Benchmark Problems for Dynamic Modeling of Intracellular Processes

Schälte

Froehlich

et al. 2019

Preprint

Self Cite

Motivation: 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.Results: 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, phospho-protein 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 largescale models based on heterogeneous relative measurements.Contact: jan.hasenauer@helmholtz-muenchen.de Supplementary information: Supplementary information are available at bioRxiv online.Supplementary code and data are available online at

Section: Normalization Results In Information Lossmentioning

confidence: 99%

Efficient parameterization of large-scale dynamic models based on relative measurements

Schälte

Froehlich

et al. 2019

Preprint

Self Cite

“…In this study, we considered application examples for which quantitative measurements are available and which are included in a collection of benchmark problems for parameter estimation, which facilitates easy reusability (Hass et al, 2019). This enables a comparison of parameter estimation using quantitative and qualitative data.…”

Section: Test Problemsmentioning

confidence: 99%

Statistical inference of mechanistic models from qualitative data using an efficient optimal scaling approach

Weindl

Hasenauer

2019

Preprint

Self Cite

Quantitative dynamical models facilitate the understanding of biological processes and the prediction of their dynamics. These models usually comprise unknown parameters, which have to be inferred from experimental data. For quantitative experimental data, there are several methods and software tools available. However, for qualitative data the available approaches are limited and computationally demanding. Here, we consider the optimal scaling method which has been developed in statistics for categorical data and has been applied to dynamical systems. This approach turns qualitative variables into quantitative ones, accounting for constraints on their relation. We derive a reduced formulation for the optimization problem defining the optimal scaling. The reduced formulation possesses the same optimal points as the established formulation but requires less degrees of freedom. Parameter estimation for dynamical models of cellular pathways revealed that the reduced formulation improves the robustness and convergence of optimizers. This resulted in substantially reduced computation times. We implemented the proposed approach in the open-source Python Parameter EStimation TOolbox (pyPESTO) to facilitate reuse and extension. The proposed approach enables efficient parameterization of quantitative dynamical models using qualitative data.

“…The small-to medium-scale examples for the benchmark study were chosen based on a collection of benchmark models (20). We chose models with different system sizes, which allowed the generation of large artificial datasets, which were sufficiently heterogeneous.…”

Section: Adaptation Of Benchmark Models and Creation Of Artificial Datamentioning

confidence: 99%

Mini-batch optimization enables training of ODE models on large-scale datasets

Stapor

Wierling

et al. 2019

Preprint

Self Cite

Quantitative dynamical models are widely used to study cellular signal processing. A critical step in modeling is the estimation of unknown model parameters from experimental data. As model sizes and datasets are steadily growing, established parameter optimization approaches for mechanistic models become computationally extremely challenging. However, mini-batch optimization methods, as employed in deep learning, have better scaling properties. In this work, we adapt, apply, and benchmark minibatch optimization for ordinary differential equation (ODE) models thereby establishing a direct link between dynamic modeling and machine learning. On our main application example, a large-scale model of cancer signaling, we benchmark mini-batch optimization against established methods, achieving better optimization results and reducing computation by more than an order of magnitude. We expect that our work will serve as a first step towards mini-batch optimization tailored to ODE models and enable modeling of even larger and more complex systems than what is currently possible.