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UID:7784@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20160620T140000
DTEND;TZID=Europe/Paris:20160620T160000
DTSTAMP:20241120T204830Z
URL:https://www.i2m.univ-amu.fr/evenements/a-computational-approach-to-the
 -description-of-the-signaling-pathways-involved-in-the-early-steps-of-meta
 stasis-laurence-calzone/
SUMMARY:Laurence Calzone (BCSBC\, Institut Curie\, Paris): A computational 
 approach to the description of the signaling pathways involved in the earl
 y steps of metastasis - Laurence Calzone
DESCRIPTION:Laurence Calzone: Cancer is driven by mutations leading to dysf
 unctions of the complex network of molecular interactions regulating signa
 lling pathways and thus\, affecting multiple cellular functions. Successfu
 l applications of systems biology methods for analysis of high-throughput 
 data require detailed reconstructions of signalling networks amenable for 
 computational analyses. For that purpose\, we have developed a comprehensi
 ve map of molecular mechanisms implicated in cancer\, the “Atlas of Canc
 er Signalling Networks” (ACSN). The resource is combined with tools for 
 map navigation and data visualization in the biological network context\, 
 which constitutes a good starting point when building a mathematical model
  to explain particular datasets.\nUsing the information gathered in ACSN\,
  we have constructed a regulatory network with the purpose of elucidating 
 the role of individual mutations or their combinations affecting the metas
 tatic development. The network was then translated into a logical model th
 at recapitulates published experimental results of known gene perturbation
 s on local invasion and migration processes\, and predict the effect of no
 t yet experimentally assessed mutations. The model has been validated usin
 g experimental data on transcriptome dynamics following TGF-β-dependent i
 nduction of Epithelial to Mesenchymal Transition in lung cancer cell lines
 .\nIn addition\, we have systematically predicted alleviating (masking) an
 d synergistic pairwise genetic interactions between the genes composing th
 e model with respect to the probability of acquiring the metastatic phenot
 ype. The genetic interaction profile for this model reveals putative candi
 dates for targeted therapy able to diminish the probability of metastasis.
  In particular\, we have shown that the double mutation Notch gain-of-func
 tion and p53 loss-of-function has the highest probability to acquire metas
 tasis\, which is in agreement with a recent published experiment in a mous
 e model of gut cancer.\nIn summary\, the mathematical model can recapitula
 te experimental mutations in both cell line and mouse models. Furthermore\
 , the model predicts new gene perturbations that affect the early steps of
  metastasis underlying potential intervention points for innovative therap
 eutic strategies in oncology.\nhttp://u900.curie.fr/fr/profile/laurence-ca
 lzone-00120
ATTACH;FMTTYPE=image/jpeg:https://www.i2m.univ-amu.fr/wp-content/uploads/2
 020/01/Laurence_Calzone.jpg
CATEGORIES:Séminaire,MABioS
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DTSTART:20160327T030000
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