Intervention and Control of Gene Regulatory Networks: Theoretical Framework and Application to Human Melanoma Gene Regulation

2013 ◽  
pp. 215-238
Author(s):  
Nidhal Bouaynaya ◽  
Roman Shterenberg ◽  
Dan Schonfeld ◽  
Hassan M. Fathallah-Shaykh
Keyword(s):  
Gene Regulation ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Theoretical Framework ◽  
Human Melanoma ◽  
Gene Regulatory ◽  
And Control

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.

scholarly journals Detecting shifts in gene regulatory networks during time-course experiments at single-time-point temporal resolution

2015 ◽  
Vol 13 (05) ◽  
pp. 1543002 ◽  
Author(s):  
Yoichi Takenaka ◽  
Shigeto Seno ◽  
Hideo Matsuda
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Time Course ◽  
Diauxic Shift ◽  
Gene Regulatory ◽  
Gene Regulations ◽  
Single Time Point ◽  
Time Point

Comprehensively understanding the dynamics of biological systems is one of the greatest challenges in biology. Vastly improved biological technologies have provided vast amounts of information that must be understood by bioinformatics and systems biology researchers. Gene regulations have been frequently modeled by ordinary differential equations or graphical models based on time-course gene expression profiles. The state-of-the-art computational approaches for analyzing gene regulations assume that their models are same throughout time-course experiments. However, these approaches cannot easily analyze transient changes at a time point, such as diauxic shift. We propose a score that analyzes the gene regulations at each time point. The score is based on the information gains of information criterion values. The method detects the shifts in gene regulatory networks (GRNs) during time-course experiments with single-time-point resolution. The effectiveness of the method is evaluated on the diauxic shift from glucose to lactose in Escherichia coli. Gene regulation shifts were detected at two time points: the first corresponding to the time at which the growth of E. coli ceased and the second corresponding to the end of the experiment, when the nutrient sources (glucose and lactose) had become exhausted. According to these results, the proposed score and method can appropriately detect the time of gene regulation shifts. The method based on the proposed score provides a new tool for analyzing dynamic biological systems. Because the score value indicates the strength of gene regulation at each time point in a gene expression profile, it can potentially infer hidden GRNs from time-course experiments.

scholarly journals Charting gene regulatory networks: strategies, challenges and perspectives

2004 ◽  
Vol 381 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Gong-Hong WEI ◽  
De-Pei LIU ◽  
Chih-Chuan LIANG
Keyword(s):  
Data Mining ◽  
Gene Regulation ◽  
Gene Regulatory Networks ◽  
Protein Interactions ◽  
Genome Annotation ◽  
Regulatory Networks ◽  
Regulatory Elements ◽  
Protein Protein Interactions ◽  
Genomics And Proteomics ◽  
Gene Regulatory

One of the foremost challenges in the post-genomic era will be to chart the gene regulatory networks of cells, including aspects such as genome annotation, identification of cis-regulatory elements and transcription factors, information on protein–DNA and protein–protein interactions, and data mining and integration. Some of these broad sets of data have already been assembled for building networks of gene regulation. Even though these datasets are still far from comprehensive, and the approach faces many important and difficult challenges, some strategies have begun to make connections between disparate regulatory events and to foster new hypotheses. In this article we review several different genomics and proteomics technologies, and present bioinformatics methods for exploring these data in order to make novel discoveries.

scholarly journals Classification-based Inference of Dynamical Models of Gene Regulatory Networks

2019 ◽  
Author(s):  
David A. Fehr ◽  
Manu ◽  
Yen Lee Loh
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Linear Optimization ◽  
Model Parameters ◽  
Gene Circuit ◽  
Gap Gene ◽  
Non Linear ◽  
Gene Regulatory

AbstractCell-fate decisions during development are controlled by densely interconnected gene regulatory networks (GRNs) consisting of many genes. Inferring and predictively modeling these GRNs is crucial for understanding development and other physiological processes. Gene circuits, coupled differential equations that represent gene product synthesis with a switch-like function, provide a biologically realistic framework for modeling the time evolution of gene expression. However, their use has been limited to smaller networks due to the computational expense of inferring model parameters from gene expression data using global non-linear optimization. Here we show that the switch-like nature of gene regulation can be exploited to break the gene circuit inference problem into two simpler optimization problems that are amenable to computationally efficient supervised learning techniques. We present FIGR (Fast Inference of Gene Regulation), a novel classification-based inference approach to determining gene circuit parameters. We demonstrate FIGR’s effectiveness on synthetic data as well as experimental data from the gap gene system of Drosophila. FIGR is faster than global non-linear optimization by nearly three orders of magnitude and its computational complexity scales much better with GRN size. On a practical level, FIGR can accurately infer the biologically realistic gap gene network in under a minute on desktop-class hardware instead of requiring hours of parallel computing. We anticipate that FIGR would enable the inference of much larger biologically realistic GRNs than was possible before. FIGR Source code is freely available at http://github.com/mlekkha/FIGR.

scholarly journals Structural and Functional Analyses of Hub MicroRNAs in an Integrated Gene Regulatory Network of Arabidopsis

2020 ◽  
Author(s):  
Zhaoxu Gao ◽  
Jun Li ◽  
Li Li ◽  
Yanzhi Yang ◽  
Jian Li ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Individual Plant ◽  
Plant Mirnas ◽  
Control Logic ◽  
Small Regulatory Rnas ◽  
Gene Regulatory ◽  
Regulatory Rnas ◽  
Functional Analyses ◽  
And Control

AbstractMicroRNAs (miRNAs) are trans-acting small regulatory RNAs that work coordinately with transcription factors (TFs) to shape the repertoires of cellular mRNA available for translation. Despite our growing knowledge of individual plant miRNAs, their global roles in gene regulatory networks remain mostly unassessed. Based on interactions reanalyzed from public databases and curated from the literature, we reconstructed an integrated miRNA network in Arabidopsis that includes 66 core TFs, 318 miRNAs, and 1712 downstream genes. We found that miRNAs occupy distinct niches and enrich miRNA-containing feed-forward loops (FFLs), particularly those in which the miRNAs are intermediate nodes. Further analyses revealed that miRNA-containing FFLs coordinate TFs located in different hierarchical layers and that intertwined miRNA-containing FFLs are associated with party and date miRNA hubs. Using the date hub MIR858A as an example, we performed detailed molecular and genetic analyses of three interconnected miRNA-containing FFLs. These analyses revealed individual functions of the selected miRNA-containing FFLs and elucidated how the date hub miRNA fulfills multiple regulatory roles. Collectively, our findings highlighted the prevalence and importance of miRNA-containing FFLs to provide new insights into the design principles and control logic of miRNA regulatory networks governing gene expression programs in plants.

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