Student Posters Repository

Ordinary differential equation (ODE) models have become an important tool in systems biology. Mechanistic ODE models allow for the integration and mechanistic interpretation of heterogeneous data. Understanding complex diseases and interpreting multi-omics datasets requires the use of large-scale models with thousands of unknown parameters, the estimation of which is computationally very costly.

We are developing and applying tools for scalable parametrization of large-scale ODE models. Our AMICI toolbox [1] allows for scalable simulation and sensitivity analysis of ODE models. For parameter estimation, we developed a C++/MPI library [2] for scalable parallel model simulation and gradient-based optimization.

The integration of these tools allows us to parametrize models with thousands of states and parameters, a scale which has so far been infeasible in systems biology [3,4]. Such large-scale models could have a huge impact in systems biomedicine by providing in silico predictions to be used for drug target identification, for personalized medicine or even virtual clinical trials.

References:

[1] Advanced Multilanguage Interface to CVODES and IDAS (AMICI), https://github.com/ICB-DCM/AMICI/
[2] Parameter estimation library parPE, https://github.com/ICB-DCM/parPE
[3] Schmiester et al., Efficient parameterization of large-scale dynamic models based on relative measurements, 2019, bioRxiv, 579045.
[4] Fröhlich et al, Efficient Parameter Estimation Enables the Prediction of Drug Response Using a Mechanistic Pan-Cancer Pathway Model, 2018, Cell Systems, 7(6):567-579.e6.