Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/97165
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dc.contributor.authorChua, Huey Engen
dc.contributor.authorZhang, Fanen
dc.contributor.authorZheng, Jieen
dc.contributor.authorMishra, Shital Kumaren
dc.contributor.authorBhowmick, Sourav Sahaen
dc.date.accessioned2015-05-22T02:13:15Zen
dc.date.accessioned2019-12-06T19:39:35Z-
dc.date.available2015-05-22T02:13:15Zen
dc.date.available2019-12-06T19:39:35Z-
dc.date.copyright2015en
dc.date.issued2015en
dc.identifier.citationMishra, S. K., Bhowmick, S. S., Chua, H. E., Zhang, F., & Zheng, J. (2015). Computational cell fate modelling for discovery of rewiring in apoptotic network for enhanced cancer drug sensitivity. BMC systems biology, 9(S4).en
dc.identifier.issn1752-0509en
dc.identifier.urihttps://hdl.handle.net/10356/97165-
dc.identifier.urihttp://hdl.handle.net/10220/25636en
dc.description.abstractThe ongoing cancer research has shown that malignant tumour cells have highly disrupted signalling transduction pathways. In cancer cells, signalling pathways are altered to satisfy the demands of continuous proliferation and survival. The changes in signalling pathways supporting uncontrolled cell growth, termed as rewiring, can lead to dysregulation of cell fates e.g. apoptosis. Hence comparative analysis of normal and oncogenic signal transduction pathways may provide insights into mechanisms of cancer drug-resistance and facilitate the discovery of novel and effective anti-cancer therapies. Here we propose a hybrid modelling approach based on ordinary differential equation (ODE) and machine learning to map network rewiring in the apoptotic pathways that may be responsible for the increase of drug sensitivity of tumour cells in triple-negative breast cancer. Our method employs Genetic Algorithm to search for the most likely network topologies by iteratively generating simulated protein phosphorylation data using ODEs and the rewired network and then fitting the simulated data with real data of cancer signalling and cell fate. Most of our predictions are consistent with experimental evidence from literature. Combining the strengths of knowledge-driven and data-driven approaches, our hybrid model can help uncover molecular mechanisms of cancer cell fate at systems level.en
dc.format.extent12 p.en
dc.language.isoenen
dc.relation.ispartofseriesBMC systems biologyen
dc.rights© 2015 Mishra et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.en
dc.subjectDRNTU::Science::Biological sciencesen
dc.titleComputational cell fate modelling for discovery of rewiring in apoptotic network for enhanced cancer drug sensitivityen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Engineeringen
dc.description.versionPublished versionen
dc.identifier.urlhttp://www.biomedcentral.com/qc/1752-0509/9/S1/S4en
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