more

documentation/amici_refs.bib

@@ -864,6 +864,27 @@ @Article{VanhoeferMat2021
   url       = {https://doi.org/10.21105/joss.03215},
 }
 
+@Misc{VillaverdePat2021,
+  author        = {Alejandro F. Villaverde and Dilan Pathirana and Fabian Fröhlich and Jan Hasenauer and Julio R. Banga},
+  title         = {A protocol for dynamic model calibration},
+  year          = {2021},
+  archiveprefix = {arXiv},
+  eprint        = {2105.12008},
+  primaryclass  = {q-bio.QM},
+}
+@article {Froehlich2021.05.20.445065,
+	author = {Fr{\"o}hlich, Fabian and Sorger, Peter K.},
+	title = {Fides: Reliable Trust-Region Optimization for Parameter Estimation of Ordinary Differential Equation Models},
+	elocation-id = {2021.05.20.445065},
+	year = {2021},
+	doi = {10.1101/2021.05.20.445065},
+	publisher = {Cold Spring Harbor Laboratory},
+	abstract = {Motivation Because they effectively represent mass action kinetics, ordinary differential equation models are widely used to describe biochemical processes. Optimization-based calibration of these models on experimental data can be challenging, even for low-dimensional problems. However, reliable model calibration is a prerequisite for many subsequent analysis steps, including uncertainty analysis, model selection and biological interpretation. Although multiple hypothesis have been advanced to explain why optimization based calibration of biochemical models is challenging, there are few comprehensive studies that test these hypothesis and tools for performing such studies are also lacking.Results We implemented an established trust-region method as a modular python framework (fides) to enable structured comparison of different approaches to ODE model calibration involving Hessian approximation schemes and trust-region subproblem solvers. We evaluate fides on a set of benchmark problems that include experimental data. We find a high variability in optimizer performance among different implementations of the same algorithm, with fides performing more reliably that other implementations investigated. Our investigation of possible sources of poor optimizer performance identify shortcomings in the widely used Gauss-Newton approximation. We address these shortcomings by proposing a novel hybrid Hessian approximation scheme that enhances optimizer performance.Availability Fides is published under the permissive BSD-3-Clause license with source code publicly available at https://github.com/fides-dev/fides. Citeable releases are archived on Zenodo.Contact fabian_froehlich{at}hms.harvard.edu and peter_sorger{at}hms.harvard.eduSupplementary information Supplementary data are available at Bioinformatics online and at https://github.com/fides-dev/fides-benchmark.Competing Interest StatementPKS is a member of the SAB or BOD member of Applied Biomath, RareCyte Inc., and Glencoe Software, which distributes a commercial version of the OMERO database; PKS is also a member of the NanoString SAB. In the last five years the Sorger lab has received research funding from Novartis and Merck. Sorger declares that none of these relationships have related to the content of this manuscript.},
+	URL = {https://www.biorxiv.org/content/early/2021/05/22/2021.05.20.445065},
+	eprint = {https://www.biorxiv.org/content/early/2021/05/22/2021.05.20.445065.full.pdf},
+	journal = {bioRxiv}
+}
+
 @Comment{jabref-meta: databaseType:bibtex;}
 
 @Comment{jabref-meta: grouping:

documentation/references.md

@@ -1,11 +1,14 @@
 # References
 
-List of publications using AMICI. Total number is 55.
+List of publications using AMICI. Total number is 57.
 
 If you applied AMICI in your work and your publication is missing, please let us know via a new Github issue.
 
 <h1 class="unnumbered" id="section">2021</h1>
 <div id="refs" class="references hanging-indent" role="doc-bibliography">
+<div id="ref-Froehlich2021.05.20.445065">
+<p>Fröhlich, Fabian, and Peter K. Sorger. 2021. “Fides: Reliable Trust-Region Optimization for Parameter Estimation of Ordinary Differential Equation Models.” <em>bioRxiv</em>. <a href="https://doi.org/10.1101/2021.05.20.445065">https://doi.org/10.1101/2021.05.20.445065</a>.</p>
+</div>
 <div id="ref-Gaspari2021">
 <p>Gaspari, Erika. 2021. “Model-Driven Design of Mycoplasma as a Vaccine Chassis.” PhD thesis, Wageningen: Wageningen University. <a href="https://doi.org/10.18174/539593">https://doi.org/10.18174/539593</a>.</p>
 </div>
@@ -27,6 +30,9 @@ If you applied AMICI in your work and your publication is missing, please let us
 <div id="ref-vanRosmalenSmi2021">
 <p>van Rosmalen, R. P., R. W. Smith, V. A. P. Martins dos Santos, C. Fleck, and M. Suarez-Diez. 2021. “Model Reduction of Genome-Scale Metabolic Models as a Basis for Targeted Kinetic Models.” <em>Metabolic Engineering</em> 64: 74–84. <a href="https://doi.org/https://doi.org/10.1016/j.ymben.2021.01.008">https://doi.org/https://doi.org/10.1016/j.ymben.2021.01.008</a>.</p>
 </div>
+<div id="ref-VillaverdePat2021">
+<p>Villaverde, Alejandro F., Dilan Pathirana, Fabian Fröhlich, Jan Hasenauer, and Julio R. Banga. 2021. “A Protocol for Dynamic Model Calibration.” <a href="http://arxiv.org/abs/2105.12008">http://arxiv.org/abs/2105.12008</a>.</p>
+</div>
 </div>
 <h1 class="unnumbered" id="section">2020</h1>
 <div id="refs" class="references hanging-indent" role="doc-bibliography">