scholarly journals Computational Methodologies for Analyzing, Modeling and Controlling Gene Regulatory Networks

2010 ◽  
Vol 2 ◽  
pp. BECB.S5594 ◽  
Author(s):  
Zahra Zamani ◽  
Amirhossein Hajihosseini ◽  
Ali Masoudi-Nejad
Keyword(s):  
Molecular Biology ◽  
Computational Methods ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
The Other ◽  
Therapeutic Interventions ◽  
Control Network ◽  
Deep Insight ◽  
Paper Briefly ◽  
Gene Regulatory

Molecular biology focuses on genes and their interactions at the transcription, regulation and protein level. Finding genes that cause certain behaviors can make therapeutic interventions more effective. Although biological tools can extract the genes and perform some analyses, without the help of computational methods, deep insight of the genetic function and its effects will not occur. On the other hand, complex systems can be modeled by networks, introducing the main data as nodes and the links in-between as the transactions occurring within the network. Gene regulatory networks are examples that are modeled and analyzed in order to gain insight of their exact functions. Since a cell's specific functionality is greatly determined by the genes it expresses, translation or the act of converting mRNA to proteins is highly regulated by the control network that directs cellular activities. This paper briefly reviews the most important computational methods for analyzing, modeling and controlling the gene regulatory networks.

Computational studies of gene regulatory networks: in numero molecular biology

2001 ◽  
Vol 2 (4) ◽  
pp. 268-279 ◽  
Author(s):  
Jeff Hasty ◽  
David McMillen ◽  
Farren Isaacs ◽  
James J. Collins
Keyword(s):  
Molecular Biology ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Computational Studies ◽  
Gene Regulatory

scholarly journals Regulation of PD1 signaling is associated with prognosis in glioblastoma multiforme

2021 ◽  
Author(s):  
Camila Lopes-Ramos ◽  
Tatiana Belova ◽  
Tess Brunner ◽  
John Quackenbush ◽  
Marieke L. Kuijjer
Keyword(s):  
Gene Regulatory Networks ◽  
Transcriptional Repression ◽  
Regulatory Networks ◽  
Treatment Options ◽  
Neoadjuvant Treatment ◽  
The Cancer Genome Atlas ◽  
Therapeutic Interventions ◽  
Short Term ◽  
Term Survival ◽  
Gene Regulatory

Glioblastoma is an aggressive cancer of the brain and spine. While analysis of glioblastoma ‘omics data has somewhat improved our understanding of the disease, it has not led to direct improvement in patient survival. Cancer survival is often characterized by differences in expression of particular genes, but the mechanisms that drive these differences are generally unknown. We therefore set out to model the regulatory mechanisms that associate with glioblastoma survival. We inferred individual patient gene regulatory networks using data from two different expression platforms from The Cancer Genome Atlas (n=522 and 431). We performed a comparative network analysis between patients with long- and short-term survival, correcting for patient age, sex, and neoadjuvant treatment status. We identified seven pathways associated with survival, all of which were involved in immune system signaling. Differential regulation of PD1 signaling was validated in an independent dataset from the German Glioma Network (n=70). We found that transcriptional repression of genes in this pathway—for which treatment options are available—was lost in short-term survivors and that this was independent of mutation burden and only weakly associated with T-cell infiltrate. These results provide a new way to stratify glioblastoma patients that uses network features as biomarkers to predict survival, and identify new potential therapeutic interventions, thus underscoring the value of analyzing gene regulatory networks in individual cancer patients.

scholarly journals Molecular Paleobiology of the Echinoderm Skeleton

2022 ◽  
Author(s):  
Jeffrey Thompson
Keyword(s):  
Molecular Biology ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Evolutionary Process ◽  
Functional Genomic ◽  
Deep Time ◽  
Recent Advances ◽  
Gene Regulatory ◽  
Multiple Levels ◽  
Promising Avenue

Molecular paleobiology provides a promising avenue to merge data from deep time, molecular biology and genomics, gaining insights into the evolutionary process at multiple levels. The echinoderm skeleton is a model for molecular paleobioloogical studies. I begin with an overview of the skeletogenic process in echinoderms, as well as a discussion of what gene regulatory networks are, and why they are of interest to paleobiologists. I then highlight recent advances in the evolution of the echinoderm skeleton from both paleobiological and molecular/functional genomic perspectives, highlighting examples where diverse approaches provide complementary insight and discussing potential of this field of research.

Methods for Reverse Engineering of Gene Regulatory Networks

2009 ◽  
pp. 497-515
Author(s):  
Hendrik Hache
Keyword(s):  
Experimental Data ◽  
Reverse Engineering ◽  
Computational Methods ◽  
High Throughput ◽  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Gene Regulatory

In this chapter, different methods and applications for reverse engineering of gene regulatory networks that have been developed in recent years are discussed and compared. Inferring gene networks from different kinds of experimental data are a challenging task that emerged, especially with the development of high throughput technologies. Various computational methods based on diverse principles were introduced to identify new regulations among genes. Mathematical aspects of the models are highlighted, and applications for reverse engineering are mentioned.

Computational methods to dissect gene regulatory networks in cancer

2017 ◽  
Vol 2 ◽  
pp. 115-122 ◽  
Author(s):  
Archana S. Iyer ◽  
Hatice U. Osmanbeyoglu ◽  
Christina S. Leslie
Keyword(s):  
Computational Methods ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Gene Regulatory
Sign in / Sign up

Export Citation Format

Share Document