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377 Publications visible to you, out of a total of 377

Abstract (Expand)

Data standards support the reliable exchange of information, the interoperability of tools, and the reproducibility of scientific results. In systems biology standards are agreed ways of structuring, describing, and associating models and data, as well as their respective parts, graphical visualization, and information about applied experimental or computational methods. Such standards also assist with describing how constituent parts interact together, or are linked, and how they are embedded in their environmental and experimental context. Here the focus will be on standards for formatting models and their content, and on metadata checklists and ontologies that support modeling.

Author: Martin Golebiewski

Date Published: 2019

Publication Type: InBook

Abstract (Expand)

Computational systems biology involves integrating heterogeneous datasets in order to generate models. These models can assist with understanding and prediction of biological phenomena. Generating datasets and integrating them into models involves a wide range of scientific expertise. As a result these datasets are often collected by one set of researchers, and exchanged with others researchers for constructing the models. For this process to run smoothly the data and models must be FAIR-findable, accessible, interoperable, and reusable. In order for data and models to be FAIR they must be structured in consistent and predictable ways, and described sufficiently for other researchers to understand them. Furthermore, these data and models must be shared with other researchers, with appropriately controlled sharing permissions, before and after publication. In this chapter we explore the different data and model standards that assist with structuring, describing, and sharing. We also highlight the popular standards and sharing databases within computational systems biology.

Authors: N. J. Stanford, M. Scharm, P. D. Dobson, M. Golebiewski, M. Hucka, V. B. Kothamachu, D. Nickerson, S. Owen, J. Pahle, U. Wittig, D. Waltemath, C. Goble, P. Mendes, J. Snoep

Date Published: 12th Oct 2019

Publication Type: Not specified

Abstract (Expand)

Physiologically based pharmacokinetic (PBPK) models have been proposed as a tool for more accurate individual pharmacokinetic (PK) predictions and model-informed precision dosing, but their application in clinical practice is still rare. This study systematically assesses the benefit of using individual patient information to improve PK predictions. A PBPK model of caffeine was stepwise personalized by using individual data on (1) demography, (2) physiology, and (3) cytochrome P450 (CYP) 1A2 phenotype of 48 healthy volunteers participating in a single-dose clinical study. Model performance was benchmarked against a caffeine base model simulated with parameters of an average individual. In the first step, virtual twins were generated based on the study subjects' demography (height, weight, age, sex), which implicated the rescaling of average organ volumes and blood flows. The accuracy of PK simulations improved compared with the base model. The percentage of predictions within 0.8-fold to 1.25-fold of the observed values increased from 45.8% (base model) to 57.8% (Step 1). However, setting physiological parameters (liver blood flow determined by magnetic resonance imaging, glomerular filtration rate, hematocrit) to measured values in the second step did not further improve the simulation result (59.1% in the 1.25-fold range). In the third step, virtual twins matching individual demography, physiology, and CYP1A2 activity considerably improved the simulation results. The percentage of data within the 1.25-fold range was 66.15%. This case study shows that individual PK profiles can be predicted more accurately by considering individual attributes and that personalized PBPK models could be a valuable tool for model-informed precision dosing approaches in the future.

Authors: Rebekka Fendt, Ute Hofmann, Annika Schneider, Elke Schaeffeler, Rolf Burghaus, Ali Yilmaz, Lars Mathias Blank, Reinhold Kerb, Jan-Frederik Schlender, Matthias Schwab, Lars Kuepfer

Date Published: 30th May 2021

Publication Type: Journal

Abstract (Expand)

In health and disease, liver cells are continuously exposed to cytokines and growth factors. While individual signal transduction pathways induced by these factors were studied in great detail, the cellular responses induced by repeated or combined stimulations are complex and less understood. Growth factor receptors on the cell surface of hepatocytes were shown to be regulated by receptor interactions, receptor trafficking and feedback regulation. Here, we exemplify how mechanistic mathematical modelling based on quantitative data can be employed to disentangle these interactions at the molecular level. Crucial is the analysis at a mechanistic level based on quantitative longitudinal data within a mathematical framework. In such multi-layered information, step-wise mathematical modelling using submodules is of advantage, which is fostered by sharing of standardized experimental data and mathematical models. Integration of signal transduction with metabolic regulation in the liver and mechanistic links to translational approaches promise to provide predictive tools for biology and personalized medicine.

Authors: Lorenza A. D’Alessandro, Ursula Klingmüller, Marcel Schilling

Date Published: 30th Jun 2022

Publication Type: Journal

Abstract

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Authors: Zhe Shen, Yan Liu, Bedair Dewidar, Junhao Hu, Ogyi Park, Teng Feng, Chengfu Xu, Chaohui Yu, Qi Li, Christoph Meyer, Iryna Ilkavets, Alexandra Müller, Carolin Stump-Guthier, Stefan Munker, Roman Liebe, Vincent Zimmer, Frank Lammert, Peter R. Mertens, Hai Li, Peter ten Dijke, Hellmut G. Augustin, Jun Li, Bin Gao, Matthias P. Ebert, Steven Dooley, Youming Li, Hong-Lei Weng

Date Published: 1st Jul 2016

Publication Type: Not specified

Abstract

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Authors: Miquel Serra-Burriel, Adrià Juanola, Feliu Serra-Burriel, Maja Thiele, Isabel Graupera, Elisa Pose, Guillem Pera, Ivica Grgurevic, Llorenç Caballeria, Salvatore Piano, Laurens van Kleef, Mathias Reichert, Dominique Roulot, Juan M Pericàs, Jörn M Schattenberg, Emmanuel A Tsochatztis, Indra Neil Guha, Montserrat Garcia-Retortillo, Rosario Hernández, Jordi Hoyo, Matilde Fuentes, Carmen Expósito, Alba Martínez, Patricia Such, Anita Madir, Sönke Detlefsen, Marta Tonon, Andrea Martini, Ann T Ma, Judith Pich, Eva Bonfill, Marta Juan, Anna Soria, Marta Carol, Jordi Gratacós-Ginès, Rosa M Morillas, Pere Toran, J M Navarrete, Antoni Torrejón, Céline Fournier, Anne Llorca, Anita Arslanow, Harry J de Koning, Fernando Cucchietti, Michael Manns, Phillip N Newsome, Rubén Hernáez, Alina Allen, Paolo Angeli, Robert J de Knegt, Tom H Karlsen, Peter Galle, Vincent Wai-Sun Wong, Núria Fabrellas, Laurent Castera, Aleksander Krag, Frank Lammert, Patrick S Kamath, Pere Ginès, Marifé Alvarez, Peter Andersen, Paolo Angeli, Alba Ardèvol, Anita Arslanow, Luca Beggiato, Zahia Ben Abdesselam, Lucy Bennett, Bajiha Boutouria, Alessandra Brocca, M. Teresa Broquetas, Llorenç Caballeria, Valeria Calvino, Judith Camacho, Aura Capdevila, Marta Carol, Laurent Castera, Marta Cervera, Fernando Cucchietti, Anna de Fuentes, Rob de Knegt, Harry J de Koning, Sonke Detlefsen, Alba Diaz, José Diéguez Bande, Vanessa Esnault, Núria Fabrellas, Josep Lluis Falcó, Rosa Fernández, Céline Fournier, Matilde Fuentes, Peter Galle, Edgar García, Montserrat García-Retortillo, Esther Garrido, Pere Ginès, Rosa Gordillo Medina, Jordi Gratacós-Ginès, Isabel Graupera, Ivica Grgurevic, Indra Neil Guha, Eva Guix, Johanne Kragh Hansen, Rebecca Harris, Elena Hernández Boluda, Rosario Hernández-Ibañez, Jordi Hoyo, Arfan Ikram, Simone Incicco, Mads Israelsen, Marta Juan, Adrià Juanola, Ralf Kaiser, Patrick S Kamath, Tom H Karlsen, Maria Kjærgaard, Marko Korenjak, Aleksander Krag, Marcin Krawczyk, Philippe Laboulaye, Irina Lambert, Frank Lammert, Simon Langkjær Sørensen, Cristina Laserna-Jiménez, Sonia Lazaro Pi, Elsa Ledain, Vincent Levy, Katrine Prier Lindvig, Anne Llorca, Vanessa Londoño, Guirec Loyer, Ann T. Ma, Anita Madir, Michael Manns, Denise Marshall, M. Lluïsa Martí, Sara Martínez, Ricard Martínez Sala, Roser Masa-Font, Jane Møller Jensen, Rosa M Morillas, Laura Muñoz, Ruth Nadal, Laura Napoleone, JM Navarrete, Phillip N Newsome, Vibeke Nielsen, Martina Pérez, Juan Manuel Pericás-Pulido, Salvatore Piano, Judit Pich, Elisa Pose, Judit Presas Escobet, Matthias Reichert, Carlota Riba, Dominique Roulot, Ana Belén Rubio, Maria Sánchez-Morata, Jörn Schattenberg, Miquel Serra-Burriel, Feliu Serra-Burriel, Louise Skovborg Just, Milan Sonneveld, Anna Soria, Christiane Stern, Patricia Such, Maja Thiele, Marta Tonon, Pere Toran, Antoni Torrejón, Emmanuel A Tsochatzis, Laurens van Kleef, Paulien van Wijngaarden, Vanessa Velázquez, Ana Viu, Susanne Nicole Weber, Tracey Wildsmith

Date Published: 1st Aug 2023

Publication Type: Journal

Abstract

Not specified

Authors: Rolf Reiter, Heiko Tzschätzsch, Florian Schwahofer, Matthias Haas, Christian Bayerl, Marion Muche, Dieter Klatt, Shreyan Majumdar, Meltem Uyanik, Bernd Hamm, Jürgen Braun, Ingolf Sack, Patrick Asbach

Date Published: 11th Nov 2019

Publication Type: Not specified

Abstract (Expand)

Alzheimer's disease (AD) is frequently accompanied by progressing weight loss, correlating with mortality. Counter-intuitively, weight loss in old age might predict AD onset but obesity in midlife increases AD risk. Furthermore, AD is associated with diabetes-like alterations in glucose metabolism. Here, we investigated metabolic features of amyloid precursor protein overexpressing APP23 female mice modeling AD upon long-term challenge with high-sucrose (HSD) or high-fat diet (HFD). Compared to wild type littermates (WT), APP23 females were less prone to mild HSD-induced and considerable HFD-induced glucose tolerance deterioration, despite unaltered glucose tolerance during normal-control diet. Indirect calorimetry revealed increased energy expenditure and hyperactivity in APP23 females. Dietary interventions, especially HFD, had weaker effects on lean and fat mass gain, steatosis and adipocyte hypertrophy of APP23 than WT mice, as shown by (1)H-magnetic-resonance-spectroscopy, histological and biochemical analyses. Proteome analysis revealed differentially regulated expression of mitochondrial proteins in APP23 livers and brains. In conclusion, hyperactivity, increased metabolic rate, and global mitochondrial dysfunction potentially add up to the development of AD-related body weight changes in APP23 females, becoming especially evident during diet-induced metabolic challenge. These findings emphasize the importance of translating this metabolic phenotyping into human research to decode the metabolic component in AD pathogenesis.

Authors: S. Schreyer, N. Berndt, J. Eckstein, M. Mulleder, S. Hemmati-Sadeghi, C. Klein, B. Abuelnor, A. Panzel, D. Meierhofer, J. Spranger, B. Steiner, S. Brachs

Date Published: 16th Apr 2021

Publication Type: Journal

Abstract

Not specified

Authors: Stefan Hoehme, Seddik Hammad, Jan Boettger, Brigitte Begher-Tibbe, Petru Bucur, Eric Vibert, Rolf Gebhardt, Jan G. Hengstler, Dirk Drasdo

Date Published: 2023

Publication Type: Journal

Abstract (Expand)

Physiological liver cell replacement is central to maintaining the organ’s high metabolic activity, although its characteristics are difficult to study in humans. Using retrospective 14C birth dating of cells, we report that human hepatocytes show continuous and lifelong turnover, maintaining the liver a young organ (average age < 3 years). Hepatocyte renewal is highly dependent on the ploidy level. Diploid hepatocytes show an seven-fold higher annual exchange rate than polyploid hepatocytes. These observations support the view that physiological liver cell renewal in humans is mainly dependent on diploid hepatocytes, whereas polyploid cells are compromised in their ability to divide. Moreover, cellular transitions between these two subpopulations are limited, with minimal contribution to the respective other ploidy class under homeostatic conditions. With these findings, we present a new integrated model of homeostatic liver cell generation in humans that provides fundamental insights into liver cell turnover dynamics.

Authors: Paula Heinke, Fabian Rost, Julian Rode, Thilo Welsch, Kanar Alkass, Joshua Feddema, Mehran Salehpour, Göran Possnert, Henrik Druid, Lutz Brusch, Olaf Bergmann

Date Published: 7th Aug 2020

Publication Type: Unpublished

Abstract (Expand)

Physiological liver cell replacement is central to maintaining the organ’s high metabolic activity, although its characteristics are difficult to study in humans. Using retrospective radiocarbon (14C) birth dating of cells, we report that human hepatocytes show continuous and lifelong turnover, allowing the liver to remain a young organ (average age <3 years). Hepatocyte renewal is highly dependent on the ploidy level. Diploid hepatocytes show more than 7-fold higher annual birth rates than polyploid hepatocytes. These observations support the view that physiological liver cell renewal in humans is mainly dependent on diploid hepatocytes, whereas polyploid cells are compromised in their ability to divide. Moreover, cellular transitions between diploid and polyploid hepatocytes are limited under homeostatic conditions. With these findings, we present an integrated model of homeostatic liver cell generation in humans that provides fundamental insights into liver cell turnover dynamics.

Authors: Paula Heinke, Fabian Rost, Julian Rode, Palina Trus, Irina Simonova, Enikő Lázár, Joshua Feddema, Thilo Welsch, Kanar Alkass, Mehran Salehpour, Andrea Zimmermann, Daniel Seehofer, Göran Possnert, Georg Damm, Henrik Druid, Lutz Brusch, Olaf Bergmann

Date Published: 1st Jun 2022

Publication Type: Journal

Abstract (Expand)

Tightly interlinked feedback regulators control the dynamics of intracellular responses elicited by the activation of signal transduction pathways. Interferon alpha (IFNalpha) orchestrates antiviral responses in hepatocytes, yet mechanisms that define pathway sensitization in response to prestimulation with different IFNalpha doses remained unresolved. We establish, based on quantitative measurements obtained for the hepatoma cell line Huh7.5, an ordinary differential equation model for IFNalpha signal transduction that comprises the feedback regulators STAT1, STAT2, IRF9, USP18, SOCS1, SOCS3, and IRF2. The model-based analysis shows that, mediated by the signaling proteins STAT2 and IRF9, prestimulation with a low IFNalpha dose hypersensitizes the pathway. In contrast, prestimulation with a high dose of IFNalpha leads to a dose-dependent desensitization, mediated by the negative regulators USP18 and SOCS1 that act at the receptor. The analysis of basal protein abundance in primary human hepatocytes reveals high heterogeneity in patient-specific amounts of STAT1, STAT2, IRF9, and USP18. The mathematical modeling approach shows that the basal amount of USP18 determines patient-specific pathway desensitization, while the abundance of STAT2 predicts the patient-specific IFNalpha signal response.

Authors: F. Kok, M. Rosenblatt, M. Teusel, T. Nizharadze, V. Goncalves Magalhaes, C. Dachert, T. Maiwald, A. Vlasov, M. Wasch, S. Tyufekchieva, K. Hoffmann, G. Damm, D. Seehofer, T. Boettler, M. Binder, J. Timmer, M. Schilling, U. Klingmuller

Date Published: 23rd Jul 2020

Publication Type: Journal

Abstract (Expand)

The epigenetic regulation of expression of genes involved in the absorption, distribution, metabolism, and excretion (ADME) of drugs contributes to interindividual variability in drug response. Epigenetic mechanisms include DNA methylation, histone modifications, and miRNAs. This review systematically outlines the influence of DNA methylation on ADME gene expression and highlights the consequences for interindividual variability in drug response or drug-induced toxicity and the implications for personalized medicine.

Authors: P. Fisel, E. Schaeffeler, M. Schwab

Date Published: 12th Apr 2016

Publication Type: Not specified

Abstract

Not specified

Authors: Yujia Li, Weronika Pioronska, Zeribe Nwosu, Weiguo Fan, MatthiasP.A. Ebert, Steven Dooley, Sai Wang

Date Published: 2022

Publication Type: Journal

Abstract (Expand)

In systems biology, one of the major tasks is to tailor model complexity to information content of the data. A useful model should describe the data and produce well-determined parameter estimates and predictions. Too small of a model will not be able to describe the data whereas a model which is too large tends to overfit measurement errors and does not provide precise predictions. Typically, the model is modified and tuned to fit the data, which often results in an oversized model. To restore the balance between model complexity and available measurements, either new data has to be gathered or the model has to be reduced. In this manuscript, we present a data-based method for reducing non-linear models. The profile likelihood is utilised to assess parameter identifiability and designate likely candidates for reduction. Parameter dependencies are analysed along profiles, providing context-dependent suggestions for the type of reduction. We discriminate four distinct scenarios, each associated with a specific model reduction strategy. Iterating the presented procedure eventually results in an identifiable model, which is capable of generating precise and testable predictions. Source code for all toy examples is provided within the freely available, open-source modelling environment Data2Dynamics based on MATLAB available at http://www.data2dynamics.org/, as well as the R packages dMod/cOde available at https://github.com/dkaschek/. Moreover, the concept is generally applicable and can readily be used with any software capable of calculating the profile likelihood.

Authors: T. Maiwald, H. Hass, B. Steiert, J. Vanlier, R. Engesser, A. Raue, F. Kipkeew, H. H. Bock, D. Kaschek, C. Kreutz, J. Timmer

Date Published: 3rd Sep 2016

Publication Type: Not specified

Abstract (Expand)

BACKGROUND: Hepatocellular carcinoma is the fifth most prevalent cancer worldwide. High tumour recurrence is the most common cause of the impaired 5-year survival rate of 26-58% after hepatectomy. The aim of this study was to investigate the impact of preoperative dynamic liver function on long-term outcome. MATERIALS AND METHODS: A total of 146 patients that underwent curative resection for HCC at our department from 2005 to 2016 were analysed. Univariate analysis was calculated using Kaplan-Meier method. Multivariable analysis was carried out with Cox regression. RESULTS: The cumulative 1-, 3-, 5-year survival rates were 83%, 42% and 14%, respectively. Multivariable Cox regression yielded that overall survival depends on disease recurrence, haemoglobin, number of tumours, liver cirrhosis, lymphatic vessel invasion, UICC stage and postoperative complications. The corresponding 1-, 3-, 5-year disease-free survival rates were 73%, 32% and 10%, respectively. Multivariable analysis yielded preoperative liver function capacity (HR 2.421; p=0.014), vascular invasion (HR 2.116; p=0.034) and UICC stage (HR 2.200; p=0.037) as risk factors associated with disease-free survival. A subanalysis with respect to the degree of functional impairment implicated that severity of liver function impairment is correlated with the disease-free survival rate. CONCLUSION: This study shows that preoperative dynamic liver function assessed by LiMAx test as well as severity of underlying liver disease have a significant impact on recurrence-free survival after curative hepatectomy. Patients presenting with impaired liver function should be evaluated for other treatment e.g. liver transplantation or receive closer oncological follow-up.

Authors: E. Bluthner, J. Bednarsch, M. Malinowski, P. Binder, J. Pratschke, M. Stockmann, M. Kaffarnik

Date Published: 9th Sep 2019

Publication Type: Not specified

Abstract (Expand)

Being the central metabolic organ of vertebrates, the liver possesses the largest repertoire of metabolic enzymes among all tissues and organs. Almost all metabolic pathways are resident in the parenchymal cell, hepatocyte, but the pathway capacities may largely differ depending on the localization of hepatocytes within the liver acinus-a phenomenon that is commonly referred to as metabolic zonation. Metabolic zonation is rather dynamic since gene expression patterns of metabolic enzymes may change in response to nutrition, drugs, hormones and pathological states of the liver (e.g., fibrosis and inflammation). This fact has to be ultimately taken into account in mathematical models aiming at the prediction of metabolic liver functions in different physiological and pathological settings. Here we present a spatially resolved kinetic tissue model of hepatic glucose metabolism which includes zone-specific temporal changes of enzyme abundances which are driven by concentration gradients of nutrients, hormones and oxygen along the hepatic sinusoids. As key modulators of enzyme expression we included oxygen, glucose and the hormones insulin and glucagon which also control enzyme activities by cAMP-dependent reversible phosphorylation. Starting with an initially non-zonated model using plasma profiles under fed, fasted and diabetic conditions, zonal patterns of glycolytic and gluconeogenetic enzymes as well as glucose uptake and release rates are created as an emergent property. We show that mechanisms controlling the adaptation of enzyme abundances to varying external conditions necessarily lead to the zonation of hepatic carbohydrate metabolism. To the best of our knowledge, this is the first kinetic tissue model which takes into account in a semi-mechanistic way all relevant levels of enzyme regulation.

Authors: N. Berndt, H. G. Holzhutter

Date Published: 12th Jan 2019

Publication Type: Journal

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