scholarly journals Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression

2020 ◽  
Vol 21 (24) ◽  
pp. 9461
Author(s):  
Aurora Savino ◽  
Paolo Provero ◽  
Valeria Poli
Keyword(s):  
Cancer Biology ◽  
Regulatory Networks ◽  
Tumour Progression ◽  
External Perturbation ◽  
Data Types ◽  
Differential Gene ◽  
Gene Regulatory ◽  
Experimental Validations ◽  
Definition Of ◽  
Future Work

Biological systems respond to perturbations through the rewiring of molecular interactions, organised in gene regulatory networks (GRNs). Among these, the increasingly high availability of transcriptomic data makes gene co-expression networks the most exploited ones. Differential co-expression networks are useful tools to identify changes in response to an external perturbation, such as mutations predisposing to cancer development, and leading to changes in the activity of gene expression regulators or signalling. They can help explain the robustness of cancer cells to perturbations and identify promising candidates for targeted therapy, moreover providing higher specificity with respect to standard co-expression methods. Here, we comprehensively review the literature about the methods developed to assess differential co-expression and their applications to cancer biology. Via the comparison of normal and diseased conditions and of different tumour stages, studies based on these methods led to the definition of pathways involved in gene network reorganisation upon oncogenes’ mutations and tumour progression, often converging on immune system signalling. A relevant implementation still lagging behind is the integration of different data types, which would greatly improve network interpretability. Most importantly, performance and predictivity evaluation of the large variety of mathematical models proposed would urgently require experimental validations and systematic comparisons. We believe that future work on differential gene co-expression networks, complemented with additional omics data and experimentally tested, will considerably improve our insights into the biology of tumours.

Differential gene regulatory networks in development and disease

2017 ◽  
Vol 75 (6) ◽  
pp. 1013-1025 ◽  
Author(s):  
Arun J. Singh ◽  
Stephen A. Ramsey ◽  
Theresa M. Filtz ◽  
Chrissa Kioussi
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Differential Gene ◽  
Gene Regulatory

On lists and other abstract data types in the calculus of constructions

2000 ◽  
Vol 10 (2) ◽  
pp. 261-276 ◽  
Author(s):  
JONATHAN P. SELDIN
Keyword(s):  
Data Type ◽  
Abstract Data Type ◽  
Abstract Data Types ◽  
Natural Numbers ◽  
Data Types ◽  
Induction Principle ◽  
Calculus Of Constructions ◽  
Abstract Data ◽  
Definition Of ◽  
Future Work

The representation of the inductively defined abstract data type for lists was left incomplete in Seldin (1997, Section 9). Here that representation is completed, and it is proved that all extra axioms needed are consistent. Among the innovations of this paper is a definition of cdr, whose definition was left for future work in Seldin (1997, Section 9). The results are then extended to other abstract data types – those of Berardi (1993). The method used to define cdr for lists is extended to obtain the definition of an inverse for each argument of each constructor of an abstract data type. These inverses are used to prove the injective property for the constructors. Also, Dedekind's method of defining the natural numbers is used to define a predicate associated with each abstract data type, and the use of this predicate makes it unnecessary to postulate the induction principle. The only axioms left to be proved are those asserting the disjointness of the co-domains of different constructors, and it is shown that those axioms can be proved consistent.

scholarly journals TRIMER: Transcription Regulation Integrated with MEtabolic Regulation

2021 ◽  
Author(s):  
Puhua Niu ◽  
Maria J. Soto ◽  
Byung-Jun Yoon ◽  
Edward R. Dougherty ◽  
Francis J. Alexander ◽  
...  
Keyword(s):  
Transcription Regulation ◽  
Regulatory Networks ◽  
Metabolic Regulation ◽  
Simulation Framework ◽  
Data Types ◽  
Metabolic Fluxes ◽  
Gene Regulatory ◽  
Traditional Approaches ◽  
Evaluation Platform ◽  
Genome Scale

ABSTRACTAdvances in bioengineering have enabled numerous bio-based commodities. Yet most traditional approaches do not extend beyond a single metabolic pathway and do not attempt to modify gene regulatory networks in order to buffer metabolic perturbations. This is despite access to near universal technologies allowing genome-scale engineering. To help overcome this limitation, we have developed a pipeline enabling analysis of Transcription Regulation Integrated with MEtabolic Regulation (TRIMER). TRIMER utilizes a Bayesian network (BN) inferred from transcriptomic data to model the transcription factor regulatory network. TRIMER then infers the probabilities of gene states that are of relevance to the metabolism of interest, and predicts metabolic fluxes resulting from deletion of transcription factors at the genome scale. Additionally, we have developed a simulation framework to mimic the TF-regulated metabolic network, capable of generating both gene expression states and metabolic fluxes, thereby providing a fair evaluation platform for benchmarking models and predictions. Here, we present this computational pipeline. We demonstrate TRIMER’s applicability to both simulated and experimental data and show that it outperforms current approaches on both data types.

scholarly journals CLIPreg: Constructing translational regulatory networks from CLIP-, Ribo- and RNA-seq

2021 ◽  
Author(s):  
Baptiste Kerouanton ◽  
Sebastian Schafer ◽  
Lena Ho ◽  
Sonia Chothani ◽  
Owen JL Rackham
Keyword(s):  
Protein Interactions ◽  
Translational Regulation ◽  
Regulatory Networks ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Critical Role ◽  
Sequencing Data ◽  
Data Types ◽  
Network Resources ◽  
Gene Regulatory

Motivation: The creation and analysis of gene regulatory networks have been the focus of bioinformatic research and underpins much of what is known about gene regulation. However, as a result of a bias in the availability of data-types that are collected, the vast majority of gene regulatory network resources and tools have focused on either transcriptional regulation or protein-protein interactions. This has left other areas of regulation, for instance translational regulation, vastly underrepresented despite them having been shown to play a critical role in both health and disease. Results: In order to address this we have developed CLIPreg, a package that integrates RNA, Ribo and CLIP- sequencing data in order to construct translational regulatory networks coordinated by RNA-binding proteins. This is the first tool of its type to be created, allowing for detailed investigation into a previously unseen layer of regulation.

scholarly journals Neuron subtype-specific effector gene expression in the Motor Ganglion of Ciona

2019 ◽  
Author(s):  
Susanne Gibboney ◽  
Kwantae Kim ◽  
Christopher J. Johnson ◽  
Jameson Orvis ◽  
Paula Martínez-Feduchi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Gene Expression Analysis ◽  
Transcriptome Profiling ◽  
Matrix Proteins ◽  
Wide Range ◽  
Specific Effector ◽  
The Central Nervous System ◽  
Differential Gene ◽  
Gene Regulatory

AbstractThe central nervous system of the Ciona larva contains only 177 neurons. The precise regulation of neuron subtype-specific morphogenesis and differentiation observed in during the formation of this minimal connectome offers a unique opportunity to dissect gene regulatory networks underlying chordate neurodevelopment. Here we compare the transcriptomes of two very distinct neuron types in the hindbrain/spinal cord homolog of Ciona, the Motor Ganglion (MG): the Descending decussating neuron (ddN, proposed homolog of Mauthner Cells in vertebrates) and the MG Interneuron 2 (MGIN2). Both types are invariantly represented by a single bilaterally symmetric left/right pair of cells in every larva. Supernumerary ddNs and MGIN2s were generated in synchronized embryos and isolated by fluorescence-activated cell sorting for transcriptome profiling. Differential gene expression analysis revealed ddN- and MGIN2-specific enrichment of a wide range of genes, including many encoding potential “effectors” of subtype-specific morphological and functional traits. More specifically, we identified the upregulation of centrosome-associated, microtubule-stabilizing/bundling proteins and extracellular matrix proteins and axon guidance cues as part of a single intrinsic regulatory program that might underlie the unique polarization of the ddNs, the only descending MG neurons that cross the midline.

scholarly journals Building gene regulatory networks from scATAC-seq and scRNA-seq using Linked Self-Organizing Maps

2018 ◽  
Author(s):  
Camden Jansen ◽  
Ricardo N. Ramirez ◽  
Nicole C. El-Ali ◽  
David Gomez-Cabrero ◽  
Jesper Tegner ◽  
...  
Keyword(s):  
Single Cell ◽  
Regulatory Networks ◽  
Time Course ◽  
Single Cells ◽  
Emergent Properties ◽  
Data Types ◽  
Self Organizing Maps ◽  
Cell Assays ◽  
Gene Regulatory ◽  
Self Organizing

AbstractRapid advances in single-cell assays have outpaced methods for analysis of those data types. Different single-cell assays show extensive variation in sensitivity and signal to noise levels. In particular, scATAC-seq generates extremely sparse and noisy datasets. Existing methods developed to analyze this data require cells amenable to pseudo-time analysis or require datasets with drastically different cell-types. We describe a novel approach using self-organizing maps (SOM) to link scATAC-seq and scRNA-seq data that overcomes these challenges and can generate draft regulatory networks. Our SOMatic package generates chromatin and gene expression SOMs separately and combines them using a linking function. We applied SOMatic on a mouse pre-B cell differentiation time-course using controlled Ikaros over-expression to recover gene ontology enrichments, identify motifs in genomic regions showing similar single-cell profiles, and generate a gene regulatory network that both recovers known interactions and predicts new Ikaros targets during the differentiation process. The ability of linked SOMs to detect emergent properties from multiple types of highly-dimensional genomic data with very different signal properties opens new avenues for integrative analysis of single-cells.

scholarly journals A comparative analysis of synthetic genetic oscillators

2010 ◽  
Vol 7 (52) ◽  
pp. 1503-1524 ◽  
Author(s):  
Oliver Purcell ◽  
Nigel J. Savery ◽  
Claire S. Grierson ◽  
Mario di Bernardo
Keyword(s):  
Experimental Data ◽  
Comparative Analysis ◽  
Time Delay ◽  
Synthetic Biology ◽  
Gene Regulatory Networks ◽  
Network Topology ◽  
Regulatory Networks ◽  
State Of The Art ◽  
Gene Regulatory ◽  
Future Work

Synthetic biology is a rapidly expanding discipline at the interface between engineering and biology. Much research in this area has focused on gene regulatory networks that function as biological switches and oscillators. Here we review the state of the art in the design and construction of oscillators, comparing the features of each of the main networks published to date, the models used for in silico design and validation and, where available, relevant experimental data. Trends are apparent in the ways that network topology constrains oscillator characteristics and dynamics. Also, noise and time delay within the network can both have constructive and destructive roles in generating oscillations, and stochastic coherence is commonplace. This review can be used to inform future work to design and implement new types of synthetic oscillators or to incorporate existing oscillators into new designs.

scholarly journals Estimation of Gene Regulatory Networks from Cancer Transcriptomics Data

Processes ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1758
Author(s):  
Seong Beom Cho
Keyword(s):  
Gene Regulatory Networks ◽  
Cancer Biology ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Classification Model ◽  
Biological Knowledge ◽  
Analysis Model ◽  
Regulatory Relationship ◽  
Transcriptomics Data ◽  
Gene Regulatory

Cancer is a genetic disease in which multiple genes are perturbed. Thus, information about the regulatory relationships between genes is necessary for the identification of biomarkers and therapeutic targets. In this review, methods for inference of gene regulatory networks (GRNs) from transcriptomics data that are used in cancer research are introduced. The methods are classified into three categories according to the analysis model. The first category includes methods that use pair-wise measures between genes, including correlation coefficient and mutual information. The second category includes methods that determine the genetic regulatory relationship using multivariate measures, which consider the expression profiles of all genes concurrently. The third category includes methods using supervised and integrative approaches. The supervised approach estimates the regulatory relationship using a supervised learning method that constructs a regression or classification model for predicting whether there is a regulatory relationship between genes with input data of gene expression profiles and class labels of prior biological knowledge. The integrative method is an expansion of the supervised method and uses more data and biological knowledge for predicting the regulatory relationship. Furthermore, simulation and experimental validation of the estimated GRNs are also discussed in this review. This review identified that most GRN inference methods are not specific for cancer transcriptome data, and such methods are required for better understanding of cancer pathophysiology. In addition, more systematic methods for validation of the estimated GRNs need to be developed in the context of cancer biology.

scholarly journals Fused regression for multi-source gene regulatory network inference

2016 ◽  
Author(s):  
Kari Y. Lam ◽  
Zachary M. Westrick ◽  
Christian L. Müller ◽  
Lionel Christiaen ◽  
Richard Bonneau
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Network Inference ◽  
Cell Types ◽  
Global Network ◽  
Simultaneous Estimation ◽  
Data Types ◽  
Gene Regulatory Network Inference ◽  
Gene Regulatory ◽  
Multiple Species

AbstractUnderstanding gene regulatory networks is critical to understanding cellular differentiation and response to external stimuli. Methods for global network inference have been developed and applied to a variety of species. Most approaches consider the problem of network inference independently in each species, despite evidence that gene regulation can be conserved even in distantly related species. Further, network inference is often confined to single data-types (single platforms) and single cell types. We introduce a method for multi-source network inference that allows simultaneous estimation of gene regulatory networks in multiple species or biological processes through the introduction of priors based on known gene relationships such as orthology incorporated using fused regression. This approach improves network inference performance even when orthology mapping and conservation are incomplete. We refine this method by presenting an algorithm that extracts the true conserved subnetwork from a larger set of potentially conserved interactions and demonstrate the utility of our method in cross species network inference. Last, we demonstrate our method’s utility in learning from data collected on different experimental platforms.

scholarly journals Inference of Genome-Scale Gene Regulatory Networks: Are There Differences in Biological and Clinical Validations?

2018 ◽  
Vol 1 (1) ◽  
pp. 138-148
Author(s):  
Frank Emmert-Streib ◽  
Matthias Dehmer
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Human Gene ◽  
Expression Data ◽  
Research Questions ◽  
Gene Regulatory ◽  
Experimental Validations ◽  
Context Specific ◽  
Genome Scale ◽  
Network Project

Causal networks, e.g., gene regulatory networks (GRNs) inferred from gene expression data, contain a wealth of information but are defying simple, straightforward and low-budget experimental validations. In this paper, we elaborate on this problem and discuss distinctions between biological and clinical validations. As a result, validation differences for GRNs reflect known differences between basic biological and clinical research questions making the validations context specific. Hence, the meaning of biologically and clinically meaningful GRNs can be very different. For a concerted approach to a problem of this size, we suggest the establishment of the HUMAN GENE REGULATORY NETWORK PROJECT which provides the information required for biological and clinical validations alike.

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