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Results for “Gene regulatory network”

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At least 19 recordsLinked to original sources
PubMed2006

Up-regulation of fetal rat lung parathyroid hormone-related protein gene regulatory network down-regulates the Sonic Hedgehog/Wnt/betacatenin gene regulatory network.

Lung development depends on endodermal Sonic Hedgehog (Shh) signaling to mesodermal Wingless/int/beta catenin (Wnt/betacatenin), followed by parathyroid hormone-related protein (PTHrP) signaling from endoderm to mesoderm. Fluid distension of fetal rat lung explants up-regulates PTHrP signaling and down-regulates Shh/Wnt/betacatenin signaling, marked by decreases in Patched, Gli, Frizzled, and Dishevelled, inducing fibroblast triglyceride uptake, type II cell saturated phosphatidylcholine, and surfactant protein-B expression. Bumetanide, which inhibits fluid distension, blocked down-regulation of the Shh/Wnt/betacatenin pathway and up-regulation of the PTHrP pathway, whereas PTHrP (1-34, 5 x 10(-7) M) treatment overcame bumetanide inhibition, and the PTHrP receptor antagonist PTHrP (7-34) amide (5 x 10(-6) M) mimicked bumetanide, indicating that PTHrP signaling mediates fluid distension-induced alveolar differentiation. Fetal rat lung explant automaturation was characterized by decreased Wnt/betacatenin signaling and increased PTHrP/PTHrP receptor signaling, up-regulating fibroblast-specific adipocyte differentiation related protein (ADRP) and peroxisome proliferator-activated receptor gamma. Wnt/betacatenin agonists (LiCl or SB415268) maintained Shh/Wnt/betacatenin signaling, blocking spontaneous up-regulation of the PTHrP pathway, whereas PTHrP or cAMP down-regulated Shh/Wnt/betacatenin signaling and stimulated PTHrP signaling for fibroblast and type II cell differentiation. This is the first evidence that alveolar fluid distension is an organizing principle for PTHrP signaling down-regulation of the Shh/Wnt/betacatenin pathway.

Animals↗
PubMed2003

Regulatory gene networks and the properties of the developmental process.

Genomic instructions for development are encoded in arrays of regulatory DNA. These specify large networks of interactions among genes producing transcription factors and signaling components. The architecture of such networks both explains and predicts developmental phenomenology. Although network analysis is yet in its early stages, some fundamental commonalities are already emerging. Two such are the use of multigenic feedback loops to ensure the progressivity of developmental regulatory states and the prevalence of repressive regulatory interactions in spatial control processes. Gene regulatory networks make it possible to explain the process of development in causal terms and eventually will enable the redesign of developmental regulatory circuitry to achieve different outcomes.

Animals↗
PubMed2006

Discovery of Time-Delayed Gene Regulatory Networks based on temporal gene expression profiling.

BACKGROUND: It is one of the ultimate goals for modern biological research to fully elucidate the intricate interplays and the regulations of the molecular determinants that propel and characterize the progression of versatile life phenomena, to name a few, cell cycling, developmental biology, aging, and the progressive and recurrent pathogenesis of complex diseases. The vast amount of large-scale and genome-wide time-resolved data is becoming increasing available, which provides the golden opportunity to unravel the challenging reverse-engineering problem of time-delayed gene regulatory networks. RESULTS: In particular, this methodological paper aims to reconstruct regulatory networks from temporal gene expression data by using delayed correlations between genes, i.e., pairwise overlaps of expression levels shifted in time relative each other. We have thus developed a novel model-free computational toolbox termed TdGRN (Time-delayed Gene Regulatory Network) to address the underlying regulations of genes that can span any unit(s) of time intervals. This bioinformatics toolbox has provided a unified approach to uncovering time trends of gene regulations through decision analysis of the newly designed time-delayed gene expression matrix. We have applied the proposed method to yeast cell cycling and human HeLa cell cycling and have discovered most of the underlying time-delayed regulations that are supported by multiple lines of experimental evidence and that are remarkably consistent with the current knowledge on phase characteristics for the cell cyclings. CONCLUSION: We established a usable and powerful model-free approach to dissecting high-order dynamic trends of gene-gene interactions. We have carefully validated the proposed algorithm by applying it to two publicly available cell cycling datasets. In addition to uncovering the time trends of gene regulations for cell cycling, this unified approach can also be used to study the complex gene regulations related to the development, aging and progressive pathogenesis of a complex disease where potential dependences between different experiment units might occurs.

Algorithms↗
PubMed2003

Inferring gene regulatory networks from time-ordered gene expression data of Bacillus subtilis using differential equations.

We describe a new method to infer a gene regulatory network, in terms of a linear system of differential equations, from time course gene expression data. As biologically the gene regulatory network is known to be sparse, we expect most coefficients in such a linear system of differential equations to be zero. In previously proposed methods, the number of nonzero coefficients in the system was limited based on ad hoc assumptions. Instead, we propose to infer the degree of sparseness of the gene regulatory network from the data, where we use Akaike's Information Criterion to determine which coefficients are nonzero. We apply our method to MMGE time course data of Bacillus subtilis.

Bacillus subtilis↗
PubMed2005

Modelling periodic oscillation in gene regulatory networks by cyclic feedback systems.

In this paper, we develop a new methodology to analyze and design periodic oscillators of biological networks, in particular gene regulatory networks with multiple genes, proteins and time delays, by using negative cyclic feedback systems. We show that negative cyclic feedback networks have no stable equilibria but stable periodic orbits when certain conditions are satisfied. Specifically, we first prove the basic properties of the biological networks composed of cyclic feedback loops, and then extend our results to general cyclic feedback network with less restriction, thereby making our theoretical analysis and design of oscillators easy to implement, even for large-scale systems. Finally, we use one circadian network formed by a period protein (PER) and per mRNA, and one biologically plausible synthetic gene network, to demonstrate the theoretical results. Since there is less restriction on the network structure, the results of this paper can be expected to apply to a wide variety of areas on modelling, analyzing and designing of biological systems.

Animals↗
PubMed2006

Perturbation avalanches and criticality in gene regulatory networks.

Boolean networks are simplified models of gene regulatory networks. We derive an approximation of the size distribution of perturbation avalanches in Boolean networks based on known results in the theory of branching processes. We show numerically that the approximation works well for different kinds of Boolean networks. It has been suggested that gene regulatory networks may be dynamically critical. To study this, as an application of the presented theory we present a novel method for estimating an order parameter from microarray data. According to the available data and our method, we find that gene regulatory networks appear to be stable and reside near the phase transition between order and chaos.

Animals↗
PubMed2004

A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data.

MOTIVATION: Signaling pathways are dynamic events that take place over a given period of time. In order to identify these pathways, expression data over time are required. Dynamic Bayesian network (DBN) is an important approach for predicting the gene regulatory networks from time course expression data. However, two fundamental problems greatly reduce the effectiveness of current DBN methods. The first problem is the relatively low accuracy of prediction, and the second is the excessive computational time. RESULTS: In this paper, we present a DBN-based approach with increased accuracy and reduced computational time compared with existing DBN methods. Unlike previous methods, our approach limits potential regulators to those genes with either earlier or simultaneous expression changes (up- or down-regulation) in relation to their target genes. This allows us to limit the number of potential regulators and consequently reduce the search space. Furthermore, we use the time difference between the initial change in the expression of a given regulator gene and its potential target gene to estimate the transcriptional time lag between these two genes. This method of time lag estimation increases the accuracy of predicting gene regulatory networks. Our approach is evaluated using time-series expression data measured during the yeast cell cycle. The results demonstrate that this approach can predict regulatory networks with significantly improved accuracy and reduced computational time compared with existing DBN approaches.

Algorithms↗
PubMed2026

Causal assessment of Bayesian gene regulatory networks from single-cell transcriptomics.

Gene regulatory network (GRN) inference is an essential tool for revealing dysregulated relationships between genes in different cell types from single-cell transcriptomic (SCT) data. GRNs based on Bayesian networks (BNs) learned from SCT data can elucidate directed regulatory relationships representing complex disease mechanisms and their interplay through graphical modeling. However, software for learning BNs from SCT data is not widely available, nor is software for evaluating the BNs' structural accuracy in representing causal relationships between genes. Here, we describe the scstruc R package. This package provides a suite of BN structure learning algorithms specifically designed to handle SCT data, to evaluate the resulting networks based on the causal relationships they represent regardless of the availability of established molecular interaction networks, and to compare regulatory relationships between conditions. We demonstrated that scstruc can identify biologically relevant differential regulatory relationships between groups on a per-cell basis.

Bayesian networks↗
PubMed2006

Statistical epistasis is a generic feature of gene regulatory networks.

Functional dependencies between genes are a defining characteristic of gene networks underlying quantitative traits. However, recent studies show that the proportion of the genetic variation that can be attributed to statistical epistasis varies from almost zero to very high. It is thus of fundamental as well as instrumental importance to better understand whether different functional dependency patterns among polymorphic genes give rise to distinct statistical interaction patterns or not. Here we address this issue by combining a quantitative genetic model approach with genotype-phenotype models capable of translating allelic variation and regulatory principles into phenotypic variation at the level of gene expression. We show that gene regulatory networks with and without feedback motifs can exhibit a wide range of possible statistical genetic architectures with regard to both type of effect explaining phenotypic variance and number of apparent loci underlying the observed phenotypic effect. Although all motifs are capable of harboring significant interactions, positive feedback gives rise to higher amounts and more types of statistical epistasis. The results also suggest that the inclusion of statistical interaction terms in genetic models will increase the chance to detect additional QTL as well as functional dependencies between genetic loci over a broad range of regulatory regimes. This article illustrates how statistical genetic methods can fruitfully be combined with nonlinear systems dynamics to elucidate biological issues beyond reach of each methodology in isolation.

Epistasis, Genetic↗
PubMed2027

Inferring Gene Regulatory Networks in Stem Cells: Methods and Applications.

Gene regulatory networks (GRNs) represent the complex interplay of transcription factors, regulatory elements, and target genes that orchestrate cellular identity and function, playing a crucial role in the differentiation and maintenance of stem cells. This chapter provides an overview of experimental and computational methodologies for inferring GRNs, with particular emphasis on single-cell approaches. We first review key experimental techniques for detecting transcription factor binding sites, chromatin accessibility, and DNA motifs, alongside essential databases that support GRN reconstruction. We then introduce computational inference methods that can be categorized into four principal frameworks: correlation-based approaches, regression and machine learning models, probabilistic and deep learning methods, and integrative or message-passing frameworks. To illustrate practical application, we present a case study applying the pySCENIC workflow to a peripheral blood mononuclear cell single-cell RNA sequencing dataset from mouse, demonstrating how regulon-based analysis can reveal cell-type-specific regulatory programs. This chapter aims to serve as a practical guide for researchers seeking to understand and implement GRN inference methodologies in stem cell biology and related fields.

Gene Regulatory Networks↗
PubMed2005

Estimating gene regulatory networks and protein-protein interactions of Saccharomyces cerevisiae from multiple genome-wide data.

MOTIVATION: Biological processes in cells are properly performed by gene regulations, signal transductions and interactions between proteins. To understand such molecular networks, we propose a statistical method to estimate gene regulatory networks and protein-protein interaction networks simultaneously from DNA microarray data, protein-protein interaction data and other genome-wide data. RESULTS: We unify Bayesian networks and Markov networks for estimating gene regulatory networks and protein-protein interaction networks according to the reliability of each biological information source. Through the simultaneous construction of gene regulatory networks and protein-protein interaction networks of Saccharomyces cerevisiae cell cycle, we predict the role of several genes whose functions are currently unknown. By using our probabilistic model, we can detect false positives of high-throughput data, such as yeast two-hybrid data. In a genome-wide experiment, we find possible gene regulatory relationships and protein-protein interactions between large protein complexes that underlie complex regulatory mechanisms of biological processes.

Algorithms↗
PubMed2026

GRNContext: an interactive web platform for contextualized gene regulatory networks visualization across human cancers.

SUMMARY: While current Gene Regulatory Network (GRN) databases provide comprehensive reference maps of potential interactions between transcription factors and target genes, they do not specify which regulatory interactions are active within specific biological contexts. This limitation is particularly critical in cancer, where transcriptional programs are inherently tissue-specific. To address this gap, we developed GRNContext, an interactive web platform designed for the visualization, exploration, and comparative analysis of gene regulatory networks contextualized across 33 cancer types from The Cancer Genome Atlas (TCGA). Our approach uses the TFLink human reference GRN as a starting point and integrates TCGA transcriptomic profiles to infer cancer-specific regulatory activity. Regulatory relevance was assessed using complementary machine learning and statistical methods, which were unified into a consensus score to prioritize and filter the most relevant candidate regulators for each target gene. By providing both curated context-specific GRNs and a user-friendly platform, GRNContext constitutes a comprehensive and accessible resource that supports mechanistic investigations, hypothesis generation, and translational research focused on transcriptional regulation in cancer. AVAILABILITY AND IMPLEMENTATION: GRNContext is supported by all major browsers and freely available on the web at https://apps.cienciavida.org/grncontext. It is implemented as a client-server web application featuring a FastAPI backend and a React frontend utilizing Cytoscape.js for interactive network visualization, all containerized via Docker for cross-platform compatibility.

Humans↗
PubMed1995

A mathematical framework for describing and analysing gene regulatory networks.

This paper presents a mathematical framework for describing and analysing gene regulatory networks by autonomous differential equations. It represents an improvement on existing frameworks in that it may handle a wider range of gene regulatory mechanisms. Gene regulatory networks are frequently threshold-dominated, i.e. genes are activated only when the concentration of certain gene products lie between definite thresholds. Here, the concept of regulatory domain is introduced to describe these regions in the phase space. To each regulatory domain is associated an indicator function whose value is 1 inside and 0 outside the domain. The indicator functions thus reflect the logical structure of the network. The sharp borders between the regulatory domains may be smoothed by replacing the logical step functions by continuous sigmoids or so-called logoid functions. A logoid function coincides with the step function outside a narrow interval around the threshold, and rises continuously from 0 to 1 inside it. Using logoids, the task of finding steady states is considerably simplified. A list of regions in phase space comprising all steady states lying close to a threshold is obtained by examining a certain type of matrix called the Logoid-Jacobian. In addition, this matrix leads to the conditions necessary for stability of the steady states. External signals may be conveniently incorporated in the form of Boolean variables. Thus the framework is well suited for studying gene regulatory networks both in single cells and multicellular systems.

Animals↗
PubMed2004

An otx cis-regulatory module: a key node in the sea urchin endomesoderm gene regulatory network.

An essential node in the gene regulatory network (GRN) for endomesoderm specification in the sea urchin embryo lies within the regulatory system of the otx gene. According to the predictions of the GRN, based on perturbation analysis and expression data, the beta1/2 transcription unit of this gene is engaged during specification in interactions with two other regulatory genes, krox and gatae. It is predicted to be driven into activity by the krox gene, to form a positively reinforcing functional loop with the gatae gene, and to respond to its own output as well. Here we isolate the relevant otx cis-regulatory element, and examine the specific input predictions of the GRN at the level of its genomic DNA sequence. This beta1/2-otx regulatory module performs the necessary functions, as shown by use of expression constructs. It requires gatae, otx, and krox inputs, as predicted, and it operates as an "AND" logic processor in that removal of any one of these inputs essentially destroys activity. The necessary target sites were identified in the module sequence, and mutation of these sites was demonstrated to produce the same respective effects on construct expression as does blocking its regulatory inputs by treatment with morpholino antisense oligonucleotides. For spatial expression in the endoderm, one particular pair of Gata sites is essential and these function synergistically with an adjacent Otx site. We thus demonstrate directly the structure/function relationships of the genomic regulatory code, at this key node of the endomesoderm GRN.

Animals↗
PubMed2025

Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome data.

Gene regulatory networks (GRNs) provide a global representation of how genetic/genomic information is transferred in living systems and are a key component in understanding genome regulation. Single-cell multiome data provide unprecedented opportunities to reconstruct GRNs at fine-grained resolution. However, the inference of GRNs is hindered by insufficient single omic profiles due to the characteristic high loss rate of single-cell sequencing data. In this study, we developed scMultiomeGRN, a deep learning framework to infer transcription factor (TF) regulatory networks via unique integration of single-cell genomic (single-cell RNA sequencing) and epigenomic (single-cell ATAC sequencing) data. We create scMultiomeGRN to elucidate these networks by conceptualizing TF network graph structures. Specifically, we build modality-specific neighbor aggregators and cross-modal attention modules to learn latent representations of TFs from single-cell multi-omics. We demonstrate that scMultiomeGRN outperforms state-of-the-art models on multiple benchmark datasets involved in diseases and health. Via scMultiomeGRN, we identified Alzheimer's disease-relevant regulatory network of SPI1 and RUNX1 for microglia. In summary, scMultiomeGRN offers a deep learning framework to identify cell type-specific gene regulatory network from single-cell multiome data.

Deep Learning↗
PubMed2003

PAINT: a promoter analysis and interaction network generation tool for gene regulatory network identification.

We have developed a bioinformatics tool named PAINT that automates the promoter analysis of a given set of genes for the presence of transcription factor binding sites. Based on coincidence of regulatory sites, this tool produces an interaction matrix that represents a candidate transcriptional regulatory network. This tool currently consists of (1) a database of promoter sequences of known or predicted genes in the Ensembl annotated mouse genome database, (2) various modules that can retrieve and process the promoter sequences for binding sites of known transcription factors, and (3) modules for visualization and analysis of the resulting set of candidate network connections. This information provides a substantially pruned list of genes and transcription factors that can be examined in detail in further experimental studies on gene regulation. Also, the candidate network can be incorporated into network identification methods in the form of constraints on feasible structures in order to render the algorithms tractable for large-scale systems. The tool can also produce output in various formats suitable for use in external visualization and analysis software. In this manuscript, PAINT is demonstrated in two case studies involving analysis of differentially regulated genes chosen from two microarray data sets. The first set is from a neuroblastoma N1E-115 cell differentiation experiment, and the second set is from neuroblastoma N1E-115 cells at different time intervals following exposure to neuropeptide angiotensin II. PAINT is available for use as an agent in BioSPICE simulation and analysis framework (www.biospice.org), and can also be accessed via a WWW interface at www.dbi.tju.edu/dbi/tools/paint/.

Angiotensin II↗
PubMed2000

Hybrid Petri net representation of gene regulatory network.

It is important to provide a representation method of gene regulatory networks which realizes the intuitions of biologists while keeping the universality in its computational ability. In this paper, we propose a method to exploit hybrid Petri net (HPN) for representing gene regulatory networks. The HPN is an extension of Petri nets which have been used to represent many kinds of systems including stochastic ones in the field of computer sciences and engineerings. Since the HPN has continuous and discrete elements, it can easily handle biological factors such as protein and mRNA concentrations. We demonstrate that, by using HPNs, it is possible to translate biological facts into HPNs in a natural manner. It should be also emphasized that a hierarchical approach is taken for our construction of the genetic switch mechanism of lambda phage which is realized by using HPNs. This hierarchical approach with HPNs makes easier the arrangement of the components in the gene regulatory network based on the biological facts and provides us a prospective view of the network. We also show some computational results of the protein dynamics of the lambda phage mechanism that is simulated and observed by implementing the HPN on a currently available tool.

Bacteriophage lambda↗
PubMed2005

LvGroucho and nuclear beta-catenin functionally compete for Tcf binding to influence activation of the endomesoderm gene regulatory network in the sea urchin embryo.

In the sea urchin embryo, specification of the endomesoderm is accomplished by the activity of a network of regulatory genes in the vegetal hemisphere, called the endomesoderm gene regulatory network (GRN). The activation of this network is mediated primarily through the activity of the Wnt pathway, though details of pathway activation remain unclear. To gain further insight into control of endomesoderm GRN activation, we have identified a sea urchin homologue of the co-repressor Groucho (LvGroucho) that has been shown to antagonize beta-catenin/Tcf activation complexes during Wnt signaling in other systems. Groucho functions by recruiting the histone deacetylase Rpd3 to the DNA template via interaction with site-specific transcription factors, resulting in localized chromatin condensation and transcriptional silencing. Our results show that the LvGroucho protein localizes to all nuclei throughout embryonic development. Interaction assays demonstrate that LvGroucho interacts with Tcf via both the Q and the WD domains of the protein. LvGroucho interacts with Tcf to antagonize the expression of key endomesoderm regulatory genes. Assays demonstrate that LvGroucho and n beta-catenin functionally compete for binding to Tcf as a major mechanism by which the Tcf-control switch is regulated. Functional analysis of the N-terminal AES197 domain of LvGroucho shows that it is sufficient to recapitulate the function of full-length LvGroucho. This finding strongly supports the conclusion that the effects of LvGro overexpression are due primarily to its interactions with Tcf and not other Groucho interacting partners, since Tcf is the only protein present in the sea urchin known to interact with AES197. Because the Q domain is unable to bind Rpd3, it was expected to behave as a dominant negative LvGroucho. Unexpectedly, overexpression of the Q domain gave functional results similar to LvGroucho and the AES197 domain. This is the first evidence for an inherent repressive function for the Q domain alone. Together, our results indicate that LvGroucho functionally competes with beta-catenin for Tcf binding, and this competitive mechanism regulates one of the earliest steps in the initiation of the sea urchin endomesoderm GRN.

Amino Acid Sequence↗