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SUMMARY:‘Executable Disease Networks: Reconstruction\, Topology\, Dynami
 cs’ - Anna Niarakis\, Université Paris-Saclay\, Evry\, France
DTSTART:20181204T113000Z
DTEND:20181204T123000Z
UID:TALK115519@talks.cam.ac.uk
CONTACT:Mala Jayasundera
DESCRIPTION:*ABSTRACT:* \nBiological processes rely on the concerted inter
 actions and regulations of thousands of molecules that form complex molecu
 lar and signalling networks. The analysis of their structure and organizat
 ion can reveal interesting topological properties that shed light onto the
  basic mechanisms that control normal cellular processes. Disruption and d
 ysregulation of these networks can lead to disease. Therefore\, the mappin
 g and accurate representation of pathways implicated\, is a primary but es
 sential step for elucidating the mechanisms underlying disease pathogenesi
 s. Disease maps have been an emerging concept as a useful and intuitive wa
 y of describing disease mechanisms in a systematic fashion. Based on infor
 mation mining\, human curation and experts’ advice\, they summarize curr
 ent biological pathway knowledge in a standard\, comprehensive representat
 ion that is both human and machine readable. Disease maps can serve as tem
 plates for visualization and analysis of omic datasets\, or they can be an
 alysed in terms of their underlying network structure. However\, their sta
 tic nature provides relatively limited understanding concerning the emergi
 ng behaviour of the system under different conditions. Computational model
 ling can reveal dynamical properties of the network by in silico simulatio
 ns and perturbations and can be further used for hypotheses testing and pr
 edictions.\n\nIn this talk I will present our efforts to establish an auto
 mated pipeline starting from a fully detailed Disease map and its analysis
  as a complex network\, all the way to the automated inference of a dynami
 cal (Boolean) model\, based on network topology and semantics\, creating t
 hus “executable” disease networks. I will use Rheumatoid Arthritis as 
 case study.\n\nI will also talk briefly about our efforts to couple signal
 ling networks based on prior knowledge with data-driven co-regulatory netw
 orks inferred from transcriptomic datasets in order to find synthetically 
 lethal partners of integrin antagonists\, in the case of Glioblastoma. \n
LOCATION:Sackler Lecture Theatre (Level 7)\, Cambridge Institute for Medic
 al Research
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