A Method to Construct Gene Regulatory Networks to Estimate and Calculate Time Delays

2013 ◽  
Vol 62 (1) ◽  
Author(s):  
Mohd Saberi Mohamad ◽  
Chai Suk Phin
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Time Delays ◽  
Time Course ◽  
Target Gene ◽  
Biological Research ◽  
Expression Change ◽  
Gene Regulatory ◽  
Regulator Genes

In general, the motive of this research is to infer gene regulatory network in order to clarify the basis consequences of biological process at the molecular level. Time course gene expression profiling dataset has been widely used in basic biological research, especially in transcription regulation studies since the microarray dataset is a short time course gene expression dataset and have lots of errors, missing value, and noise.  In this research, R library is implemented in this method to construct gene regulatory which aims to estimate and calculate the time delays between genes and transcription factor. Time delay is the parameters of the modeled time delay linear regression models and a time lag during gene expression change of the regulator genes toward target gene expression. The constructed gene regulatory network provided information of time delays between expression change in regulator genes and its target gene which can be applied to investigate important time-related biological process in cells. The result of time delays and regulation patterns in gene regulatory network may contribute into biological research such as cell development, cell cycle, and cell differentiation in any of living cells.

scholarly journals Biomarker Prioritisation and Power Estimation Using Ensemble Gene Regulatory Network Inference

2020 ◽  
Vol 21 (21) ◽  
pp. 7886
Author(s):  
Furqan Aziz ◽  
Animesh Acharjee ◽  
John A. Williams ◽  
Dominic Russ ◽  
Laura Bravo-Merodio ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Network Inference ◽  
Partial Least Square ◽  
Least Square ◽  
Ductal Adenocarcinoma ◽  
Gene Regulatory Network Inference ◽  
Gene Regulatory ◽  
Regulator Genes

Inferring the topology of a gene regulatory network (GRN) from gene expression data is a challenging but important undertaking for gaining a better understanding of gene regulation. Key challenges include working with noisy data and dealing with a higher number of genes than samples. Although a number of different methods have been proposed to infer the structure of a GRN, there are large discrepancies among the different inference algorithms they adopt, rendering their meaningful comparison challenging. In this study, we used two methods, namely the MIDER (Mutual Information Distance and Entropy Reduction) and the PLSNET (Partial least square based feature selection) methods, to infer the structure of a GRN directly from data and computationally validated our results. Both methods were applied to different gene expression datasets resulting from inflammatory bowel disease (IBD), pancreatic ductal adenocarcinoma (PDAC), and acute myeloid leukaemia (AML) studies. For each case, gene regulators were successfully identified. For example, for the case of the IBD dataset, the UGT1A family genes were identified as key regulators while upon analysing the PDAC dataset, the SULF1 and THBS2 genes were depicted. We further demonstrate that an ensemble-based approach, that combines the output of the MIDER and PLSNET algorithms, can infer the structure of a GRN from data with higher accuracy. We have also estimated the number of the samples required for potential future validation studies. Here, we presented our proposed analysis framework that caters not only to candidate regulator genes prediction for potential validation experiments but also an estimation of the number of samples required for these experiments.

scholarly journals DeltaNeTS+: Elucidating the mechanism of drugs and diseases using gene expression and transcriptional regulatory networks

2019 ◽  
Author(s):  
Heeju Noh ◽  
Ziyi Hua ◽  
Panagiotis Chrysinas ◽  
Jason E. Shoemaker ◽  
Rudiyanto Gunawan
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Network Structure ◽  
Regulatory Network ◽  
Gene Network ◽  
Time Course ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Direct Gene ◽  
Gene Regulatory

AbstractBackgroundKnowledge on the molecular targets of diseases and drugs is crucial for elucidating disease pathogenesis and mechanism of action of drugs, and for driving drug discovery and treatment formulation. In this regard, high-throughput gene transcriptional profiling has become a leading technology, generating whole-genome data on the transcriptional alterations caused by diseases or drug compounds. However, identifying direct gene targets, especially in the background of indirect (downstream) effects, based on differential gene expressions is difficult due to the complexity of gene regulatory network governing the gene transcriptional processes.ResultsIn this work, we developed a network analysis method, called DeltaNeTS+, for inferring direct gene targets of drugs and diseases from gene transcriptional profiles. DeltaNeTS+ relies on a gene regulatory network model to identify direct perturbations to the transcription of genes. Importantly, DeltaNeTS+ is able to combine both steady-state and time-course gene expression profiles, as well as to leverage information on the gene network structure that is increasingly becoming available for a multitude of organisms, including human. We demonstrated the power of DeltaNeTS+ in predicting gene targets using gene expression data in complex organisms, including Caenorhabditis elegans and human cell lines (T-cell and Calu-3). More specifically, in an application to time-course gene expression profiles of influenza A H1N1 (swine flu) and H5N1 (avian flu) infection, DeltaNeTS+ shed light on the key differences of dynamic cellular perturbations caused by the two influenza strains.ConclusionDeltaNeTS+ is an enabling tool to infer gene transcriptional perturbations caused by diseases and drugs from gene transcriptional profiles. By incorporating available information on gene network structure, DeltaNeTS+ produces accurate predictions of direct gene targets from a small sample size (~10s). DeltaNeTS+ can freely downloaded from http://www.github.com/cabsel/deltanetsplus.

scholarly journals Study on the Relationship between the miRNA-centered ceRNA Regulatory Network and Fatigue

2021 ◽  
Author(s):  
Xingzhe Yang ◽  
Feng Li ◽  
Jie Ma ◽  
Yan Liu ◽  
Xuejiao Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Hot Spot ◽  
Genetic Research ◽  
Noncoding Rnas ◽  
Messenger Rnas ◽  
Effective Prevention ◽  
Gene Regulatory ◽  
The Relationship

AbstractIn recent years, the incidence of fatigue has been increasing, and the effective prevention and treatment of fatigue has become an urgent problem. As a result, the genetic research of fatigue has become a hot spot. Transcriptome-level regulation is the key link in the gene regulatory network. The transcriptome includes messenger RNAs (mRNAs) and noncoding RNAs (ncRNAs). MRNAs are common research targets in gene expression profiling. Noncoding RNAs, including miRNAs, lncRNAs, circRNAs and so on, have been developed rapidly. Studies have shown that miRNAs are closely related to the occurrence and development of fatigue. MiRNAs can regulate the immune inflammatory reaction in the central nervous system (CNS), regulate the transmission of nerve impulses and gene expression, regulate brain development and brain function, and participate in the occurrence and development of fatigue by regulating mitochondrial function and energy metabolism. LncRNAs can regulate dopaminergic neurons to participate in the occurrence and development of fatigue. This has certain value in the diagnosis of chronic fatigue syndrome (CFS). CircRNAs can participate in the occurrence and development of fatigue by regulating the NF-κB pathway, TNF-α and IL-1β. The ceRNA hypothesis posits that in addition to the function of miRNAs in unidirectional regulation, mRNAs, lncRNAs and circRNAs can regulate gene expression by competitive binding with miRNAs, forming a ceRNA regulatory network with miRNAs. Therefore, we suggest that the miRNA-centered ceRNA regulatory network is closely related to fatigue. At present, there are few studies on fatigue-related ncRNA genes, and most of these limited studies are on miRNAs in ncRNAs. However, there are a few studies on the relationship between lncRNAs, cirRNAs and fatigue. Less research is available on the pathogenesis of fatigue based on the ceRNA regulatory network. Therefore, exploring the complex mechanism of fatigue based on the ceRNA regulatory network is of great significance. In this review, we summarize the relationship between miRNAs, lncRNAs and circRNAs in ncRNAs and fatigue, and focus on exploring the regulatory role of the miRNA-centered ceRNA regulatory network in the occurrence and development of fatigue, in order to gain a comprehensive, in-depth and new understanding of the essence of the fatigue gene regulatory network.

scholarly journals HSCVFNT: Inference of Time-Delayed Gene Regulatory Network Based on Complex-Valued Flexible Neural Tree Model

2018 ◽  
Vol 19 (10) ◽  
pp. 3178 ◽  
Author(s):  
Bin Yang ◽  
Yuehui Chen ◽  
Wei Zhang ◽  
Jiaguo Lv ◽  
Wenzheng Bao ◽  
...  
Keyword(s):  
Time Series ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Target Gene ◽  
Repair System ◽  
Tree Model ◽  
Scoring Method ◽  
Maximum Information ◽  
Gene Regulatory ◽  
Complex Valued

Gene regulatory network (GRN) inference can understand the growth and development of animals and plants, and reveal the mystery of biology. Many computational approaches have been proposed to infer GRN. However, these inference approaches have hardly met the need of modeling, and the reducing redundancy methods based on individual information theory method have bad universality and stability. To overcome the limitations and shortcomings, this thesis proposes a novel algorithm, named HSCVFNT, to infer gene regulatory network with time-delayed regulations by utilizing a hybrid scoring method and complex-valued flexible neural network (CVFNT). The regulations of each target gene can be obtained by iteratively performing HSCVFNT. For each target gene, the HSCVFNT algorithm utilizes a novel scoring method based on time-delayed mutual information (TDMI), time-delayed maximum information coefficient (TDMIC) and time-delayed correlation coefficient (TDCC), to reduce the redundancy of regulatory relationships and obtain the candidate regulatory factor set. Then, the TDCC method is utilized to create time-delayed gene expression time-series matrix. Finally, a complex-valued flexible neural tree model is proposed to infer the time-delayed regulations of each target gene with the time-delayed time-series matrix. Three real time-series expression datasets from (Save Our Soul) SOS DNA repair system in E. coli and Saccharomyces cerevisiae are utilized to evaluate the performance of the HSCVFNT algorithm. As a result, HSCVFNT obtains outstanding F-scores of 0.923, 0.8 and 0.625 for SOS network and (In vivo Reverse-Engineering and Modeling Assessment) IRMA network inference, respectively, which are 5.5%, 14.3% and 72.2% higher than the best performance of other state-of-the-art GRN inference methods and time-delayed methods.

Deciphering the transcription factor-microRNA-target gene regulatory network associated with graphene oxide cytotoxicity

2018 ◽  
Vol 12 (9) ◽  
pp. 1014-1026 ◽  
Author(s):  
Masoumeh Farahani ◽  
Mostafa Rezaei–Tavirani ◽  
Hakimeh Zali ◽  
Afsaneh Arefi Oskouie ◽  
Meisam Omidi ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Graphene Oxide ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Target Gene ◽  
Microrna Target ◽  
Gene Regulatory

scholarly journals Odd-paired controls frequency doubling in Drosophila segmentation by altering the pair-rule gene regulatory network

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Erik Clark ◽  
Michael Akam
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Frequency Doubling ◽  
Drosophila Embryo ◽  
Anteroposterior Patterning ◽  
Regulatory Interactions ◽  
Pair Rule Gene ◽  
Gene Regulatory ◽  
Pair Rule

The Drosophila embryo transiently exhibits a double-segment periodicity, defined by the expression of seven 'pair-rule' genes, each in a pattern of seven stripes. At gastrulation, interactions between the pair-rule genes lead to frequency doubling and the patterning of 14 parasegment boundaries. In contrast to earlier stages of Drosophila anteroposterior patterning, this transition is not well understood. By carefully analysing the spatiotemporal dynamics of pair-rule gene expression, we demonstrate that frequency-doubling is precipitated by multiple coordinated changes to the network of regulatory interactions between the pair-rule genes. We identify the broadly expressed but temporally patterned transcription factor, Odd-paired (Opa/Zic), as the cause of these changes, and show that the patterning of the even-numbered parasegment boundaries relies on Opa-dependent regulatory interactions. Our findings indicate that the pair-rule gene regulatory network has a temporally modulated topology, permitting the pair-rule genes to play stage-specific patterning roles.

Sign in / Sign up

Export Citation Format

Share Document