Evan J. Molinelli\u00a0equal contributor,\u00a0Anil Korkut\u00a0equal contributor,\u00a0Weiqing Wang\u00a0equal contributor,\u00a0Martin L. Miller,\u00a0Nicholas P. Gauthier,\u00a0Xiaohong Jing,\u00a0Poorvi Kaushik,\u00a0Qin He,\u00a0Gordon Mills,\u00a0David B. Solit,\u00a0Christine A. Pratilas,\u00a0Martin Weigt,\u00a0Alfredo Braunstein,\u00a0Andrea Pagnani,\u00a0Riccardo Zecchina,\u00a0Chris Sander<\/p>\n
Abstract:<\/strong><\/p>\n We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived\u00a0de novo<\/em>, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology.<\/p>\n <\/p>\n","protected":false},"excerpt":{"rendered":" Perturbation Biology: Inferring Signaling Networks in Cellular Systems Evan J. Molinelli\u00a0equal contributor,\u00a0Anil Korkut\u00a0equal contributor,\u00a0Weiqing Wang\u00a0equal contributor,\u00a0Martin L. Miller,\u00a0Nicholas P. Gauthier,\u00a0Xiaohong Jing,\u00a0Poorvi Kaushik,\u00a0Qin He,\u00a0Gordon Mills,\u00a0David B. Solit,\u00a0Christine A. Pratilas,\u00a0Martin Weigt,\u00a0Alfredo Braunstein,\u00a0Andrea Pagnani,\u00a0Riccardo Zecchina,\u00a0Chris Sander Abstract: We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may […]<\/p>\n","protected":false},"author":88,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[3,13],"tags":[17,15,18,16,14,4],"_links":{"self":[{"href":"https:\/\/blogs.discovery.wisc.edu\/sysbiojournalclub\/wp-json\/wp\/v2\/posts\/89"}],"collection":[{"href":"https:\/\/blogs.discovery.wisc.edu\/sysbiojournalclub\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.discovery.wisc.edu\/sysbiojournalclub\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.discovery.wisc.edu\/sysbiojournalclub\/wp-json\/wp\/v2\/users\/88"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.discovery.wisc.edu\/sysbiojournalclub\/wp-json\/wp\/v2\/comments?post=89"}],"version-history":[{"count":2,"href":"https:\/\/blogs.discovery.wisc.edu\/sysbiojournalclub\/wp-json\/wp\/v2\/posts\/89\/revisions"}],"predecessor-version":[{"id":91,"href":"https:\/\/blogs.discovery.wisc.edu\/sysbiojournalclub\/wp-json\/wp\/v2\/posts\/89\/revisions\/91"}],"wp:attachment":[{"href":"https:\/\/blogs.discovery.wisc.edu\/sysbiojournalclub\/wp-json\/wp\/v2\/media?parent=89"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.discovery.wisc.edu\/sysbiojournalclub\/wp-json\/wp\/v2\/categories?post=89"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.discovery.wisc.edu\/sysbiojournalclub\/wp-json\/wp\/v2\/tags?post=89"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}