# Difference between revisions of "Lessons Learned from Quantitative Dynamical Modeling in Systems Biology"

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== Lessons Learned from Quantitative Dynamical Modeling in Systems Biology == | == Lessons Learned from Quantitative Dynamical Modeling in Systems Biology == | ||

+ | [[https://doi.org/10.1371/journal.pone.0074335: 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 | ||

+ | |||

=== Summary === | === 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. | 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 === | === Study outcomes === | ||

− | In this paper, the following | + | In this paper, the following approaches were compared: |

* Outcome O1: The reduction in compuatation time was shown if ODE models are fitted in a parallel implementation | * 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 O2: The bias of parameter estimation was smaller if error parameters are estimated simultaneously instead of estimating measurement errors as a preprocessing step by averaging over replicates. |

− | * Outcome O3: | + | * Outcome O3: Stochastic optimization algorithms exhibited a weak performance compared to deterministic optimization methods |

− | * Outcome O4: | + | * Outcome O4: Derivatives calculated by sensitivities was superior to "finite differences" |

+ | * Outcome O5: Reparametrization of the model equations improved the performance for one model | ||

+ | |||

+ | The paper discusses further aspects which are outside the benchmarking scope. | ||

+ | |||

− | + | === Application settings === | |

− | |||

Three models are investigated: | Three models are investigated: | ||

# A toy model was used to obtain study outcome Ox | # A toy model was used to obtain study outcome Ox | ||

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− | ==== | + | === Study design and evidence level === |

+ | The following aspects should be considered if the level of evidence is assessd. | ||

+ | |||

+ | ==== Outcome O3 ==== | ||

+ | * Two application models (Becker and Bachmann) were used for this outcome | ||

+ | * Untuned, standard configuration parameters were used for stochastic optimization | ||

+ | |||

+ | ==== Outcome O4 ==== | ||

+ | * Two application models (Becker and Bachmann) were used for this outcome | ||

+ | * The observed performance benefit could be explained by illstrating non-smooth outcomes for finite differences if a parameter is varied and by showing a dependency on the finite difference step-size. Both issues did not occur for the solution of sensitivity equations. | ||

+ | |||

+ | ==== Outcome O5 ==== | ||

+ | * Two application models (Becker and Bachmann) were used for this outcome, but the performance benefit was only visible for the Bachmann model | ||

+ | |||

+ | === Further References === | ||

+ | V. Becker, M. Schilling, J. Bachmann, U. Baumann, A. Raue, T. Maiwald, J. Timmer, U. Klingmueller. | ||

+ | Covering a broad dynamic range: Information processing at the erythropoietin receptor. Science 328, 2010, 1404-1408 | ||

− | + | J. Bachmann, A. Raue, M. Schilling, M. BÃ¶hm, A.C. Pfeifer, C. Kreutz, D. Kaschek, H. Busch, N. Gretz, W.D. Lehmann, J. Timmer, U. Klingmueller. | |

− | + | Division of labor by dual feedback regulators controls JAK2/STAT5 signaling over broad ligand range. Mol. Sys. Bio. 7, 2011, 516 | |

− |

## Revision as of 14:31, 7 August 2018

## Contents

## Lessons Learned from Quantitative Dynamical Modeling in Systems Biology

[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

### 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 approaches were compared:

- 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 instead of estimating measurement errors as a preprocessing step by averaging over replicates.
- Outcome O3: Stochastic optimization algorithms exhibited a weak performance compared to deterministic optimization methods
- Outcome O4: Derivatives calculated by sensitivities was superior to "finite differences"
- Outcome O5: Reparametrization of the model equations improved the performance for one model

The paper discusses further aspects which are outside the benchmarking scope.

### 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

### Study design and evidence level

The following aspects should be considered if the level of evidence is assessd.

#### Outcome O3

- Two application models (Becker and Bachmann) were used for this outcome
- Untuned, standard configuration parameters were used for stochastic optimization

#### Outcome O4

- Two application models (Becker and Bachmann) were used for this outcome
- The observed performance benefit could be explained by illstrating non-smooth outcomes for finite differences if a parameter is varied and by showing a dependency on the finite difference step-size. Both issues did not occur for the solution of sensitivity equations.

#### Outcome O5

- Two application models (Becker and Bachmann) were used for this outcome, but the performance benefit was only visible for the Bachmann model

### Further References

V. Becker, M. Schilling, J. Bachmann, U. Baumann, A. Raue, T. Maiwald, J. Timmer, U. Klingmueller. Covering a broad dynamic range: Information processing at the erythropoietin receptor. Science 328, 2010, 1404-1408

J. Bachmann, A. Raue, M. Schilling, M. BÃ¶hm, A.C. Pfeifer, C. Kreutz, D. Kaschek, H. Busch, N. Gretz, W.D. Lehmann, J. Timmer, U. Klingmueller. Division of labor by dual feedback regulators controls JAK2/STAT5 signaling over broad ligand range. Mol. Sys. Bio. 7, 2011, 516