Multistability and Multicellularity: Cell Fates as High-Dimensional Attractors of Gene Regulatory Networks

2006 ◽  
pp. 293-326 ◽  
Author(s):  
Sui Huang
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
High Dimensional ◽  
Cell Fates ◽  
Gene Regulatory

scholarly journals Independence screening for high dimensional nonlinear additive ODE models with applications to dynamic gene regulatory networks

2018 ◽  
Vol 37 (17) ◽  
pp. 2630-2644
Author(s):  
Hongqi Xue ◽  
Shuang Wu ◽  
Yichao Wu ◽  
Juan C. Ramirez Idarraga ◽  
Hulin Wu
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
High Dimensional ◽  
Ode Models ◽  
Gene Regulatory

scholarly journals Precision of Tissue Patterning is Controlled by Dynamical Properties of Gene Regulatory Networks

2019 ◽  
Author(s):  
Katherine Exelby ◽  
Edgar Herrera-Delgado ◽  
Lorena Garcia Perez ◽  
Ruben Perez-Carrasco ◽  
Andreas Sagner ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Expression Patterns ◽  
Cell Fates ◽  
Gene Circuits ◽  
Stochastic Fluctuations ◽  
Network Properties ◽  
Gene Regulatory

AbstractDuring development, gene regulatory networks allocate cell fates by partitioning tissues into spatially organised domains of gene expression. How the sharp boundaries that delineate these gene expression patterns arise, despite the stochasticity associated with gene regulation, is poorly understood. We show, in the vertebrate neural tube, using perturbations of coding and regulatory regions, that the structure of the regulatory network contributes to boundary precision. This is achieved, not by reducing noise in individual genes, but by the configuration of the network modulating the ability of stochastic fluctuations to initiate gene expression changes. We use a computational screen to identify network properties that influence boundary precision, revealing two dynamical mechanisms by which small gene circuits attenuate the effect of noise in order to increase patterning precision. These results highlight design principles of gene regulatory networks that produce precise patterns of gene expression.

scholarly journals Precision of tissue patterning is controlled by dynamical properties of gene regulatory networks

Development ◽  
2021 ◽  
Vol 148 (4) ◽  
pp. dev197566
Author(s):  
Katherine Exelby ◽  
Edgar Herrera-Delgado ◽  
Lorena Garcia Perez ◽  
Ruben Perez-Carrasco ◽  
Andreas Sagner ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Expression Patterns ◽  
Cell Fates ◽  
Gene Circuits ◽  
Stochastic Fluctuations ◽  
Network Properties ◽  
Gene Regulatory

ABSTRACTDuring development, gene regulatory networks allocate cell fates by partitioning tissues into spatially organised domains of gene expression. How the sharp boundaries that delineate these gene expression patterns arise, despite the stochasticity associated with gene regulation, is poorly understood. We show, in the vertebrate neural tube, using perturbations of coding and regulatory regions, that the structure of the regulatory network contributes to boundary precision. This is achieved, not by reducing noise in individual genes, but by the configuration of the network modulating the ability of stochastic fluctuations to initiate gene expression changes. We use a computational screen to identify network properties that influence boundary precision, revealing two dynamical mechanisms by which small gene circuits attenuate the effect of noise in order to increase patterning precision. These results highlight design principles of gene regulatory networks that produce precise patterns of gene expression.

scholarly journals A general index for linear and nonlinear correlations for high dimensional genomic data

BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhihao Yao ◽  
Jing Zhang ◽  
Xiufen Zou
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
High Dimensional Data ◽  
Kernel Functions ◽  
High Dimensional ◽  
Vector Correlation ◽  
General Index ◽  
Gene Regulatory ◽  
Linear And Nonlinear ◽  
Rv Coefficient

Abstract Background With the advance of high throughput sequencing, high-dimensional data are generated. Detecting dependence/correlation between these datasets is becoming one of most important issues in multi-dimensional data integration and co-expression network construction. RNA-sequencing data is widely used to construct gene regulatory networks. Such networks could be more accurate when methylation data, copy number aberration data and other types of data are introduced. Consequently, a general index for detecting relationships between high-dimensional data is indispensable. Results We proposed a Kernel-Based RV-coefficient, named KBRV, for testing both linear and nonlinear correlation between two matrices by introducing kernel functions into RV2 (the modified RV-coefficient). Permutation test and other validation methods were used on simulated data to test the significance and rationality of KBRV. In order to demonstrate the advantages of KBRV in constructing gene regulatory networks, we applied this index on real datasets (ovarian cancer datasets and exon-level RNA-Seq data in human myeloid differentiation) to illustrate its superiority over vector correlation. Conclusions We concluded that KBRV is an efficient index for detecting both linear and nonlinear relationships in high dimensional data. The correlation method for high dimensional data has possible applications in the construction of gene regulatory network.

Gene regulatory networks directing myeloid and lymphoid cell fates within the immune system

2008 ◽  
Vol 20 (4) ◽  
pp. 228-235 ◽  
Author(s):  
Peter Laslo ◽  
Jagan M.R. Pongubala ◽  
David W. Lancki ◽  
Harinder Singh
Keyword(s):  
Immune System ◽  
Gene Regulatory Networks ◽  
Lymphoid Cell ◽  
Regulatory Networks ◽  
Cell Fates ◽  
Gene Regulatory

scholarly journals High-dimensional Bayesian network inference from systems genetics data using genetic node ordering

2018 ◽  
Author(s):  
Lingfei Wang ◽  
Pieter Audenaert ◽  
Tom Michoel
Keyword(s):  
Genetic Variation ◽  
Bayesian Network ◽  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Network Inference ◽  
High Dimensional ◽  
Systems Genetics ◽  
Gene Regulatory ◽  
Bayesian Network Inference

AbstractStudying the impact of genetic variation on gene regulatory networks is essential to understand the biological mechanisms by which genetic variation causes variation in phenotypes. Bayesian networks provide an elegant statistical approach for multi-trait genetic mapping and modelling causal trait relationships. However, inferring Bayesian gene networks from high-dimensional genetics and genomics data is challenging, because the number of possible networks scales super-exponentially with the number of nodes, and the computational cost of conventional Bayesian network inference methods quickly becomes prohibitive. We propose an alternative method to infer high-quality Bayesian gene networks that easily scales to thousands of genes. Our method first reconstructs a node ordering by conducting pairwise causal inference tests between genes, which then allows to infer a Bayesian network via a series of independent variable selection problems, one for each gene. We demonstrate using simulated and real systems genetics data that this results in a Bayesian network with equal, and sometimes better, likelihood than the conventional methods, while having a significantly higher over-lap with groundtruth networks and being orders of magnitude faster. Moreover our method allows for a unified false discovery rate control across genes and individual edges, and thus a rigorous and easily interpretable way for tuning the sparsity level of the inferred network. Bayesian network inference using pairwise node ordering is a highly efficient approach for reconstructing gene regulatory networks when prior information for the inclusion of edges exists or can be inferred from the available data.

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