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dweindl · Apr 20, 2024 · 3601eb8 · 3601eb8
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diff --git a/documentation/amici_refs.bib b/documentation/amici_refs.bib
@@ -1291,6 +1291,21 @@ @Article{KissVen2024
   url              = {https://doi.org/10.1093/nar/gkae123},
 }
 
+@Article{DoresicGre2024,
+  author           = {Domagoj Dore{\v s}i{\'c} and Stephan Grein and Jan Hasenauer},
+  journal          = {bioRxiv},
+  title            = {Efficient parameter estimation for ODE models of cellular processes using semi-quantitative data},
+  year             = {2024},
+  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 non-linear (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. Here, 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).Competing Interest StatementThe authors have declared no competing interest.},
+  creationdate     = {2024-04-20T13:05:06},
+  doi              = {10.1101/2024.01.26.577371},
+  elocation-id     = {2024.01.26.577371},
+  eprint           = {https://www.biorxiv.org/content/early/2024/01/30/2024.01.26.577371.full.pdf},
+  modificationdate = {2024-04-20T13:05:06},
+  publisher        = {Cold Spring Harbor Laboratory},
+  url              = {https://www.biorxiv.org/content/early/2024/01/30/2024.01.26.577371},
+}
+
 @Comment{jabref-meta: databaseType:bibtex;}
 
 @Comment{jabref-meta: grouping:

diff --git a/documentation/references.md b/documentation/references.md
@@ -1,6 +1,6 @@
 # References
 
-List of publications using AMICI. Total number is 84.
+List of publications using AMICI. Total number is 85.
 
 If you applied AMICI in your work and your publication is missing, please let us know via a new GitHub issue.
 
@@ -14,6 +14,12 @@ If you applied AMICI in your work and your publication is missing, please let us
 <h1 class="unnumbered" id="section">2024</h1>
 <div id="refs" class="references csl-bib-body hanging-indent"
 role="list">
+<div id="ref-DoresicGre2024" class="csl-entry" role="listitem">
+Dorešić, Domagoj, Stephan Grein, and Jan Hasenauer. 2024.
+<span>“Efficient Parameter Estimation for ODE Models of Cellular
+Processes Using Semi-Quantitative Data.”</span> <em>bioRxiv</em>. <a
+href="https://doi.org/10.1101/2024.01.26.577371">https://doi.org/10.1101/2024.01.26.577371</a>.
+</div>
 <div id="ref-KissVen2024" class="csl-entry" role="listitem">
 Kiss, Anna E, Anuroop V Venkatasubramani, Dilan Pathirana, Silke Krause,
 Aline Campos Sparr, Jan Hasenauer, Axel Imhof, Marisa Müller, and Peter