Efficient Parameter Estimation Enables the Prediction of Drug Response Using a Mechanistic Pan-Cancer Pathway Model

Fröhlich, Fabian; Keßler, T.; Weindl, Daniel; Shadrin, Alexey; Schmiester, Leonard; Hache, Hendrik; Muradyan, Artur; Schütte, Moritz; Lim, Jin Hee; Heinig, Matthias; Theis, Fabian J.; Lehrach, Hans; Wierling, Christoph; Lange, Bodo; Hasenauer, Jan

doi:10.1016/j.cels.2018.10.013

Cited by 126 publications

(185 citation statements)

References 82 publications

(103 reference statements)

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“…In Loos et al (2018), hierarchical optimization was performed using objective gradients computed via forward sensitivity analysis. However, for large-scale models adjoint sensitivity analysis has shown to be orders of magnitude faster (Fröhlich et al, 2018), because essentially here the evaluation of state sensitivities is circumvented by defining an adjoint state p ∈ R nx which does not scale in the number of parameters (Fröhlich et al, 2017b).…”

Section: Combining Hierarchical Optimization and Adjoint Sensitivity mentioning

confidence: 99%

“…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;Fröhlich et al, 2018). In Fröhlich et al (2018), we demonstrated how such a large-scale mechanistic model integrating various cancer-related signaling pathways is able to, e.g., predict the response of cancer cells to drug combinations based on measurements for single treatment responses, a task which is commonly not possible with statistical models.…”

Section: Introductionmentioning

confidence: 99%

“…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;Fröhlich et al, 2018). In Fröhlich et al (2018), we demonstrated how such a large-scale mechanistic model integrating various cancer-related signaling pathways is able to, e.g., predict the response of cancer cells to drug combinations based on measurements for single treatment responses, a task which is commonly not possible with statistical models. Overall, mechanistic models can pave the way to personalized medicine by integrating patient specific information, and thus creating virtual patients (Kühn and Lehrach, 2012;Ogilvie et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Scalable methods have been developed to address the problem of computational complexity, e.g. adjoint sensitivity analysis (Fujarewicz et al, 2005;Lu et al, 2012;Fröhlich et al, 2017b) and parallelization (Penas et al, 2015;Fröhlich et al, 2018). Complementary, large-scale transcriptomics, proteomics and pharmacological datasets have been acquired and have been made publicly available in databases such as the Cancer Cell Line Encyclopedia (CCLE) (Barretina et al, 2012), the Genomics of Drug Sensitivity in Cancer (GDSC) project (Eduati et al, 2017) and the MD Anderson Cell Lines Project (MCLP) (Li et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Our method is more general and achieves a better scaling behaviour than the existing ones (Weber et al, 2011;Loos et al, 2018). We apply it to estimate parameters for the large-scale pan-cancer signaling model from Fröhlich et al (2018).…”

Section: Introductionmentioning

confidence: 99%

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

Schmiester

Schälte

Froehlich

et al. 2019

Preprint

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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

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Section: Combining Hierarchical Optimization and Adjoint Sensitivity mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Schmiester

Schälte

Froehlich

et al. 2019

Preprint

Self Cite

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Connecting Logical Models to Omics Data

Béal

Rémy

Calzone

2023

Symbolic Approaches to Modeling and Analysis of Biological Systems

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Systems biology of angiogenesis signaling: Computational models and omics

Zhang

Wang

Oliveira

et al. 2021

WIREs Mechanisms of Disease

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Angiogenesis is a highly regulated multiscale process that involves a plethora of cells, their cellular signal transduction, activation, proliferation, differentiation, as well as their intercellular communication. The coordinated execution and integration of such complex signaling programs is critical for physiological angiogenesis to take place in normal growth, development, exercise, and wound healing, while its dysregulation is critically linked to many major human diseases such as cancer, cardiovascular diseases, and ocular disorders; it is also crucial in regenerative medicine. Although huge efforts have been devoted to drug development for these diseases by investigation of angiogenesis‐targeted therapies, only a few therapeutics and targets have proved effective in humans due to the innate multiscale complexity and nonlinearity in the process of angiogenic signaling. As a promising approach that can help better address this challenge, systems biology modeling allows the integration of knowledge across studies and scales and provides a powerful means to mechanistically elucidate and connect the individual molecular and cellular signaling components that function in concert to regulate angiogenesis. In this review, we summarize and discuss how systems biology modeling studies, at the pathway‐, cell‐, tissue‐, and whole body‐levels, have advanced our understanding of signaling in angiogenesis and thereby delivered new translational insights for human diseases. This article is categorized under: Cardiovascular Diseases > Computational Models Cancer > Computational Models

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Efficient Parameter Estimation Enables the Prediction of Drug Response Using a Mechanistic Pan-Cancer Pathway Model

Cited by 126 publications

References 82 publications

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

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

Connecting Logical Models to Omics Data

Systems biology of angiogenesis signaling: Computational models and omics

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