scholarly journals Computational Tools for Strain Optimization by Tuning the Optimal Level of Gene Expression

2012 ◽  
pp. 251-258 ◽  
Author(s):  
Emanuel Gonçalves ◽  
Isabel Rocha ◽  
Miguel Rocha
Keyword(s):  
Gene Expression ◽  
Optimal Level ◽  
Computational Tools ◽  
Strain Optimization

scholarly journals Uncovering the genetic blueprint of the C. elegans nervous system

2020 ◽  
Vol 117 (52) ◽  
pp. 33570-33577
Author(s):  
István A. Kovács ◽  
Dániel L. Barabási ◽  
Albert-László Barabási
Keyword(s):  
Gene Expression ◽  
Expression Profiling ◽  
Expression Patterns ◽  
Computational Framework ◽  
Computational Tools ◽  
C Elegans ◽  
Junction Formation ◽  
Cell Gene Expression ◽  
Genetic Mechanisms ◽  
Cell Gene

Despite rapid advances in connectome mapping and neuronal genetics, we lack theoretical and computational tools to unveil, in an experimentally testable fashion, the genetic mechanisms that govern neuronal wiring. Here we introduce a computational framework to link the adjacency matrix of a connectome to the expression patterns of its neurons, helping us uncover a set of genetic rules that govern the interactions between neurons in contact. The method incorporates the biological realities of the system, accounting for noise from data collection limitations, as well as spatial restrictions. The resulting methodology allows us to infer a network of 19 innexin interactions that govern the formation of gap junctions in Caenorhabditis elegans, five of which are already supported by experimental data. As advances in single-cell gene expression profiling increase the accuracy and the coverage of the data, the developed framework will allow researchers to systematically infer experimentally testable connection rules, offering mechanistic predictions for synapse and gap junction formation.

scholarly journals ImmuCellDB: An Indicative Database of Immune Cell Composition From Different Tissues and Disease Conditions in Mouse and Human

2021 ◽  
Vol 12 ◽  
Author(s):  
Ziyi Chen ◽  
Han Na ◽  
Aiping Wu
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Immune Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Expression Data ◽  
Computational Tools ◽  
Cell Composition ◽  
Cell Abundance ◽  
Different Tissues

Immune cell composition is highly divergent across different tissues and diseases. A comprehensive resource of tissue immune cells across different conditions in mouse and human will thus provide great understanding of the immune microenvironment of many diseases. Recently, computational methods for estimating immune cell abundance from tissue transcriptome data have been developed and are now widely used. Using these computational tools, large-scale estimation of immune cell composition across tissues and conditions should be possible using gene expression data collected from public databases. In total, 266 tissue types and 706 disease types in humans, as well as 143 tissue types and 61 disease types, and 206 genotypes in mouse had been included in a database we have named ImmuCellDB (http://wap-lab.org:3200/ImmuCellDB/). In ImmuCellDB, users can search and browse immune cell proportions based on tissues, disease or genotype in mouse or humans. Additionally, the variation and correlation of immune cell abundance and gene expression level between different conditions can be compared and viewed in this database. We believe that ImmuCellDB provides not only an indicative view of tissue-dependent or disease-dependent immune cell profiles, but also represents an easy way to pre-determine immune cell abundance and gene expression profiles for specific situations.

scholarly journals Computational biology for ageing

2011 ◽  
Vol 366 (1561) ◽  
pp. 51-63 ◽  
Author(s):  
Daniela Wieser ◽  
Irene Papatheodorou ◽  
Matthias Ziehm ◽  
Janet M. Thornton
Keyword(s):  
Gene Expression ◽  
Computational Biology ◽  
High Throughput ◽  
Computational Analysis ◽  
Data Interpretation ◽  
Easy Access ◽  
Computational Tools ◽  
Computational Approaches ◽  
Future Developments ◽  
Pathway Analyses

High-throughput genomic and proteomic technologies have generated a wealth of publicly available data on ageing. Easy access to these data, and their computational analysis, is of great importance in order to pinpoint the causes and effects of ageing. Here, we provide a description of the existing databases and computational tools on ageing that are available for researchers. We also describe the computational approaches to data interpretation in the field of ageing including gene expression, comparative and pathway analyses, and highlight the challenges for future developments. We review recent biological insights gained from applying bioinformatics methods to analyse and interpret ageing data in different organisms, tissues and conditions.

scholarly journals Uncovering the genetic blueprint of the C. elegans nervous system

2020 ◽  
Author(s):  
István A. Kovács ◽  
Dániel L. Barabási ◽  
Albert-László Barabási
Keyword(s):  
Gene Expression ◽  
Expression Profiling ◽  
Expression Patterns ◽  
Computational Framework ◽  
Computational Tools ◽  
C Elegans ◽  
Junction Formation ◽  
Cell Gene Expression ◽  
Genetic Mechanisms ◽  
Cell Gene

Despite rapid advances in connectome mapping and neuronal genetics, we lack theoretical and computational tools to unveil, in an experimentally testable fashion, the genetic mechanisms that govern neuronal wiring. Here we introduce a computational framework to link the adjacency matrix of a connectome to the expression patterns of its neurons, helping us uncover a set of genetic rules that govern the interactions between adjacent neurons. The method incorporates the biological realities of the system, accounting for noise from data collection limitations, as well as spatial restrictions. The resulting methodology allows us to infer a network of 19 innexin interactions that govern the formation of gap junctions in C. elegans, five of which are already supported by experimental data. As advances in single-cell gene expression profiling increase the accuracy and the coverage of the data, the developed framework will allow researchers to systematically infer experimentally testable connection rules, offering mechanistic predictions for synapse and gap junction formation.

Molecular and Microscopic Analysis of Altered Gene Expression in Transgenic and Gene Targeted Mice

1996 ◽  
Vol 54 ◽  
pp. 12-13
Author(s):  
W. K. Jones ◽  
J. Robbins
Keyword(s):  
Gene Expression ◽  
Pulmonary Veins ◽  
Microscopic Analysis ◽  
Mouse Tissue ◽  
Adult Heart ◽  
Heavy Chains ◽  
Altered Gene Expression ◽  
Altered Gene ◽  
Pulmonary Myocardium

Two myosin heavy chains (MyHC) are expressed in the mammalian heart and are differentially regulated during development. In the mouse, the α-MyHC is expressed constitutively in the atrium. At birth, the β-MyHC is downregulated and replaced by the α-MyHC, which is the sole cardiac MyHC isoform in the adult heart. We have employed transgenic and gene-targeting methodologies to study the regulation of cardiac MyHC gene expression and the functional and developmental consequences of altered α-MyHC expression in the mouse.We previously characterized an α-MyHC promoter capable of driving tissue-specific and developmentally correct expression of a CAT (chloramphenicol acetyltransferase) marker in the mouse. Tissue surveys detected a small amount of CAT activity in the lung (Fig. 1a). The results of in situ hybridization analyses indicated that the pattern of CAT transcript in the adult heart (Fig. 1b, top panel) is the same as that of α-MyHC (Fig. 1b, lower panel). The α-MyHC gene is expressed in a layer of cardiac muscle (pulmonary myocardium) associated with the pulmonary veins (Fig. 1c). These studies extend our understanding of α-MyHC expression and delimit a third cardiac compartment.

Linking transcription, RNA polymerase II elongation and alternative splicing

2020 ◽  
Vol 477 (16) ◽  
pp. 3091-3104 ◽  
Author(s):  
Luciana E. Giono ◽  
Alberto R. Kornblihtt
Keyword(s):  
Gene Expression ◽  
Alternative Splicing ◽  
Rna Polymerase Ii ◽  
Environmental Changes ◽  
Transcription Elongation ◽  
Single Gene ◽  
Regulation Of Gene Expression ◽  
Regulation Of Transcription ◽  
Stepping Stones ◽  
Eukaryotic Transcription

Gene expression is an intricately regulated process that is at the basis of cell differentiation, the maintenance of cell identity and the cellular responses to environmental changes. Alternative splicing, the process by which multiple functionally distinct transcripts are generated from a single gene, is one of the main mechanisms that contribute to expand the coding capacity of genomes and help explain the level of complexity achieved by higher organisms. Eukaryotic transcription is subject to multiple layers of regulation both intrinsic — such as promoter structure — and dynamic, allowing the cell to respond to internal and external signals. Similarly, alternative splicing choices are affected by all of these aspects, mainly through the regulation of transcription elongation, making it a regulatory knob on a par with the regulation of gene expression levels. This review aims to recapitulate some of the history and stepping-stones that led to the paradigms held today about transcription and splicing regulation, with major focus on transcription elongation and its effect on alternative splicing.

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