GMO free systems optimization of wine yeast for wine production by massive scale directed evolution
Programme: ERASysAPP
SEEK ID: https://fairdomhub.org/projects/25
Public web page: Not specified
Organisms: Saccharomyces cerevisiae
FAIRDOM PALs: Payam Ghiaci, Nikolay Martyushenko
Project created: 10th Nov 2014
Related items
- People (7)
- Programmes (1)
- Institutions (3)
- Investigations (1)
- Studies (1)
- Assays (1)
- Data files (2)
- SOPs (1)
Projects: WineSys, INBioPharm, BioZEment 2.0, CoolWine, Auromega
Institutions: Norwegian University of Science and Technology
https://orcid.org/0000-0002-9125-326XExpertise: Systems Biology
Tools: network theory
Projects: STREAM, SCaRAB, WineSys, INBioPharm
Institutions: SINTEF, Norwegian University of Science and Technology
Expertise: Metabolomics
Institutions: University of Gothenburg, European Molecular Biology Laboratory
Projects: WineSys
Institutions: Norwegian University of Science and Technology
Institutions: Universitat Rovira i Virgili
Projects: WineSys
Institutions: Norwegian University of Science and Technology
Institutions: University of Gothenburg
The main objective of the ERANET proposal Systems Biology Applications - ERASysAPP (app = application = translational systems biology) is to promote multidimensional and complementary European systems biology projects, programmes and research initiatives on a number of selected research topics. Inter alia, ERASysAPP will initiate, execute and monitor a number of joint transnational calls on systems biology research projects with a particular focus on applications - or in other words so called ...
Projects: SysVirDrug, SysMilk, SysMetEx, MetApp, IMOMESIC, WineSys, CropClock, SYSTERACT, XyloCut, RootBook, ROBUSTYEAST, LEANPROT, ErasysApp Funders
Web page: https://www.cobiotech.eu/about-cobiotech/erasysapp
Gene co-epxression network analyses are common in the study of large scale biological data sets. In this study, we have developed a methodology for the comparison of pairs of co-expression networks using the s-core network peeling approach. We apply the methodology to gene-expression data for human and mouse.
Submitter: Eivind Almaas
Studies: Use of s-core/ s-core+ analysis to conduct a comparative gene co-express...
Assays: Application of developed network methodology on human and mouse data.
Snapshots: No snapshots
s-core/ s-core+ network peeling is a methodology to identify cores of weighted complex networks.
Submitter: Eivind Almaas
Investigation: Development of methods for comparing gene co-ex...
Assays: Application of developed network methodology on human and mouse data.
Snapshots: No snapshots
We have developed a method for comparative analysis of pairs of complex networks based on gene co-expression analysis. We apply this modeling analysis to data set for gene expressions in multiple tissues of mus musculus and homo sapiens.
Submitter: Eivind Almaas
Biological problem addressed: Model Analysis Type
Investigation: Development of methods for comparing gene co-ex...
Organisms: No organisms
Models: No Models
SOPs: README file for s-core / s-core+ perl script
Data files: Networks from human and mouse gene co-expressio..., Perl program for computing s-core and s-core+
Snapshots: No snapshots
Creators: Eivind Almaas, Marius Eidsaa
Submitter: Eivind Almaas
Creators: Eivind Almaas, Marius Eidsaa
Submitter: Eivind Almaas
This README file describes how the s-core / s-core+ analysis perl script is to be executed together with data files.
Creators: Eivind Almaas, Marius Eidsaa
Submitter: Eivind Almaas