Lessons Learned from Quantitative Dynamical Modeling in Systems Biology

Lessons Learned from Quantitative Dynamical Modeling in Systems Biology

Summary

This paper consideres modelling intracellular interaction networks with ordinary differential equation models (ODEs). Several aspects for robust and efficient estimation of model parameters were investigated.

Study outcomes

In this paper, the following comparisons were performed:

• Outcome O1: The reduction in compuatation time was shown if ODE models are fitted in a parallel implementation
• Outcome O2: The bias of parameter estimation was smaller if error parameters are estimated simultaneously
• Outcome O3:
• Outcome O4:

Study design

Application settings

Three models are investigated:

1. A toy model was used to obtain study outcome Ox
2. The so-called Becker model REF with 16 parameters and 85 experimental data points was used to derive study outcomes Ox and Ox.
3. The so-called Bachmann model REF with 115 paraemters and 541 experimental data points was used to derive study outcomes O1 and

Reference

[Lessons Learned from Quantitative Dynamical Modeling in Systems Biology] Raue A, Schilling M, Bachmann J, Matteson A, Schelke M, et al. (2013) Lessons Learned from Quantitative Dynamical Modeling in Systems Biology. PLOS ONE 8(9): e74335. https://doi.org/10.1371/journal.pone.0074335