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Journal of Biosciences

, Volume 43, Issue 2, pp 295–306 | Cite as

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

  • Arya A Das
  • Elizabeth Jacob
Article

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.

Keywords

Agent-based model data analytics ErbB signalling re-engineering reverse engineering 

Notes

Acknowledgements

This work was supported by a CSIR Network Project on Genomics and Informatics Solutions for Integrating Biology (GENESIS). AAD acknowledges CSIR, for financial support through the Senior Research Fellowship. We thank Jacob Abraham, architecture student at VNIT, Nagpur, India, for creating the artwork of the graphical abstract.

Supplementary material

12038_2018_9747_MOESM1_ESM.docx (848 kb)
Supplementary material 1 (DOCX 847 kb)

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Copyright information

© Indian Academy of Sciences 2018

Authors and Affiliations

  1. 1.Computational Modelling and Simulation Unit, Council of Scientific and Industrial ResearchNational Institute for Interdisciplinary Science and Technology (CSIR-NIIST)ThiruvananthapuramIndia

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