Perturbation biology: inferring signaling networks in cellular systems.
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 t...
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2013
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oai:doaj.org-article:b42abf9d37ee45299b42ecb7866bb94f2021-11-18T05:53:17ZPerturbation biology: inferring signaling networks in cellular systems.1553-734X1553-735810.1371/journal.pcbi.1003290https://doaj.org/article/b42abf9d37ee45299b42ecb7866bb94f2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24367245/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358We 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 de novo, 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.Evan J MolinelliAnil KorkutWeiqing WangMartin L MillerNicholas P GauthierXiaohong JingPoorvi KaushikQin HeGordon MillsDavid B SolitChristine A PratilasMartin WeigtAlfredo BraunsteinAndrea PagnaniRiccardo ZecchinaChris SanderPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 12, p e1003290 (2013) |
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| collection |
DOAJ |
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| topic |
Biology (General) QH301-705.5 |
| spellingShingle |
Biology (General) QH301-705.5 Evan J Molinelli Anil Korkut Weiqing Wang Martin L Miller Nicholas P Gauthier Xiaohong Jing Poorvi Kaushik Qin He Gordon Mills David B Solit Christine A Pratilas Martin Weigt Alfredo Braunstein Andrea Pagnani Riccardo Zecchina Chris Sander Perturbation biology: inferring signaling networks in cellular systems. |
| description |
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 de novo, 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. |
| format |
article |
| author |
Evan J Molinelli Anil Korkut Weiqing Wang Martin L Miller Nicholas P Gauthier Xiaohong Jing Poorvi Kaushik Qin He Gordon Mills David B Solit Christine A Pratilas Martin Weigt Alfredo Braunstein Andrea Pagnani Riccardo Zecchina Chris Sander |
| author_facet |
Evan J Molinelli Anil Korkut Weiqing Wang Martin L Miller Nicholas P Gauthier Xiaohong Jing Poorvi Kaushik Qin He Gordon Mills David B Solit Christine A Pratilas Martin Weigt Alfredo Braunstein Andrea Pagnani Riccardo Zecchina Chris Sander |
| author_sort |
Evan J Molinelli |
| title |
Perturbation biology: inferring signaling networks in cellular systems. |
| title_short |
Perturbation biology: inferring signaling networks in cellular systems. |
| title_full |
Perturbation biology: inferring signaling networks in cellular systems. |
| title_fullStr |
Perturbation biology: inferring signaling networks in cellular systems. |
| title_full_unstemmed |
Perturbation biology: inferring signaling networks in cellular systems. |
| title_sort |
perturbation biology: inferring signaling networks in cellular systems. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2013 |
| url |
https://doaj.org/article/b42abf9d37ee45299b42ecb7866bb94f |
| work_keys_str_mv |
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