scholarly journals From omics to systems biology: Exploring the mystery box of microbial life

2011 ◽  
Vol 32 (4) ◽  
pp. 147
Author(s):  
Martin Ostrowski ◽  
Sasha Tetu ◽  
Karl Hassan ◽  
Anahit Penesyan ◽  
Kent Lim ◽  
...  
Keyword(s):  
Experimental Data ◽  
Systems Biology ◽  
Molecular Biology ◽  
High Throughput ◽  
Individual Gene ◽  
Microbial Life

Microbial molecular biology has traditionally used very reductionist approaches; for example, find a gene of interest, clone it or knock it out and see if you can detect a phenotype. The genomics era has opened up the possibility of analysing microbes and communities at a systems level by combining high-throughput experimental data from genomic, transcriptomic, proteomic and phenomic techniques. This parallels earlier reductionist approaches by going from DNA to RNA to protein to phenotype, albeit on a global rather than individual gene scale.

scholarly journals Synthetic Biology Open Language (SBOL) Version 2.3

2019 ◽  
Vol 16 (2) ◽  
Author(s):  
Curtis Madsen ◽  
Angel Goñi Moreno ◽  
Umesh P ◽  
Zachary Palchick ◽  
Nicholas Roehner ◽  
...  
Keyword(s):  
Experimental Data ◽  
Metabolic Engineering ◽  
Systems Biology ◽  
Synthetic Biology ◽  
Molecular Biology ◽  
Special Issue ◽  
Design Elements ◽  
Biological Design ◽  
Support Organization ◽  
Synthetic Biology Open Language

AbstractSynthetic biology builds upon the techniques and successes of genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. The field still faces substantial challenges, including long development times, high rates of failure, and poor reproducibility. One method to ameliorate these problems is to improve the exchange of information about designed systems between laboratories. The synthetic biology open language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, filling a need not satisfied by other pre-existing standards. This document details version 2.3.0 of SBOL, which builds upon version 2.2.0 published in last year’s JIB Standards in Systems Biology special issue. In particular, SBOL 2.3.0 includes means of succinctly representing sequence modifications, such as insertion, deletion, and replacement, an extension to support organization and attachment of experimental data derived from designs, and an extension for describing numerical parameters of design elements. The new version also includes specifying types of synthetic biology activities, unambiguous locations for sequences with multiple encodings, refinement of a number of validation rules, improved figures and examples, and clarification on a number of issues related to the use of external ontology terms.

scholarly journals Whole-genome approaches for large-scale gene identification and expression analysis in mammalian preimplantation embryos

2005 ◽  
Vol 17 (2) ◽  
pp. 37 ◽  
Author(s):  
James Adjaye
Keyword(s):  
Systems Biology ◽  
Molecular Biology ◽  
Regenerative Medicine ◽  
High Throughput ◽  
Molecular Basis ◽  
Large Scale ◽  
Transcriptional Control ◽  
Preimplantation Embryos ◽  
Gene Identification ◽  
Whole Genome

The elucidation, unravelling and understanding of the molecular basis of transcriptional control during preimplantion development is of utmost importance if we are to intervene and eliminate or reduce abnormalities associated with growth, disease and infertility by applying assisted reproduction. Importantly, these studies should enhance our knowledge of basic reproductive biology and its application to regenerative medicine and livestock production. A major obstacle impeding progress in these areas is the ability to successfully generate molecular portraits of preimplantation embryos from their minute amounts of RNA. The present review describes the various approaches whereby classical embryology fuses with molecular biology, high-throughput genomics and systems biology to address and solve questions related to early development in mammals.

Co-expressional conservation in virulence and stress related genes of three Gammaproteobacterial species: Escherichia coli, Salmonella enterica and Pseudomonas aeruginosa

2015 ◽  
Vol 11 (11) ◽  
pp. 3137-3148
Author(s):  
Nazanin Hosseinkhan ◽  
Peyman Zarrineh ◽  
Hassan Rokni-Zadeh ◽  
Mohammad Reza Ashouri ◽  
Ali Masoudi-Nejad
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Pseudomonas Aeruginosa ◽  
Systems Biology ◽  
Gene Expression Data ◽  
High Throughput ◽  
Expression Data ◽  
P Gene ◽  
Stress Related Genes ◽  
High Throughput Gene Expression

Gene co-expression analysis is one of the main aspects of systems biology that uses high-throughput gene expression data.

scholarly journals Integrating Molecular Biology and Bioinformatics Education

2018 ◽  
Author(s):  
Boas Pucker ◽  
Hanna Marie Schilbert ◽  
Sina Franziska Schumacher
Keyword(s):  
Molecular Biology ◽  
Cooperative Learning ◽  
Peer Review ◽  
High Throughput ◽  
Teaching Method ◽  
Integrated Education ◽  
High Throughput Data ◽  
Wet Lab ◽  
Bioinformatics Education ◽  
Increasing Demand

Combined awareness about the power and limitations of bioinformatics and molecular biology enables advanced research based on high-throughput data. Despite an increasing demand for scientists with a combined background in both fields, the education in dry lab and wet lab is often separated. This work describes an example of integrated education with focus on genomics and transcriptomics. Participants learn computational and molecular biology methods in the same practical course. Peer-review is applied as a teaching method to foster cooperative learning of students with heterogeneous backgrounds. Evaluation results indicate acceptance and appreciation of this approach.

scholarly journals ReactomeFIViz: the Reactome FI Cytoscape app for pathway and network-based data analysis

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 146 ◽  
Author(s):  
Guanming Wu ◽  
Eric Dawson ◽  
Adrian Duong ◽  
Robin Haw ◽  
Lincoln Stein
Keyword(s):  
Experimental Data ◽  
Data Analysis ◽  
Graphical Models ◽  
High Throughput ◽  
Interaction Network ◽  
Large Data ◽  
Relevant Information ◽  
Data Sets ◽  
Data Types ◽  
Biological Studies

High-throughput experiments are routinely performed in modern biological studies. However, extracting meaningful results from massive experimental data sets is a challenging task for biologists. Projecting data onto pathway and network contexts is a powerful way to unravel patterns embedded in seemingly scattered large data sets and assist knowledge discovery related to cancer and other complex diseases. We have developed a Cytoscape app called “ReactomeFIViz”, which utilizes a highly reliable gene functional interaction network and human curated pathways from Reactome and other pathway databases. This app provides a suite of features to assist biologists in performing pathway- and network-based data analysis in a biologically intuitive and user-friendly way. Biologists can use this app to uncover network and pathway patterns related to their studies, search for gene signatures from gene expression data sets, reveal pathways significantly enriched by genes in a list, and integrate multiple genomic data types into a pathway context using probabilistic graphical models. We believe our app will give researchers substantial power to analyze intrinsically noisy high-throughput experimental data to find biologically relevant information.

scholarly journals High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes

2016 ◽  
Author(s):  
Marta R. Hidalgo ◽  
Cankut Cubuk ◽  
Alicia Amadoz ◽  
Francisco Salavert ◽  
José Carbonell-Caballero ◽  
...  
Keyword(s):  
High Throughput ◽  
Gene Mutations ◽  
Diagnostic Value ◽  
Therapeutic Interventions ◽  
Biological Knowledge ◽  
Individual Gene ◽  
Main Challenge ◽  
Activity Measurements ◽  
Cancer Types ◽  
Or Gene

AbstractUnderstanding the aspects of the cell functionality that account for disease or drug action mechanisms is a main challenge for precision medicine. Here we propose a new method that models cell signaling using biological knowledge on signal transduction. The method recodes individual gene expression values (and/or gene mutations) into accurate measurements of changes in the activity of signaling circuits, which ultimately constitute high-throughput estimations of cell functionalities caused by gene activity within the pathway. Moreover, such estimations can be obtained either at cohort-level, in case/control comparisons, or personalized for individual patients. The accuracy of the method is demonstrated in an extensive analysis involving 5640 patients from 12 different cancer types. Circuit activity measurements not only have a high diagnostic value but also can be related to relevant disease outcomes such as survival, and can be used to assess therapeutic interventions.

scholarly journals Integrating Molecular Biology and Bioinformatics Education

2018 ◽  
Author(s):  
Boas Pucker ◽  
Hanna Marie Schilbert ◽  
Sina Franziska Schumacher
Keyword(s):  
Molecular Biology ◽  
Cooperative Learning ◽  
Peer Review ◽  
High Throughput ◽  
Teaching Method ◽  
Integrated Education ◽  
High Throughput Data ◽  
Wet Lab ◽  
Bioinformatics Education ◽  
Increasing Demand

Combined awareness about the power and limitations of bioinformatics and molecular biology enables advanced research based on high-throughput data. Despite an increasing demand for scientists with a combined background in both fields, the education in dry lab and wet lab is often separated. This work describes an example of integrated education with focus on genomics and transcriptomics. Participants learn computational and molecular biology methods in the same practical course. Peer-review is applied as a teaching method to foster cooperative learning of students with heterogeneous backgrounds. Evaluation results indicate acceptance and appreciation of this approach.

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