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Fit-Preserving Refinement of the ErbB Signalling Pathway

Bogdan Iancu, Usman Sanwal, Cristian Gratie, Ion Petre, Fit-Preserving Refinement of the ErbB Signalling Pathway. TUCS Technical Reports 1187, TUCS, 2017.

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 signaling 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

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BibTeX entry:

@TECHREPORT{tIaSaGrPe17a,
  title = {Fit-Preserving Refinement of the ErbB Signalling Pathway},
  author = {Iancu, Bogdan and Sanwal, Usman and Gratie, Cristian and Petre, Ion},
  number = {1187},
  series = {TUCS Technical Reports},
  publisher = {TUCS},
  year = {2017},
}

Belongs to TUCS Research Unit(s): Computational Biomodeling Laboratory (Combio Lab)

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