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In silico prediction of ErbB signal activation from receptor expression profiles through a data analytics pipeline

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dc.contributor.author Arya, A D
dc.contributor.author Elizabeth, Jacob
dc.date.accessioned 2018-07-26T05:53:29Z
dc.date.available 2018-07-26T05:53:29Z
dc.date.issued 2018-06
dc.identifier.citation Journal of Biosciences, 43(2):295-306 en_US
dc.description.abstract The ErbB signalling pathway has been studied extensively owing to its role in normal physiology and its dysregulation in cancer. Reverse engineering by mathematical models use the reductionist approach to characterize the network components. For an emergent, system-level view of the network, we propose a data analytics pipeline that can learn from the data generated by reverse engineering and use it to re-engineer the system with an agent-based approach. Data from a kinetic model that estimates the parameters by fitting to experiments on cell lines, were encoded into rules, for the interactions of the molecular species (agents) involved in biochemical reactions. The agent model, a digital representation of the cell line system, tracks the activation of ErbB1-3 receptors on binding with ligands, resulting in their dimerization, phosphorylation, trafficking and stimulation of downstream signalling through P13-Akt and Erk pathways. The analytics pipeline has been used to mechanistically link HER expression profile to receptor dimerization and activation of downstream signalling pathways. When applied to drug studies, the efficacy of a drug can be investigated in silico. The anti-tumour activity of Pertuzumab, a monoclonal antibody that inhibits HER2 dimerization, was simulated by blocking 80% of the cellular HER2 available, to observe the effect on signal activation. en_US
dc.language.iso en en_US
dc.publisher Indian Academy of Sciences en_US
dc.subject Agent-based model en_US
dc.subject data analytics en_US
dc.subject ErbB signalling en_US
dc.subject re-engineering en_US
dc.subject reverse engineering en_US
dc.title In silico prediction of ErbB signal activation from receptor expression profiles through a data analytics pipeline en_US
dc.type Article en_US

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    Journal Articles authored by NIIST researchers published in 2018

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