Update references

doc/amici_refs.bib

@@ -1693,6 +1693,48 @@ @Article{LakrisenkoIse2026
   url              = {https://www.biorxiv.org/content/early/2026/02/14/2026.02.12.705506},
 }
 
+@Misc{JostThi2026,
+  author           = {Paul Jonas Jost and Christoph Thiele and Jan Hasenauer},
+  title            = {Balancing label resolution and computational cost in dynamical models of lipid metabolism},
+  year             = {2026},
+  archiveprefix    = {arXiv},
+  creationdate     = {2026-06-18T13:25:20},
+  eprint           = {2606.13620},
+  modificationdate = {2026-06-18T13:25:20},
+  primaryclass     = {q-bio.QM},
+  url              = {https://arxiv.org/abs/2606.13620},
+}
+
+@Article{WielandBlu2026,
+  author           = {Wieland, Vincent and Blum, Tobias and Iriady, Irina and Reverte-Salisa, Laia and Pathirana, Dilan and F{\"o}rster, Irmgard and Weighardt, Heike and Hasenauer, Jan},
+  journal          = {bioRxiv},
+  title            = {Mathematical Modeling of the Canonical Aryl Hydrocarbon Receptor Pathway},
+  year             = {2026},
+  abstract         = {The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor involved in xenobiotic sensing, as well as development, immunity, and tissue homeostasis. AhR signaling can proceed through a canonical and non-canonical pathway; the present study focuses on the canonical pathway. While ligand-dependent differences in binding affinities and direct ligand degradation kinetics are well known, and subtle differences in ligand binding can shape downstream signaling, it is still unclear which biochemical reaction steps within the canonical pathway are responsible for distinct ligand-specific transcriptional responses. Here, we developed a mechanistic ordinary differential equation model of the canonical AhR pathway. We calibrated the model to time-resolved qPCR measurements of Cyp1a1 and Ahrr mRNA in mouse bone-marrow-derived macrophages exposed to structurally diverse, environmentally relevant ligands with known immunomodulatory activity (3-methylcholanthrene, indolo[3,2-b]carbazole, and bisphenol A) using global optimization under a heteroskedastic likelihood. To dissect ligand specificity, we evaluated 528 candidate models that allow one or two ligand-involving reaction rate constants to vary. Akaike-based model selection reveals a dominant dynamical regime governed by promoter occupancy and target-gene mRNA synthesis, indicating that ligand-specific transcriptional responses are primarily encoded at the level of transcriptional regulation rather than upstream signaling events. The resulting model is made available in SBML and PEtab formats for reproducibility, and to enable further research into whether ligand-specific effects are conserved or rewired across cell types.Competing Interest StatementThe authors have declared no competing interest.Deutsche Forschungsgemeinschaft, https://ror.org/018mejw64, EXC 2047 - 390685813, EXC 2151 - 390873048, SFB 1454 - 432325352European Research Council, Consolidator Grant - 101126146},
+  creationdate     = {2026-06-18T13:25:20},
+  doi              = {10.64898/2026.05.05.722708},
+  elocation-id     = {2026.05.05.722708},
+  eprint           = {https://www.biorxiv.org/content/early/2026/05/08/2026.05.05.722708.full.pdf},
+  modificationdate = {2026-06-18T13:25:20},
+  publisher        = {Cold Spring Harbor Laboratory},
+  url              = {https://www.biorxiv.org/content/early/2026/05/08/2026.05.05.722708},
+}
+
+@Article{GreinPen2026,
+  author           = {Grein, Stephan and Penas, David R. and Weindl, Daniel and Lakrisenko, Polina and Banga, Julio R. and Hasenauer, Jan},
+  journal          = {bioRxiv},
+  title            = {Large-scale analysis of optimisation methods for parameter estimation problems in the life sciences},
+  year             = {2026},
+  abstract         = {Dynamic models are central to the computational life sciences but typically contain unknown parameters that must be inferred from experimental data. High-throughput measurements have made this task increasingly challenging, yielding high-dimensional search spaces and non-convex objectives with many local optima. This makes the choice of optimisation method critical. However, existing empirical studies either consider only a limited number of benchmark problems or only a narrow spectrum of local, global and hybrid optimisation methods. Here, we present a comprehensive benchmark of a broad range of optimisation methods on a curated collection of parameter estimation problems, comprising 990 method-problem-pairs executed on two independent supercomputing infrastructures. Our evaluation quantifies success rates, solution quality and computational cost, revealing characteristic strengths and limitations of each approach. We find that optimisation methods separated into clear performance tiers. Building on these results, we implemented a new hybrid strategy that combines enhanced scatter search with the best-performing local solver, which showed robust performance and improved on the other scatter-search variants we tested. Our results provide practical guidance for selecting optimisation methods and thereby support more accurate and reliable model calibration.Competing Interest StatementThe authors have declared no competing interest.Bundesministerium f{\"u}r Forschung, Technologie und Raumfahrt, https://ror.org/04pz7b180, 01ZX1916ADeutsche Forschungsgemeinschaft, 390685813, 390873048, 432325352, 450149205, 443187771European Union, https://ror.org/019w4f821, PID2023-146275NB-C22},
+  creationdate     = {2026-07-14T14:56:22},
+  doi              = {10.64898/2026.07.11.737731},
+  elocation-id     = {2026.07.11.737731},
+  eprint           = {https://www.biorxiv.org/content/early/2026/07/13/2026.07.11.737731.full.pdf},
+  modificationdate = {2026-07-14T14:56:22},
+  publisher        = {Cold Spring Harbor Laboratory},
+  url              = {https://www.biorxiv.org/content/early/2026/07/13/2026.07.11.737731},
+}
+
 @Comment{jabref-meta: databaseType:bibtex;}
 
 @Comment{jabref-meta: grouping:

doc/references.md

@@ -1,6 +1,6 @@
 # References
 
-List of publications using AMICI. Total number is 111.
+List of publications using AMICI. Total number is 114.
 
 If you applied AMICI in your work and your publication is missing, please let us know via a new
 [GitHub issue](https://github.com/AMICI-dev/AMICI/issues/new?labels=documentation&title=Add+publication&body=AMICI+was+used+in+this+manuscript:+DOI).
@@ -17,13 +17,33 @@ If you applied AMICI in your work and your publication is missing, please let us
 <h1 class="unnumbered" id="section">2026</h1>
 <div id="refs" class="references csl-bib-body hanging-indent"
 data-entry-spacing="0" role="list">
+<div id="ref-GreinPen2026" class="csl-entry" role="listitem">
+Grein, Stephan, David R. Penas, Daniel Weindl, Polina Lakrisenko, Julio
+R. Banga, and Jan Hasenauer. 2026. <span>“Large-Scale Analysis of
+Optimisation Methods for Parameter Estimation Problems in the Life
+Sciences.”</span> <em>bioRxiv</em>. <a
+href="https://doi.org/10.64898/2026.07.11.737731">https://doi.org/10.64898/2026.07.11.737731</a>.
+</div>
+<div id="ref-JostThi2026" class="csl-entry" role="listitem">
+Jost, Paul Jonas, Christoph Thiele, and Jan Hasenauer. 2026.
+<span>“Balancing Label Resolution and Computational Cost in Dynamical
+Models of Lipid Metabolism.”</span> <a
+href="https://arxiv.org/abs/2606.13620">https://arxiv.org/abs/2606.13620</a>.
+</div>
 <div id="ref-LakrisenkoIse2026" class="csl-entry" role="listitem">
 Lakrisenko, Polina, Jörg Isensee, Tim Hucho, Daniel Weindl, and Jan
 Hasenauer. 2026. <span>“A Mechanistic Model of Protein Kinase
 <span>A</span> Dynamics Under Pro- and Anti-Nociceptive Inputs.”</span>
 <em>bioRxiv</em>. <a
 href="https://doi.org/10.64898/2026.02.12.705506">https://doi.org/10.64898/2026.02.12.705506</a>.
 </div>
+<div id="ref-WielandBlu2026" class="csl-entry" role="listitem">
+Wieland, Vincent, Tobias Blum, Irina Iriady, Laia Reverte-Salisa, Dilan
+Pathirana, Irmgard Förster, Heike Weighardt, and Jan Hasenauer. 2026.
+<span>“Mathematical Modeling of the Canonical Aryl Hydrocarbon Receptor
+Pathway.”</span> <em>bioRxiv</em>. <a
+href="https://doi.org/10.64898/2026.05.05.722708">https://doi.org/10.64898/2026.05.05.722708</a>.
+</div>
 </div>
 <h1 class="unnumbered" id="section">2025</h1>
 <div id="refs" class="references csl-bib-body hanging-indent"

doc/usage_by_year.csv

@@ -10,4 +10,4 @@ year,citations
 2023,12
 2024,15
 2025,14
-2026,1
+2026,4