scholarly journals Identification of transcription factors regulating senescence in wheat through gene regulatory network modelling

2018 ◽  
Author(s):  
Philippa Borrill ◽  
Sophie A. Harrington ◽  
James Simmonds ◽  
Cristobal Uauy
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Time Series Data ◽  
Nutrient Content ◽  
Series Data ◽  
Network Modelling ◽  
Polyploid Wheat ◽  
Gene Regulatory ◽  
Developmental Programme

AbstractSenescence is a tightly regulated developmental programme which is coordinated by transcription factors. Identifying these transcription factors in crops will provide opportunities to tailor the senescence process to different environmental conditions and regulate the balance between yield and grain nutrient content. Here we use ten time points of gene expression data alongside gene network modelling to identify transcription factors regulating senescence in polyploid wheat. We observe two main phases of transcription changes during senescence: early downregulation of housekeeping and metabolic processes followed by upregulation of transport and hormone related genes. We have identified transcription factor families associated with these early and later waves of differential expression. Using gene regulatory network modelling alongside complementary publicly available datasets we identified candidate transcription factors for controlling senescence. We validated the function of one of these candidate transcription factors in senescence using wheat chemically-induced mutants. This study lays the ground work to understand the transcription factors which regulate senescence in polyploid wheat and exemplifies the integration of time-series data with publicly available expression atlases and networks to identify candidate regulatory genes.

Inferring Gene Regulatory Network Models from Time-Series Data Using Metaheuristics

2020 ◽  
Author(s):  
Jose Eduardo H. da Silva ◽  
Heder S. Betnardino ◽  
Helio J.C. Barbosa ◽  
Alex B. Vieira ◽  
Luciana C.D. Campos ◽  
...  
Keyword(s):  
Time Series ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Time Series Data ◽  
Network Models ◽  
Series Data ◽  
Gene Regulatory

scholarly journals Identification of Transcription Factors Regulating Senescence in Wheat through Gene Regulatory Network Modelling

2019 ◽  
Vol 180 (3) ◽  
pp. 1740-1755 ◽  
Author(s):  
Philippa Borrill ◽  
Sophie A. Harrington ◽  
James Simmonds ◽  
Cristobal Uauy
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Network Modelling ◽  
Gene Regulatory

scholarly journals PALLAS: Penalized mAximum LikeLihood and pArticle Swarms for Inference of Gene Regulatory Networks from Time Series Data

2020 ◽  
Author(s):  
Yukun Tan ◽  
Fernando B. Lima Neto ◽  
Ulisses Braga Neto
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Time Series Data ◽  
Series Data ◽  
Particle Swarm Algorithm ◽  
Penalized Maximum Likelihood ◽  
Particle Swarms ◽  
Gene Regulatory

AbstractWe present PALLAS, a practical method for gene regulatory network (GRN) inference from time series data, which employs penalized maximum likelihood and particle swarms for optimization. PALLAS is based on the Partially-Observable Boolean Dynamical System (POBDS) model and thus does not require ad-hoc binarization of the data. The penalty in the likelihood is a LASSO regularization term, which encourages the resulting network to be sparse. PALLAS is able to scale to large networks under no prior knowledge, by virtue of a novel continuous-discrete Fish School Search particle swarm algorithm for efficient simultaneous maximization of the penalized likelihood over the discrete space of networks and the continuous space of observational parameters. The performance of PALLAS is demonstrated by a comprehensive set of experiments using synthetic data generated from real and artificial networks, as well as real time series microarray and RNA-seq data, where it is compared to several other well-known methods for gene regulatory network inference. The results show that PALLAS can infer GRNs efficiently and accurately. PALLAS is a fully-fledged program with a commandline user interface, written in python, and available on GitHub (https://github.com/yukuntan92/PALLAS).

scholarly journals Approximate inference of gene regulatory network models from RNA-Seq time series data

2017 ◽  
Author(s):  
Thomas Thorne
Keyword(s):  
Time Series ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Time Series Data ◽  
Stem Cell Differentiation ◽  
Series Data ◽  
Model Parameters ◽  
Rna Seq ◽  
Sparse Regression ◽  
Gene Regulatory

AbstractInference of gene regulatory network structures from RNA-Seq data is challenging due to the nature of the data, as measurements take the form of counts of reads mapped to a given gene. Here we present a model for RNA-Seq time series data that applies a negative binomial distribution for the observations, and uses sparse regression with a horseshoe prior to learn a dynamic Bayesian network of interactions between genes. We use a variational inference scheme to learn approximate posterior distributions for the model parameters. The methodology is benchmarked on synthetic data designed to replicate the distribution of real world RNA-Seq data. We compare our method to other sparse regression approaches and information theoretic methods. We demonstrate an application of our method to a publicly available human neuronal stem cell differentiation RNA-Seq time series.

scholarly journals The identification of transcription factors expressed in the notochord of Ciona intestinalis adds new potential players to the brachyury gene regulatory network

2011 ◽  
Vol 240 (9) ◽  
pp. spcone-spcone
Author(s):  
Diana S. José-Edwards ◽  
Pierre Kerner ◽  
Jamie E. Kugler ◽  
Wei Deng ◽  
Di Jiang ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Ciona Intestinalis ◽  
Gene Regulatory
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