Condensing Biochemistry into Gene Regulatory Networks

2014 ◽  
Vol 4 (3) ◽  
pp. 1-25
Author(s):  
Alberto de la Fuente
Keyword(s):  
Gene Regulation ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Network Models ◽  
Transcriptional Regulatory Networks ◽  
Modern Biology ◽  
Genome Wide ◽  
Transcriptional Regulatory ◽  
Biochemical Systems ◽  
Gene Regulatory

Gene Regulatory Networks are models of gene regulation. Inferring such model from genome-wide gene-expression measurements is one of the key challenges in modern biology, and a large number of algorithms have been proposed for this task. As there is still much confusion in the current literature as to what precisely Gene Regulatory Networks are, it is important to provide a definition that is as unambiguous as possible. In this paper the author provides such a definition and explain what Gene Regulatory Networks are in terms of the underlying biochemical processes. The author will use a linear approximation to the in general non-linear kinetics underlying interactions in biochemical systems and show how a biochemical system can be ‘condensed' into a more compact description, i.e. Gene Regulatory Networks. Important differences between the defined Gene Regulatory Networks and other network models for gene regulation, i.e. Transcriptional Regulatory Networks and Co-Expression Networks, are also discussed.

What are Gene Regulatory Networks?

2010 ◽  
pp. 1-27 ◽  
Author(s):  
Alberto de la Fuente
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Network Models ◽  
Transcriptional Regulatory Networks ◽  
Biochemical Processes ◽  
Transcriptional Regulatory ◽  
Biochemical Systems ◽  
Gene Regulatory

This book deals with algorithms for inferring and analyzing Gene Regulatory Networks using mainly gene expression data. What precisely are the Gene Regulatory Networks that are inferred by such algorithms from this type of data? There is still much confusion in the current literature and it is important to start a book about computational methods for Gene Regulatory Networks with a definition that is as unambiguous as possible. In this chapter, I provide a definition and try to clearly explain what Gene Regulatory Networks are in terms of the underlying biochemical processes. To do the latter in a formal way, I will use a linear approximation to the in general non-linear kinetics underlying interactions in biochemical systems and show how a biochemical system can be ‘condensed’ into the more compact description of Gene Regulatory Networks. Important differences between the defined Gene Regulatory Networks and other network models for gene regulation, such as Transcriptional Regulatory Networks and Co-Expression Networks, will be highlighted.

scholarly journals Predicting synchronized gene coexpression patterns from fibration symmetries in gene regulatory networks in bacteria

2020 ◽  
Author(s):  
Ian Leifer ◽  
Mishael Sanchez ◽  
Cecilia Ishida ◽  
Hernan Makse
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Network Structure ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Networks ◽  
Conceptual Tool ◽  
Gene Coexpression ◽  
Expression Of Genes ◽  
Transcriptional Regulatory ◽  
Gene Regulatory

Abstract Background: Gene regulatory networks coordinate the expression of genes across physiological states and ensure a synchronized expression of genes in cellular subsystems, critical for the coherent functioning of cells. Here we address the questions whether it is possible to predict gene synchronization from network structure alone. We have recently shown that synchronized gene expression may be predicted from symmetries in the transcriptional regulatory networks (TRN) and described by the concept of symmetry fibrations. We showed that symmetry fibrations partition the genes into groups called fibers based on the symmetries of their 'input trees', the set of paths in the network through which signals can reach a gene. In idealized dynamic gene expression models, all genes in a fiber are perfectly synchronized, while less idealized models -- with gene input functions differencing between genes -- predict symmetry breaking and desynchronization. Results: To study the functional role of gene fibers and to test whether some of the fiber-induced coexpression remains in reality, we analyze gene fibrations for the transcription networks of E. coli and B. subtilis and confront them with expression data. We find approximate gene coexpression patterns consistent with symmetry fibrations with idealized gene expression dynamics. This shows that network structure alone provides useful information about gene synchronization, and suggest that gene input functions within fibers may be further streamlined by evolutionary pressures to realize a coexpression of genes. Conclusions: Thus, gene fibrations provides a sound conceptual tool to describe tunable coexpression induced by network topology and shaped by mechanistic details of gene expression.

scholarly journals An Integrated Database of Small RNAs and Their Interplay With Transcriptional Gene Regulatory Networks in Corynebacteria

2021 ◽  
Vol 12 ◽  
Author(s):  
Mariana Teixeira Dornelles Parise ◽  
Doglas Parise ◽  
Flavia Figueira Aburjaile ◽  
Anne Cybelle Pinto Gomide ◽  
Rodrigo Bentes Kato ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Networks ◽  
Small Rnas ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Networks ◽  
Bacterial Gene ◽  
Bacterial Gene Expression ◽  
Transcriptional Regulatory ◽  
Animal Pathogens ◽  
Gene Regulatory

Small RNAs (sRNAs) are one of the key players in the post-transcriptional regulation of bacterial gene expression. These molecules, together with transcription factors, form regulatory networks and greatly influence the bacterial regulatory landscape. Little is known concerning sRNAs and their influence on the regulatory machinery in the genus Corynebacterium, despite its medical, veterinary and biotechnological importance. Here, we expand corynebacterial regulatory knowledge by integrating sRNAs and their regulatory interactions into the transcriptional regulatory networks of six corynebacterial species, covering four human and animal pathogens, and integrate this data into the CoryneRegNet database. To this end, we predicted sRNAs to regulate 754 genes, including 206 transcription factors, in corynebacterial gene regulatory networks. Amongst them, the sRNA Cd-NCTC13129-sRNA-2 is predicted to directly regulate ydfH, which indirectly regulates 66 genes, including the global regulator glxR in C. diphtheriae. All of the sRNA-enriched regulatory networks of the genus Corynebacterium have been made publicly available in the newest release of CoryneRegNet(www.exbio.wzw.tum.de/coryneregnet/) to aid in providing valuable insights and to guide future experiments.

scholarly journals Programmatic access to bacterial regulatory networks with regutools

2020 ◽  
Vol 36 (16) ◽  
pp. 4532-4534
Author(s):  
Joselyn Chávez ◽  
Carmina Barberena-Jonas ◽  
Jesus E Sotelo-Fonseca ◽  
José Alquicira-Hernández ◽  
Heladia Salgado ◽  
...  
Keyword(s):  
Data Structures ◽  
Gene Regulatory Networks ◽  
Binding Sites ◽  
Regulatory Networks ◽  
R Package ◽  
Transcriptional Regulatory Networks ◽  
Dna Binding Sites ◽  
Transcriptional Regulatory ◽  
Gene Regulatory ◽  
Programmatic Access

Abstract Summary RegulonDB has collected, harmonized and centralized data from hundreds of experiments for nearly two decades and is considered a point of reference for transcriptional regulation in Escherichia coli K12. Here, we present the regutools R package to facilitate programmatic access to RegulonDB data in computational biology. regutools gives researchers the possibility of writing reproducible workflows with automated queries to RegulonDB. The regutools package serves as a bridge between RegulonDB data and the Bioconductor ecosystem by reusing the data structures and statistical methods powered by other Bioconductor packages. We demonstrate the integration of regutools with Bioconductor by analyzing transcription factor DNA binding sites and transcriptional regulatory networks from RegulonDB. We anticipate that regutools will serve as a useful building block in our progress to further our understanding of gene regulatory networks. Availability and implementation regutools is an R package available through Bioconductor at bioconductor.org/packages/regutools.

scholarly journals Programmatic access to bacterial regulatory networks with regutools

2020 ◽  
Author(s):  
Joselyn Chávez ◽  
Carmina Barberena-Jonas ◽  
Jesus E. Sotelo-Fonseca ◽  
José Alquicira-Hernández ◽  
Heladia Salgado ◽  
...  
Keyword(s):  
Data Structures ◽  
Gene Regulatory Networks ◽  
Binding Sites ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Networks ◽  
Link Type ◽  
Dna Binding Sites ◽  
Transcriptional Regulatory ◽  
Gene Regulatory ◽  
Programmatic Access

AbstractSummaryRegulonDB has collected, harmonized and centralized data from hundreds of experiments for nearly two decades and is considered a point of reference for transcriptional regulation in Escherichia coli K12. Here, we present the regutools R package to facilitate programmatic access to RegulonDB data in computational biology. regutools gives researchers the possibility of writing reproducible workflows with automated queries to RegulonDB. The regutools package serves as a bridge between RegulonDB data and the Bioconductor ecosystem by reusing the data structures and statistical methods powered by other Bioconductor packages. We demonstrate the integration of regutools with Bioconductor by analyzing transcription factor DNA binding sites and transcriptional regulatory networks from RegulonDB. We anticipate that regutools will serve as a useful building block in our progress to further our understanding of gene regulatory networks.Availability and Implementationregutools is an R package available through Bioconductor at bioconductor.org/packages/regutools.Contactgithub.com/ComunidadBioInfo/regutools, [email protected], [email protected].

scholarly journals Inference of gene regulatory networks from genome-wide knockout fitness data

2012 ◽  
Vol 29 (3) ◽  
pp. 338-346 ◽  
Author(s):  
L. Wang ◽  
X. Wang ◽  
A. P. Arkin ◽  
M. S. Samoilov
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Genome Wide ◽  
Gene Regulatory

Genome-wide RNA structure changes during human neurogenesis modulate gene regulatory networks

2021 ◽  
Author(s):  
Jiaxu Wang ◽  
Tong Zhang ◽  
Zhang Yu ◽  
Wen Ting Tan ◽  
Ming Wen ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Rna Structure ◽  
Regulatory Networks ◽  
Structure Changes ◽  
Genome Wide ◽  
Gene Regulatory
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