# Lessons Learned from Quantitative Dynamical Modeling in Systems Biology

## Contents

## 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:

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

#### Evidence level

### 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