Establishing large-scale gene regulatory networks using a gene-knowledge-embedded evolutionary computation method

2011 ◽  
Author(s):  
Li-Sun Shu ◽  
Hui-Ling Huang ◽  
Shinn-Jang Ho ◽  
Shinn-Ying Ho
Keyword(s):  
Evolutionary Computation ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Large Scale ◽  
Computation Method ◽  
Gene Regulatory

scholarly journals Mapping Gene Regulatory Networks in Drosophila Eye Development by Large-Scale Transcriptome Perturbations and Motif Inference

Cell Reports ◽  
2014 ◽  
Vol 9 (6) ◽  
pp. 2290-2303 ◽  
Author(s):  
Delphine Potier ◽  
Kristofer Davie ◽  
Gert Hulselmans ◽  
Marina Naval Sanchez ◽  
Lotte Haagen ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Large Scale ◽  
Eye Development ◽  
Drosophila Eye ◽  
Mapping Gene ◽  
Gene Regulatory

INFERENCE OF LARGE-SCALE GENE REGULATORY NETWORKS USING REGRESSION-BASED NETWORK APPROACH

2009 ◽  
Vol 07 (04) ◽  
pp. 717-735 ◽  
Author(s):  
HASEONG KIM ◽  
JAE K. LEE ◽  
TAESUNG PARK
Keyword(s):  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Large Scale ◽  
Simple Procedure ◽  
Simulated Data ◽  
Computation Time ◽  
Network Approach ◽  
Global Regulators ◽  
Gene Regulatory

The gene regulatory network modeling plays a key role in search for relationships among genes. Many modeling approaches have been introduced to find the causal relationship between genes using time series microarray data. However, they have been suffering from high dimensionality, overfitting, and heavy computation time. Further, the selection of a best model among several possible competing models is not guaranteed that it is the best one. In this study, we propose a simple procedure for constructing large scale gene regulatory networks using a regression-based network approach. We determine the optimal out-degree of network structure by using the sum of squared coefficients which are obtained from all appropriate regression models. Through the simulated data, accuracy of estimation and robustness against noise are computed in order to compare with the vector autoregressive regression model. Our method shows high accuracy and robustness for inferring large-scale gene networks. Also it is applied to Caulobacter crecentus cell cycle data consisting of 1472 genes. It shows that many genes are regulated by two transcription factors, ctrA and gcrA, that are known for global regulators.

scholarly journals DCI: learning causal differences between gene regulatory networks

2021 ◽  
Author(s):  
Anastasiya Belyaeva ◽  
Chandler Squires ◽  
Caroline Uhler
Keyword(s):  
Gene Expression ◽  
Causal Inference ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Large Scale ◽  
Developmental Time ◽  
Supplementary Information ◽  
Causal Graph ◽  
Causal Graphs ◽  
Gene Regulatory

Abstract Summary Designing interventions to control gene regulation necessitates modeling a gene regulatory network by a causal graph. Currently, large-scale gene expression datasets from different conditions, cell types, disease states, and developmental time points are being collected. However, application of classical causal inference algorithms to infer gene regulatory networks based on such data is still challenging, requiring high sample sizes and computational resources. Here, we describe an algorithm that efficiently learns the differences in gene regulatory mechanisms between different conditions. Our difference causal inference (DCI) algorithm infers changes (i.e. edges that appeared, disappeared, or changed weight) between two causal graphs given gene expression data from the two conditions. This algorithm is efficient in its use of samples and computation since it infers the differences between causal graphs directly without estimating each possibly large causal graph separately. We provide a user-friendly Python implementation of DCI and also enable the user to learn the most robust difference causal graph across different tuning parameters via stability selection. Finally, we show how to apply DCI to single-cell RNA-seq data from different conditions and cell states, and we also validate our algorithm by predicting the effects of interventions. Availability and implementation Python package freely available at http://uhlerlab.github.io/causaldag/dci. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Phase transitions and assortativity in models of gene regulatory networks evolved under different selection processes

2021 ◽  
Vol 18 (177) ◽  
Author(s):  
Brandon Alexander ◽  
Alexandra Pushkar ◽  
Michelle Girvan
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Large Scale ◽  
Target Genes ◽  
Network Evolution ◽  
Network Nodes ◽  
Individual Gene ◽  
Damage Propagation ◽  
Selection Processes ◽  
Gene Regulatory

We study a simplified model of gene regulatory network evolution in which links (regulatory interactions) are added via various selection rules that are based on the structural and dynamical features of the network nodes (genes). Similar to well-studied models of ‘explosive’ percolation, in our approach, links are selectively added so as to delay the transition to large-scale damage propagation, i.e. to make the network robust to small perturbations of gene states. We find that when selection depends only on structure, evolved networks are resistant to widespread damage propagation, even without knowledge of individual gene propensities for becoming ‘damaged’. We also observe that networks evolved to avoid damage propagation tend towards disassortativity (i.e. directed links preferentially connect high degree ‘source’ genes to low degree ‘target’ genes and vice versa). We compare our simulations to reconstructed gene regulatory networks for several different species, with genes and links added over evolutionary time, and we find a similar bias towards disassortativity in the reconstructed networks.

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