diff --git a/documentation/amici_refs.bib b/documentation/amici_refs.bib index 4c31869d87..ef283eaf52 100644 --- a/documentation/amici_refs.bib +++ b/documentation/amici_refs.bib @@ -1100,19 +1100,20 @@ @Article{FroehlichGer2023 } @Article{FroehlichSor2022, - author = {Fröhlich, Fabian AND Sorger, Peter K.}, - journal = {PLOS Computational Biology}, - title = {Fides: Reliable trust-region optimization for parameter estimation of ordinary differential equation models}, - year = {2022}, - month = {07}, - number = {7}, - pages = {1-28}, - volume = {18}, - abstract = {Ordinary differential equation (ODE) models are widely used to study biochemical reactions in cellular networks since they effectively describe the temporal evolution of these networks using mass action kinetics. The parameters of these models are rarely known a priori and must instead be estimated by calibration using experimental data. Optimization-based calibration of ODE models on is often challenging, even for low-dimensional problems. Multiple hypotheses have been advanced to explain why biochemical model calibration is challenging, including non-identifiability of model parameters, but there are few comprehensive studies that test these hypotheses, likely because tools for performing such studies are also lacking. Nonetheless, reliable model calibration is essential for uncertainty analysis, model comparison, and biological interpretation. We implemented an established trust-region method as a modular Python framework (fides) to enable systematic comparison of different approaches to ODE model calibration involving a variety of Hessian approximation schemes. We evaluated fides on a recently developed corpus of biologically realistic benchmark problems for which real experimental data are available. Unexpectedly, we observed high variability in optimizer performance among different implementations of the same mathematical instructions (algorithms). Analysis of possible sources of poor optimizer performance identified limitations in the widely used Gauss-Newton, BFGS and SR1 Hessian approximation schemes. We addressed these drawbacks with a novel hybrid Hessian approximation scheme that enhances optimizer performance and outperforms existing hybrid approaches. When applied to the corpus of test models, we found that fides was on average more reliable and efficient than existing methods using a variety of criteria. We expect fides to be broadly useful for ODE constrained optimization problems in biochemical models and to be a foundation for future methods development.}, - creationdate = {2023-04-15T08:12:41}, - doi = {10.1371/journal.pcbi.1010322}, - publisher = {Public Library of Science}, - url = {https://doi.org/10.1371/journal.pcbi.1010322}, + author = {Fröhlich, Fabian and Sorger, Peter K.}, + journal = {PLOS Computational Biology}, + title = {Fides: Reliable trust-region optimization for parameter estimation of ordinary differential equation models}, + year = {2022}, + month = {07}, + number = {7}, + pages = {1-28}, + volume = {18}, + abstract = {Ordinary differential equation (ODE) models are widely used to study biochemical reactions in cellular networks since they effectively describe the temporal evolution of these networks using mass action kinetics. The parameters of these models are rarely known a priori and must instead be estimated by calibration using experimental data. Optimization-based calibration of ODE models on is often challenging, even for low-dimensional problems. Multiple hypotheses have been advanced to explain why biochemical model calibration is challenging, including non-identifiability of model parameters, but there are few comprehensive studies that test these hypotheses, likely because tools for performing such studies are also lacking. Nonetheless, reliable model calibration is essential for uncertainty analysis, model comparison, and biological interpretation. We implemented an established trust-region method as a modular Python framework (fides) to enable systematic comparison of different approaches to ODE model calibration involving a variety of Hessian approximation schemes. We evaluated fides on a recently developed corpus of biologically realistic benchmark problems for which real experimental data are available. Unexpectedly, we observed high variability in optimizer performance among different implementations of the same mathematical instructions (algorithms). Analysis of possible sources of poor optimizer performance identified limitations in the widely used Gauss-Newton, BFGS and SR1 Hessian approximation schemes. We addressed these drawbacks with a novel hybrid Hessian approximation scheme that enhances optimizer performance and outperforms existing hybrid approaches. When applied to the corpus of test models, we found that fides was on average more reliable and efficient than existing methods using a variety of criteria. We expect fides to be broadly useful for ODE constrained optimization problems in biochemical models and to be a foundation for future methods development.}, + creationdate = {2023-04-15T08:12:41}, + doi = {10.1371/journal.pcbi.1010322}, + modificationdate = {2024-02-23T18:10:55}, + publisher = {Public Library of Science}, + url = {https://doi.org/10.1371/journal.pcbi.1010322}, } @Article{ErdemMut2022, @@ -1163,15 +1164,6 @@ @InBook{Froehlich2023 url = {https://doi.org/10.1007/978-1-0716-3008-2_3}, } -@Misc{SluijsZho2023, - author = {Bob van Sluijs and Tao Zhou and Britta Helwig and Mathieu Baltussen and Frank Nelissen and Hans Heus and Wilhelm Huck}, - title = {Inverse Design of Enzymatic Reaction Network States}, - year = {2023}, - creationdate = {2023-07-06T10:39:46}, - doi = {10.21203/rs.3.rs-2646906/v1}, - modificationdate = {2023-07-06T10:40:37}, -} - @Article{BuckBas2023, author = {Michèle C. Buck and Lisa Bast and Judith S. Hecker and Jennifer Rivière and Maja Rothenberg-Thurley and Luisa Vogel and Dantong Wang and Immanuel Andrä and Fabian J. Theis and Florian Bassermann and Klaus H. Metzeler and Robert A.J. Oostendorp and Carsten Marr and Katharina S. Götze}, journal = {iScience}, @@ -1251,6 +1243,38 @@ @Misc{HuckBal2023 publisher = {Research Square Platform LLC}, } +@Article{LangPen2024, + author = {Lang, Paul F. and Penas, David R. and Banga, Julio R. and Weindl, Daniel and Novak, Bela}, + journal = {PLOS Computational Biology}, + title = {Reusable rule-based cell cycle model explains compartment-resolved dynamics of 16 observables in RPE-1 cells}, + year = {2024}, + month = {01}, + number = {1}, + pages = {1-24}, + volume = {20}, + abstract = {The mammalian cell cycle is regulated by a well-studied but complex biochemical reaction system. Computational models provide a particularly systematic and systemic description of the mechanisms governing mammalian cell cycle control. By combining both state-of-the-art multiplexed experimental methods and powerful computational tools, this work aims at improving on these models along four dimensions: model structure, validation data, validation methodology and model reusability. We developed a comprehensive model structure of the full cell cycle that qualitatively explains the behaviour of human retinal pigment epithelial-1 cells. To estimate the model parameters, time courses of eight cell cycle regulators in two compartments were reconstructed from single cell snapshot measurements. After optimisation with a parallel global optimisation metaheuristic we obtained excellent agreements between simulations and measurements. The PEtab specification of the optimisation problem facilitates reuse of model, data and/or optimisation results. Future perturbation experiments will improve parameter identifiability and allow for testing model predictive power. Such a predictive model may aid in drug discovery for cell cycle-related disorders.}, + creationdate = {2024-01-24T20:02:16}, + doi = {10.1371/journal.pcbi.1011151}, + modificationdate = {2024-02-23T18:10:08}, + publisher = {Public Library of Science}, + url = {https://doi.org/10.1371/journal.pcbi.1011151}, +} + +@Article{SluijsZho2024, + author = {van Sluijs, Bob and Zhou, Tao and Helwig, Britta and Baltussen, Mathieu G. and Nelissen, Frank H. T. and Heus, Hans A. and Huck, Wilhelm T. S.}, + journal = {Nature Communications}, + title = {Iterative design of training data to control intricate enzymatic reaction networks}, + year = {2024}, + issn = {2041-1723}, + month = feb, + number = {1}, + volume = {15}, + creationdate = {2024-02-23T17:09:35}, + doi = {10.1038/s41467-024-45886-9}, + modificationdate = {2024-02-23T17:09:35}, + publisher = {Springer Science and Business Media LLC}, +} + @Comment{jabref-meta: databaseType:bibtex;} @Comment{jabref-meta: grouping: diff --git a/documentation/references.md b/documentation/references.md index 00c3f40cc8..2164037aaf 100644 --- a/documentation/references.md +++ b/documentation/references.md @@ -1,6 +1,6 @@ # References -List of publications using AMICI. Total number is 82. +List of publications using AMICI. Total number is 83. If you applied AMICI in your work and your publication is missing, please let us know via a new GitHub issue. @@ -11,17 +11,35 @@ If you applied AMICI in your work and your publication is missing, please let us } +

2024

+
+
+Lang, Paul F., David R. Penas, Julio R. Banga, Daniel Weindl, and Bela +Novak. 2024. “Reusable Rule-Based Cell Cycle Model Explains +Compartment-Resolved Dynamics of 16 Observables in RPE-1 Cells.” +PLOS Computational Biology 20 (1): 1–24. https://doi.org/10.1371/journal.pcbi.1011151. +
+
+Sluijs, Bob van, Tao Zhou, Britta Helwig, Mathieu G. Baltussen, Frank H. +T. Nelissen, Hans A. Heus, and Wilhelm T. S. Huck. 2024. +“Iterative Design of Training Data to Control Intricate Enzymatic +Reaction Networks.” Nature Communications 15 (1). https://doi.org/10.1038/s41467-024-45886-9. +
+

2023

-
+role="list"> +
Buck, Michèle C., Lisa Bast, Judith S. Hecker, Jennifer Rivière, Maja Rothenberg-Thurley, Luisa Vogel, Dantong Wang, et al. 2023. “Progressive Disruption of Hematopoietic Architecture from Clonal Hematopoiesis to MDS.” iScience, 107328. https://doi.org/10.1016/j.isci.2023.107328.
-
+
Contento, Lorenzo, Noemi Castelletti, Elba Raimúndez, Ronan Le Gleut, Yannik Schälte, Paul Stapor, Ludwig Christian Hinske, et al. 2023. “Integrative Modelling of Reported Case Numbers and Seroprevalence @@ -29,14 +47,14 @@ Reveals Time-Dependent Test Efficiency and Infectious Contacts.” Epidemics 43: 100681. https://doi.org/10.1016/j.epidem.2023.100681.
-
+
Contento, Lorenzo, Paul Stapor, Daniel Weindl, and Jan Hasenauer. 2023. “A More Expressive Spline Representation for SBML Models Improves Code Generation Performance in AMICI.” bioRxiv. https://doi.org/10.1101/2023.06.29.547120.
-
+
Fröhlich, Fabian. 2023. “A Practical Guide for the Efficient Formulation and Calibration of Large, Energy- and Rule-Based Models of Cellular Signal Transduction.” In Computational Modeling of @@ -44,29 +62,28 @@ Signaling Networks, edited by Lan K. Nguyen, 59–86. New York, NY: Springer US. https://doi.org/10.1007/978-1-0716-3008-2_3.
-
+
Fröhlich, Fabian, Luca Gerosa, Jeremy Muhlich, and Peter K Sorger. 2023. “Mechanistic Model of MAPK Signaling Reveals How Allostery and Rewiring Contribute to Drug Resistance.” Molecular Systems Biology 19 (2): e10988. https://doi.org/10.15252/msb.202210988.
-
+
Hasenauer, Jan, Simon Merkt, Solomon Ali, Esayas Gudina, Wondimagegn Adissu, Maximilian Münchhoff, Alexander Graf, et al. 2023. “Long-Term Monitoring of SARS-CoV-2 Seroprevalence and Variants in Ethiopia Provides Prediction for Immunity and Cross-Immunity.” https://doi.org/10.21203/rs.3.rs-3307821/v1.
-
+
Huck, Wilhelm, Mathieu Baltussen, Thijs de Jong, Quentin Duez, and William Robinson. 2023. “Chemical Reservoir Computation in a Self-Organizing Reaction Network.” Research Square Platform LLC. https://doi.org/10.21203/rs.3.rs-3487081/v1.
-
+
Lakrisenko, Polina, Paul Stapor, Stephan Grein, Łukasz Paszkowski, Dilan Pathirana, Fabian Fröhlich, Glenn Terje Lines, Daniel Weindl, and Jan Hasenauer. 2023. “Efficient Computation of Adjoint Sensitivities @@ -74,37 +91,31 @@ at Steady-State in ODE Models of Biochemical Reaction Networks.” PLOS Computational Biology 19 (1): 1–19. https://doi.org/10.1371/journal.pcbi.1010783.
-
+
Mendes, Pedro. 2023. “Reproducibility and FAIR Principles: The Case of a Segment Polarity Network Model.” Frontiers in Cell and Developmental Biology 11. https://doi.org/10.3389/fcell.2023.1201673.
-
+
Mishra, Shekhar, Ziyu Wang, Michael J. Volk, and Huimin Zhao. 2023. “Design and Application of a Kinetic Model of Lipid Metabolism in Saccharomyces Cerevisiae.” Metabolic Engineering 75: 12–18. https://doi.org/10.1016/j.ymben.2022.11.003.
-
+
Raimúndez, Elba, Michael Fedders, and Jan Hasenauer. 2023. “Posterior Marginalization Accelerates Bayesian Inference for Dynamical Models of Biological Processes.” iScience, September, 108083. https://doi.org/10.1016/j.isci.2023.108083.
-
-Sluijs, Bob van, Tao Zhou, Britta Helwig, Mathieu Baltussen, Frank -Nelissen, Hans Heus, and Wilhelm Huck. 2023. “Inverse Design of -Enzymatic Reaction Network States.” https://doi.org/10.21203/rs.3.rs-2646906/v1. -
-
+
Tunedal, Kajsa, Federica Viola, Belén Casas Garcia, Ann Bolger, Fredrik H. Nyström, Carl Johan Östgren, Jan Engvall, et al. 2023. “Haemodynamic Effects of Hypertension and Type 2 Diabetes: -Insights from a 4d Flow 4D Flow MRI-based Personalized Cardiovascular Mathematical Model.” The Journal of Physiology n/a (n/a). https://doi.org/10.1113/JP284652. @@ -112,8 +123,8 @@ href="https://doi.org/10.1113/JP284652">https://doi.org/10.1113/JP284652.

2022

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+role="list"> +
Albadry, Mohamed, Sebastian Höpfl, Nadia Ehteshamzad, Matthias König, Michael Böttcher, Jasna Neumann, Amelie Lupp, et al. 2022. “Periportal Steatosis in Mice Affects Distinct Parameters of @@ -121,7 +132,7 @@ Pericentral Drug Metabolism.” Scientific Reports 12 (1): 21825. https://doi.org/10.1038/s41598-022-26483-6.
-
+
Erdem, Cemal, Arnab Mutsuddy, Ethan M. Bensman, William B. Dodd, Michael M. Saint-Antoine, Mehdi Bouhaddou, Robert C. Blake, et al. 2022. “A Scalable, Open-Source Implementation of a Large-Scale @@ -129,21 +140,21 @@ Mechanistic Model for Single Cell Proliferation and Death Signaling.” Nature Communications 13 (1): 3555. https://doi.org/10.1038/s41467-022-31138-1.
-
-Fröhlich, Peter K., Fabian AND Sorger. 2022. “Fides: Reliable +
+Fröhlich, Fabian, and Peter K. Sorger. 2022. “Fides: Reliable Trust-Region Optimization for Parameter Estimation of Ordinary Differential Equation Models.” PLOS Computational Biology 18 (7): 1–28. https://doi.org/10.1371/journal.pcbi.1010322.
-
+
Massonis, Gemma, Alejandro F Villaverde, and Julio R Banga. 2022. “Improving dynamic predictions with ensembles of observable models.” Bioinformatics, November. https://doi.org/10.1093/bioinformatics/btac755.
-
+
Meyer, Kristian, Mikkel Søes Ibsen, Lisa Vetter-Joss, Ernst Broberg Hansen, and Jens Abildskov. 2022. “Industrial Ion-Exchange Chromatography Development Using Discontinuous Galerkin Methods Coupled @@ -151,21 +162,21 @@ with Forward Sensitivity Analysis.” Journal of Chromatography A, 463741. https://doi.org/10.1016/j.chroma.2022.463741.
-
+
Schmucker, Robin, Gabriele Farina, James Faeder, Fabian Fröhlich, Ali Sinan Saglam, and Tuomas Sandholm. 2022. “Combination Treatment Optimization Using a Pan-Cancer Pathway Model.” PLOS Computational Biology 17 (12): 1–22. https://doi.org/10.1371/journal.pcbi.1009689.
-
+
Sluijs, Bob van, Roel J. M. Maas, Ardjan J. van der Linden, Tom F. A. de Greef, and Wilhelm T. S. Huck. 2022. “A Microfluidic Optimal Experimental Design Platform for Forward Design of Cell-Free Genetic Networks.” Nature Communications 13 (1): 3626. https://doi.org/10.1038/s41467-022-31306-3.
-
+
Stapor, Paul, Leonard Schmiester, Christoph Wierling, Simon Merkt, Dilan Pathirana, Bodo M. H. Lange, Daniel Weindl, and Jan Hasenauer. 2022. “Mini-batch optimization enables training of @@ -173,7 +184,7 @@ ODE models on large-scale datasets.” Nature Communications 13 (1): 34. https://doi.org/10.1038/s41467-021-27374-6.
-
+
Sundqvist, Nicolas, Sebastian Sten, Peter Thompson, Benjamin Jan Andersson, Maria Engström, and Gunnar Cedersund. 2022. “Mechanistic Model for Human Brain Metabolism and Its Connection @@ -181,8 +192,7 @@ to the Neurovascular Coupling.” PLOS Computational Biology 18 (12): 1–24. https://doi.org/10.1371/journal.pcbi.1010798.
-
+
Villaverde, Alejandro F., Elba Raimúndez, Jan Hasenauer, and Julio R. Banga. 2022. “Assessment of Prediction Uncertainty Quantification Methods in Systems Biology.” IEEE/ACM Transactions on @@ -192,16 +202,16 @@ href="https://doi.org/10.1109/TCBB.2022.3213914">https://doi.org/10.1109/TCBB.20

2021

-
+role="list"> +
Adlung, Lorenz, Paul Stapor, Christian Tönsing, Leonard Schmiester, Luisa E. Schwarzmüller, Lena Postawa, Dantong Wang, et al. 2021. -“Cell-to-Cell Variability in Jak2/Stat5 Pathway Components and +“Cell-to-Cell Variability in JAK2/STAT5 Pathway Components and Cytoplasmic Volumes Defines Survival Threshold in Erythroid Progenitor Cells.” Cell Reports 36 (6): 109507. https://doi.org/10.1016/j.celrep.2021.109507.
-
+
Bast, Lisa, Michèle C. Buck, Judith S. Hecker, Robert A. J. Oostendorp, Katharina S. Götze, and Carsten Marr. 2021. “Computational Modeling of Stem and Progenitor Cell Kinetics Identifies Plausible @@ -209,13 +219,13 @@ Hematopoietic Lineage Hierarchies.” iScience 24 (2): 102120. https://doi.org/10.1016/j.isci.2021.102120.
-
+
Gaspari, Erika. 2021. “Model-Driven Design of Mycoplasma as a Vaccine Chassis.” PhD thesis, Wageningen: Wageningen University. https://doi.org/10.18174/539593.
-
+
Gudina, Esayas Kebede, Solomon Ali, Eyob Girma, Addisu Gize, Birhanemeskel Tegene, Gadissa Bedada Hundie, Wondewosen Tsegaye Sime, et al. 2021. “Seroepidemiology and model-based @@ -224,13 +234,13 @@ front-line hospital workers and communities.” The Lancet Global Health 9 (11): e1517–27. https://doi.org/10.1016/S2214-109X(21)00386-7.
-
+
Maier, Corinna. 2021. “Bayesian Data Assimilation and Reinforcement Learning for Model-Informed Precision Dosing in Oncology.” Doctoralthesis, Universität Potsdam. https://doi.org/10.25932/publishup-51587.
-
+
Raimúndez, Elba, Erika Dudkin, Jakob Vanhoefer, Emad Alamoudi, Simon Merkt, Lara Fuhrmann, Fan Bai, and Jan Hasenauer. 2021. “COVID-19 Outbreak in Wuhan Demonstrates the Limitations of Publicly Available @@ -239,42 +249,41 @@ Case Numbers for Epidemiological Modeling.” Epidemics href="https://doi.org/10.1016/j.epidem.2021.100439">https://doi.org/10.1016/j.epidem.2021.100439.
+role="listitem"> Schmiester, Leonard, Daniel Weindl, and Jan Hasenauer. 2021. “Efficient Gradient-Based Parameter Estimation for Dynamic Models Using Qualitative Data.” bioRxiv. https://doi.org/10.1101/2021.02.06.430039.
-
+
Städter, Philipp, Yannik Schälte, Leonard Schmiester, Jan Hasenauer, and Paul L. Stapor. 2021. “Benchmarking of Numerical Integration Methods for ODE Models of Biological Systems.” Scientific Reports 11 (1): 2696. https://doi.org/10.1038/s41598-021-82196-2.
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+
Sten, Sebastian, Henrik Podéus, Nicolas Sundqvist, Fredrik Elinder, Maria Engström, and Gunnar Cedersund. 2021. “A Multi-Data Based Quantitative Model for the Neurovascular Coupling in the Brain.” bioRxiv. https://doi.org/10.1101/2021.03.25.437053.
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+
Tomasoni, Danilo, Alessio Paris, Stefano Giampiccolo, Federico Reali, Giulia Simoni, Luca Marchetti, Chanchala Kaddi, et al. 2021. “QSPcc Reduces Bottlenecks in Computational Model Simulations.” Communications Biology 4 (1): 1022. https://doi.org/10.1038/s42003-021-02553-9.
-
+
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.” Metabolic Engineering 64: 74–84. https://doi.org/10.1016/j.ymben.2021.01.008.
-
+
Vanhoefer, Jakob, Marta R. A. Matos, Dilan Pathirana, Yannik Schälte, and Jan Hasenauer. 2021. “Yaml2sbml: Human-Readable and -Writable Specification of ODE Models and Their Conversion to @@ -282,8 +291,7 @@ Specification of ODE Models and Their Conversion to (61): 3215. https://doi.org/10.21105/joss.03215.
-
+
Villaverde, Alejandro F, Dilan Pathirana, Fabian Fröhlich, Jan Hasenauer, and Julio R Banga. 2021. “A protocol for dynamic model calibration.” Briefings in @@ -293,16 +301,16 @@ href="https://doi.org/10.1093/bib/bbab387">https://doi.org/10.1093/bib/bbab387

2020

-
+role="list"> +
Alabert, Constance, Carolin Loos, Moritz Voelker-Albert, Simona Graziano, Ignasi Forné, Nazaret Reveron-Gomez, Lea Schuh, et al. 2020. “Domain Model Explains Propagation Dynamics and Stability of -Histone H3k27 and H3k36 Methylation Landscapes.” Cell +Histone H3K27 and H3K36 Methylation Landscapes.” Cell Reports 30 (January): 1223–1234.e8. https://doi.org/10.1016/j.celrep.2019.12.060.
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+
Erdem, Cemal, Ethan M. Bensman, Arnab Mutsuddy, Michael M. Saint-Antoine, Mehdi Bouhaddou, Robert C. Blake, Will Dodd, et al. 2020. “A Simple and Efficient Pipeline for Construction, Merging, @@ -310,7 +318,7 @@ Expansion, and Simulation of Large-Scale, Single-Cell Mechanistic Models.” bioRxiv. https://doi.org/10.1101/2020.11.09.373407.
-
+
Gerosa, Luca, Christopher Chidley, Fabian Fröhlich, Gabriela Sanchez, Sang Kyun Lim, Jeremy Muhlich, Jia-Yun Chen, et al. 2020. “Receptor-Driven ERK Pulses Reconfigure MAPK Signaling and Enable @@ -318,21 +326,20 @@ Persistence of Drug-Adapted BRAF-Mutant Melanoma Cells.” Cell Systems. https://doi.org/10.1016/j.cels.2020.10.002.
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+
Kuritz, Karsten, Alain R Bonny, João Pedro Fonseca, and Frank Allgöwer. 2020. “PDE-Constrained Optimization for Estimating Population Dynamics over Cell Cycle from Static Single Cell Measurements.” bioRxiv. https://doi.org/10.1101/2020.03.30.015909.
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+
Maier, Corinna, Niklas Hartung, Charlotte Kloft, Wilhelm Huisinga, and Jana de Wiljes. 2020. “Reinforcement Learning and Bayesian Data Assimilation for Model-Informed Precision Dosing in Oncology.” https://arxiv.org/abs/2006.01061.
-
+
Schälte, Yannik, and Jan Hasenauer. 2020. “Efficient exact inference for dynamical systems with noisy measurements using sequential approximate Bayesian @@ -340,23 +347,22 @@ computation.” Bioinformatics 36 (Supplement_1): i551–59. https://doi.org/10.1093/bioinformatics/btaa397.
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+
Schmiester, Leonard, Daniel Weindl, and Jan Hasenauer. 2020. “Parameterization of Mechanistic Models from Qualitative Data Using an Efficient Optimal Scaling Approach.” Journal of Mathematical Biology, July. https://doi.org/10.1007/s00285-020-01522-w.
-
+
Schuh, Lea, Carolin Loos, Daniil Pokrovsky, Axel Imhof, Ralph A. W. -Rupp, and Carsten Marr. 2020. “H4k20 Methylation Is Differently +Rupp, and Carsten Marr. 2020. “H4K20 Methylation Is Differently Regulated by Dilution and Demethylation in Proliferating and Cell-Cycle-Arrested Xenopus Embryos.” Cell Systems 11 (6): 653–662.e8. https://doi.org/10.1016/j.cels.2020.11.003.
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+
Sten, Sebastian. 2020. “Mathematical Modeling of Neurovascular Coupling.” Linköping University Medical Dissertations. PhD thesis, Linköping UniversityLinköping UniversityLinköping University, @@ -365,14 +371,14 @@ Health Sciences, Center for Medical Image Science; Visualization (CMIV); Linköping University, Division of Diagnostics; Specialist Medicine. https://doi.org/10.3384/diss.diva-167806.
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+
Sten, Sebastian, Fredrik Elinder, Gunnar Cedersund, and Maria Engström. 2020. “A Quantitative Analysis of Cell-Specific Contributions and the Role of Anesthetics to the Neurovascular Coupling.” NeuroImage 215: 116827. https://doi.org/10.1016/j.neuroimage.2020.116827.
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+
Tsipa, Argyro, Jake Alan Pitt, Julio R. Banga, and Athanasios Mantalaris. 2020. “A Dual-Parameter Identification Approach for Data-Based Predictive Modeling of Hybrid Gene Regulatory Network-Growth @@ -383,16 +389,15 @@ href="https://doi.org/10.1007/s00449-020-02360-2">https://doi.org/10.1007/s00449

2019

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+role="list"> +
Dharmarajan, Lekshmi, Hans-Michael Kaltenbach, Fabian Rudolf, and Joerg Stelling. 2019. “A Simple and Flexible Computational Framework for Inferring Sources of Heterogeneity from Single-Cell Dynamics.” Cell Systems 8 (1): 15–26.e11. https://doi.org/10.1016/j.cels.2018.12.007.
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+
Fischer, David S., Anna K. Fiedler, Eric Kernfeld, Ryan M. J. Genga, Aimée Bastidas-Ponce, Mostafa Bakhti, Heiko Lickert, Jan Hasenauer, Rene Maehr, and Fabian J. Theis. 2019. “Inferring Population Dynamics @@ -400,22 +405,21 @@ from Single-Cell RNA-Sequencing Time Series Data.” Nature Biotechnology 37: 461–68. https://doi.org/10.1038/s41587-019-0088-0.
-
+
Gregg, Robert W, Saumendra N Sarkar, and Jason E Shoemaker. 2019. “Mathematical Modeling of the cGAS Pathway Reveals Robustness of -DNA Sensing to Trex1 Feedback.” Journal of Theoretical +DNA Sensing to TREX1 Feedback.” Journal of Theoretical Biology 462 (February): 148–57. https://doi.org/10.1016/j.jtbi.2018.11.001.
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+
Kapfer, Eva-Maria, Paul Stapor, and Jan Hasenauer. 2019. “Challenges in the Calibration of Large-Scale Ordinary Differential Equation Models.” IFAC-PapersOnLine 52 (26): 58–64. https://doi.org/10.1016/j.ifacol.2019.12.236.
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+
Nousiainen, Kari, Jukka Intosalmi, and Harri Lähdesmäki. 2019. “A Mathematical Model for Enhancer Activation Kinetics During Cell Differentiation.” In Algorithms for Computational @@ -423,50 +427,47 @@ Biology, edited by Ian Holmes, Carlos Martı́n-Vide, and Miguel A. Vega-Rodrı́guez, 191–202. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-18174-1_14.
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+
Pedretscher, B., B. Kaltenbacher, and O. Pfeiler. 2019. “Parameter Identification and Uncertainty Quantification in Stochastic State Space Models and Its Application to Texture Analysis.” Applied Numerical Mathematics 146: 38–54. https://doi.org/10.1016/j.apnum.2019.06.020.
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+
Pitt, Jake Alan, and Julio R Banga. 2019. “Parameter Estimation in Models of Biological Oscillators: An Automated Regularised Estimation Approach.” BMC Bioinformatics 20 (February): 82. https://doi.org/10.1186/s12859-019-2630-y.
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+
Schmiester, Leonard, Yannik Schälte, Fabian Fröhlich, Jan Hasenauer, and Daniel Weindl. 2019. “Efficient parameterization of large-scale dynamic models based on relative measurements.” Bioinformatics, July. https://doi.org/10.1093/bioinformatics/btz581.
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Terje Lines, Glenn, Łukasz Paszkowski, Leonard Schmiester, Daniel Weindl, Paul Stapor, and Jan Hasenauer. 2019. “Efficient Computation of Steady States in Large-Scale ODE Models of Biochemical Reaction Networks.” IFAC-PapersOnLine 52 (26): 32–37. https://doi.org/10.1016/j.ifacol.2019.12.232.
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Villaverde, Alejandro F., Elba Raimúndez, Jan Hasenauer, and Julio R. Banga. 2019. “A Comparison of Methods for Quantifying Prediction Uncertainty in Systems Biology.” IFAC-PapersOnLine 52 (26): 45–51. https://doi.org/10.1016/j.ifacol.2019.12.234.
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Wang, Dantong, Paul Stapor, and Jan Hasenauer. 2019. “Dirac Mixture Distributions for the Approximation of Mixed Effects Models.” IFAC-PapersOnLine 52 (26): 200–206. https://doi.org/10.1016/j.ifacol.2019.12.258.
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Watanabe, Simon Berglund. 2019. “Identifiability of Parameters in PBPK Models.” Master’s thesis, Chalmers University of Technology / Department of Mathematical Sciences. https://hdl.handle.net/20.500.

2018

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Ballnus, Benjamin, Steffen Schaper, Fabian J Theis, and Jan Hasenauer. 2018. “Bayesian Parameter Estimation for Biochemical Reaction Networks Using Region-Based Adaptive Parallel Tempering.” Bioinformatics 34 (13): i494–501. https://doi.org/10.1093/bioinformatics/bty229.
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Bast, Lisa, Filippo Calzolari, Michael Strasser, Jan Hasenauer, Fabian J. Theis, Jovica Ninkovic, and Carsten Marr. 2018. “Subtle Changes in Clonal Dynamics Underlie the Age-Related Decline in Neurogenesis.” Cell Reports. https://doi.org/10.1016/j.celrep.2018.11.088.
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Fröhlich, Fabian, Thomas Kessler, Daniel Weindl, Alexey Shadrin, Leonard Schmiester, Hendrik Hache, Artur Muradyan, et al. 2018. “Efficient Parameter Estimation Enables the Prediction of Drug Response Using a @@ -498,7 +499,7 @@ Mechanistic Pan-Cancer Pathway Model.” Cell Systems 7 (6): 567–579.e6. https://doi.org/10.1016/j.cels.2018.10.013.
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Fröhlich, Fabian, Anita Reiser, Laura Fink, Daniel Woschée, Thomas Ligon, Fabian Joachim Theis, Joachim Oskar Rädler, and Jan Hasenauer. 2018. “Multi-Experiment Nonlinear Mixed Effect Modeling of @@ -506,50 +507,48 @@ Single-Cell Translation Kinetics After Transfection.” Npj Systems Biology and Applications 5 (1): 1. https://doi.org/10.1038/s41540-018-0079-7.
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Kaltenbacher, Barbara, and Barbara Pedretscher. 2018. “Parameter Estimation in SDEs via the Fokker–Planck Equation: Likelihood Function and Adjoint Based Gradient Computation.” Journal of Mathematical Analysis and Applications 465 (2): 872–84. https://doi.org/10.1016/j.jmaa.2018.05.048.
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Loos, Carolin, Sabrina Krause, and Jan Hasenauer. 2018. “Hierarchical Optimization for the Efficient Parametrization of ODE Models.” Bioinformatics 34 (24): 4266–73. https://doi.org/10.1093/bioinformatics/bty514.
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Loos, Carolin, Katharina Moeller, Fabian Fröhlich, Tim Hucho, and Jan Hasenauer. 2018. “A Hierarchical, Data-Driven Approach to Modeling Single-Cell Populations Predicts Latent Causes of Cell-to-Cell Variability.” Cell Systems 6 (5): 593–603. https://doi.org/10.1016/j.cels.2018.04.008.
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Pitt, Jake Alan, Lucian Gomoescu, Constantinos C. Pantelides, Benoît Chachuat, and Julio R. Banga. 2018. “Critical Assessment of Parameter Estimation Methods in Models of Biological Oscillators.” IFAC-PapersOnLine 51 (19): 72–75. https://doi.org/10.1016/j.ifacol.2018.09.040.
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Schälte, Y., P. Stapor, and J. Hasenauer. 2018. “Evaluation of Derivative-Free Optimizers for Parameter Estimation in Systems Biology.” FAC-PapersOnLine 51 (19): 98–101. https://doi.org/10.1016/j.ifacol.2018.09.025.
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Stapor, Paul, Fabian Fröhlich, and Jan Hasenauer. 2018. “Optimization and Profile Calculation of ODE Models Using Second Order Adjoint Sensitivity Analysis.” Bioinformatics 34 (13): i151–59. https://doi.org/10.1093/bioinformatics/bty230.
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Villaverde, Alejandro F, Fabian Fröhlich, Daniel Weindl, Jan Hasenauer, and Julio R Banga. 2018. “Benchmarking optimization methods for parameter estimation in large kinetic @@ -559,36 +558,35 @@ href="https://doi.org/10.1093/bioinformatics/bty736">https://doi.org/10.1093/bio

2017

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Ballnus, B., S. Hug, K. Hatz, L. Görlitz, J. Hasenauer, and F. J. Theis. 2017. “Comprehensive Benchmarking of Markov Chain Monte Carlo Methods for Dynamical Systems.” BMC Syst. Biol. 11 (63). https://doi.org/10.1186/s12918-017-0433-1.
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Fröhlich, F., B. Kaltenbacher, F. J. Theis, and J. Hasenauer. 2017. “Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks.” PLoS Comput. Biol. 13 (1): e1005331. https://doi.org/10.1371/journal.pcbi.1005331.
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Fröhlich, F., F. J. Theis, J. O. Rädler, and J. Hasenauer. 2017. “Parameter Estimation for Dynamical Systems with Discrete Events and Logical Operations.” Bioinformatics 33 (7): 1049–56. https://doi.org/10.1093/bioinformatics/btw764.
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Kazeroonian, A., F. J. Theis, and J. Hasenauer. 2017. “A Scalable Moment-Closure Approximation for Large-Scale Biochemical Reaction Networks.” Bioinformatics 33 (14): i293–300. https://doi.org/10.1093/bioinformatics/btx249.
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Maier, C., C. Loos, and J. Hasenauer. 2017. “Robust Parameter Estimation for Dynamical Systems from Outlier-Corrupted Data.” Bioinformatics 33 (5): 718–25. https://doi.org/10.1093/bio

2016

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Boiger, R., J. Hasenauer, S. Hross, and B. Kaltenbacher. 2016. “Integration Based Profile Likelihood Calculation for PDE Constrained Parameter Estimation Problems.” Inverse Prob. 32 (12): 125009. https://doi.org/10.1088/0266-5611/32/12/125009.
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Fiedler, A., S. Raeth, F. J. Theis, A. Hausser, and J. Hasenauer. 2016. “Tailored Parameter Optimization Methods for Ordinary Differential Equation Models with Steady-State Constraints.” BMC Syst. Biol. 10 (80). https://doi.org/10.1186/s12918-016-0319-7.
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Fröhlich, F., P. Thomas, A. Kazeroonian, F. J. Theis, R. Grima, and J. Hasenauer. 2016. “Inference for Stochastic Chemical Kinetics Using Moment Equations and System Size Expansion.” PLoS Comput. Biol. 12 (7): e1005030. https://doi.org/10.1371/journal.pcbi.1005030.
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Hross, S., A. Fiedler, F. J. Theis, and J. Hasenauer. 2016. “Quantitative Comparison of Competing PDE Models for Pom1p Dynamics in Fission Yeast.” In Proc. 6th @@ -628,8 +626,7 @@ Findeisen, E. Bullinger, E. Balsa-Canto, and K. Bernaerts, 49:264–69. 26. IFAC-PapersOnLine. https://doi.org/10.1016/j.ifacol.2016.12.136.
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Kazeroonian, A., F. Fröhlich, A. Raue, F. J. Theis, and J. Hasenauer. 2016. “CERENA: Chemical REaction Network Analyzer – A Toolbox for the @@ -637,7 +634,7 @@ Simulation and Analysis of Stochastic Chemical Kinetics.” PLoS ONE 11 (1): e0146732. https://doi.org/10.1371/journal.pone.0146732.
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Loos, C., A. Fiedler, and J. Hasenauer. 2016. “Parameter Estimation for Reaction Rate Equation Constrained Mixture Models.” In Proc. 13th Int. Conf. Comp. Meth. Syst. @@ -648,8 +645,8 @@ href="https://doi.org/10.1007/978-3-319-45177-0">https://doi.org/10.1007/978-3-3

2015

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Loos, C., C. Marr, F. J. Theis, and J. Hasenauer. 2015. “Computational Methods in Systems Biology.” In, edited by O. Roux and J. Bourdon, 9308:52–63. Lecture Notes in Computer Science.