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Bram Thijssen, Tjeerd M Dijkstra, Tom Heskes, and Lodewyk F Wessels (2018)

Bayesian data integration for quantifying the contribution of diverse measurements to parameter estimates

Bioinformatics, 34(5):803-811.

Motivation: Computational models in biology are frequently underdetermined, due to limits in our
capacity to measure biological systems. In particular, mechanistic models often contain parameters
whose values are not constrained by a single type ofmeasurement. Itmay be possible to achieve better
model determination by combining the information contained in different types of measurements.
Bayesian statistics provides a convenient framework for this, allowing a quantification of the reduction
in uncertainty with each additional measurement type. We wished to explore whether such integration
is feasible and whether it can allow computationalmodels to bemore accurately determined.
Results: We created an ordinary differential equation model of cell cycle regulation in budding yeast
and integrated data from 13 different studies covering different experimental techniques. We found
that for some parameters, a single type of measurement, relative time course mRNA expression, is
sufficient to constrain them. Other parameters, however, were only constrained when two types of
measurements were combined, namely relative time course and absolute transcript concentration.
Comparing the estimates to measurements from three additional, independent studies, we found that
the degradation and transcription rates indeed matched the model predictions in order of magnitude.
The predicted translation rate was incorrect however, thus revealing a deficiency in the model. Since
this parameter was not constrained by any of the measurement types separately, it was only possible
to falsify the model when integratingmultiple types ofmeasurements. In conclusion, this study shows
that integratingmultiplemeasurement types can allow models to be more accurately determined.
Availability and implementation: The models and files required for running the inference are
included in the Supplementary information.

10.1093/bioinformatics/btx666