Inferring stable gene regulatory networks from steady-state data

2015 ◽  
Author(s):  
Joy E. Larvie ◽  
Mohammad S. Gorji ◽  
Abdollah Homaifar
Keyword(s):  
Steady State ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Stable Gene ◽  
State Data ◽  
Gene Regulatory

Inference of gene regulatory networks based on nonlinear ordinary differential equations

2020 ◽  
Vol 36 (19) ◽  
pp. 4885-4893 ◽  
Author(s):  
Baoshan Ma ◽  
Mingkun Fang ◽  
Xiangtian Jiao
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Steady State ◽  
Differential Equations ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Time Series Data ◽  
Series Data ◽  
State Data ◽  
Gene Regulatory

Abstract Motivation Gene regulatory networks (GRNs) capture the regulatory interactions between genes, resulting from the fundamental biological process of transcription and translation. In some cases, the topology of GRNs is not known, and has to be inferred from gene expression data. Most of the existing GRNs reconstruction algorithms are either applied to time-series data or steady-state data. Although time-series data include more information about the system dynamics, steady-state data imply stability of the underlying regulatory networks. Results In this article, we propose a method for inferring GRNs from time-series and steady-state data jointly. We make use of a non-linear ordinary differential equations framework to model dynamic gene regulation and an importance measurement strategy to infer all putative regulatory links efficiently. The proposed method is evaluated extensively on the artificial DREAM4 dataset and two real gene expression datasets of yeast and Escherichia coli. Based on public benchmark datasets, the proposed method outperforms other popular inference algorithms in terms of overall score. By comparing the performance on the datasets with different scales, the results show that our method still keeps good robustness and accuracy at a low computational complexity. Availability and implementation The proposed method is written in the Python language, and is available at: https://github.com/lab319/GRNs_nonlinear_ODEs Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Integration of Steady-State and Temporal Gene Expression Data for the Inference of Gene Regulatory Networks

PLoS ONE ◽  
2013 ◽  
Vol 8 (8) ◽  
pp. e72103 ◽  
Author(s):  
Yi Kan Wang ◽  
Daniel G. Hurley ◽  
Santiago Schnell ◽  
Cristin G. Print ◽  
Edmund J. Crampin
Keyword(s):  
Gene Expression ◽  
Steady State ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Expression Data ◽  
Temporal Gene Expression ◽  
Gene Regulatory

Inference of signaling and gene regulatory networks by steady-state perturbation experiments: structure and accuracy

2005 ◽  
Vol 232 (3) ◽  
pp. 427-441 ◽  
Author(s):  
Michael Andrec ◽  
Boris N. Kholodenko ◽  
Ronald M. Levy ◽  
Eduardo Sontag
Keyword(s):  
Steady State ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Gene Regulatory

scholarly journals The Developmental Transcriptome for Lytechinus variegatus Exhibits Temporally Punctuated Gene Expression Changes

2019 ◽  
Author(s):  
John D. Hogan ◽  
Jessica L. Keenan ◽  
Lingqi Luo ◽  
Dakota Y. Hawkins ◽  
Jonas Ibn-Salem ◽  
...  
Keyword(s):  
Gene Expression ◽  
Larval Development ◽  
Sea Urchin ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Lytechinus Variegatus ◽  
Stable Gene ◽  
Major Transitions ◽  
Gene Regulatory ◽  
Embryonic And Larval Development

AbstractEmbryonic development is arguably the most complex process an organism undergoes during its lifetime, and understanding this complexity is best approached with a systems-level perspective. The sea urchin has become a highly valuable model organism for understanding developmental specification, morphogenesis, and evolution. As a non-chordate deuterostome, the sea urchin occupies an important evolutionary niche between protostomes and vertebrates. Lytechinus variegatus (Lv) is an Atlantic species that has been well studied, and which has provided important insights into signal transduction, patterning, and morphogenetic changes during embryonic and larval development. The Pacific species, Strongylocentrotus purpuratus (Sp), is another well-studied sea urchin, particularly for gene regulatory networks (GRNs) and cis-regulatory analyses. A well-annotated genome and transcriptome for Sp are available, but similar resources have not been developed for Lv. Here, we provide an analysis of the Lv transcriptome at 11 timepoints during embryonic and larval development. The data indicate that the gene regulatory networks that underlie specification are well-conserved among sea urchin species. We show that the major transitions in variation of embryonic transcription divide the developmental time series into four distinct, temporally sequential phases. Our work shows that sea urchin development occurs via sequential intervals of relatively stable gene expression states that are punctuated by abrupt transitions.

scholarly journals BioSwitch: a tool for the detection of bistability and multi-steady state behaviour in signalling and gene regulatory networks

2019 ◽  
Author(s):  
Pencho Yordanov ◽  
Joerg Stelling ◽  
Irene Otero-Muras
Keyword(s):  
Steady State ◽  
Cell Fate ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Reaction Network ◽  
Numerical Continuation ◽  
Signalling Pathways ◽  
State Behaviour ◽  
Gene Regulatory ◽  
Steady State Behaviour

Abstract Motivation Multi-steady state behaviour, and in particular multi-stability, provides biological systems with the capacity to take reliable decisions (such as cell fate determination). A problem arising frequently in systems biology is to elucidate whether a signal transduction mechanism or a gene regulatory network has the capacity for multi-steady state behaviour, and consequently for a switch-like response to stimuli. Bifurcation diagrams are a powerful instrument in non-linear analysis to study the qualitative and quantitative behaviour of equilibria including bifurcation into different equilibrium branches and bistability. However, in the context of signalling pathways, the inherent large parametric uncertainty hampers the (direct) use of standard bifurcation tools. Results We present BioSwitch, a toolbox to detect multi-steady state behaviour in signalling pathways and gene regulatory networks. The tool combines results from chemical reaction network theory with global optimization to efficiently detect whether a signalling pathway has the capacity to undergo a saddle node bifurcation, and in case of multi-stationarity, provides the exact coordinates of the bifurcation where to start a numerical continuation analysis with standard bifurcation tools, leading to two different branches of equilibria. Bistability detection in the G1/S transition pathway of Saccharomyces cerevisiae is included as an illustrative example. Availability and implementation BioSwitch runs under the popular MATLAB computational environment, and is available at https://sites.google.com/view/bioswitch.

scholarly journals Comparison of Gene Regulatory Networks via Steady-State Trajectories

2007 ◽  
Vol 2007 ◽  
pp. 1-11 ◽  
Author(s):  
Marcel Brun ◽  
Seungchan Kim ◽  
Woonjung Choi ◽  
Edward R. Dougherty
Keyword(s):  
Steady State ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Gene Regulatory
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