A Synthesis Method of Gene Regulatory Networks based on Gene Expression by Network Learning

Regulatory Network ◽

Regulatory Networks ◽

Synthesis Method ◽

Gene Expression Pattern ◽

Cellular Functions ◽

Network Learning ◽

Complementary Approach ◽

Investigating gene regulatory networks is important to understand mechanisms of cellular functions. Recently, the synthesis of gene regulatory networks having desired functions has become of interest to many researchers because it is a complementary approach to understanding gene regulatory networks, and it could be the first step in controlling living cells. In this chapter, we discuss a synthesis problem in gene regulatory networks by network learning. The problem is to determine parameters of a gene regulatory network such that it possesses given gene expression pattern sequences as desired properties. We also discuss a controller synthesis method of gene regulatory networks. Some experiments illustrate the performance of this method.

Predicting genotype-specific gene regulatory networks

10.1101/2021.01.18.427134 ◽

2021 ◽

Author(s):

Deborah Weighill ◽

Marouen Ben Guebila ◽

Kimberly Glass ◽

John Quackenbush ◽

John Platig

Keyword(s):

Gene Expression ◽

Gene Regulatory Network ◽

Genetic Variants ◽

Regulatory Network ◽

Regulatory Networks ◽

Specific Gene ◽

Disease Etiology ◽

Gene Regulatory ◽

The Impact

AbstractThe majority of disease-associated genetic variants are thought to have regulatory effects, including the disruption of transcription factor (TF) binding and the alteration of downstream gene expression. Identifying how a person’s genotype affects their individual gene regulatory network has the potential to provide important insights into disease etiology and to enable improved genotype-specific disease risk assessments and treatments. However, the impact of genetic variants is generally not considered when constructing gene regulatory networks. To address this unmet need, we developed EGRET (Estimating the Genetic Regulatory Effect on TFs), which infers a genotype-specific gene regulatory network (GRN) for each individual in a study population by using message passing to integrate genotype-informed TF motif predictions - derived from individual genotype data, the predicted effects of variants on TF binding and gene expression, and TF motif predictions - with TF protein-protein interactions and gene expression. Comparing EGRET networks for two blood-derived cell lines identified genotype-associated cell-line specific regulatory differences which were subsequently validated using allele-specific expression, chromatin accessibility QTLs, and differential TF binding from ChIP-seq. In addition, EGRET GRNs for three cell types across 119 individuals captured regulatory differences associated with disease in a cell-type-specific manner. Our analyses demonstrate that EGRET networks can capture the impact of genetic variants on complex phenotypes, supporting a novel fine-scale stratification of individuals based on their genetic background. EGRET is available through the Network Zoo R package (netZooR v0.9; netzoo.github.io).

Neural Gene Network Constructor: A Neural Based Model for Reconstructing Gene Regulatory Network

10.1101/842369 ◽

2019 ◽

Author(s):

Zhang Zhang ◽

Lifei Wang ◽

Shuo Wang ◽

Ruyi Tao ◽

Jingshu Xiao ◽

...

Keyword(s):

Gene Expression ◽

Gene Regulatory Network ◽

Gene Expression Data ◽

Network Structure ◽

Regulatory Network ◽

Gene Network ◽

Regulatory Networks ◽

Expression Data ◽

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.

A synthesis method of gene regulatory networks by network learning -extension to generalized models

2008 SICE Annual Conference ◽

10.1109/sice.2008.4655079 ◽

2008 ◽

Author(s):

Yoshihiro Mori ◽

Yasuaki Kuroe ◽

Takehiro Mori

Keyword(s):

Regulatory Networks ◽

Synthesis Method ◽

Network Learning ◽

Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

eLife ◽

10.7554/elife.51254 ◽

2020 ◽

Vol 9 ◽

Cited By ~ 16

Author(s):

Christopher A Jackson ◽

Dayanne M Castro ◽

Giuseppe-Antonio Saldi ◽

Richard Bonneau ◽

David Gresham

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Regulatory Networks ◽

Target Genes ◽

Single Cell Rna Sequencing ◽

Understanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse transcriptionally barcoded gene deletion mutants in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We benchmarked a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,228 interactions.

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

10.1101/721043 ◽

2019 ◽

Cited By ~ 1

Author(s):

Katherine Exelby ◽

Edgar Herrera-Delgado ◽

Lorena Garcia Perez ◽

Ruben Perez-Carrasco ◽

Andreas Sagner ◽

...

Keyword(s):

Gene Expression ◽

Regulatory Network ◽

Regulatory Networks ◽

Expression Patterns ◽

Cell Fates ◽

Gene Circuits ◽

Stochastic Fluctuations ◽

Network Properties ◽

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.

Evolution of Mendelian dominance in gene regulatory networks associated with phenotypic robustness

10.1101/2021.01.11.426187 ◽

2021 ◽

Author(s):

Kenji Okubo ◽

Kunihiko Kaneko

Keyword(s):

Gene Expression ◽

Regulatory Networks ◽

Target Genes ◽

Gene Expression Pattern ◽

Mendelian Inheritance ◽

Gene Expression Dynamics ◽

Phenotypic Robustness ◽

Robustness To Noise ◽

AbstractMendelian inheritance is a fundamental law of genetics. Considering two alleles in a diploid, a phenotype of a heterotype is dominated by a particular homotype according to the law of dominance. This picture is usually based on simple genotype-phenotype mapping in which one gene regulates one phenotype. However, in reality, some interactions between genes can result in deviation from Mendelian dominance.Here, by using the numerical evolution of diploid gene regulatory networks (GRNs), we discuss whether Mendelian dominance evolves beyond the classical case of one-to-one genotype-phenotype mapping. We examine whether complex genotype-phenotype mapping can achieve Mendelian dominance through the evolution of the GRN with interacting genes. Specifically, we extend the GRN model to a diploid case, in which two GRN matrices are added to give gene expression dynamics, and simulate evolution with meiosis and recombination. Our results reveal that Mendelian dominance evolves even under complex genotype-phenotype mapping. This dominance is achieved via a group of genotypes that differ from each other but have a common phenotype given by the expression of target genes. Calculating the degree of dominance shows that it increases through the evolution, correlating closely with the decrease in phenotypic fluctuations and the increase in robustness to initial noise. This evolution of Mendelian dominance is associated with phenotypic robustness against meiosis-induced genome mixing, whereas sexual recombination arising from the mixing of chromosomes from the parents further enhances dominance and robustness. Owing to this dominance, the robustness to genetic differences increases, while the optimal fitness is sustained up to a large difference between the two genomes. In summary, Mendelian dominance is achieved by groups of genotypes that are associated with the increase in phenotypic robustness to noise.Author summaryMendelian dominance is one of the most fundamental laws in genetics. When two conflicting characters occur in a single diploid, the dominant character is always chosen. Assuming that one gene makes one character, this law is simple to grasp. However, in reality, phenotypes are generated via interactions between several genes, which may alter Mendel’s dominance law. The evolution of robustness to noise and mutations has been investigated extensively using complex expression dynamics with gene regulatory networks. Here, we applied gene-expression dynamics with complex interactions to the case of a diploid and simulated the evolution of the gene regulatory network to generate the optimal phenotype given by a certain gene expression pattern. Interestingly, after evolution, Mendelian dominance is achieved via a group of genes. This group-based Mendelian dominance is shaped by phenotype insensitivity to genome mixing by meiosis and evolves concurrently with the robustness to noise. By focusing on the influence of phenotypic robustness, which has received considerable attention recently, our result provides a novel perspective as to why Mendel’s law of dominance is commonly observed.

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

Development ◽

10.1242/dev.197566 ◽

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 ◽

Regulatory Network ◽

Regulatory Networks ◽

Expression Patterns ◽

Cell Fates ◽

Gene Circuits ◽

Stochastic Fluctuations ◽

Network Properties ◽

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.

Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

10.1101/581678 ◽

2019 ◽

Cited By ~ 6

Author(s):

Christopher A Jackson ◽

Dayanne M Castro ◽

Giuseppe-Antonio Saldi ◽

Richard Bonneau ◽

David Gresham

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Regulatory Networks ◽

Network Reconstruction ◽

Single Cell Rna Sequencing ◽

AbstractUnderstanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for transcriptionally barcoding gene deletion mutants and performing scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse genotypes in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We developed, and benchmarked, a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,018 interactions. Our study establishes a general approach to gene regulatory network reconstruction from scRNAseq data that can be employed in any organism.