Development of a high-throughput quantitative immunofluorescence method and stochastic modeling of signalling networks
Lead Research Organisation:
Imperial College London
Department Name: Life Sciences
Abstract
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Organisations
People |
ORCID iD |
Michael Stumpf (Principal Investigator) |
Publications
Liepe J
(2013)
Maximizing the information content of experiments in systems biology.
in PLoS computational biology
Mc Mahon SS
(2014)
Information theory and signal transduction systems: from molecular information processing to network inference.
in Seminars in cell & developmental biology
Michailovici I
(2014)
Nuclear to cytoplasmic shuttling of ERK promotes differentiation of muscle stem/progenitor cells.
in Development (Cambridge, England)
Silk D
(2014)
Model selection in systems biology depends on experimental design.
in PLoS computational biology
Toni T
(2012)
Elucidating the in vivo phosphorylation dynamics of the ERK MAP kinase using quantitative proteomics data and Bayesian model selection.
in Molecular bioSystems
Description | We are now able to understand how signals flow through eukaryotic cells much better. |
Exploitation Route | We have received an HFSP grant and several fellowships which continue the collaboration with Japan. |
Sectors | Agriculture, Food and Drink,Healthcare,Pharmaceuticals and Medical Biotechnology |
Description | This has been a phenomenal research training opportunity and allowed most members of my group to spend some time in a leading international systems biology experimental group. We have also gained much better understanding of how signalling across MAPK works. |
First Year Of Impact | 2011 |
Sector | Agriculture, Food and Drink,Digital/Communication/Information Technologies (including Software),Education,Healthcare,Pharmaceuticals and Medical Biotechnology |
Impact Types | Cultural,Economic,Policy & public services |
Description | HFSP |
Amount | $1,200,000 (USD) |
Organisation | Human Frontier Science Program (HFSP) |
Sector | Charity/Non Profit |
Country | France |
Start | 01/2012 |
End | 02/2015 |
Title | InformationMeasures.jl |
Description | A Julia package to infer gene regulatory networks using information theoretical approaches. |
Type Of Technology | Software |
Year Produced | 2016 |
Impact | This is a very fast (up to 500 times faster than current R packages) and accurate means of applying bi- and multi-variate information theoretical measures. |
URL | https://github.com/Tchanders/InformationMeasures.jl |