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12:08 EDT 30th March 2020 | BioPortfolio

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Showing "Overview Gene Regulatory Network Inference Based Differential Equation" PubMed Articles 1–25 of 51,000+

Overview of Gene Regulatory Network Inference Based on Differential Equation Models.

Reconstruction of gene regulatory networks (GRN) plays an important role in understanding the complexity, functionality and pathways of biological systems, which could support the design of new drugs for diseases. Because differential equation models are flexible and strong, these models have been utilized to identify biochemical reactions and gene regulatory networks. This paper investigates the differential equation models for reverse engineering gene regulatory networks. We introduce three kinds of diffe...


Systems-Genetics-Based Inference of a Core Regulatory Network Underlying White Fat Browning.

Recruitment of brite/beige cells, known as browning of white adipose tissue (WAT), is an efficient way to turn an energy-storing organ into an energy-dissipating one and may therefore be of therapeutic value in combating obesity. However, a comprehensive understanding of the regulatory mechanisms mediating WAT browning is still lacking. Here, we exploit the large natural variation in WAT browning propensity between inbred mouse strains to gain an inclusive view of the core regulatory network coordinating th...

Time-Varying Differential Network Analysis for Revealing Network Rewiring over Cancer Progression.

To reveal how gene regulatory networks change over cancer development, multiple time-varying differential networks between adjacent cancer stages should be estimated simultaneously. Since the network rewiring may be driven by the perturbation of certain individual genes, there may be some hub nodes shared by these differential networks. Although several methods have been developed to estimate differential networks from gene expression data, most of them are designed for estimating a single differential netw...


Bifurcation analysis of bistable and oscillatory dynamics in biological networks using the root-locus method.

Most of the biological systems including gene regulatory networks can be described well by ordinary differential equation models with rational non-linearities. These models are derived either based on the reaction kinetics or by curve fitting to experimental data. This study demonstrates the applicability of the root-locus-based bifurcation analysis method for studying the complex dynamics of such models. The effectiveness of the bifurcation analysis in determining the exact parameter regions in each of whi...

Differential regulatory network-based quantification and prioritization of key genes underlying cancer drug resistance based on time-course RNA-seq data.

Drug resistance is a major cause for the failure of cancer chemotherapy or targeted therapy. However, the molecular regulatory mechanisms controlling the dynamic evolvement of drug resistance remain poorly understood. Thus, it is important to develop methods for identifying key gene regulatory mechanisms of the resistance to specific drugs. In this study, we developed a data-driven computational framework, DryNetMC, using a differential regulatory network-based modeling and characterization strategy to quan...

Probabilistic inference of binary Markov random fields in spiking neural networks through mean-field approximation.

Recent studies have suggested that the cognitive process of the human brain is realized as probabilistic inference and can be further modeled by probabilistic graphical models like Markov random fields. Nevertheless, it remains unclear how probabilistic inference can be implemented by a network of spiking neurons in the brain. Previous studies have tried to relate the inference equation of binary Markov random fields to the dynamic equation of spiking neural networks through belief propagation algorithm and...

Network inference with ensembles of bi-clustering trees.

Network inference is crucial for biomedicine and systems biology. Biological entities and their associations are often modeled as interaction networks. Examples include drug protein interaction or gene regulatory networks. Studying and elucidating such networks can lead to the comprehension of complex biological processes. However, usually we have only partial knowledge of those networks and the experimental identification of all the existing associations between biological entities is very time consuming a...

Non-coding RNA regulatory networks.

It is well established that the vast majority of human RNA transcripts do not encode for proteins and that non-coding RNAs regulate cell physiology and shape cellular functions. A subset of them is involved in gene regulation at different levels, from epigenetic gene silencing to post-transcriptional regulation of mRNA stability. Notably, the aberrant expression of many non-coding RNAs has been associated with aggressive pathologies. Rapid advances in network biology indicates that the robustness of cellula...

IgGeneUsage: differential gene usage in immune repertoires.

Decoding the properties of immune repertoires is key to understanding the adaptive immune response to challenges such as viral infection. One important quantitative property is differential usage of Ig genes between biological conditions. Yet, most analyses for differential Ig gene usage are performed qualitatively or with inadequate statistical methods. Here we introduce IgGeneUsage, a computational tool for the analysis of differential Ig gene usage. IgGeneUsage employs Bayesian inference with hierarchica...

RUNX1-ETO Depletion in t(8;21) AML Leads to C/EBPα- and AP-1-Mediated Alterations in Enhancer-Promoter Interaction.

Acute myeloid leukemia (AML) is associated with mutations in transcriptional and epigenetic regulator genes impairing myeloid differentiation. The t(8;21)(q22;q22) translocation generates the RUNX1-ETO fusion protein, which interferes with the hematopoietic master regulator RUNX1. We previously showed that the maintenance of t(8;21) AML is dependent on RUNX1-ETO expression. Its depletion causes extensive changes in transcription factor binding, as well as gene expression, and initiates myeloid differentiati...

The detection and analysis of differential regulatory communities in lung cancer.

The tumorgenesis process of lung cancer involves the regulatory dysfunctions of multiple pathways. Although many signaling pathways have been identified to be associated with lung cancer, there are little quantitative models of how inactions between genes change during the process from normal to cancer. These changes belong to different dynamic co-expressions patterns. We quantitatively analyzed differential co-expression of gene pairs in four datasets. Each dataset included a large number of lung cancer an...

LiPLike: Towards gene regulatory network predictions of high certainty.

High correlation in expression between regulatory elements is a persistent obstacle for the reverse-engineering of gene regulatory networks. If two potential regulators have matching expression patterns, it becomes challenging to differentiate between them, thus increasing the risk of false positive identifications.

Acquisition of multipotent and migratory neural crest cells in vertebrate evolution.

The emergence of multipotent and migratory neural crest (NC) cells defines a key evolutionary transition from invertebrates to vertebrates. Studies in vertebrates have identified a complex gene regulatory network that governs sequential stages of NC ontogeny. Comparative analysis has revealed extensive conservation of the overall architecture of the NC gene regulatory network between jawless and jawed vertebrates. Among invertebrates, urochordates express putative NC gene homologs in the neural plate border...

Identification of lncRNA-associated differential subnetworks in oesophageal squamous cell carcinoma by differential co-expression analysis.

Differential expression analysis has led to the identification of important biomarkers in oesophageal squamous cell carcinoma (ESCC). Despite enormous contributions, it has not harnessed the full potential of gene expression data, such as interactions among genes. Differential co-expression analysis has emerged as an effective tool that complements differential expression analysis to provide better insight of dysregulated mechanisms and indicate key driver genes. Here, we analysed the differential co-expres...

Gene network reverse engineering: The Next Generation.

A master equation for spin systems far from equilibrium.

The quantum dynamics of spin systems is often treated by a differential equation known as the master equation, which describes the trajectories of spin observables such as magnetization components, spin state populations, and coherences between spin states. The master equation describes how a perturbed spin system returns to a state of thermal equilibrium with a finite-temperature environment. The conventional master equation, which has the form of an inhomogeneous differential equation, applies to cases wh...

Equation for evolution of temporal width of a solute band migrating in chromatographic column.

An alternative differential equation to model the development of the temporal width of a solute band during its migration in a chromatographic column is developed. This model uses the solute residency, the inverse of the solute velocity, as parameter, which has the advantage to be an always finite quantity even at the column outlet in GC-MS where carrier gas velocity approaches infinity. The new differential equation is derived from a known equation describing the evolution of the spatial variance of migrat...

Stability and oscillation analysis of a gene regulatory network with multiple time delays and diffusion rate.

In genetic regulatory networks (GRNs), the diffusion rate of mRNA and protein play a key role in regulatory mechanisms of gene expression, especially in translation and transcription. However, the influence of diffusion rate on oscillatory gene expression is not well understood. In this paper, by considering the diffusion rate of mRNA and protein, a novel GRN is proposed. Then, two basic problems of such network, i.e. stability and oscillation, are solved in detail. Moreover, the properties of oscillation a...

Cell differentiation: what have we learned in 50 years?

I revisit two theories of cell differentiation in multicellular organisms published a half-century ago, Stuart Kauffman's global gene regulatory dynamics (GGRD) model and Roy Britten's and Eric Davidson's modular gene regulatory network (MGRN) model, in light of newer knowledge of mechanisms of gene regulation in the metazoans (animals). The two models continue to inform hypotheses and computational studies of differentiation of lineage-adjacent cell types. However, their shared notion (based on bacterial r...

Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe.

Here, we present Scribe (https://github.com/aristoteleo/Scribe-py), a toolkit for detecting and visualizing causal regulatory interactions between genes and explore the potential for single-cell experiments to power network reconstruction. Scribe employs restricted directed information to determine causality by estimating the strength of information transferred from a potential regulator to its downstream target. We apply Scribe and other leading approaches for causal network reconstruction to several types...

Cancer driver gene discovery in transcriptional regulatory networks using influence maximization approach.

Cancer driver genes (CDGs) are the genes whose mutations cause tumor growth. Several computational methods have been previously developed for finding CDGs. Most of these methods are sequence-based, that is, they rely on finding key mutations in genomic data to predict CDGs. In the present work, we propose iMaxDriver as a network-based tool for predicting driver genes by application of influence maximization algorithm on human transcriptional regulatory network (TRN). In the first step of this approach, the ...

A simplified modelling framework facilitates more complex representations of plant circadian clocks.

The circadian clock orchestrates biological processes so that they occur at specific times of the day, thereby facilitating adaptation to diurnal and seasonal environmental changes. In plants, mathematical modelling has been comprehensively integrated with experimental studies to gain a better mechanistic understanding of the complex genetic regulatory network comprising the clock. However, with an increasing number of circadian genes being discovered, there is a pressing need for methods facilitating the e...

Characterizing the involvement of FaMADS9 in the regulation of strawberry fruit receptacle development.

FaMADS9 is the strawberry (Fragaria x ananassa) gene that exhibits the highest homology to the tomato (Solanum lycopersicum) RIN gene. Transgenic lines were obtained in which FaMADS9 was silenced. The fruits of these lines did not show differences in basic parameters, such as fruit firmness or colour, but exhibited lower Brix values in three of the four independent lines. The gene ontology MapMan category that was most enriched among the differentially expressed genes in the receptacles at the white stage c...

Supervised Learning of Gene Regulatory Networks.

Identifying the entirety of gene regulatory interactions in a biological system offers the possibility to determine the key molecular factors that affect important traits on the level of cells, tissues, and whole organisms. Despite the development of experimental approaches and technologies for identification of direct binding of transcription factors (TFs) to promoter regions of downstream target genes, computational approaches that utilize large compendia of transcriptomics data are still the predominant ...

TREEasy: an automated workflow to infer gene trees, species trees, and phylogenetic networks from multilocus data.

Multilocus genomic datasets can be used to infer a rich set of information about the evolutionary history of a lineage, including gene trees, species trees, and phylogenetic networks. However, user-friendly tools to run such integrated analyses are lacking, and workflows often require tedious reformatting and handling time to shepherd data through a series of individual programs. Here, we present a tool written in Python-TREEasy-that performs automated sequence alignment (with MAFFT), gene tree inference (w...


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