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

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    • Keywords


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

    • Abstract


      The ErbB signalling pathway has been studied extensively owing to its role in normal physiology and its dysregulation incancer. 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 datagenerated by reverse engineering and use it to re-engineer the system with an agent-based approach. Data from a kineticmodel that estimates the parameters by fitting to experiments on cell lines, were encoded into rules, for the interactions ofthe molecular species (agents) involved in biochemical reactions. The agent model, a digital representation of the cell linesystem, 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 beenused to mechanistically link HER expression profile to receptor dimerization and activation of downstream signallingpathways. When applied to drug studies, the efficacy of a drug can be investigated in silico. The anti-tumour activity ofPertuzumab, a monoclonal antibody that inhibits HER2 dimerization, was simulated by blocking 80% of the cellular HER2available, to observe the effect on signal activation.

    • Author Affiliations



      1. Computational Modelling and Simulation Unit, Council of Scientific and Industrial Research, National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Thiruvananthapuram 695 019, India
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    • Supplementary Material

  • Journal of Biosciences | News

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      Posted on July 25, 2019

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