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 DeltaNeTS+: elucidating the mechanism of drugs and diseases using gene expression and transcriptional regulatory networks

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Heeju Noh ◽  
Ziyi Hua ◽  
Panagiotis Chrysinas ◽  
Jason E. Shoemaker ◽  
Rudiyanto Gunawan
Keyword(s):  
Gene Expression ◽  
Network Structure ◽  
Regulatory Network ◽  
Gene Network ◽  
Time Course ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Expression Data ◽  
Direct Gene ◽  
Gene Regulatory

Abstract Background Knowledge 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. Results In this work, we developed a network analysis method, called DeltaNeTS+, for inferring direct gene targets of drugs and diseases from gene transcriptional profiles. DeltaNeTS+ uses a gene regulatory network model to identify direct perturbations to the transcription of genes using gene expression data. Importantly, DeltaNeTS+ is able to combine both steady-state and time-course expression profiles, as well as leverage information on the gene network structure. 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. Conclusion DeltaNeTS+ is a powerful network analysis tool for inferring gene targets from gene expression profiles. As demonstrated in the case studies, by incorporating available information on gene network structure, DeltaNeTS+ produces accurate predictions of direct gene targets from a small sample size (~ 10 s). Integrating static and dynamic expression data with transcriptional network structure extracted from genomic information, as enabled by DeltaNeTS+, is crucial toward personalized medicine, where treatments can be tailored to individual patients. DeltaNeTS+ can be freely downloaded from http://www.github.com/cabsel/deltanetsplus.

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 Modeling Gene Networks inSaccharomyces cerevisiaeBased on Gene Expression Profiles

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Yulin Zhang ◽  
Kebo Lv ◽  
Shudong Wang ◽  
Jionglong Su ◽  
Dazhi Meng
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Clustering Coefficient ◽  
Anaerobic Conditions ◽  
Gene Regulatory ◽  
Aerobic And Anaerobic ◽  
Aerobic And Anaerobic Conditions

Detailed and innovative analysis of gene regulatory network structures may reveal novel insights to biological mechanisms. Here we study how gene regulatory network inSaccharomyces cerevisiaecan differ under aerobic and anaerobic conditions. To achieve this, we discretized the gene expression profiles and calculated the self-entropy of down- and upregulation of gene expression as well as joint entropy. Based on these quantities the uncertainty coefficient was calculated for each gene triplet, following which, separate gene logic networks were constructed for the aerobic and anaerobic conditions. Four structural parameters such as average degree, average clustering coefficient, average shortest path, and average betweenness were used to compare the structure of the corresponding aerobic and anaerobic logic networks. Five genes were identified to be putative key components of the two energy metabolisms. Furthermore, community analysis using the Newman fast algorithm revealed two significant communities for the aerobic but only one for the anaerobic network. David Gene Functional Classification suggests that, under aerobic conditions, one such community reflects the cell cycle and cell replication, while the other one is linked to the mitochondrial respiratory chain function.

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