NETWORK EVOLUTION
Integrated evolutionary analyses of genetic and drug interaction networks in yeast
| Coordinatore | MAGYAR TUDOMANYOS AKADEMIA SZEGEDI BIOLOGIAI KOZPONTJA
Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie. |
| Nazionalità Coordinatore | Hungary [HU] |
| Totale costo | 1˙280˙000 € |
| EC contributo | 1˙280˙000 € |
| Programma | FP7-IDEAS-ERC
Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) |
| Code Call | ERC-2007-StG |
| Funding Scheme | ERC-SG |
| Anno di inizio | 2008 |
| Periodo (anno-mese-giorno) | 2008-07-01 - 2013-06-30 |
Partecipanti
| # | ||||
|---|---|---|---|---|
| 1 |
MAGYAR TUDOMANYOS AKADEMIA SZEGEDI BIOLOGIAI KOZPONTJA
Organization address
address: Temesvari krt. 62 contact info |
HU (SZEGED) | hostInstitution | 0.00 |
| 2 |
MAGYAR TUDOMANYOS AKADEMIA SZEGEDI BIOLOGIAI KOZPONTJA
Organization address
address: Temesvari krt. 62 contact info |
HU (SZEGED) | hostInstitution | 0.00 |
Mappa
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Obiettivo del progetto (Objective)
'The ability of cellular systems to adapt to genetic and environmental perturbations is a fundamental but poorly understood process both at the molecular and evolutionary level. There are both physiological and evolutionary reasonings why mutations often have limited impact on cellular growth. First, perturbations that hit one target often have no effect on the overall performance of a complex system (such as metabolic networks), as perturbations can be adjusted by reorganizing fluxes in metabolic networks, or changing regulation and expression of genes. Second, due to the fast evolvability of microbes, the effect of a perturbation can readily be alleviated by the evolution of compensatory mutations at other sites of the network. Understanding the extent of intrinsic and evolved robustness in cellular systems demands integrated analyses that combine functional genomics and computational systems biology with microbial evolutionary experiments. In collaboration with several leading research teams in the field, we plan to investigate the following issues. First, we will ask how accurately genome-scale metabolic network models can predict the impact of genetic deletions and other non-heritable perturbations. Second, to understand how the impact of genetic and drug perturbations can be mitigated during evolution, we will pursue a large-scale lab evolutionary protocol, and compare the results with predictions of computational models. Our work may suggest avenues of research on the general rules of acquired drug resistance in microbes.'