scholarly journals NetREX: Network Rewiring using EXpression - Towards Context Specific Regulatory Networks

2017 ◽  
Author(s):  
Yijie Wang ◽  
Dong-Yeon Cho ◽  
Hangnoh Lee ◽  
Justin Fear ◽  
Brian Oliver ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Regulatory Network ◽  
Gene Network ◽  
Regulatory Networks ◽  
Tissue Type ◽  
Expression Data ◽  
Gene Regulatory ◽  
Context Specific ◽  
Tf Gene ◽  
Biological Context

AbstractUnderstanding gene regulation is a fundamental step towards understanding of how cells function and respond to environmental cues and perturbations. An important step in this direction is the ability to infer the transcription factor (TF)-gene regulatory network (GRN). However gene regulatory networks are typically constructed disregarding the fact that regulatory programs are conditioned on tissue type, developmental stage, sex, and other factors. Due to lack of the biological context specificity, these context-agnostic networks may not provide insight for revealing the precise actions of genes for a specific biological system under concern. Collecting multitude of features required for a reliable construction of GRNs such as physical features (TF binding, chromatin accessibility) and functional features (correlation of expression or chromatin patterns) for every context of interest is costly. Therefore we need methods that is able to utilize the knowledge about a context-agnostic network (or a network constructed in a related context) for construction of a context specific regulatory network.To address this challenge we developed a computational approach that utilizes expression data obtained in a specific biological context such as a particular development stage, sex, tissue type and a GRN constructed in a different but related context (alternatively an incomplete or a noisy network for the same context) to construct a context specific GRN. Our method, NetREX, is inspired by network component analysis (NCA) that estimates TF activities and their influences on target genes given predetermined topology of a TF-gene network. To predict a network under a different condition, NetREX removes the restriction that the topology of the TF-gene network is fixed and allows for adding and removing edges to that network. To solve the corresponding optimization problem, which is non-convex and non-smooth, we provide a general mathematical framework allowing use of the recently proposed Proximal Alternative Linearized Maximization technique and prove that our formulation has the properties required for convergence.We tested our NetREX on simulated data and subsequently applied it to gene expression data in adult females from 99 hemizygotic lines of the Drosophila deletion (DrosDel) panel. The networks predicted by NetREX showed higher biological consistency than alternative approaches. In addition, we used the list of recently identified targets of the Doublesex (DSX) transcription factor to demonstrate the predictive power of our method.

scholarly journals Accurate differential analysis of transcription factor activity from gene expression

2018 ◽  
Author(s):  
Viren Amin ◽  
Murat Can Cobanoglu
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Expression Data ◽  
Differential Analysis ◽  
Gene Regulatory ◽  
Context Specific ◽  
Tf Gene ◽  
Perturbation Estimation

AbstractWe present EPEE (Effector and Perturbation Estimation Engine), a method for differential analysis of transcription factor (TF) activity from gene expression data. EPEE addresses two principal challenges in the field, namely incorporating context-specific TF-gene regulatory networks, and accounting for the fact that TF activity inference is intrinsically coupled for all TFs that share targets. Our validations in well-studied immune and cancer contexts show that addressing the overlap challenge and using state-of-the-art regulatory networks enable EPEE to consistently produce accurate results. (Accessible at: https://github.com/Cobanoglu-Lab/EPEE)

scholarly journals Neural Gene Network Constructor: A Neural Based Model for Reconstructing Gene Regulatory Network

2019 ◽  
Author(s):  
Zhang Zhang ◽  
Lifei Wang ◽  
Shuo Wang ◽  
Ruyi Tao ◽  
Jingshu Xiao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Network Structure ◽  
Regulatory Network ◽  
Gene Network ◽  
Regulatory Networks ◽  
Expression Data ◽  
Gene Regulatory

SummaryReconstructing gene regulatory networks (GRNs) and inferring the gene dynamics are important to understand the behavior and the fate of the normal and abnormal cells. Gene regulatory networks could be reconstructed by experimental methods or from gene expression data. Recent advances in Single Cell RNA sequencing technology and the computational method to reconstruct trajectory have generated huge scRNA-seq data tagged with additional time labels. Here, we present a deep learning model “Neural Gene Network Constructor” (NGNC), for inferring gene regulatory network and reconstructing the gene dynamics simultaneously from time series gene expression data. NGNC is a model-free heterogenous model, which can reconstruct any network structure and non-linear dynamics. It consists of two parts: a network generator which incorporating gumbel softmax technique to generate candidate network structure, and a dynamics learner which adopting multiple feedforward neural networks to predict the dynamics. We compare our model with other well-known frameworks on the data set generated by GeneNetWeaver, and achieve the state of the arts results both on network reconstruction and dynamics learning.

scholarly journals TReNCo: Topologically associating domain (TAD) aware regulatory network construction

2021 ◽  
Author(s):  
Christopher Bennett ◽  
Viren Amin ◽  
Daehwan Kim ◽  
Murat Cobanoglu ◽  
Venkat Malladi
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Cardiac Development ◽  
Network Construction ◽  
Biologically Relevant ◽  
Topologically Associated Domains ◽  
Gene Regulatory ◽  
Context Specific ◽  
Tf Gene

AbstractThere has long been a desire to understand, describe, and model gene regulatory networks controlling numerous biologically meaningful processes like differentiation. Despite many notable improvements to models over the years, many models do not accurately capture subtle biological and chemical characteristics of the cell such as high-order chromatin domains of the chromosomes. Topologically Associated Domains (TAD) are one of these genomic regions that are enriched for contacts within themselves. Here we present TAD-aware Regulatory Network Construction or TReNCo, a memory-lean method utilizing epigenetic marks of enhancer and promoter activity, and gene expression to create context-specific transcription factor-gene regulatory networks. TReNCo utilizes common assay’s, ChIP-seq, RNA-seq, and TAD boundaries as a hard cutoff, instead of distance based, to efficiently create context-specific TF-gene regulatory networks. We used TReNCo to define the enhancer landscape and identify transcription factors (TFs) that drive the cardiac development of the mouse. Our results show that we are able to build specialized adjacency regulatory network graphs containing biologically relevant connections and time dependent dynamics.

scholarly journals Improving gene regulatory network structure using redundancy reduction in the MRNET algorithm

RSC Advances ◽  
2017 ◽  
Vol 7 (37) ◽  
pp. 23222-23233 ◽  
Author(s):  
Wei Liu ◽  
Wen Zhu ◽  
Bo Liao ◽  
Haowen Chen ◽  
Siqi Ren ◽  
...  
Keyword(s):  
Systems Biology ◽  
Gene Regulatory Network ◽  
Gene Regulatory Networks ◽  
Network Structure ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Central Problem ◽  
Expression Data ◽  
Redundancy Reduction ◽  
Gene Regulatory

Inferring gene regulatory networks from expression data is a central problem in systems biology.

PAGeneRN

2020 ◽  
pp. 1052-1075 ◽  
Author(s):  
Dina Elsayad ◽  
A. Ali ◽  
Howida A. Shedeed ◽  
Mohamed F. Tolba
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Network Analysis ◽  
Gene Regulatory Network ◽  
Gene Expression Data ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Expression Data ◽  
Gene Expression Data Analysis ◽  
Gene Regulatory

The gene expression analysis is an important research area of Bioinformatics. The gene expression data analysis aims to understand the genes interacting phenomena, gene functionality and the genes mutations effect. The Gene regulatory network analysis is one of the gene expression data analysis tasks. Gene regulatory network aims to study the genes interactions topological organization. The regulatory network is critical for understanding the pathological phenotypes and the normal cell physiology. There are many researches that focus on gene regulatory network analysis but unfortunately some algorithms are affected by data size. Where, the algorithm runtime is proportional to the data size, therefore, some parallel algorithms are presented to enhance the algorithms runtime and efficiency. This work presents a background, mathematical models and comparisons about gene regulatory networks analysis different techniques. In addition, this work proposes Parallel Architecture for Gene Regulatory Network (PAGeneRN).

EVIDENCE OF SCALE-FREE TOPOLOGY IN GENE REGULATORY NETWORK OF HUMAN TISSUES

2008 ◽  
Vol 19 (02) ◽  
pp. 283-290 ◽  
Author(s):  
M. ANDRECUT ◽  
S. A. KAUFFMAN ◽  
A. M. MADNI
Keyword(s):  
Gene Regulatory Network ◽  
Gene Regulatory Networks ◽  
Regulatory Network ◽  
Gene Network ◽  
Regulatory Networks ◽  
Regulatory Gene ◽  
Human Tissues ◽  
Scale Free ◽  
The Common ◽  
Gene Regulatory

We report the reconstruction of the topology of gene regulatory network in human tissues. The results show that the connectivity of the regulatory gene network is characterized by a scale-free distribution. This result supports the hypothesis that scale-free networks may represent the common blueprint for gene regulatory networks.

scholarly journals Exact Reconstruction of Gene Regulatory Networks using Compressive Sensing

2014 ◽  
Author(s):  
Young Hwan Chang ◽  
Joe W. Gray ◽  
Claire J. Tomlin
Keyword(s):  
Compressive Sensing ◽  
Gene Regulatory Networks ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
A Priori ◽  
Graph Structure ◽  
Expression Data ◽  
Exact Reconstruction ◽  
Time Series Gene Expression ◽  
Gene Regulatory

Background: We consider the problem of reconstructing a gene regulatory network structure from limited time series gene expression data, without any a priori knowledge of connectivity. We assume that the network is sparse, meaning the connectivity among genes is much less than full connectivity. We develop a method for network reconstruction based on compressive sensing, which takes advantage of the network's sparseness. Results: For the case in which all genes are accessible for measurement, and there is no measurement noise, we show that our method can be used to exactly reconstruct the network. For the more general problem, in which hidden genes exist and all measurements are contaminated by noise, we show that our method leads to reliable reconstruction. In both cases, coherence of the model is used to assess the ability to reconstruct the network and to design new experiments. For each problem, a set of numerical examples is presented. Conclusions: The method provides a guarantee on how well the inferred graph structure represents the underlying system, reveals deficiencies in the data and model, and suggests experimental directions to remedy the deficiencies.

scholarly journals Backward evolution from gene network dynamics

2018 ◽  
Author(s):  
Merzu Kebede Belete ◽  
Daniel A. Charlebois ◽  
Gábor Balázsi
Keyword(s):  
Population Dynamics ◽  
Gene Regulatory Network ◽  
Gene Regulatory Networks ◽  
Regulatory Network ◽  
Gene Network ◽  
Regulatory Networks ◽  
Network Dynamics ◽  
Regulator Gene ◽  
Gene Regulatory ◽  
Population Fitness

AbstractGene expression is controlled by regulator genes that together with effector genes form gene regulatory networks. How mutation in the genes comprising gene regulatory networks influences cell population dynamics has not been adequately investigated. In this study, we develop mathematical models to study how a mutation in a regulator gene that reaches the effector gene with a time delay affects short-term and long-term population growth. Using theory and experiment, we find a paradoxical outcome of evolution where a mutation in a regulator gene leads to an interaction between gene regulatory network and population dynamics, causing in certain cases a permanent decrease in population fitness in a constant environment.Significance StatementThe properties of a cell are largely the products of its proteins, synthesized at rates depending on the regulation of protein coding genes. Single-cell measurements show that genetically identical cells can differ radically in their protein levels, partially due to the random production and degradation of proteins. It is currently unknown how mutants arise and spread in populations affected by such biological variability. We use computer simulations and evolution experiments to study how a mutant spreads in a population that carries a synthetic drug resistance gene network. Our results show for the first time a paradoxical outcome of evolution, where an initially beneficial mutation can interact with gene regulatory network dynamics and cause a permanent decrease in population fitness in the same environment.

scholarly journals Constructing gene regulatory networks using epigenetic data

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Abhijeet Rajendra Sonawane ◽  
Dawn L. DeMeo ◽  
John Quackenbush ◽  
Kimberly Glass
Keyword(s):  
Transcription Factor ◽  
Gene Regulatory Networks ◽  
Regulatory Network ◽  
Message Passing ◽  
Regulatory Networks ◽  
Transcription Factor Binding ◽  
Network Reconstruction ◽  
Sequence Motif ◽  
Factor Binding ◽  
Gene Regulatory

AbstractThe biological processes that drive cellular function can be represented by a complex network of interactions between regulators (transcription factors) and their targets (genes). A cell’s epigenetic state plays an important role in mediating these interactions, primarily by influencing chromatin accessibility. However, how to effectively use epigenetic data when constructing a gene regulatory network remains an open question. Almost all existing network reconstruction approaches focus on estimating transcription factor to gene connections using transcriptomic data. In contrast, computational approaches for analyzing epigenetic data generally focus on improving transcription factor binding site predictions rather than deducing regulatory network relationships. We bridged this gap by developing SPIDER, a network reconstruction approach that incorporates epigenetic data into a message-passing framework to estimate gene regulatory networks. We validated SPIDER’s predictions using ChIP-seq data from ENCODE and found that SPIDER networks are both highly accurate and include cell-line-specific regulatory interactions. Notably, SPIDER can recover ChIP-seq verified transcription factor binding events in the regulatory regions of genes that do not have a corresponding sequence motif. The networks estimated by SPIDER have the potential to identify novel hypotheses that will allow us to better characterize cell-type and phenotype specific regulatory mechanisms.

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