Update references

doc/using_pypesto.bib

@@ -11,7 +11,6 @@ @Article{FalcoCoh2023
   timestamp    = {2023-07-20},
   doi          = {10.1098/rsif.2023.0184},
   publisher    = {The Royal Society},
-  url          = {https://doi.org/10.1098/rsif.2023.0184},
 }
 
 @Article{LakrisenkoSta2023,
@@ -27,7 +26,6 @@ @Article{LakrisenkoSta2023
   creationdate = {2023-01-26T11:19:52},
   doi          = {10.1371/journal.pcbi.1010783},
   publisher    = {Public Library of Science},
-  url          = {https://doi.org/10.1371/journal.pcbi.1010783},
 }
 
 @Article{SchmiesterSch2021,
@@ -44,7 +42,6 @@ @Article{SchmiesterSch2021
   doi          = {10.1371/journal.pcbi.1008646},
   publisher    = {Public Library of Science},
   timestamp    = {2021-01-30},
-  url          = {https://doi.org/10.1371/journal.pcbi.1008646},
 }
 
 @Article{MishraWan2023,
@@ -59,7 +56,6 @@ @Article{MishraWan2023
   creationdate = {2023-01-26T11:31:17},
   doi          = {https://doi.org/10.1016/j.ymben.2022.11.003},
   keywords     = {Lipid metabolism, Kinetic model, Free fatty acid, Fatty alcohol},
-  url          = {https://www.sciencedirect.com/science/article/pii/S1096717622001380},
 }
 
 @Article{FroehlichSor2022,
@@ -75,7 +71,6 @@ @Article{FroehlichSor2022
   creationdate = {2023-01-26T11:31:44},
   doi          = {10.1371/journal.pcbi.1010322},
   publisher    = {Public Library of Science},
-  url          = {https://doi.org/10.1371/journal.pcbi.1010322},
 }
 
 @Article{FroehlichGer2022,
@@ -91,7 +86,6 @@ @Article{FroehlichGer2022
   modificationdate = {2024-05-13T09:29:21},
   publisher        = {Cold Spring Harbor Laboratory},
   ranking          = {rank1},
-  url              = {https://www.biorxiv.org/content/early/2022/02/18/2022.02.17.480899},
 }
 
 @Article{GerosaChi2020,
@@ -109,7 +103,6 @@ @Article{GerosaChi2020
   creationdate = {2023-01-26T11:32:57},
   doi          = {10.1016/j.cels.2020.10.002},
   publisher    = {Elsevier},
-  url          = {https://doi.org/10.1016/j.cels.2020.10.002},
 }
 
 @Article{SchmiesterWei2021,
@@ -126,7 +119,6 @@ @Article{SchmiesterWei2021
   creationdate = {2023-01-26T11:33:16},
   doi          = {10.1093/bioinformatics/btab512},
   eprint       = {https://academic.oup.com/bioinformatics/article-pdf/37/23/4493/41641709/btab512.pdf},
-  url          = {https://doi.org/10.1093/bioinformatics/btab512},
 }
 
 @Article{SchmiesterWei2020,
@@ -143,7 +135,6 @@ @Article{SchmiesterWei2020
   doi          = {10.1007/s00285-020-01522-w},
   refid        = {Schmiester2020},
   timestamp    = {2021-01-30},
-  url          = {https://doi.org/10.1007/s00285-020-01522-w},
 }
 
 @InProceedings{DuttaShi2021,
@@ -162,7 +153,6 @@ @InProceedings{DuttaShi2021
   keywords     = {Machine Learning, Flaky tests, Extreme Value Theory},
   location     = {Athens, Greece},
   numpages     = {12},
-  url          = {https://doi.org/10.1145/3468264.3468615},
 }
 
 @Article{ContentoCas2021,
@@ -176,7 +166,6 @@ @Article{ContentoCas2021
   elocation-id = {2021.10.01.21263052},
   eprint       = {https://www.medrxiv.org/content/early/2021/10/01/2021.10.01.21263052.full.pdf},
   publisher    = {Cold Spring Harbor Laboratory Press},
-  url          = {https://www.medrxiv.org/content/early/2021/10/01/2021.10.01.21263052},
 }
 
 @Article{AlbadryHoe2022,
@@ -192,7 +181,6 @@ @Article{AlbadryHoe2022
   creationdate = {2023-01-26T11:34:50},
   doi          = {10.1038/s41598-022-26483-6},
   refid        = {Albadry2022},
-  url          = {https://doi.org/10.1038/s41598-022-26483-6},
 }
 
 @Article{FischerHolzhausenRoe2023,
@@ -206,7 +194,6 @@ @Article{FischerHolzhausenRoe2023
   elocation-id = {2023.01.17.523407},
   eprint       = {https://www.biorxiv.org/content/early/2023/01/19/2023.01.17.523407.full.pdf},
   publisher    = {Cold Spring Harbor Laboratory},
-  url          = {https://www.biorxiv.org/content/early/2023/01/19/2023.01.17.523407},
 }
 
 @Article{KissVen2024,
@@ -222,7 +209,6 @@ @Article{KissVen2024
   doi              = {10.1093/nar/gkae123},
   eprint           = {https://academic.oup.com/nar/advance-article-pdf/doi/10.1093/nar/gkae123/56756494/gkae123.pdf},
   modificationdate = {2024-02-28T18:27:01},
-  url              = {https://doi.org/10.1093/nar/gkae123},
 }
 
 @Article{DoresicGre2024,
@@ -237,7 +223,6 @@ @Article{DoresicGre2024
   eprint           = {https://www.biorxiv.org/content/early/2024/01/30/2024.01.26.577371.full.pdf},
   modificationdate = {2024-04-20T13:06:42},
   publisher        = {Cold Spring Harbor Laboratory},
-  url              = {https://www.biorxiv.org/content/early/2024/01/30/2024.01.26.577371},
 }
 
 @Article{ArrudaSch2023,
@@ -252,7 +237,6 @@ @Article{ArrudaSch2023
   eprint           = {https://www.biorxiv.org/content/early/2023/08/23/2023.08.22.554273.full.pdf},
   modificationdate = {2024-04-22T12:56:00},
   publisher        = {Cold Spring Harbor Laboratory},
-  url              = {https://www.biorxiv.org/content/early/2023/08/23/2023.08.22.554273},
 }
 
 @Article{MerktAli2024,
@@ -283,4 +267,74 @@ @Article{FalcoCoh2024a
   publisher        = {Elsevier BV},
 }
 
+@Article{HoepflAlb2024,
+  author           = {Höpfl, Sebastian and Albadry, Mohamed and Dahmen, Uta and Herrmann, Karl-Heinz and Kindler, Eva Marie and König, Matthias and Reichenbach, Jürgen Rainer and Tautenhahn, Hans-Michael and Wei, Weiwei and Zhao, Wan-Ting and Radde, Nicole Erika},
+  journal          = {Bioinformatics},
+  title            = {{Bayesian modelling of time series data (BayModTS) - a FAIR workflow to process sparse and highly variable data}},
+  year             = {2024},
+  issn             = {1367-4811},
+  month            = {05},
+  pages            = {btae312},
+  abstract         = {{Systems biology aims to better understand living systems through mathematical modelling of experimental and clinical data. A pervasive challenge in quantitative dynamical modelling is the integration of time series measurements, which often have high variability and low sampling resolution. Approaches are required to utilise such information while consistently handling uncertainties.We present BayModTS (Bayesian Modelling of Time Series data), a new FAIR (Findable, Accessible, Interoperable and Reusable) workflow for processing and analysing sparse and highly variable time series data. BayModTS consistently transfers uncertainties from data to model predictions, including process knowledge via parameterised models. Further, credible differences in the dynamics of different conditions can be identified by filtering noise. To demonstrate the power and versatility of BayModTS, we applied it to three hepatic datasets gathered from three different species and with different measurement techniques: (i) blood perfusion measurements by magnetic resonance imaging in rat livers after portal vein ligation, (ii) pharmacokinetic time series of different drugs in normal and steatotic mice, and (iii) CT-based volumetric assessment of human liver remnants after clinical liver resection.The BayModTS codebase is available on GitHub at https://github.com/Systems-Theory-in-Systems-Biology/BayModTS. The repository contains a Python script for the executable BayModTS workflow and a widely applicable SBML (Systems Biology Markup Language) model for retarded transient functions. In addition, all examples from the paper are included in the repository. Data and code of the application examples are stored on DaRUS https://doi.org/10.18419/darus-3876. The raw MRI ROI voxel data were uploaded to DaRUS https://doi.org/10.18419/darus-3878. The steatosis metabolite data are published on FairdomHub 10.15490/fairdomhub.1.study.1070.1.}},
+  creationdate     = {2024-05-16T07:58:55},
+  doi              = {10.1093/bioinformatics/btae312},
+  eprint           = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btae312/57572667/btae312.pdf},
+  modificationdate = {2024-05-16T07:58:55},
+}
+
+@Misc{LakrisenkoPat2024,
+  author           = {Polina Lakrisenko and Dilan Pathirana and Daniel Weindl and Jan Hasenauer},
+  title            = {Exploration of methods for computing sensitivities in ODE models at dynamic and steady states},
+  year             = {2024},
+  archiveprefix    = {arXiv},
+  creationdate     = {2024-05-30T09:47:51},
+  eprint           = {2405.16524},
+  modificationdate = {2024-05-30T09:47:51},
+  primaryclass     = {q-bio.QM},
+}
+
+@Misc{PhilippsKoe2024,
+  author           = {Maren Philipps and Antonia Körner and Jakob Vanhoefer and Dilan Pathirana and Jan Hasenauer},
+  title            = {Non-Negative Universal Differential Equations With Applications in Systems Biology},
+  year             = {2024},
+  archiveprefix    = {arXiv},
+  creationdate     = {2024-06-28T08:40:06},
+  eprint           = {2406.14246},
+  modificationdate = {2024-06-28T08:40:06},
+  primaryclass     = {q-bio.QM},
+  url              = {https://arxiv.org/abs/2406.14246},
+}
+
+@Article{SchmiesterBra2024,
+  author           = {Schmiester, Leonard and Brasó-Maristany, Fara and González-Farré, Blanca and Pascual, Tomás and Gavilá, Joaquín and Tekpli, Xavier and Geisler, Jürgen and Kristensen, Vessela N. and Frigessi, Arnoldo and Prat, Aleix and Köhn-Luque, Alvaro},
+  journal          = {Clinical Cancer Research},
+  title            = {{Computational Model Predicts Patient Outcomes in Luminal B Breast Cancer Treated with Endocrine Therapy and CDK4/6 Inhibition}},
+  year             = {2024},
+  issn             = {1078-0432},
+  month            = {07},
+  pages            = {OF1-OF9},
+  abstract         = {{Development of a computational biomarker to predict, prior to treatment, the response to CDK4/6 inhibition (CDK4/6i) in combination with endocrine therapy in patients with breast cancer.A mechanistic mathematical model that accounts for protein signaling and drug mechanisms of action was developed and trained on extensive, publicly available data from breast cancer cell lines. The model was built to provide a patient-specific response score based on the expression of six genes (CCND1, CCNE1, ESR1, RB1, MYC, and CDKN1A). The model was validated in five independent cohorts of 148 patients in total with early-stage or advanced breast cancer treated with endocrine therapy and CDK4/6i. Response was measured either by evaluating Ki67 levels and PAM50 risk of relapse (ROR) after neoadjuvant treatment or by evaluating progression-free survival (PFS).The model showed significant association with patient’s outcomes in all five cohorts. The model predicted high Ki67 [area under the curve; AUC (95\\% confidence interval, CI) of 0.80 (0.64–0.92), 0.81 (0.60–1.00) and 0.80 (0.65–0.93)] and high PAM50 ROR [AUC of 0.78 (0.64–0.89)]. This observation was not obtained in patients treated with chemotherapy. In the other cohorts, patient stratification based on the model prediction was significantly associated with PFS [hazard ratio (HR) = 2.92 (95\\% CI, 1.08–7.86), P = 0.034 and HR = 2.16 (1.02 4.55), P = 0.043].A mathematical modeling approach accurately predicts patient outcome following CDK4/6i plus endocrine therapy that marks a step toward more personalized treatments in patients with Luminal B breast cancer.}},
+  creationdate     = {2024-08-01T09:44:04},
+  doi              = {10.1158/1078-0432.CCR-24-0244},
+  eprint           = {https://aacrjournals.org/clincancerres/article-pdf/doi/10.1158/1078-0432.CCR-24-0244/3478451/ccr-24-0244.pdf},
+  modificationdate = {2024-08-01T09:44:04},
+  url              = {https://doi.org/10.1158/1078-0432.CCR-24-0244},
+}
+
+@InProceedings{JacksonCha2023,
+  author           = {Jackson, Clayton and Chardon, Matthieu and Wang, Y. Curtis and Rudi, Johann and Tresch, Matthew and Heckman, Charles J. and Quinn, Roger D.},
+  booktitle        = {Biomimetic and Biohybrid Systems},
+  title            = {Multimodal Parameter Inference for a Canonical Motor Microcircuit Controlling Rat Hindlimb Motion},
+  year             = {2023},
+  address          = {Cham},
+  editor           = {Meder, Fabian and Hunt, Alexander and Margheri, Laura and Mura, Anna and Mazzolai, Barbara},
+  pages            = {38--51},
+  publisher        = {Springer Nature Switzerland},
+  abstract         = {This work explored synaptic strengths in a computational neuroscience model of a controller for the hip joint of a rat which consists of Ia interneurons, Renshaw cells, and the associated motor neurons. This circuit has been referred to as the Canonical Motor Microcircuit (CMM). It is thought that the CMM acts to modulate motor neuron activity at the output stage. We first created a biomechanical model of a rat hindlimb consisting of a pelvis, femur, shin, foot, and flexor-extensor muscle pairs modeled with a Hill muscle model. We then modeled the CMM using non-spiking leaky-integrator neural models connected with conductance-based synapses. To tune the parameters in the network, we implemented an automated approach for parameter search using the Markov chain Monte Carlo (MCMC) method to solve a parameter estimation problem in a Bayesian inference framework. As opposed to traditional optimization techniques, the MCMC method identifies probability densities over the multidimensional space of parameters. This allows us to see a range of likely parameters that produce model outcomes consistent with animal data, determine if the distribution of likely parameters is uni- or multi-modal, as well as evaluate the significance and sensitivity of each parameter. This approach will allow for further analysis of the circuit, specifically, the function and significance of Ia feedback and Renshaw cells.},
+  creationdate     = {2024-09-06T15:49:21},
+  doi              = {10.1007/978-3-031-39504-8_3},
+  isbn             = {978-3-031-39504-8},
+  modificationdate = {2024-09-06T15:49:47},
+}
+
 @Comment{jabref-meta: databaseType:bibtex;}