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SUMMARY:Plenary Lecture 14: Ecosystems Biology: from data to control of mi
 crobial communities - Wilmes\, P (Universit du Luxembourg)
DTSTART:20141128T093000Z
DTEND:20141128T100500Z
UID:TALK56413@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Co-authors: Emilie Muller (University of Luxembourg)\, Anna He
 intz-Buschart (University of Luxembourg)\, Shaman Narayanasamy (University
  of Luxembourg)\, Cdric Laczny (University of Luxembourg) \n\nMixed microb
 ial communities are complex and dynamic systems. Integrated omics (combine
 d metagenomics\, metatranscriptomics\, metaproteomics and metabolomics) ar
 e currently gaining momentum for detailed descriptions of community struct
 ure\, function and dynamics in situ as well as offering the potential to d
 iscover novel functionalities within the framework of Eco-Systems Biology.
  We have developed an integrative workflow comprising wet- and dry-lab met
 hodologies to enable systematic measurements of microbial communities over
  space and time\, and the integration and analysis of the resulting multi-
 meta-omic data. Two distinct approaches have been developed allowing the d
 econvolution of integrated omic data either at the population- or communit
 y-level. By resolving multi-omic data at the population-level\, we have un
 covered patterns which suggest that in our model microbial community (lipi
 d accumulating microbial consortia from a biological wastewater treatment 
 tank) t he dominance of a microbial generalist is linked to finely tuned r
 esource usage. Analysis of reconstructed metabolic networks has resulted i
 n the identification of possible keystone genes\, analogous to keystone sp
 ecies in species interaction networks. Integrated omics will likely become
  the future standard for the large-scale characterization of microbial con
 sortia within an Eco-Systems Biology framework. In particular\, by integra
 ting information from genome to metabolome\, integrated omics allows decon
 voluton of structure-function relationships by identifying key members and
  functionalities. For example\, identified keystone species and/or genes l
 ikely represent driver nodes which may be exploited in view of future cont
 rol strategies. However\, to test emerging hypotheses and formulate predic
 tive models\, which support such endeavours\, an iterative discovery-drive
 n planning approach is required. This should ultimately allow the manipula
 tion of microbial communities and steer them towards desired outcomes.\n
LOCATION:Seminar Room 1\, Newton Institute
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