From a801f3c4eef2bafb89ef08b005c56835ec8248ad Mon Sep 17 00:00:00 2001 From: Polina Lakrisenko Date: Thu, 10 Apr 2025 09:06:52 +0200 Subject: [PATCH] add new references --- src/benchmark_refs.bib | 31 +++++++++++++++++++++++++++++++ 1 file changed, 31 insertions(+) diff --git a/src/benchmark_refs.bib b/src/benchmark_refs.bib index e4706c31..aea0aa0f 100644 --- a/src/benchmark_refs.bib +++ b/src/benchmark_refs.bib @@ -1,3 +1,34 @@ +@article{LakrisenkoPat2024, + author = {Lakrisenko, Polina AND Pathirana, Dilan AND Weindl, Daniel AND Hasenauer, Jan}, + journal = {PLOS ONE}, + publisher = {Public Library of Science}, + title = {Benchmarking methods for computing local sensitivities in ordinary differential equation models at dynamic and steady states}, + year = {2024}, + month = {10}, + volume = {19}, + url = {https://doi.org/10.1371/journal.pone.0312148}, + pages = {1-19}, + abstract = {Estimating parameters of dynamic models from experimental data is a challenging, and often computationally-demanding task. It requires a large number of model simulations and objective function gradient computations, if gradient-based optimization is used. In many cases, steady-state computation is a part of model simulation, either due to steady-state data or an assumption that the system is at steady state at the initial time point. Various methods are available for steady-state and gradient computation. Yet, the most efficient pair of methods (one for steady states, one for gradients) for a particular model is often not clear. In order to facilitate the selection of methods, we explore six method pairs for computing the steady state and sensitivities at steady state using six real-world problems. The method pairs involve numerical integration or Newton’s method to compute the steady-state, and—for both forward and adjoint sensitivity analysis—numerical integration or a tailored method to compute the sensitivities at steady-state. Our evaluation shows that all method pairs provide accurate steady-state and gradient values, and that the two method pairs that combine numerical integration for the steady-state with a tailored method for the sensitivities at steady-state were the most robust, and amongst the most computationally-efficient. We also observed that while Newton’s method for steady-state computation yields a substantial speedup compared to numerical integration, it may lead to a large number of simulation failures. Overall, our study provides a concise overview across current methods for computing sensitivities at steady state. While our study shows that there is no universally-best method pair, it also provides guidance to modelers in choosing the right methods for a problem at hand.}, + number = {10}, + doi = {10.1371/journal.pone.0312148}, +} + +@article{DorešićGre2024, + author = {Dorešić, Domagoj and Grein, Stephan and Hasenauer, Jan}, + title = {Efficient parameter estimation for ODE models of cellular processes using semi-quantitative data}, + journal = {Bioinformatics}, + volume = {40}, + number = {Supplement_1}, + pages = {i558-i566}, + year = {2024}, + month = {06}, + abstract = {Quantitative dynamical models facilitate the understanding of biological processes and the prediction of their dynamics. The parameters of these models are commonly estimated from experimental data. Yet, experimental data generated from different techniques do not provide direct information about the state of the system but a nonlinear (monotonic) transformation of it. For such semi-quantitative data, when this transformation is unknown, it is not apparent how the model simulations and the experimental data can be compared.We propose a versatile spline-based approach for the integration of a broad spectrum of semi-quantitative data into parameter estimation. We derive analytical formulas for the gradients of the hierarchical objective function and show that this substantially increases the estimation efficiency. Subsequently, we demonstrate that the method allows for the reliable discovery of unknown measurement transformations. Furthermore, we show that this approach can significantly improve the parameter inference based on semi-quantitative data in comparison to available methods.Modelers can easily apply our method by using our implementation in the open-source Python Parameter EStimation TOolbox (pyPESTO) available at https://github.com/ICB-DCM/pyPESTO.}, + issn = {1367-4811}, + doi = {10.1093/bioinformatics/btae210}, + url = {https://doi.org/10.1093/bioinformatics/btae210}, + eprint = {https://academic.oup.com/bioinformatics/article-pdf/40/Supplement\_1/i558/58354973/btae210.pdf}, +} + @article{RaimundezFed2023, title = {Posterior marginalization accelerates Bayesian inference for dynamical models of biological processes}, journal = {iScience},