Fit-preserving refinement of the ErbB signalling pathway

Bogdan Iancu, Muhammad Usman Sanwal, Cristian Gratie, Ion Petre

Research output: Book/Journal/ReportCommissioned reportProfessional

Abstract

The construction of large scale biological models is a laborious task, which is
often addressed by adopting iterative routines for model augmentation, adding
certain details to an initial high level abstraction of the biological phenomenon of
interest. However, refitting a model at every step of its development is time consuming
and computationally intensive. In this context, fit-preserving data refinement
brings about an effective alternative by providing adequate parameter values
that ensure fit preservation at every refinement step. We address here the implementation
of fit-preserving data refinement for a model of the ErbB signalling
pathway, which is extended to include four different types of receptor tyrosine
kinases, ErbB1-4, and two types of ligands, EGF and HRG. We build an extensive
model, which ensures a good fit by construction with notably less effort than what
a parameter estimation routine would require.
Original languageEnglish
PublisherTurku Centre for Computer Science
Number of pages16
Volume1187
ISBN (Print)ISBN 978-952-12-3582-5
Publication statusPublished - 2017
MoE publication typeD4 Published development or research report or study

Keywords

  • Computational modelling; model construction; refinement; ErbB signalling pathway; ODE-based models; Event-B; invariant.

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