scholarly journals Reconstruction of the Regulatory Network for Bacillus subtilis and Reconciliation with Gene Expression Data

2016 ◽  
Vol 7 ◽  
Author(s):  
José P. Faria ◽  
Ross Overbeek ◽  
Ronald C. Taylor ◽  
Neal Conrad ◽  
Veronika Vonstein ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bacillus Subtilis ◽  
Gene Expression Data ◽  
Regulatory Network ◽  
Expression Data

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).

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.

Prediction of key transcription factors during skin regeneration by combining gene expression data and regulatory network information analysis

2019 ◽  
Vol 12 (03) ◽  
pp. 1950024
Author(s):  
Ping Huang ◽  
Peng Ge ◽  
Qing-Fen Tian ◽  
Guo-Bao Huang
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Gene Expression Data ◽  
Regulatory Network ◽  
Skin Regeneration ◽  
Local Network ◽  
Great Promise ◽  
Expression Data ◽  
Network Information ◽  
Ppi Networks

Purpose: Burn is one of the most common injuries in clinical practice. The use of transcription factors (TFs) has been reported to reverse the epigenetic rewiring process and has great promise for skin regeneration. To better identify key TFs for skin reprogramming, we proposed a predictive system that conjoint analyzed gene expression data and regulatory network information. Methods: Firstly, the gene expression data in skin tissues were downloaded and the LIMMA package was used to identify differential-expressed genes (DEGs). Then three ways, including identification of TFs from the DEGs, enrichment analysis of TFs by a Fisher’s test, the direct and network-based influence degree analysis of TFs, were used to identify the key TFs related to skin regeneration. Finally, to obtain most comprehensive combination of TFs, the coverage extent of all the TFs were analyzed by Venn diagrams. Results: The top 30 TFs combinations with higher coverage were acquired. Especially, TFAP2A, ZEB1, and NFKB1 exerted greater regulatory influence on other DEGs in the local network and presented relatively higher degrees in the protein–protein interaction (PPI) networks. Conclusion: These TFs identification could give a deeper understanding of the molecular mechanism of cell trans-differentiation, and provide a reference for the skin regeneration and burn treatment.

scholarly journals Approximated Gene Expression Trajectories (AGETs) for Gene Regulatory Network Inference on Cell Tracks

2022 ◽  
Author(s):  
Kay Spiess ◽  
Timothy Fulton ◽  
Seogwon Hwang ◽  
Kane Toh ◽  
Dillan Saunders ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pattern Formation ◽  
Gene Regulatory Network ◽  
Gene Expression Data ◽  
Regulatory Network ◽  
Quantitative Data ◽  
Expression Data ◽  
Cell Movements ◽  
Reverse Engineer ◽  
Gene Regulatory

The study of pattern formation has benefited from reverse-engineering gene regulatory network (GRN) structure from spatio-temporal quantitative gene expression data. Traditional approaches omit tissue morphogenesis, hence focusing on systems where the timescales of pattern formation and morphogenesis can be separated. In such systems, pattern forms as an emergent property of the underlying GRN. This is not the case in many animal patterning systems, where patterning and morphogenesis are simultaneous. To address pattern formation in these systems we need to adapt our methodologies to explicitly accommodate cell movements and tissue shape changes. In this work we present a novel framework to reverse-engineer GRNs underlying pattern formation in tissues experiencing morphogenetic changes and cell rearrangements. By combination of quantitative data from live and fixed embryos we approximate gene expression trajectories (AGETs) in single cells and use a subset to reverse-engineer candidate GRNs using a Markov Chain Monte Carlo approach. GRN fit is assessed by simulating on cell tracks (live-modelling) and comparing the output to quantitative data-sets. This framework outputs candidate GRNs that recapitulate pattern formation at the level of the tissue and the single cell. To our knowledge, this inference methodology is the first to integrate cell movements and gene expression data, making it possible to reverse-engineer GRNs patterning tissues undergoing morphogenetic changes.

scholarly journals PEAK Predicts Gene Regulatory Network Linkages during Sea Urchin Development with High Sensitivity from Gene Expression Data Alone

2021 ◽  
Author(s):  
Jingyi Zhang ◽  
Farhan Ibrahim ◽  
Doaa Altarawy ◽  
Lenwood S Heath ◽  
Sarah Tulin
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Sea Urchin ◽  
Gene Expression Data ◽  
Regulatory Network ◽  
Gene Networks ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Gene Interactions ◽  
Expression Data

Abstract BackgroundGene regulatory network (GRN) inference can now take advantage of powerful machine learning algorithms to predict the entire landscape of gene-to-gene interactions with the potential to complement traditional experimental methods in building gene networks. However, the dynamical nature of embryonic development -- representing the time-dependent interactions between thousands of transcription factors, signaling molecules, and effector genes -- is one of the most challenging arenas for GRN prediction. ResultsIn this work, we show that successful GRN predictions for developmental systems from gene expression data alone can be obtained with the Priors Enriched Absent Knowledge (PEAK) network inference algorithm. PEAK is a noise-robust method that models gene expression dynamics via ordinary differential equations and selects the best network based on information-theoretic criteria coupled with the machine learning algorithm Elastic net. We test our GRN prediction methodology using two gene expression data sets for the purple sea urchin (S. purpuratus) and cross-check our results against existing GRN models that have been constructed and validated by over 30 years of experimental results. Our results found a remarkably high degree of sensitivity in identifying known gene interactions in the network (maximum 76.32%). We also generated 838 novel predictions for interactions that have not yet been described, which provide a resource for researchers to use to further complete the sea urchin GRN. ConclusionsGRN predictions that match known gene interactions can be produced using gene expression data alone from developmental time series experiments.

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