ and cell‐based therapies and help bring drug discovery into a new era. The inventory of biological parts that are essential for life on this planet (...) is becoming increasingly complete. The postgenomic era has provided encyclopedic information on gene‐function correlations and systems biology is now delivering comprehensive details on the dynamics of biochemical reaction networks in living organisms. The time has come to reassemble these catalogued items and design new biological devices and systems with novel and useful functionality in a rational and systematic manner. This is the premise of synthetic biology, a constructive systems biology discipline likely to become the life science revolution of the 21st century. (shrink)

Recent research shows that a faulty or sub‐optimally operating metabolic network can often be rescued by the targeted removal of enzyme‐coding genes – the exact opposite of what traditional gene therapy would suggest. Predictions go as far as to assert that certain gene knockouts can restore the growth of otherwise nonviable gene‐deficient cells. Many questions follow from this discovery: What are the underlying mechanisms? How generalizable is this effect? What are the potential applications? Here, I approach these (...) questions from the perspective of compensatory perturbations on networks. Relations are drawn between such synthetic rescues and naturally occurring cascades of reaction inactivation, as well as their analogs in physical and other biological networks. I specially discuss how rescue interactions can lead to the rational design of antagonistic drug combinations that select against resistance and how they can illuminate medical research on cancer, antibiotics, and metabolic diseases. Editor's suggested further reading in BioEssaysThe evolutionary context of robust and redundant cell biological mechanisms AbstractReprogramming cell fates: reconciling rarity with robustness Abstract. (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)

Synthetic biology is an emerging engineering discipline that attempts to design and rewire biological components, so as to achieve new functions in a robust and predictable manner. The new tools and strategies provided by synthetic biology have the potential to improve therapeutics for neurodegenerative diseases. In particular, synthetic biology will help design small molecules, proteins, gene networks, and vectors to target disease‐related genes. Ultimately, new intelligent delivery systems will provide targeted and sustained therapeutic benefits. New (...) treatments will arise from combining ‘protect and repair’ strategies: the use of drug treatments, the promotion of neurotrophic factor synthesis, and gene targeting. Going beyond RNAi and artificial transcription factors, site‐specific genome modification is likely to play an increasing role, especially with newly available gene editing tools such as CRISPR/Cas9 systems. Taken together, these advances will help develop safe and long‐term therapies for many brain diseases in human patients. (shrink)

Synthetic genomics is a new field of research in which small DNA pieces are assembled in a series of steps into whole genomes. The highly reduced genomes of host‐associated bacteria are now being used as models for de novo synthesis of small genomes in the laboratory. Bacteria with the smallest genomes identified in nature provide nutrients to their hosts, such as amino acids, co‐factors and vitamins. Comparative genomics of these bacteria enables predictions to be made about the gene (...) sets required for core cellular functions and the associated metabolic network for the biosynthesis of host‐selected compounds. Synthetic biology may ultimately enable researchers to make customized cell‐specific organelles for the production and delivery of drugs to humans and domestic animals. Synthetic genomics may also become the method of choice for functional analyses of genes and genomes from bacteria that cannot be cultivated in the laboratory. (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)

Graphical AbstractCRISPR activation (CRISPRa) in bacteria is an attractive method for programmable gene activation. In article number 1900252, Yang Liu and Baojun Wang summarize the current state-of-the-art in this area of high potential, and present the powerful features and capabilities of a newly reported eukaryote-like, σ54-dependent CRISPRa system. The eukaryote-like bacterial CRISPRa enables novel designs in synthetic gene regulation and promotes research in the σ54-dependent gene networks with broad applications envisioned.

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)

The potential of the vertebrate limb as a model system to study developmental mechanisms is particularly well illustrated by the analysis of the Hox gene network. These genes are probably involved in the establishment of patterns encoding positional information. Their functional organisation during both limb and trunk development are very similar and seem to involve the progressive activation in time, along the chromosome, of a battery of genes whose products could differentially instruct those cells where they are expressed. This (...) process may be common to all organisms that develop according to an anterior‐posterior morphogenetic progression. The possible linkage of this system to a particular mechanism of segmentation as well as its phylogenetic implications are discussed. (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)

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)

Cells can transit between a range of stable epithelial and mesenchymal states and this has allowed the evolution of complex body forms. Epithelial to mesenchymal transition (EMT) and its reverse, mesenchymal to epithelial transition (MET), occur sequentially in development and organogenesis. EMT often accompanies transitions between stem‐like cells and their more differentiated progeny, as occurs at gastrulation, although the relevance of this had not been clarified. New findings from the cancer and cell reprogramming fields suggest that EMT and MET can (...) act as essential portals to stem cell character. Here, we review these findings in the broader context of EMT and MET with emphasis on stem cell biology. Using the heart as an example, we also explore the potential role of EMT/MET in organ regeneration. Understanding EMT and MET at a network level will give us new tools to probe stem cell character and enhance tissue repair. (shrink)

This article examines how minimal genome research mobilizes philosophical concepts such as minimality and essentiality. Following a historical approach the article aims to uncover what function this terminology plays and which problems are raised by them. Specifically, four historical moments are examined, linked to the work of Harold J. Morowitz, Mitsuhiro Itaya, Eugene Koonin and Arcady Mushegian, and J. Craig Venter. What this survey shows is a historical shift away from historical questions about life or descriptive questions about specific organisms (...) towards questions that explore biological possibilities: what are possible forms of minimal genomes, regardless of whether they exist in nature? Moreover, it highlights a fundamental ambiguity at work in minimal genome research between a universality claim and a standardization claim: does a minimal genome refer to the minimal gene set for any organism whatsoever? Or does it refer rather to a gene set that will provide stable, robust and predictable behaviour, suited for biotechnological applications? Two diagnoses are proposed for this ambiguity: a philosophical diagnosis of how minimal genome research either misunderstands the ontology of biological entities or philosophically misarticulates scientific practice. Secondly, a historical diagnosis that suggests that this ambiguity is part of a broader shift towards technoscience. (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)

Like biological species, languages change over time. As noted by Darwin, there are many parallels between language evolution and biological evolution. Insights into these parallels have also undergone change in the past 150 years. Just like genes, words change over time, and language evolution can be likened to genome evolution accordingly, but what kind of evolution? There are fundamental differences between eukaryotic and prokaryotic evolution. In the former, natural variation entails the gradual accumulation of minor mutations in alleles. In the (...) latter, lateral gene transfer is an integral mechanism of natural variation. The study of language evolution using biological methods has attracted much interest of late, most approaches focusing on language tree construction. These approaches may underestimate the important role that borrowing plays in language evolution. Network approaches that were originally designed to study lateral gene transfer may provide more realistic insights into the complexities of language evolution.Editor's suggested further reading in BioEssaysLinguistic evidence supports date for Homeric epics Abstract. (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)

Understanding the dynamics of information is crucial to many areas of research, both inside and outside of philosophy. Using computer simulations of three kinds of information, germs, genes, and memes, we show that the mechanism of information transfer often swamps network structure in terms of its effects on both the dynamics and the fitness of the information. This insight has both obvious and subtle implications for a number of questions in philosophy, including questions about the nature of information, whether there (...) is genetic information, and how to arrange scientific communities. (shrink)

The Streptococcus pyogenes CRISPR‐Cas system has gained widespread application as a genome editing and gene regulation tool as simultaneous cellular delivery of the Cas9 protein and guide RNAs enables recognition of specific DNA sequences. The recent discovery that Cas9 can also bind and cleave RNA in an RNA‐programmable manner indicates the potential utility of this system as a universal nucleic acid‐recognition technology. RNA‐targeted Cas9 (RCas9) could allow identification and manipulation of RNA substrates in live cells, empowering the study of (...) cellular gene expression, and could ultimately spawn patient‐ and disease‐specific diagnostic and therapeutic tools. Here we describe the development of RCas9 and compare it to previous methods for RNA targeting, including engineered RNA‐binding proteins and other types of CRISPR‐Cas systems. We discuss potential uses ranging from live imaging of transcriptional dynamics to patient‐specific therapies and applications in synthetic biology. (shrink)

Frequent lateral genetic transfer undermines the existence of a unique “tree of life” that relates all organisms. Vertical inheritance is nonetheless of vital interest in the study of microbial evolution, and knowing the “tree of cells” can yield insights into ecological continuity, the rates of change of different cellular characters, and the evolutionary plasticity of genomes. Notwithstanding within-species recombination, the relationships most frequently recovered from genomic data at shallow to moderate taxonomic depths are likely to reflect cellular inheritance. At the (...) same time, it is clear that several types of ‘average signals’ from whole genomes can be highly misleading, and the existence of a central tendency must not be taken as prima facie evidence of vertical descent. Phylogenetic networks offer an attractive solution, since they can be formulated in ways that mitigate the misleading aspects of hybrid evolutionary signals in genomes. But the connections in a network typically show genetic relatedness without distinguishing between vertical and lateral inheritance of genetic material. The solution may lie in a compromise between strict tree-thinking and network paradigms: build a phylogenetic network, but identify the set of connections in the network that are potentially due to vertical descent. Even if a single tree cannot be unambiguously identified, choosing a subnetwork of putative vertical connections can still lead to drastic reductions in the set of candidate vertical hypotheses. (shrink)

CRISPR (clustered regularly interspaced short palindromic repeats) activation (CRISPRa) in bacteria is an attractive method for programmable gene activation. Recently, a eukaryote‐like, σ54‐dependent CRISPRa system has been reported. It exhibits high dynamic ranges and permits flexible target site selection. Here, an overview of the existing strategies of CRISPRa in bacteria is presented, and the characteristics and design principles of the CRISPRa system are introduced. Possible scenarios for applying the eukaryote‐like CRISPRa system is discussed with corresponding suggestions for performance optimization (...) and future functional expansion. The authors envision the new eukaryote‐like CRISPRa system enabling novel designs in multiplexed gene regulation and promoting research in the σ54‐dependent gene regulatory networks among a variety of biotechnology relevant or disease‐associated bacterial species. (shrink)

A proposal for a new model of the evolution of gene regulation networks and development that draws on work from artificial intelligence and philosophy of mind. Each of us is a collection of more than ten trillion cells, busy performing tasks crucial to our continued existence. Gene regulation networks, consisting of a subset of genes called transcription factors, control cellular activity, producing the right gene activities for the many situations that the multiplicity of cells in (...) our bodies face. Genes working together make up a truly ingenious system. In this book, Roger Sansom investigates how gene regulation works and how such a refined but simple system evolved. Sansom describes in detail two frameworks for understanding gene regulation. The first, developed by the theoretical biologist Stuart Kauffman, holds that gene regulation networks are fundamentally systems that repeat patterns of gene expression. Sansom finds Kauffman's framework an inadequate explanation for how cells overcome the difficulty of development. Sansom proposes an alternative: the connectionist framework. Drawing on work from artificial intelligence and philosophy of mind, he argues that the key lies in how multiple transcription factors combine to regulate a single gene, acting in a way that is qualitatively consistent. This allows the expression of genes to be finely tuned to the variable microenvironments of cells. Because of the nature of both development and its evolution, we can gain insight into the developmental process when we identify gene regulation networks as the controllers of development. The ingenuity of genes is explained by how gene regulation networks evolve to control development. (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)

In the multidisciplinary field of developmental cognitive neuroscience, statistical associations between levels of description play an increasingly important role. One example of such associations is the observation of correlations between relatively common gene variants and individual differences in behavior. It is perhaps surprising that such associations can be detected despite the remoteness of these levels of description, and the fact that behavior is the outcome of an extended developmental process involving interaction of the whole organism with a variable environment. (...) Given that they have been detected, how do such associations inform cognitive-level theories? To investigate this question, we employed a multiscale computational model of development, using a sample domain drawn from the field of language acquisition. The model comprised an artificial neural network model of past-tense acquisition trained using the backpropagation learning algorithm, extended to incorporate population modeling and genetic algorithms. It included five levels of description—four internal: genetic, network, neurocomputation, behavior; and one external: environment. Since the mechanistic assumptions of the model were known and its operation was relatively transparent, we could evaluate whether cross-level associations gave an accurate picture of causal processes. We established that associations could be detected between artificial genes and behavioral variation, even under polygenic assumptions of a many-to-one relationship between genes and neurocomputational parameters, and when an experience-dependent developmental process interceded between the action of genes and the emergence of behavior. We evaluated these associations with respect to their specificity, to their developmental stability, and to their replicability, as well as considering issues of missing heritability and gene–environment interactions. We argue that gene–behavior associations can inform cognitive theory with respect to effect size, specificity, and timing. The model demonstrates a means by which researchers can undertake multiscale modeling with respect to cognition and develop highly specific and complex hypotheses across multiple levels of description. (shrink)

The dissertation explores the way that large-scale research projects in human genetics influence and are influenced by various social and political issues in contemporary U.S. society. In short, the dissertation argues that the same cultural assumptions which make research projects like the Human Genome Project and human behavioral genetics research seem like promising and worthwhile endeavors simultaneously lead to the results of these projects getting used to define the terms that various social issues are discussed in. In cases where the (...) issues involve conflicting agendas, those cultural assumptions that drive the research projects often point towards a specific form of resolution, a form that is often implicitly or explicitly supported by the results of the research projects themselves. ;There is, in other words, a complex relationship of mutual support between certain kinds of research projects in human genetics, the legal and social decisions made in various areas in political life, and the kinds of discourse that can exist around those same areas. This relationship is pernicious insofar as those assumptions that encourage the research and influence the way the research is used are assumptions that many people would question the wisdom of uncritically accepting. And indeed, they are assumptions that, I argue, have strong racist and sexist overtones, assumptions born out of a radically anti-egalitarian framework. Political decisions and social discourse that might otherwise seem radically racist, sexist, or classist can, once they are being made in the shadow of research undertaken under these same kinds of assumptions, be made to seem somehow natural, inevitable, or at the very least, scientifically well-motivated. (shrink)

Combining the communicative language competence model and the perspective of multimodal research, this research proposes a research framework for oral communicative competence under the multimodal perspective. This not only truly reflects the language communicative competence but also fully embodies the various contents required for assessment in the basic attributes of spoken language. Aiming at the feature sparseness of the user evaluation matrix, this paper proposes a feature weight assignment algorithm based on the English spoken category keyword dictionary and user search (...) records. The algorithm is mainly based on the self-built English oral category classification dictionary and converts the user’s query vector into a user-English-speaking type vector. Through the calculation rules proposed in this paper, the target user’s preference score for a specific type of spoken English is obtained, and this score is assigned to the unrated item of the original user’s feature matrix as the initial starting score. At the same time, in order to solve the problem of insufficient user similarity calculation accuracy, a user similarity calculation algorithm based on “Synonyms Cilin Extended Edition” and search records is proposed. The algorithm introduces “Synonyms Cilin” to calculate the correlation between the semantic items, vocabulary, and query vector in the user query record to obtain the similarity between users and finally gives a user similarity calculation that integrates user ratings and query vectors method. For the task of Chinese grammar error correction, this article uses two methods of predicting the relationship between words in the corpus, Word2Vec and GloVe, to train the word vectors of different dimensions and use the word vectors to represent the text features of the experimental samples, avoiding sentences brought by word segmentation. On the basis of word vectors, the advantages and disadvantages of CNN, LSTM, and SVM models in this shared task are analyzed through experimental data. The comparative experiment shows that the method in this paper has achieved relatively good results. (shrink)

The aberrations of a gene can influence it and the functions of its neighbour genes in gene interaction network, leading to the development of carcinogenesis of normal cells. In consideration of gene interaction network as a complex network, previous studies have made efforts on the driver attribute filling of genes via network properties of nodes and network propagation of mutations. However, there are still obstacles from problems of small size of cancer samples and the existence of drivers (...) without property of network neighbours, limiting the discovery of cancer driver genes. To address these obstacles, we propose an efficient modularity subspace based concept learning model. Our model can overcome the curse of dimensionality due to small samples via dimension reduction in the task of attribute concept learning and explore the features of genes through modularity subspace beyond the network neighbours. The evaluation analysis also demonstrates the superiority of our model in the task of driver attribute filling on two gene interaction networks. Generally, our model shows a promising prospect in the application of interaction network analysis of tumorigenesis. (shrink)

Predicting the phenotype of an organism from its genotype is a central question in genetics. Most importantly, we would like to find out if the perturbation of a single gene may be the cause of a disease. However, our current ability to predict the phenotypic effects of perturbations of individual genes is limited. Network models of genes are one tool for tackling this problem. In a recent study, (Lee et al.) it has been shown that network models covering the (...) majority of genes of an organism can be used for accurately predicting phenotypic effects of gene perturbations in multicellular organisms. BioEssays 30:707–710, 2008. © 2008 Wiley Periodicals, 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)

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)

NCG 4.0 is the latest update of the Network of Cancer Genes, a web-based repository of systems-level properties of cancer genes. In its current version, the database collects information on 537 known (i.e. experimentally supported) and 1463 candidate (i.e. inferred using statistical methods) cancer genes. Candidate cancer genes derive from the manual revision of 67 original publications describing the mutational screening of 3460 human exomes and genomes in 23 different cancer types. For all 2000 cancer genes, duplicability, evolutionary origin, expression, (...) functional annotation, interaction network with other human proteins and with microRNAs are reported. In addition to providing a substantial update of cancer-related information, NCG 4.0 also introduces two new features. The first is the annotation of possible false-positive cancer drivers, defined as candidate cancer genes inferred from large-scale screenings whose association with cancer is likely to be spurious. The second is the description of the systems-level properties of 64 human microRNAs that are causally involved in cancer progression (oncomiRs). Owing to the manual revision of all information, NCG 4.0 constitutes a complete and reliable resource on human coding and non-coding genes whose deregulation drives cancer onset and/or progression. NCG 4.0 can also be downloaded as a free application for Android smart phones. (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)

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.

The generation of synthetic data is becoming a fundamental task in the daily life of any organization due to the new protection data laws that are emerging. Because of the rise in the use of Artificial Intelligence, one of the most recent proposals to address this problem is the use of Generative Adversarial Networks. These types of networks have demonstrated a great capacity to create synthetic data with very good performance. The goal of synthetic data (...) generation is to create data that will perform similarly to the original dataset for many analysis tasks, such as classification. The problem of GANs is that in a classification problem, GANs do not take class labels into account when generating new data, it is treated as any other attribute. This research work has focused on the creation of new synthetic data from datasets with different characteristics with a Conditional Generative Adversarial Network. CGANs are an extension of GANs where the class label is taken into account when the new data is generated. The performance of our results has been measured in two different ways: firstly, by comparing the results obtained with classification algorithms, both in the original datasets and in the data generated; secondly, by checking that the correlation between the original data and those generated is minimal. (shrink)

The combination of decreased genotyping costs and prolific social media use is fueling a personal genetic testing industry in which consumers purchase and interact with genetic risk information online. Consumers and their genetic risk profiles are protected in some respects by the 2008 federal Genetic Information Nondiscrimination Act (GINA), which forbids the discriminatory use of genetic information by employers and health insurers; however, practical and technical limitations undermine its enforceability, given the everyday practices of online social networking and its impact (...) on the workplace. In the Web 2.0 era, employers in most states can legally search about job candidates and employees online, probing social networking sites for personal information that might bear on hiring and employment decisions. We examine GINA's protections for online sharing of genetic information as well as its limitations, and propose policy recommendations to address current gaps that leave employees? genetic information vulnerable in a Web-based world. (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)

Gene expression network is also a type of complex network. It is challenging to analyze the gene expression network through relevant knowledge and algorithms of a complex network. In this paper, the existing characteristics of genes are analyzed from various indexes of the gene expression network to analyze key genes and TOP genes. Firstly, gene chip data are screened, gene data with obvious characteristics are selected, and relevant clustering characteristics are analyzed. Then, the complex (...) class='Hi'>gene network structure is established, and gene networks with different threshold shapes and different sizes are selected. Finally, the relevant indexes and PR values after the PageRank algorithm are analyzed for complex networks under different thresholds, thus establishing the TOP gene and PR sequence. (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)

Synthetic biology research is often described in terms of programming cells through the introduction of synthetic genes. Genetic material is seemingly attributed with a high level of causal responsibility. We discuss genetic causation in synthetic biology and distinguish three gene concepts differing in their assumptions of genetic control. We argue that synthetic biology generally employs a difference-making approach to establishing genetic causes, and that this approach does not commit to a specific notion of genetic program (...) or genetic control. Still, we suggest that a strong program concept of genetic material can be used as a successful heuristic in certain areas of synthetic biology. Its application requires control of causal context, and may stand in need of a modular decomposition of the target system. We relate different modularity concepts to the discussion of genetic causation and point to possible advantages of and important limitations to seeking modularity in synthetic biology systems. (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)

The vast majority (> 95%) of single-gene mutations in yeast affect not only the expression of the mutant gene, but also the expression of many other genes. These data suggest the presence of a previously uncharacterized ‘gene expression network’—a set of interactions between genes which dictate gene expression in the native cell environment. Here, we quantitatively analyze the gene expression network revealed by microarray expression data from 273 different yeast gene deletion mutants.(1) We find (...) that gene expression interactions form a robust, error-tolerant ‘scale-free’ network, similar to metabolic pathways(2) and artificial networks such as power grids and the internet.(3-5) Because the connectivity between genes in the gene expression network is unevenly distributed, a scale-free organization helps make organisms resistant to the deleterious effects of mutation, and is thus highly adaptive. The existence of a gene expression network poses practical considerations for the study of gene function, since most mutant phenotypes are the result of changes in the expression of many genes. Using principles of scale-free network topology, we propose that fragmenting the gene expression network via ‘genome-engineering’ may be a viable and practical approach to isolating gene function. BioEssays 24:267–274, 2002. © 2002 Wiley Periodicals, Inc.; DOI 10.1002/bies.10054. (shrink)

This paper reports on the meeting of the Sounding Board of the EU Reprogenetics Project that was held in Budapest, Hungary, 6–9 November 2005. The Reprogenetics Project runs from 2004 until 2007 and has a brief to study the ethical aspects of human reproductive cloning and germline gene therapy. Discussions during The Budapest Meeting are reported in depth in this paper as well as the initiatives to involve the participating groups and others in ongoing collaborations with the goal of (...) forming an integrated network of European resources in the fields of ethics of science. (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)

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)