Agency: Cordis | Branch: FP7 | Program: CP-IP | Phase: HEALTH-2007-2.1.1-6 | Award Amount: 16.02M | Year: 2008
Lipids are central to the regulation and control of cellular processes by acting as basic building units for biomembranes, the platforms for the vast majority of cellular functions. Recent developments in lipid mass spectrometry have set the scene for a completely new way to understand the composition of membranes, cells and tissues in space and time by allowing the precise identification and quantification of alterations of the total lipid profile after specific perturbations. In combination with advanced proteome and transcriptome analysis tools and novel imaging techniques using RNA interference, it is now possible to unravel the complex network between lipids, genes and proteins in an integrated lipidomics approach. This project application of the European Lipidomics Initiative (ELife; www.lipidomics.net) will address lipid droplets (LD) as dynamic organelles with regard to composition, metabolism and regulation. LD are the hallmark of energy overload diseases with a major health care impact in Europe. The project will exploit recent advances in lipidomics to establish high-throughput methods to define drugable targets and novel biomarkers related to LD lipid and protein species, their interaction and regulation during assembly, disassembly and storage. Translational research from mouse to man applied to LD pathology is a cornerstone of this project at the interface between research and development. To maximize the value of the assembled data generated throughout the project, LipidomicNet as a detailed special purpose Wiki formate data base will be developed and integrated into the existing Lipidomics Expertise Platform (LEP) established through the SSA ELife project (www.lipidomics-expertise.de). ELife collaborates with the NIH initiative LIPID MAPS (www.lipidmaps.org) and the Japanese pendant Lipidbank (www.lipidbank.jp) and is connected to the Danubian Biobank consortium (SSA DanuBiobank, www.danubianbiobank.de) for clinical lipidomics.
News Article | November 23, 2016
This report studies Immune Analysis System in Global market, especially in North America, Europe, China, Japan, Southeast Asia and India, focuses on top manufacturers in global market, with Production, price, revenue and market share for each manufacturer, covering BioTek DAS Roche TECAN BIOSCIENCE GRIFOLS BIOBASE HYBIOME CIOM MEDICAL PERLONG SIMENS BECKMAN COULTER GeteinBiotech ThermoFisher Fenghua Market Segment by Regions, this report splits Global into several key Regions, with production, consumption, revenue, market share and growth rate of Immune Analysis System in these regions, from 2011 to 2021 (forecast), like North America Europe China Japan Southeast Asia India Split by product type, with production, revenue, price, market share and growth rate of each type, can be divided into Type I Type II Type III Split by application, this report focuses on consumption, market share and growth rate of Immune Analysis System in each application, can be divided into Application 1 Application 2 Application 3 Global Immune Analysis System Market Research Report 2016 1 Immune Analysis System Market Overview 1.1 Product Overview and Scope of Immune Analysis System 1.2 Immune Analysis System Segment by Type 1.2.1 Global Production Market Share of Immune Analysis System by Type in 2015 1.2.2 Type I 1.2.3 Type II 1.2.4 Type III 1.3 Immune Analysis System Segment by Application 1.3.1 Immune Analysis System Consumption Market Share by Application in 2015 1.3.2 Application 1 1.3.3 Application 2 1.3.4 Application 3 1.4 Immune Analysis System Market by Region 1.4.1 North America Status and Prospect (2011-2021) 1.4.2 Europe Status and Prospect (2011-2021) 1.4.3 China Status and Prospect (2011-2021) 1.4.4 Japan Status and Prospect (2011-2021) 1.4.5 Southeast Asia Status and Prospect (2011-2021) 1.4.6 India Status and Prospect (2011-2021) 1.5 Global Market Size (Value) of Immune Analysis System (2011-2021) 7 Global Immune Analysis System Manufacturers Profiles/Analysis 7.1 BioTek 7.1.1 Company Basic Information, Manufacturing Base and Its Competitors 7.1.2 Immune Analysis System Product Type, Application and Specification 126.96.36.199 Type I 188.8.131.52 Type II 7.1.3 BioTek Immune Analysis System Production, Revenue, Price and Gross Margin (2015 and 2016) 7.1.4 Main Business/Business Overview 7.2 DAS 7.2.1 Company Basic Information, Manufacturing Base and Its Competitors 7.2.2 Immune Analysis System Product Type, Application and Specification 184.108.40.206 Type I 220.127.116.11 Type II 7.2.3 DAS Immune Analysis System Production, Revenue, Price and Gross Margin (2015 and 2016) 7.2.4 Main Business/Business Overview 7.3 Roche 7.3.1 Company Basic Information, Manufacturing Base and Its Competitors 7.3.2 Immune Analysis System Product Type, Application and Specification 18.104.22.168 Type I 22.214.171.124 Type II 7.3.3 Roche Immune Analysis System Production, Revenue, Price and Gross Margin (2015 and 2016) 7.3.4 Main Business/Business Overview 7.4 TECAN 7.4.1 Company Basic Information, Manufacturing Base and Its Competitors 7.4.2 Immune Analysis System Product Type, Application and Specification 126.96.36.199 Type I 188.8.131.52 Type II 7.4.3 TECAN Immune Analysis System Production, Revenue, Price and Gross Margin (2015 and 2016) 7.4.4 Main Business/Business Overview 7.5 BIOSCIENCE 7.5.1 Company Basic Information, Manufacturing Base and Its Competitors 7.5.2 Immune Analysis System Product Type, Application and Specification 184.108.40.206 Type I 220.127.116.11 Type II 7.5.3 BIOSCIENCE Immune Analysis System Production, Revenue, Price and Gross Margin (2015 and 2016) 7.5.4 Main Business/Business Overview 7.6 GRIFOLS 7.6.1 Company Basic Information, Manufacturing Base and Its Competitors 7.6.2 Immune Analysis System Product Type, Application and Specification 18.104.22.168 Type I 22.214.171.124 Type II 7.6.3 GRIFOLS Immune Analysis System Production, Revenue, Price and Gross Margin (2015 and 2016) 7.6.4 Main Business/Business Overview 7.7 BIOBASE 7.7.1 Company Basic Information, Manufacturing Base and Its Competitors 7.7.2 Immune Analysis System Product Type, Application and Specification 126.96.36.199 Type I 188.8.131.52 Type II 7.7.3 BIOBASE Immune Analysis System Production, Revenue, Price and Gross Margin (2015 and 2016) 7.7.4 Main Business/Business Overview 7.8 HYBIOME 7.8.1 Company Basic Information, Manufacturing Base and Its Competitors 7.8.2 Immune Analysis System Product Type, Application and Specification 184.108.40.206 Type I 220.127.116.11 Type II 7.8.3 HYBIOME Immune Analysis System Production, Revenue, Price and Gross Margin (2015 and 2016) 7.8.4 Main Business/Business Overview 7.9 CIOM MEDICAL 7.9.1 Company Basic Information, Manufacturing Base and Its Competitors 7.9.2 Immune Analysis System Product Type, Application and Specification 18.104.22.168 Type I 22.214.171.124 Type II 7.9.3 CIOM MEDICAL Immune Analysis System Production, Revenue, Price and Gross Margin (2015 and 2016) 7.9.4 Main Business/Business Overview 7.10 PERLONG 7.10.1 Company Basic Information, Manufacturing Base and Its Competitors 7.10.2 Immune Analysis System Product Type, Application and Specification 126.96.36.199 Type I 188.8.131.52 Type II 7.10.3 PERLONG Immune Analysis System Production, Revenue, Price and Gross Margin (2015 and 2016) 7.10.4 Main Business/Business Overview 7.11 SIMENS 7.12 BECKMAN COULTER 7.13 GeteinBiotech 7.14 ThermoFisher 7.15 Fenghua
Agency: Cordis | Branch: FP7 | Program: MC-ITN | Phase: FP7-PEOPLE-ITN-2008 | Award Amount: 1.91M | Year: 2009
We will educate and train young scientists to apply an interdisciplinary systems-based approach to complex biological questions using plant reproduction as a model system. Systems approaches are not routinely taught, have been identified as an area requiring urgent PhD-level training in some European countries and require network-scale working practices. We have assembled a team with International research reputations in two key areas for the success of this project: half are experimental and half are computational/mathematical scientists. We will place 9 PhD students under the supervision of this team, focussed on this single biological problem. Students assigned to experimental groups will use advanced techniques to generate data for the computational groups. The computational students will inform, analyse, interpret and model the data and their models will be validated by the experimental groups. A series of laboratory placements will ensure a wide range of subject-specific training and exchanges between the experimental and computational groups will lead to a greater understanding at the interface of these disciplines. The integration of a team of PhD students into this project, so that they each see their contributions as essential and integral parts of the success of the strategy, combined with the supervison and co-ordinated discipline-specific and generic training schedule will produce a cohort of young scientists trained in systems biology. The core skills and approaches that will be instilled into the young scientists and the integration of industry into the project, will equip them to take a systems approach to any biological question and prepare them for a career in an academic or industrial environment. Scientific outcomes will include the use of advanced techniques to provide the quantity and quality of data required to model floral regulation, the use of computational approaches to generate predictive models and their experimental validation.
Agency: Cordis | Branch: FP7 | Program: BSG-SME | Phase: SME-1 | Award Amount: 2.23M | Year: 2010
The activity of genes is absolutely essential for all life from viruses and bacteria to crops and human beings. Despite the many technological breakthroughs within life science research during the last 20-30 years, we are however still far away from fully understanding the activity of genes and which factor influence (regulates) the gene activity. Such knowledge is, by nature, of very high importance and very high value to life science researchers globally, and the ANGS project consortium will therefore develop a suite of algorithms, methods, and software tools that are significantly better at analyzing and understanding gene regulation than what exists today. The consortium consists of three SMEs that all are poised to take advantage of the new developments in genome sequencing (CLC bio, BIOBASE, and deCODE genetics), and 4 academic partners (Oxford University, Goettingen University, Renyi Institute, and NCSB). The SMEs will commercialize the results to help ensure that EU companies are established among the world leaders within solutions for analysis of gene regulation. The primary focus of the consortium will be to develop methods for including the massive amounts of genomic data that is being generated using the revolutionary Next Generation Sequencing technologies an amount of data that will increase exponentially in the coming years, and that is virtually impossible to analyze with any reasonable success by existing methods and in existing software. The software suite, the ANGS engine will thus make it possible to include up to a thousand genomes, e.g. from the 10,000 genome project as knowledge input in gene regulation analysis. Such software will be able to provide completely new knowledge and will thus have tremendous value to life science researcher globally, including pharmaceutical companies, biotech companies, agricultural companies, biofuel companies, research hospitals, as well as universities and governmental research organizations
Kaufmann K.,Business Unit Bioscience |
Nagasaki M.,Tokyo Medical University |
Jauregui R.,BIOBASE GmbH
In Silico Biology | Year: 2010
We present a dynamical model of the gene network controlling flower development in Arabidopsis thaliana. The network is centered at the regulation of the floral organ identity genes (AP1, AP2, AP3, PI and AG) and ends with the transcription factor complexes responsible for differentiation of floral organs. We built and simulated the regulatory interactions that determine organ specificity using an extension of hybrid Petri nets as implemented in Cell Illustrator. The network topology is characterized by two main features: (1) the presence of multiple autoregulatory feedback loops requiring the formation of protein complexes, and (2) the role of spatial regulators determining floral patterning. The resulting network shows biologically coherent expression patterns for the involved genes, and simulated mutants produce experimentally validated changes in organ expression patterns. The requirement of heteromeric higher-order protein complex formation for positive autoregulatory feedback loops attenuates stochastic fluctuations in gene expression, enabling robust organ-specific gene expression patterns. If autoregulation is mediated by monomers or homodimers of proteins, small variations in initial protein levels can lead to biased production of homeotic proteins, ultimately resulting in homeosis. We also suggest regulatory feedback loops involving miRNA loci by which homeotic genes control the activity of their spatial regulators. © 2010-IOS Press and Bioinformation Systems e.V. and the authors. All rights reserved.
Doetsch M.,Helmholtz Center for Infection Research |
Doetsch M.,University of Vienna |
Gluch A.,Helmholtz Center for Infection Research |
Gluch A.,BIOBASE GmbH |
And 5 more authors.
PLoS ONE | Year: 2012
Evidence is presented for the involvement of the interplay between transcription factor Yin Yang 1 (YY1) and poly(ADP-ribose) polymerase-1 (PARP-1) in the regulation of mouse PARP-1 gene (muPARP-1) promoter activity. We identified potential YY1 binding motifs (BM) at seven positions in the muPARP-1 core-promoter (-574/+200). Binding of YY1 was observed by the electrophoretic supershift assay using anti-YY1 antibody and linearized or supercoiled forms of plasmids bearing the core promoter, as well as with 30 bp oligonucleotide probes containing the individual YY1 binding motifs and four muPARP-1 promoter fragments. We detected YY1 binding to BM1 (-587/-558), BM4 (-348/-319) and a very prominent association with BM7 (+86/+115). Inspection of BM7 reveals overlap of the muPARP-1 translation start site with the Kozak sequence and YY1 and PARP-1 recognition sites. Site-directed mutagenesis of the YY1 and PARP-1 core motifs eliminated protein binding and showed that YY1 mediates PARP-1 binding next to the Kozak sequence. Transfection experiments with a reporter gene under the control of the muPARP-1 promoter revealed that YY1 binding to BM1 and BM4 independently repressed the promoter. Mutations at these sites prevented YY1 binding, allowing for increased reporter gene activity. In PARP-1 knockout cells subjected to PARP-1 overexpression, effects similar to YY1 became apparent; over expression of YY1 and PARP-1 revealed their synergistic action. Together with our previous findings these results expand the PARP-1 autoregulatory loop principle by YY1 actions, implying rigid limitation of muPARP-1 expression. The joint actions of PARP-1 and YY1 emerge as important contributions to cell homeostasis. © 2012 Doetsch et al.
Pachov G.V.,Heidelberg Institute for Theoretical Studies HITS GGmbH |
Gabdoulline R.R.,Heidelberg Institute for Theoretical Studies HITS GGmbH |
Gabdoulline R.R.,University of Heidelberg |
Gabdoulline R.R.,BIOBASE GmbH |
Wade R.C.,Heidelberg Institute for Theoretical Studies HITS GGmbH
Nucleic Acids Research | Year: 2011
Several different models of the linker histone (LH)-nucleosome complex have been proposed, but none of them has unambiguously revealed the position and binding sites of the LH on the nucleosome. Using Brownian dynamics-based docking together with normal mode analysis of the nucleosome to account for the flexibility of two flanking 10bp long linker DNAs (L-DNA), we identified binding modes of the H5-LH globular domain (GH5) to the nucleosome. For a wide range of nucleosomal conformations with the L-DNA ends less than 65 apart, one dominant binding mode was identified for GH5 and found to be consistent with fluorescence recovery after photobleaching (FRAP) experiments. GH5 binds asymmetrically with respect to the nucleosomal dyad axis, fitting between the nucleosomal DNA and one of the L-DNAs. For greater distances between L-DNA ends, docking of GH5 to the L-DNA that is more restrained and less open becomes favored. These results suggest a selection mechanism by which GH5 preferentially binds one of the L-DNAs and thereby affects DNA dynamics and accessibility and contributes to formation of a particular chromatin fiber structure. The two binding modes identified would, respectively, favor a tight zigzag chromatin structure or a loose solenoid chromatin fiber. © 2011 The Author(s).
Alamanova D.,BIOBASE GmbH |
Stegmaier P.,BIOBASE GmbH |
Kel A.,BIOBASE GmbH
BMC Bioinformatics | Year: 2010
Background: Knowledge of transcription factor-DNA binding patterns is crucial for understanding gene transcription. Numerous DNA-binding proteins are annotated as transcription factors in the literature, however, for many of them the corresponding DNA-binding motifs remain uncharacterized.Results: The position weight matrices (PWMs) of transcription factors from different structural classes have been determined using a knowledge-based statistical potential. The scoring function calibrated against crystallographic data on protein-DNA contacts recovered PWMs of various members of widely studied transcription factor families such as p53 and NF-κB. Where it was possible, extensive comparison to experimental binding affinity data and other physical models was made. Although the p50p50, p50RelB, and p50p65 dimers belong to the same family, particular differences in their PWMs were detected, thereby suggesting possibly different in vivo binding modes. The PWMs of p63 and p73 were computed on the basis of homology modeling and their performance was studied using upstream sequences of 85 p53/p73-regulated human genes. Interestingly, about half of the p63 and p73 hits reported by the Match algorithm in the altogether 126 promoters lay more than 2 kb upstream of the corresponding transcription start sites, which deviates from the common assumption that most regulatory sites are located more proximal to the TSS. The fact that in most of the cases the binding sites of p63 and p73 did not overlap with the p53 sites suggests that p63 and p73 could influence the p53 transcriptional activity cooperatively. The newly computed p50p50 PWM recovered 5 more experimental binding sites than the corresponding TRANSFAC matrix, while both PWMs showed comparable receiver operator characteristics.Conclusions: A novel algorithm was developed to calculate position weight matrices from protein-DNA complex structures. The proposed algorithm was extensively validated against experimental data. The method was further combined with Homology Modeling to obtain PWMs of factors for which crystallographic complexes with DNA are not yet available. The performance of PWMs obtained in this work in comparison to traditionally constructed matrices demonstrates that the structure-based approach presents a promising alternative to experimental determination of transcription factor binding properties. © 2010 Alamanova et al; licensee BioMed Central Ltd.
Gabdoulline R.,Heinrich Heine University Düsseldorf |
Eckweiler D.,Helmholtz Center for Infection Research |
Kel A.,GeneXplain GmbH |
Kel A.,BIOBASE GmbH |
And 2 more authors.
Nucleic Acids Research | Year: 2012
We present the webserver 3D transcription factor (3DTF) to compute position-specific weight matrices (PWMs) of transcription factors using a knowledge-based statistical potential derived from crystallographic data on protein-DNA complexes. Analysis of available structures that can be used to construct PWMs shows that there are hundreds of 3D structures from which PWMs could be derived, as well as thousands of proteins homologous to these. Therefore, we created 3DTF, which delivers binding matrices given the experimental or modeled protein-DNA complex. The webserver can be used by biologists to derive novel PWMs for transcription factors lacking known binding sites and is freely accessible at http://www.gene-regulation.com/pub/programs/ 3dtf/. © 2012 The Author(s).
Agency: Cordis | Branch: FP7 | Program: CP-IP | Phase: HEALTH-2007-1.1-1 | Award Amount: 14.82M | Year: 2008
The GEN2PHEN project aims to unify human and model organism genetic variation databases towards increasingly holistic views into Genotype-To-Phenotype (G2P) data, and to link this system into other biomedical knowledge sources via genome browser functionality. The project will establish the technological building-blocks needed for the evolution of todays diverse G2P databases into a future seamless G2P biomedical knowledge environment. The project will then utilise these elements to construct an operational first-version of that knowledge environment, by the projects end. This will consist of a European-centred but globally-networked hierarchy of bioinformatics GRID-linked databases, tools and standards, all tied into the Ensembl genome browser. The project has the following specific objectives: 1) To analyse the G2P field and thus determine emerging needs and practices; 2) To develop key standards for the G2P database field; 3) To create generic database components, services, and integration infrastructures for the G2P database domain; 4) To create search modalities and data presentation solutions for G2P knowledge; 5) To facilitate the process of populating G2P databases; 6) To build a major G2P internet portal; 7) To deploy GEN2PHEN solutions to the community; 8) To address system durability and long-term financing; 9) To undertake a whole-system utility and validation pilot study GEN2PHEN Consortium members have been selected from a talented pool of European research groups and companies that are interested in the G2P database challenge. Additionally, a few non-EU participants have been included to bring extra capabilities to the initiative. The final constellation is characterised by broad and proven competence, a network of established working relationships, and high-level roles/connections within other significant projects in this domain.