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Article ; Online: Robust calibration of hierarchical population models for heterogeneous cell populations.

Loos, Carolin / Hasenauer, Jan

2019 Volume 488, Page(s) 110118

Abstract: Cellular heterogeneity is known to have important effects on signal processing and cellular decision making. To understand these processes, multiple classes of mathematical models have been introduced. The hierarchical population model builds a novel ... ...

Abstract	Cellular heterogeneity is known to have important effects on signal processing and cellular decision making. To understand these processes, multiple classes of mathematical models have been introduced. The hierarchical population model builds a novel class which allows for the mechanistic description of heterogeneity and explicitly takes into account subpopulation structures. However, this model requires a parametric distribution assumption for the cell population and, so far, only the normal distribution has been employed. Here, we incorporate alternative distribution assumptions into the model, assess their robustness against outliers and evaluate their influence on the performance of model calibration in a simulation study and a real-world application example. We found that alternative distributions provide reliable parameter estimates even in the presence of outliers, and can in fact increase the convergence of model calibration.
MeSH term(s)	Calibration ; Computer Simulation ; Models, Statistical ; Models, Theoretical ; Normal Distribution
Language	English
Publishing date	2019-12-19
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2972-5
ISSN	1095-8541 ; 0022-5193
ISSN (online)	1095-8541
ISSN	0022-5193
DOI	10.1016/j.jtbi.2019.110118
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Article ; Online: Dissecting the antibody-OME: past, present, and future.

Loos, Carolin / Lauffenburger, Douglas A / Alter, Galit

Current opinion in immunology

2020 Volume 65, Page(s) 89–96

Abstract: Humoral immunity is key to protection for nearly all licensed vaccines. Yet, the design of vaccines has been more difficult for some of our most deadly killers (e.g. HIV, influenza, Dengue virus, etc.), likely due to our incomplete understanding of the ... ...

Abstract	Humoral immunity is key to protection for nearly all licensed vaccines. Yet, the design of vaccines has been more difficult for some of our most deadly killers (e.g. HIV, influenza, Dengue virus, etc.), likely due to our incomplete understanding of the precise immunological mechanisms associated with protection. Humoral immunity is governed both by B-cells and their bi-functional secreted antibodies, all of which have a unique capacity to evolve during an immune response. Current OMIC technologies capture individual features of the humoral immune response, providing a glimpse into humoral components (Fab/Fc/B-cell-omic), but fail to provide a wholistic view of the humoral response as a collective functional arm. Here, we dissect current OMIC strategies reviewing experimental and computational approaches, that if integrated could provide a true systems-level view of the humoral immune response.
MeSH term(s)	Animals ; Antibodies, Viral/immunology ; Humans ; Immunity, Humoral/immunology
Chemical Substances	Antibodies, Viral
Language	English
Publishing date	2020-08-02
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	1035767-1
ISSN	1879-0372 ; 0952-7915
ISSN (online)	1879-0372
ISSN	0952-7915
DOI	10.1016/j.coi.2020.06.003
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Article ; Online: Comparison of null models for combination drug therapy reveals Hand model as biochemically most plausible.

Sinzger, Mark / Vanhoefer, Jakob / Loos, Carolin / Hasenauer, Jan

Scientific reports

2019 Volume 9, Issue 1, Page(s) 3002

Abstract: Null models for the effect of combination therapies are widely used to evaluate synergy and antagonism of drugs. Due to the relevance of null models, their suitability is continuously discussed. Here, we contribute to the discussion by investigating the ... ...

Abstract	Null models for the effect of combination therapies are widely used to evaluate synergy and antagonism of drugs. Due to the relevance of null models, their suitability is continuously discussed. Here, we contribute to the discussion by investigating the properties of five null models. Our study includes the model proposed by David J. Hand, which we refer to as Hand model. The Hand model has been introduced almost 20 years ago but hardly was used and studied. We show that the Hand model generalizes the principle of dose equivalence compared to the Loewe model and resolves the ambiguity of the Tallarida model. This provides a solution to the persisting conflict about the compatibility of two essential model properties: the sham combination principle and the principle of dose equivalence. By embedding several null models into a common framework, we shed light in their biochemical validity and provide indications that the Hand model is biochemically most plausible. We illustrate the practical implications and differences between null models by examining differences of null models on published data.
MeSH term(s)	Computer Simulation/standards ; Drug Interactions ; Drug Therapy, Combination/adverse effects ; Drug Therapy, Combination/methods ; Models, Theoretical
Language	English
Publishing date	2019-02-28
Publishing country	England
Document type	Comparative Study ; Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-019-38907-x
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Book ; Online: Mathematical modeling of variability in intracellular signaling

Loos, Carolin / Hasenauer, Jan

2019

Abstract: Cellular signaling is essential in information processing and decision making. Therefore, a variety of experimental approaches have been developed to study signaling on bulk and single-cell level. Single-cell measurements of signaling molecules ... ...

Abstract	Cellular signaling is essential in information processing and decision making. Therefore, a variety of experimental approaches have been developed to study signaling on bulk and single-cell level. Single-cell measurements of signaling molecules demonstrated a substantial cell-to-cell variability, raising questions about its causes and mechanisms and about how cell populations cope with or exploit cellular heterogeneity. To gain insights from single-cell signaling data, analysis and modeling approaches have been introduced. This review discusses these modeling approaches, with a focus on recent advances in the development and calibration of mechanistic models. Additionally, it outlines current and future challenges.
Keywords	Quantitative Biology - Quantitative Methods
Publishing date	2019-04-17
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes.

Fröhlich, Fabian / Loos, Carolin / Hasenauer, Jan

Methods in molecular biology (Clifton, N.J.)

2018 Volume 1883, Page(s) 385–422

Abstract: Ordinary differential equation models have become a standard tool for the mechanistic description of biochemical processes. If parameters are inferred from experimental data, such mechanistic models can provide accurate predictions about the behavior of ... ...

Abstract	Ordinary differential equation models have become a standard tool for the mechanistic description of biochemical processes. If parameters are inferred from experimental data, such mechanistic models can provide accurate predictions about the behavior of latent variables or the process under new experimental conditions. Complementarily, inference of model structure can be used to identify the most plausible model structure from a set of candidates, and, thus, gain novel biological insight. Several toolboxes can infer model parameters and structure for small- to medium-scale mechanistic models out of the box. However, models for highly multiplexed datasets can require hundreds to thousands of state variables and parameters. For the analysis of such large-scale models, most algorithms require intractably high computation times. This chapter provides an overview of the state-of-the-art methods for parameter and model inference, with an emphasis on scalability.
MeSH term(s)	Algorithms ; Biochemical Phenomena ; Data Interpretation, Statistical ; Datasets as Topic ; Models, Biological ; Systems Biology/instrumentation ; Systems Biology/methods
Language	English
Publishing date	2018-12-13
Publishing country	United States
Document type	Journal Article ; Review
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-4939-8882-2_16
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Article ; Online: Hierarchical optimization for the efficient parametrization of ODE models.

Loos, Carolin / Krause, Sabrina / Hasenauer, Jan

Bioinformatics (Oxford, England)

2018 Volume 34, Issue 24, Page(s) 4266–4273

Abstract: Motivation: Mathematical models are nowadays important tools for analyzing dynamics of cellular processes. The unknown model parameters are usually estimated from experimental data. These data often only provide information about the relative changes ... ...

Abstract	Motivation: Mathematical models are nowadays important tools for analyzing dynamics of cellular processes. The unknown model parameters are usually estimated from experimental data. These data often only provide information about the relative changes between conditions, hence, the observables contain scaling parameters. The unknown scaling parameters and corresponding noise parameters have to be inferred along with the dynamic parameters. The nuisance parameters often increase the dimensionality of the estimation problem substantially and cause convergence problems. Results: In this manuscript, we propose a hierarchical optimization approach for estimating the parameters for ordinary differential equation (ODE) models from relative data. Our approach restructures the optimization problem into an inner and outer subproblem. These subproblems possess lower dimensions than the original optimization problem, and the inner problem can be solved analytically. We evaluated accuracy, robustness and computational efficiency of the hierarchical approach by studying three signaling pathways. The proposed approach achieved better convergence than the standard approach and required a lower computation time. As the hierarchical optimization approach is widely applicable, it provides a powerful alternative to established approaches. Availability and implementation: The code is included in the MATLAB toolbox PESTO which is available at http://github.com/ICB-DCM/PESTO. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Computational Biology/methods ; Models, Biological ; Signal Transduction ; Software/standards
Language	English
Publishing date	2018-07-16
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/bty514
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Article ; Online: Hierarchical optimization for the efficient parametrization of ODE models

Loos, Carolin / Krause, Sabrina / Hasenauer, Jan

Bioinformatics. 2018 Dec. 15, v. 34, no. 24, p. 4266-4273

2018 , Page(s) 4266–4273

Abstract: Mathematical models are nowadays important tools for analyzing dynamics of cellular processes. The unknown model parameters are usually estimated from experimental data. These data often only provide information about the relative changes between ... ...

Abstract	Mathematical models are nowadays important tools for analyzing dynamics of cellular processes. The unknown model parameters are usually estimated from experimental data. These data often only provide information about the relative changes between conditions, hence, the observables contain scaling parameters. The unknown scaling parameters and corresponding noise parameters have to be inferred along with the dynamic parameters. The nuisance parameters often increase the dimensionality of the estimation problem substantially and cause convergence problems. In this manuscript, we propose a hierarchical optimization approach for estimating the parameters for ordinary differential equation (ODE) models from relative data. Our approach restructures the optimization problem into an inner and outer subproblem. These subproblems possess lower dimensions than the original optimization problem, and the inner problem can be solved analytically. We evaluated accuracy, robustness and computational efficiency of the hierarchical approach by studying three signaling pathways. The proposed approach achieved better convergence than the standard approach and required a lower computation time. As the hierarchical optimization approach is widely applicable, it provides a powerful alternative to established approaches. The code is included in the MATLAB toolbox PESTO which is available at http://github.com/ICB-DCM/PESTO Supplementary data are available at Bioinformatics online.
Keywords	bioinformatics ; differential equation ; system optimization
Language	English
Dates of publication	2018-1215
Size	p. 4266-4273
Publishing place	Oxford University Press
Document type	Article ; Online
Note	NAL-AP-2-clean ; Use and reproduction
ZDB-ID	1468345-3
ISSN	1367-4811 ; 1460-2059
ISSN	1367-4811 ; 1460-2059
DOI	10.1093/bioinformatics/bty514
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: H4K20 Methylation Is Differently Regulated by Dilution and Demethylation in Proliferating and Cell-Cycle-Arrested Xenopus Embryos.

Schuh, Lea / Loos, Carolin / Pokrovsky, Daniil / Imhof, Axel / Rupp, Ralph A W / Marr, Carsten

Cell systems

2020 Volume 11, Issue 6, Page(s) 653–662.e8

Abstract: DNA replication during cell division leads to dilution of histone modifications and can thus affect chromatin-mediated gene regulation, raising the question of how the cell-cycle shapes the histone modification landscape, particularly during ... ...

Abstract	DNA replication during cell division leads to dilution of histone modifications and can thus affect chromatin-mediated gene regulation, raising the question of how the cell-cycle shapes the histone modification landscape, particularly during embryogenesis. We tackled this problem by manipulating the cell cycle during early Xenopus laevis embryogenesis and analyzing in vivo histone H4K20 methylation kinetics. The global distribution of un-, mono-, di-, and tri-methylated histone H4K20 was measured by mass spectrometry in normal and cell-cycle-arrested embryos over time. Using multi-start maximum likelihood optimization and quantitative model selection, we found that three specific biological methylation rate constants were required to explain the measured H4K20 methylation state kinetics. While demethylation is essential for regulating H4K20 methylation kinetics in non-cycling cells, demethylation is very likely dispensable in rapidly dividing cells of early embryos, suggesting that cell-cycle-mediated dilution of H4K20 methylation is an essential regulatory component for shaping its epigenetic landscape during early development. A record of this paper's transparent peer review process is included in the Supplemental Information.
MeSH term(s)	Animals ; Cell Cycle Checkpoints/genetics ; Cell Proliferation ; Demethylation ; Methylation ; Xenopus laevis/embryology
Language	English
Publishing date	2020-12-08
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2854138-8
ISSN	2405-4720 ; 2405-4712
ISSN (online)	2405-4720
ISSN	2405-4712
DOI	10.1016/j.cels.2020.11.003
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Article ; Online: Systems serology-based comparison of antibody effector functions induced by adjuvanted vaccines to guide vaccine design.

Loos, Carolin / Coccia, Margherita / Didierlaurent, Arnaud M / Essaghir, Ahmed / Fallon, Jonathan K / Lauffenburger, Douglas / Luedemann, Corinne / Michell, Ashlin / van der Most, Robbert / Zhu, Alex Lee / Alter, Galit / Burny, Wivine

NPJ vaccines

2023 Volume 8, Issue 1, Page(s) 34

Abstract: The mechanisms by which antibodies confer protection vary across vaccines, ranging from simple neutralization to functions requiring innate immune recruitment via Fc-dependent mechanisms. The role of adjuvants in shaping the maturation of antibody- ... ...

Abstract	The mechanisms by which antibodies confer protection vary across vaccines, ranging from simple neutralization to functions requiring innate immune recruitment via Fc-dependent mechanisms. The role of adjuvants in shaping the maturation of antibody-effector functions remains under investigated. Using systems serology, we compared adjuvants in licensed vaccines (AS01
Language	English
Publishing date	2023-03-08
Publishing country	England
Document type	Journal Article
ISSN	2059-0105
ISSN (online)	2059-0105
DOI	10.1038/s41541-023-00613-1
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Article ; Online: Robust parameter estimation for dynamical systems from outlier-corrupted data.

Maier, Corinna / Loos, Carolin / Hasenauer, Jan

Bioinformatics (Oxford, England)

2017 Volume 33, Issue 5, Page(s) 718–725

Abstract: Motivation: Dynamics of cellular processes are often studied using mechanistic mathematical models. These models possess unknown parameters which are generally estimated from experimental data assuming normally distributed measurement noise. Outlier ... ...

Abstract	Motivation: Dynamics of cellular processes are often studied using mechanistic mathematical models. These models possess unknown parameters which are generally estimated from experimental data assuming normally distributed measurement noise. Outlier corruption of datasets often cannot be avoided. These outliers may distort the parameter estimates, resulting in incorrect model predictions. Robust parameter estimation methods are required which provide reliable parameter estimates in the presence of outliers. Results: In this manuscript, we propose and evaluate methods for estimating the parameters of ordinary differential equation models from outlier-corrupted data. As alternatives to the normal distribution as noise distribution, we consider the Laplace, the Huber, the Cauchy and the Student's t distribution. We assess accuracy, robustness and computational efficiency of estimators using these different distribution assumptions. To this end, we consider artificial data of a conversion process, as well as published experimental data for Epo-induced JAK/STAT signaling. We study how well the methods can compensate and discover artificially introduced outliers. Our evaluation reveals that using alternative distributions improves the robustness of parameter estimates. Availability and implementation: The MATLAB implementation of the likelihood functions using the distribution assumptions is available at Bioinformatics online. Contact: jan.hasenauer@helmholtz-muenchen.de. Supplementary information: Supplementary material are available at Bioinformatics online.
Language	English
Publishing date	2017-03-01
Publishing country	England
Document type	Journal Article
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btw703
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