A comparison of methods for quantifying prediction uncertainty in systems biology

Revision as of 13:27, 25 February 2020 by Bwday (talk | contribs) (Design for Outcome O2)


Villaverde, Alejandro F., et al. "A comparison of methods for quantifying prediction uncertainty in systems biology." IFAC-PapersOnLine 52.26 (2019): 45-51.

Permanent link to the paper


Three methods for quantifying prediction uncertainty in ODE models are assessed. Here, prediction uncertainty does not refer to estimated parameters, but to the uncertatinty of state trajectories. The three methods are: Fisher Information Matrix (FIM), Prediction Posetrior (PP), Ensemble Consensus (ENS).

Study outcomes

Outcome O1

For a small, fully-observed ODE model (α-pinene), all three methods yield nearly same results consistent with the known true trajectories. For a larger, only-partially observed ODE model (JAK2/STAT5), PP and ENS yield better accuracy than FIM. However, even for PP and ENS, confidence levels do not cover the truth.

Outcome O2

The computational cost of the three models is differing, especially for large problems: FIM (small), ENS (intermediate), PP (high)

Study design and evidence level

General aspects

Synthetic data is generated for two examplary ODE models (one smaller, one larger), given a true parameter set. The sample correlation coefficient is used to quantify agreement between predicted and true state trajectories.

Design for Outcome O1

Sample correlation coefficient is compared for the three different methods for the two different models, respectively.

Further comments and aspects


The list of cited or related literature is placed here.