Genetic Regulatory Interactions Research Articles

Molecular and Hormonal Mechanisms Regulating Fleshy Fruit Ripening.

This article focuses on the molecular and hormonal mechanisms underlying the control of fleshy fruit ripening and quality. Recent research on tomato shows that ethylene, acting through transcription factors, is responsible for the initiation of tomato ripening. Several other hormones, including abscisic acid (ABA), jasmonic acid (JA) and brassinosteroids (BR), promote ripening by upregulating ethylene biosynthesis genes in different fruits. Changes to histone marks and DNA methylation are associated with the activation of ripening genes and are necessary for ripening initiation. Light, detected by different photoreceptors and operating through ELONGATED HYPOCOTYL 5(HY5), also modulates ripening. Re-evaluation of the roles of ‘master regulators’ indicates that MADS-RIN, NAC-NOR, Nor-like1 and other MADS and NAC genes, together with ethylene, promote the full expression of genes required for further ethylene synthesis and change in colour, flavour, texture and progression of ripening. Several different types of non-coding RNAs are involved in regulating expression of ripening genes, but further clarification of their diverse mechanisms of action is required. We discuss a model that integrates the main hormonal and genetic regulatory interactions governing the ripening of tomato fruit and consider variations in ripening regulatory circuits that operate in other fruits.

Heart morphogenesis gene regulatory networks revealed by temporal expression analysis.

During embryogenesis the heart forms as a linear tube that then undergoes multiple simultaneous morphogenetic events to obtain its mature shape. To understand the gene regulatory networks (GRNs) driving this phase of heart development, during which many congenital heart disease malformations likely arise, we conducted an RNA-seq timecourse in zebrafish from 30 hpf to 72 hpf and identified 5861 genes with altered expression. We clustered the genes by temporal expression pattern, identified transcription factor binding motifs enriched in each cluster, and generated a model GRN for the major gene batteries in heart morphogenesis. This approach predicted hundreds of regulatory interactions and found batteries enriched in specific cell and tissue types, indicating that the approach can be used to narrow the search for novel genetic markers and regulatory interactions. Subsequent analyses confirmed the GRN using two mutants, Tbx5 and nkx2-5, and identified sets of duplicated zebrafish genes that do not show temporal subfunctionalization. This dataset provides an essential resource for future studies on the genetic/epigenetic pathways implicated in congenital heart defects and the mechanisms of cardiac transcriptional regulation.

Minimal metabolic pathway structure is consistent with associated biomolecular interactions.

Pathways are a universal paradigm for functionally describing cellular processes. Even though advances in high-throughput data generation have transformed biology, the core of our biological understanding, and hence data interpretation, is still predicated on human-defined pathways. Here, we introduce an unbiased, pathway structure for genome-scale metabolic networks defined based on principles of parsimony that do not mimic canonical human-defined textbook pathways. Instead, these minimal pathways better describe multiple independent pathway-associated biomolecular interaction datasets suggesting a functional organization for metabolism based on parsimonious use of cellular components. We use the inherent predictive capability of these pathways to experimentally discover novel transcriptional regulatory interactions in Escherichia coli metabolism for three transcription factors, effectively doubling the known regulatory roles for Nac and MntR. This study suggests an underlying and fundamental principle in the evolutionary selection of pathway structures; namely, that pathways may be minimal, independent, and segregated.

Competition for Catalytic Resources Alters Biological Network Dynamics

Genetic regulation networks orchestrate many complex cellular behaviors. Dynamic operations that take place within cells are thus dependent on the gene expression machinery, enabled by powerful enzymes such as polymerases, ribosomes, or nucleases. These generalist enzymes typically process many different substrates, potentially leading to competitive situations: by saturating the common enzyme, one substrate may down-regulate its competitors. However, most theoretical or experimental models simply omit these effects, focusing on the pattern of genetic regulatory interactions as the main determinant of network function. We show here that competition effects have important outcomes, which can be spotted within the global dynamics of experimental systems. Further we demonstrate that enzyme saturation creates a layer of cross couplings that may foster, but also hamper, the expected behavior of synthetic biology constructs.

Inferring Genetic Interactions via a Data-Driven Second Order Model.

Genetic/transcriptional regulatory interactions are shown to predict partial components of signaling pathways, which have been recognized as vital to complex human diseases. Both activator (A) and repressor (R) are known to coregulate their common target gene (T). Xu et al. (2002) proposed to model this coregulation by a fixed second order response surface (called the RS algorithm), in which T is a function of A, R, and AR. Unfortunately, the RS algorithm did not result in a sufficient number of genetic interactions (GIs) when it was applied to a group of 51 yeast genes in a pilot study. Thus, we propose a data-driven second order model (DDSOM), an approximation to the non-linear transcriptional interactions, to infer genetic and transcriptional regulatory interactions. For each triplet of genes of interest (A, R, and T), we regress the expression of T at time t + 1 on the expression of A, R, and AR at time t. Next, these well-fitted regression models (viewed as points in R3) are collected, and the center of these points is used to identify triples of genes having the A-R-T relationship or GIs. The DDSOM and RS algorithms are first compared on inferring transcriptional compensation interactions of a group of yeast genes in DNA synthesis and DNA repair using microarray gene expression data; the DDSOM algorithm results in higher modified true positive rate (about 75%) than that of the RS algorithm, checked against quantitative RT-polymerase chain reaction results. These validated GIs are reported, among which some coincide with certain interactions in DNA repair and genome instability pathways in yeast. This suggests that the DDSOM algorithm has potential to predict pathway components. Further, both algorithms are applied to predict transcriptional regulatory interactions of 63 yeast genes. Checked against the known transcriptional regulatory interactions queried from TRANSFAC, the proposed also performs better than the RS algorithm.

Construction of Probabilistic Boolean Networks from a Prescribed Transition Probability Matrix: A Maximum Entropy Rate Approach

AbstractModeling genetic regulatory networks is an important problem in genomic research. Boolean Networks (BNs) and their extensions Probabilistic Boolean Networks (PBNs) have been proposed for modeling genetic regulatory interactions. In a PBN, its steady-state distribution gives very important information about the long-run behavior of the whole network. However, one is also interested in system synthesis which requires the construction of networks. The inverse problem is ill-posed and challenging, as there may be many networks or no network having the given properties, and the size of the problem is huge. The construction of PBNs from a given transition-probability matrix and a given set of BNs is an inverse problem of huge size. We propose a maximum entropy approach for the above problem. Newton's method in conjunction with the Conjugate Gradient (CG) method is then applied to solving the inverse problem. We investigate the convergence rate of the proposed method. Numerical examples are also given to demonstrate the effectiveness of our proposed method.

Structure–function relations are subtle in genetic regulatory networks

Recent studies have yielded insights into structure–function relations in genetic regulatory networks. Models of feed-forward loops show that the input–output behavior depends critically on the input signal as well as transcription interactions. Models of induction of the lac operon in Escherichia coli reveal the importance of metabolism in determining genetic regulatory network behavior. Combined experimental and computational studies of activation by MarA in E. coli show how mechanisms of transcription regulation, hidden at the level of genetic regulatory networks, can influence behavior. Together these studies illustrate that gene regulation is critically influenced by factors beyond the topology of genetic regulatory interactions. Prediction of the specific information processing roles of gene circuits is more difficult than we would like, but it is still possible. Thinking about evolution of proteins and networks might make it easier.

A matrix perturbation method for computing the steady-state probability distributions of probabilistic Boolean networks with gene perturbations

Modeling genetic regulatory interactions is an important issue in systems biology. Probabilistic Boolean networks (PBNs) have been proved to be a useful tool for the task. The steady-state probability distribution of a PBN gives important information about the captured genetic network. The computation of the steady-state probability distribution involves the construction of the transition probability matrix of the PBN. The size of the transition probability matrix is 2 n × 2 n where n is the number of genes. Although given the number of genes and the perturbation probability in a perturbed PBN, the perturbation matrix is the same for different PBNs, the storage requirement for this matrix is huge if the number of genes is large. Thus an important issue is developing computational methods from the perturbation point of view. In this paper, we analyze and estimate the steady-state probability distribution of a PBN with gene perturbations. We first analyze the perturbation matrix. We then give a perturbation matrix analysis for the captured PBN problem and propose a method for computing the steady-state probability distribution. An approximation method with error analysis is then given for further reducing the computational complexity. Numerical experiments are given to demonstrate the efficiency of the proposed methods.

On the Choice and Number of Microarrays for Transcriptional Regulatory Network Inference

BackgroundTranscriptional regulatory network inference (TRNI) from large compendia of DNA microarrays has become a fundamental approach for discovering transcription factor (TF)-gene interactions at the genome-wide level. In correlation-based TRNI, network edges can in principle be evaluated using standard statistical tests. However, while such tests nominally assume independent microarray experiments, we expect dependency between the experiments in microarray compendia, due to both project-specific factors (e.g., microarray preparation, environmental effects) in the multi-project compendium setting and effective dependency induced by gene-gene correlations. Herein, we characterize the nature of dependency in an Escherichia coli microarray compendium and explore its consequences on the problem of determining which and how many arrays to use in correlation-based TRNI.ResultsWe present evidence of substantial effective dependency among microarrays in this compendium, and characterize that dependency with respect to experimental condition factors. We then introduce a measure neff of the effective number of experiments in a compendium, and find that corresponding to the dependency observed in this particular compendium there is a huge reduction in effective sample size i.e., neff = 14.7 versus n = 376. Furthermore, we found that the neff of select subsets of experiments actually exceeded neff of the full compendium, suggesting that the adage 'less is more' applies here. Consistent with this latter result, we observed improved performance in TRNI using subsets of the data compared to results using the full compendium. We identified experimental condition factors that trend with changes in TRNI performance and neff , including growth phase and media type. Finally, using the set of known E. coli genetic regulatory interactions from RegulonDB, we demonstrated that false discovery rates (FDR) derived from neff -adjusted p-values were well-matched to FDR based on the RegulonDB truth set.ConclusionsThese results support utilization of neff as a potent descriptor of microarray compendia. In addition, they highlight a straightforward correlation-based method for TRNI with demonstrated meaningful statistical testing for significant edges, readily applicable to compendia from any species, even when a truth set is not available. This work facilitates a more refined approach to construction and utilization of mRNA expression compendia in TRNI.

Generating probabilistic Boolean networks from a prescribed stationary distribution

Modeling gene regulation is an important problem in genomic research. Boolean networks (BN) and its generalization probabilistic Boolean networks (PBNs) have been proposed to model genetic regulatory interactions. BN is a deterministic model while PBN is a stochastic model. In a PBN, on one hand, its stationary distribution gives important information about the long-run behavior of the network. On the other hand, one may be interested in system synthesis which requires the construction of networks from the observed stationary distribution. This results in an inverse problem which is ill-posed and challenging. Because there may be many networks or no network having the given properties and the size of the inverse problem is huge. In this paper, we consider the problem of constructing PBNs from a given stationary distribution and a set of given Boolean Networks (BNs). We first formulate the inverse problem as a constrained least squares problem. We then propose a heuristic method based on Conjugate Gradient (CG) algorithm, an iterative method, to solve the resulting least squares problem. We also introduce an estimation method for the parameters of the PBNs. Numerical examples are then given to demonstrate the effectiveness of the proposed methods.

Inferring genetic interactions via a nonlinear model and an optimization algorithm

BackgroundBiochemical pathways are gradually becoming recognized as central to complex human diseases and recently genetic/transcriptional interactions have been shown to be able to predict partial pathways. With the abundant information made available by microarray gene expression data (MGED), nonlinear modeling of these interactions is now feasible. Two of the latest advances in nonlinear modeling used sigmoid models to depict transcriptional interaction of a transcription factor (TF) for a target gene, but do not model cooperative or competitive interactions of several TFs for a target.ResultsAn S-shape model and an optimization algorithm (GASA) were developed to infer genetic interactions/transcriptional regulation of several genes simultaneously using MGED. GASA consists of a genetic algorithm (GA) and a simulated annealing (SA) algorithm, which is enhanced by a steepest gradient descent algorithm to avoid being trapped in local minimum. Using simulated data with various degrees of noise, we studied how GASA with two model selection criteria and two search spaces performed. Furthermore, GASA was shown to outperform network component analysis, the time series network inference algorithm (TSNI), GA with regular GA (GAGA) and GA with regular SA. Two applications are demonstrated. First, GASA is applied to infer a subnetwork of human T-cell apoptosis. Several of the predicted interactions are supported by the literature. Second, GASA was applied to infer the transcriptional factors of 34 cell cycle regulated targets in S. cerevisiae, and GASA performed better than one of the latest advances in nonlinear modeling, GAGA and TSNI. Moreover, GASA is able to predict multiple transcription factors for certain targets, and these results coincide with experiments confirmed data in YEASTRACT.ConclusionsGASA is shown to infer both genetic interactions and transcriptional regulatory interactions well. In particular, GASA seems able to characterize the nonlinear mechanism of transcriptional regulatory interactions (TIs) in yeast, and may be applied to infer TIs in other organisms. The predicted genetic interactions of a subnetwork of human T-cell apoptosis coincide with existing partial pathways, suggesting the potential of GASA on inferring biochemical pathways.

Modeling of gene regulatory networks with hybrid differential evolution and particle swarm optimization

In the last decade, recurrent neural networks (RNNs) have attracted more efforts in inferring genetic regulatory networks (GRNs), using time series gene expression data from microarray experiments. This is critically important for revealing fundamental cellular processes, investigating gene functions, and understanding their relations. However, RNNs are well known for training difficulty. Traditional gradient descent-based methods are easily stuck in local minima and the computation of the derivatives is also not always possible. Here, the performance of three evolutionary-swarm computation technology-based methods, known as differential evolution (DE), particle swarm optimization (PSO), and the hybrid of DE and PSO (DEPSO), in training RNNs is investigated. Furthermore, the gene networks are reconstructed via the identification of the gene interactions, which are explained through corresponding connection weight matrices. The experimental results on two data sets studied in this paper demonstrate that the DEPSO algorithm performs better in RNN training. Also, the RNN-based model can provide meaningful insight in capturing the nonlinear dynamics of genetic networks and revealing genetic regulatory interactions.

An approximation method for solving the steady-state probability distribution of probabilistic Boolean networks

Probabilistic Boolean networks (PBNs) have been proposed to model genetic regulatory interactions. The steady-state probability distribution of a PBN gives important information about the captured genetic network. The computation of the steady-state probability distribution usually includes construction of the transition probability matrix and computation of the steady-state probability distribution. The size of the transition probability matrix is 2(n)-by-2(n) where n is the number of genes in the genetic network. Therefore, the computational costs of these two steps are very expensive and it is essential to develop a fast approximation method. In this article, we propose an approximation method for computing the steady-state probability distribution of a PBN based on neglecting some Boolean networks (BNs) with very small probabilities during the construction of the transition probability matrix. An error analysis of this approximation method is given and theoretical result on the distribution of BNs in a PBN with at most two Boolean functions for one gene is also presented. These give a foundation and support for the approximation method. Numerical experiments based on a genetic network are given to demonstrate the efficiency of the proposed method.

The proneural determinant MASH1 regulates forebrainDlx1/2expression through the I12b intergenic enhancer

Establishment of neuronal networks is an extremely complex process involving the interaction of a diversity of neuronal cells. During mammalian development, these highly organized networks are formed through the differentiation of multipotent neuronal progenitors into multiple neuronal cell lineages. In the developing forebrain of mammals, the combined function of the Dlx1, Dlx2, Dlx5 and Dlx6 homeobox genes is necessary for the differentiation of the GABAergic interneurons born in the ventricular and subventricular zones of the ventral telencephalon, as well as for the migration of these neurons to the hippocampus, cerebral cortex and olfactory bulbs. The 437 bp I12b enhancer sequence in the intergenic region of the Dlx1/2 bigene cluster is involved in the forebrain regulation of Dlx1/2. Using DNase I footprinting, we identified six regions of I12b potentially bound by transcription factors. Mutagenesis of each binding site affected the expression of reporter constructs in transgenic mice. However, the effects of impairing protein-DNA interactions were not uniform across the forebrain Dlx1/2 expression domains, suggesting that distinct regulatory interactions are taking place in the different populations of neuronal precursors. Analyses of protein-DNA interactions provide evidence of a direct role for MASH1 in Dlx1/2 regulation in the forebrain. DLX proteins play a crucial role in the maintenance of their own expression, as shown by transgenic and co-transfection experiments. These studies suggest that the seemingly continuous domains of Dlx gene expression in the telencephalon and diencephalon are in fact the combination of distinct cell populations within which different genetic regulatory interactions take place.

Inferring genetic regulatory logic from expression data

High-throughput molecular genetics methods allow the collection of data about the expression of genes at different time points and under different conditions. The challenge is to infer gene regulatory interactions from these data and to get an insight into the mechanisms of genetic regulation. We propose a model for genetic regulatory interactions, which has a biologically motivated Boolean logic semantics, but is of a probabilistic nature, and is hence able to confront noisy biological processes and data. We propose a method for learning the model from data based on the Bayesian approach and utilizing Gibbs sampling. We tested our method with previously published data of the Saccharomyces cerevisiae cell cycle and found relations between genes consistent with biological knowledge.

Genexp--a genetic network simulation environment.

An environment for simulation of dynamics of genetic regulatory networks is presented. The model is based on the recurrent neural network principle and allows to interactively simulate various genetic regulatory interactions under different features of the system. The results are displayed graphically. http://proteom.biomed.cas.cz/genexp

Genetic Regulation of Root Hair Development in Arabidopsis Thaliana: A Network Model

The root epidermis of Arabidopsis thaliana is formed by alternate files of hair and non-hair cells. Epidermal cells overlying two cortex cells eventually develop a hair, while those overlying only one cortex cell do not. Here we propose a network model that integrates most of the available genetic and molecular data on the regulatory and signaling pathways underlying root epidermal differentiation. The network architecture includes two pathways; one formed by the genesTTG , R homolog, GL2 and CPC, and the other one by the signal transduction proteins ETR1 and CTR1. Both parallel pathways regulate the activity of AXR2 and RHD6, which in turn control the development of root hairs. The regulatory network was simulated as a dynamical system of eight discrete state variables. The distinction between epidermal cells contacting one or two cortical cells was accounted for by fixing the initial states of CPC and ETR1 proteins. The model allows for predictions of mutants and pharmacological effects because it includes the ethylene receptor. The dynamical system reaches one of the six stable states depending upon the initial state of the CPC variable and the ethylene receptor. Two of the stable states describe the activation patterns observed in mature trichoblasts (hair cells) and atrichoblasts (non-hair cells) in the wild-type phenotype and under normal ethylene availability. The other four states correspond to changes in the number of hair cells due to experimentally induced changes in ethylene availability. This model provides a hypothesis on the interactions among genes that encode transcription factors that regulate root hair development and the proteins involved in the ethylene transduction pathway. This is the first effort to use a dynamical system to understand the complex genetic regulatory interactions that ruleArabidopsis primary root development. The advantages of this type of models over static schematic representations are discussed.

2 Localization of bicoid Message during Drosophila Oogenesis

This chapter discusses the determinant nature of Drosophila development that results from molecular asymmetries present in the egg before fertilization. There are only a few such positional markers in the egg, specifying the anterior and posterior poles and the dorsal-ventral axis. During early embryogenesis, a complex cascade of genetic regulatory interactions refines this coarse positional information into the detailed pattern of the embryo. The molecular asymmetry that marks the anterior end of the Drosophila eggs is the bicoid messenger RNA. Bicoid message is produced only during oogenesis and is stably localized at one end of the oocyte, egg, and early embryo. The signal for localization, within bicoid message, appears to be the secondary structure of the 3' untranslated end.