Abstract
Mechanistic models of biochemical reaction networks facilitate the quantitative understanding of biological processes and the integration of heterogeneous datasets. However, some biological processes require the consideration of comprehensive reaction networks and therefore large-scale models. Parameter estimation for such models poses great challenges, in particular when the data are on a relative scale. Here, we propose a novel hierarchical approach combining (i) the efficient analytic evaluation of optimal scaling, offset and error model parameters with (ii) the scalable evaluation of objective function gradients using adjoint sensitivity analysis. We evaluate the properties of the methods by parameterizing a pan-cancer ordinary differential equation model (>1000 state variables, >4000 parameters) using relative protein, phosphoprotein and viability measurements. The hierarchical formulation improves optimizer performance considerably. Furthermore, we show that this approach allows estimating error model parameters with negligible computational overhead when no experimental estimates are available, providing an unbiased way to weight heterogeneous data. Overall, our hierarchical formulation is applicable to a wide range of models, and allows for the efficient parameterization of large-scale models based on heterogeneous relative measurements. Supplementary code and data are available online at http://doi.org/10.5281/zenodo.3254429 and http://doi.org/10.5281/zenodo.3254441. Supplementary data are available at Bioinformatics online.
Highlights
In systems biology, mechanistic ordinary differential equation (ODE) models are widely used to deepen the understanding of biological processes
We evaluate the properties of the methods by parameterizing a pan-cancer ordinary differential equation model (>1000 state variables, >4000 parameters) using relative protein, phosphoprotein and viability measurements
The hierarchical formulation improves optimizer performance considerably. We show that this approach allows estimating error model parameters with negligible computational overhead when no experimental estimates are available, providing an unbiased way to weight heterogeneous data
Summary
Mechanistic ordinary differential equation (ODE) models are widely used to deepen the understanding of biological processes. Applications range from the description of signaling pathways (Klipp et al, 2005) to the prediction of drug responses (Hass et al, 2017) and patient survival (Fey et al, 2015). With the availability of scalable computational methods and increasing computing power, larger and larger models have been developed to capture the intricacies of biological regulatory networks more accurately (Bouhaddou et al, 2018; Frohlich et al, 2018). In Frohlich et al (2018), we demonstrated how such a large-scale.
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