Publications

What is a Publication?
23 Publications visible to you, out of a total of 23

Abstract (Expand)

A multitude of pharmacokinetics studies have been published. However, due to the lack of an open database, pharmacokinetics data, as well as the corresponding meta-information, have been difficult to access. We present PK-DB (https://pk-db.com), an open database for pharmacokinetics information from clinical trials. PK-DB provides curated information on (i) characteristics of studied patient cohorts and subjects (e.g. age, bodyweight, smoking status, genetic variants); (ii) applied interventions (e.g. dosing, substance, route of application); (iii) pharmacokinetic parameters (e.g. clearance, half-life, area under the curve) and (iv) measured pharmacokinetic time-courses. Key features are the representation of experimental errors, the normalization of measurement units, annotation of information to biological ontologies, calculation of pharmacokinetic parameters from concentration-time profiles, a workflow for collaborative data curation, strong validation rules on the data, computational access via a REST API as well as human access via a web interface. PK-DB enables meta-analysis based on data from multiple studies and data integration with computational models. A special focus lies on meta-data relevant for individualized and stratified computational modeling with methods like physiologically based pharmacokinetic (PBPK), pharmacokinetic/pharmacodynamic (PK/PD), or population pharmacokinetic (pop PK) modeling.

Authors: J. Grzegorzewski, J. Brandhorst, K. Green, D. Eleftheriadou, Y. Duport, F. Barthorscht, A. Koller, D. Y. J. Ke, S. De Angelis, M. Konig

Date Published: 5th Nov 2020

Publication Type: Journal

Abstract (Expand)

Biological models often contain elements that have inexact numerical values, since they are based on values that are stochastic in nature or data that contains uncertainty. The Systems Biology Markup Language (SBML) Level 3 Core specification does not include an explicit mechanism to include inexact or stochastic values in a model, but it does provide a mechanism for SBML packages to extend the Core specification and add additional syntactic constructs. The SBML Distributions package for SBML Level 3 adds the necessary features to allow models to encode information about the distribution and uncertainty of values underlying a quantity.

Authors: Lucian P. Smith, Stuart L. Moodie, Frank T. Bergmann, Colin Gillespie, Sarah M. Keating, Matthias König, Chris J. Myers, Maciek J. Swat, Darren J. Wilkinson, Michael Hucka

Date Published: 1st Aug 2020

Publication Type: Journal

Abstract (Expand)

Systems biology has experienced dramatic growth in the number, size and complexity of computational models describing biology. To reproduce simulation results and reuse models, researchers need to exchange precise and unambiguous descriptions of model structure and meaning. SBML (the Systems Biology Markup Language) is a community-developed format for this purpose. The latest edition, called SBML Level 3, has a modular structure, with a core suited to representing reaction-based models, and packages that extend the core with features suited for a variety of model types. Examples include constraint-based models, reaction-diffusion models, logical network models, and rule-based models. SBML and its rich software ecosystem have transformed the way systems biologists build and interact with models, and has played an important role in increasing model interoperability and reuse over the past two decades. More recently, a rise of multiscale models of whole cells and organs, and new data sources such as single cells measurements and live imaging, have precipitated new ways of integrating data and models. SBML Level 3 provides the foundation needed to support this evolution.

Authors: SM Keating, D Waltemath, M König, F Zhang, A Dräger, C Chaouiya, FT Bergmann, A Finney, CS Gillespie, T Helikar, S Hoops, RS Malik-Sheriff, SL Moodie, II Moraru, CJ Myers, A Naldi, BG Olivier, S Sahle, JC Schaff, LP Smith, MJ Swat, DT, L Watanabe, DJ Wilkinson, ML Blinov, K Begley, JR Faeder, HF Gómez, TM Hamm, Y Inagaki, W Liebermeister, AL Lister, D Lucio, E Mjolsness, CJ Proctor, K Raman, N Rodriguez, CA Shaffer, BE Shapiro, J Stelling, N Swainston, N Tanimura, J Wagner, M Meier-Schellersheim, HM Sauro, B Palsson, H Bolouri, H Kitano, Akira Funahashi, H Hermjakob, JC Doyle, M Hucka, SBML Community members

Date Published: 1st Jul 2020

Publication Type: Journal

Abstract

sbmlsim: Python utilities for simulating SBML models available at https://github.com/matthiaskoenig/sbmlsim.

Author: Matthias König

Date Published: 1st Jul 2020

Publication Type: Misc

Abstract (Expand)

This paper presents a report on outcomes of the 10th Computational Modeling in Biology Network (COMBINE) meeting that was held in Heidelberg, Germany, in July of 2019. The annual event brings together researchers, biocurators and software engineers to present recent results and discuss future work in the area of standards for systems and synthetic biology. The COMBINE initiative coordinates the development of various community standards and formats for computational models in the life sciences. Over the past 10 years, COMBINE has brought together standard communities that have further developed and harmonized their standards for better interoperability of models and data. COMBINE 2019 was co-located with a stakeholder workshop of the European EU-STANDS4PM initiative that aims at harmonized data and model standardization for in silico models in the field of personalized medicine, as well as with the FAIRDOM PALs meeting to discuss findable, accessible, interoperable and reusable (FAIR) data sharing. This report briefly describes the work discussed in invited and contributed talks as well as during breakout sessions. It also highlights recent advancements in data, model, and annotation standardization efforts. Finally, this report concludes with some challenges and opportunities that this community will face during the next 10 years.

Authors: Dagmar Waltemath, Martin Golebiewski, Michael L Blinov, Padraig Gleeson, Henning Hermjakob, Michael Hucka, Esther Thea Inau, Sarah M Keating, Matthias König, Olga Krebs, Rahuman S Malik-Sheriff, David Nickerson, Ernst Oberortner, Herbert M Sauro, Falk Schreiber, Lucian Smith, Melanie I Stefan, Ulrike Wittig, Chris J Myers

Date Published: 29th Jun 2020

Publication Type: Journal

Abstract (Expand)

This special issue of the Journal of Integrative Bioinformatics presents papers related to the 10th COMBINE meeting together with the annual update of COMBINE standards in systems and synthetic biology.Not specified

Authors: Falk Schreiber, Björn Sommer, Tobias Czauderna, Martin Golebiewski, Thomas E. Gorochowski, Michael Hucka, Sarah M. Keating, Matthias König, Chris Myers, David Nickerson, Dagmar Waltemath

Date Published: 29th Jun 2020

Publication Type: Journal

Abstract (Expand)

A standardized approach to annotating computational biomedical models and their associated files can facilitate model reuse and reproducibility among research groups, enhance search and retrieval of models and data, and enable semantic comparisons between models. Motivated by these potential benefits and guided by consensus across the COmputational Modeling in BIology NEtwork (COMBINE) community, we have developed a specification for encoding annotations in Open Modeling and EXchange (OMEX)-formatted archives. Distributing modeling projects within these archives is a best practice established by COMBINE, and the OMEX metadata specification presented here provides a harmonized, community-driven approach for annotating a variety of standardized model and data representation formats within an archive. The specification primarily includes technical guidelines for encoding archive metadata, so that software tools can more easily utilize and exchange it, thereby spurring broad advancements in model reuse, discovery, and semantic analyses.

Authors: Maxwell L. Neal, John H. Gennari, Dagmar Waltemath, David P. Nickerson, Matthias König

Date Published: 25th Jun 2020

Publication Type: Journal

Abstract (Expand)

PK-DB is a database and web interface for pharmacokinetics data and information from clinical trials as well as pre-clinical research. PK-DB allows to curate pharmacokinetics data integrated with the corresponding meta-information. PK-DB is available at https://pk-db.com

Authors: Matthias König, Jan Grzegorzewski

Date Published: 1st Jun 2020

Publication Type: Misc

Abstract

Not specified

Authors: C. Lieven, M. E. Beber, B. G. Olivier, F. T. Bergmann, M. Ataman, P. Babaei, J. A. Bartell, L. M. Blank, S. Chauhan, K. Correia, C. Diener, A. Drager, B. E. Ebert, J. N. Edirisinghe, J. P. Faria, A. M. Feist, G. Fengos, R. M. T. Fleming, B. Garcia-Jimenez, V. Hatzimanikatis, W. van Helvoirt, C. S. Henry, H. Hermjakob, M. J. Herrgard, A. Kaafarani, H. U. Kim, Z. King, S. Klamt, E. Klipp, J. J. Koehorst, M. Konig, M. Lakshmanan, D. Y. Lee, S. Y. Lee, S. Lee, N. E. Lewis, F. Liu, H. Ma, D. Machado, R. Mahadevan, P. Maia, A. Mardinoglu, G. L. Medlock, J. M. Monk, J. Nielsen, L. K. Nielsen, J. Nogales, I. Nookaew, B. O. Palsson, J. A. Papin, K. R. Patil, M. Poolman, N. D. Price, O. Resendis-Antonio, A. Richelle, I. Rocha, B. J. Sanchez, P. J. Schaap, R. S. Malik Sheriff, S. Shoaie, N. Sonnenschein, B. Teusink, P. Vilaca, J. O. Vik, J. A. H. Wodke, J. C. Xavier, Q. Yuan, M. Zakhartsev, C. Zhang

Date Published: 4th Mar 2020

Publication Type: Journal

Abstract

sbmlutils is a collection of python utilities for working with SBML models implemented on top of libSBML and other libraries available from https://github.com/matthiaskoenig/sbmlutils

Author: Matthias König

Date Published: 1st Mar 2020

Publication Type: Misc

Abstract (Expand)

To address the issue of reproducibility in computational modeling we developed the concept of an executable simulation model (EXSIMO). An EXSIMO combines model, data and code with the execution environment to run the computational analysis in an automated manner using tools from software engineering. Key components are i) models, data and code for the computational analysis; ii) tests for models, data and code; and iii) an automation layer to run tests and execute the analysis. An EXSIMO combines version control, model, data, units, annotations, analysis, reports, execution environment, testing, continuous integration and release. We applied the concept to perform a replication study of a computational analysis of hepatic glucose metabolism in the liver. The corresponding EXSIMO is available from https://github.com/matthiaskoenig/exsimo.

Author: Matthias König

Date Published: 6th Jan 2020

Publication Type: Unpublished

Abstract (Expand)

EXSIMO: EXecutable SImulation MOdel; Data, model and code for executable simulation model of hepatic glucose metabolism Reports: https://matthiaskoenig.github.io/exsimo/ Docker images:: https://hub.docker.com/r/matthiaskoenig/exsimo Github releases: https://github.com/matthiaskoenig/exsimo/releases

Author: Matthias König

Date Published: 2020

Publication Type: Misc

Abstract (Expand)

Numerical modeling of biological systems has become an important assistance for understanding and predicting hepatic diseases like non‐alcoholic fatty liver disease (NAFLD) or the detoxification of drugs and toxines by the liver. We developed a model for the simulation of hepatic function‐perfusion processes using a multiscale and multiphase approach. Here, the liver lobules are described using a homogenization approach with a coupled set of partial differential equations (PDE) based on the Theory of Porous Media (TPM) to describe the coupled blood transport and tissue deformation. For the description of metabolic processes on cellular scale ordinary differential equations (ODE) are used. For many practical and clinical applications, e.g. optimization procedures or uncertainty quantification, a fast but reliable computation is required. Thus, we use a non‐linear model order reduction (MOR) based on an artificial neural network (ANN) for the prediction of simulation results. The practicability of this approach is shown in a comparison between the high fidelity numerical simulation of a NAFLD and the predicted results by the ANN.

Authors: Lena Lambers, Tim Ricken, Matthias König

Date Published: 1st Nov 2019

Publication Type: Journal

Abstract (Expand)

A multitude of pharmacokinetics studies have been published. However, due to the lack of an open database, pharmacokinetics data, as well as the corresponding meta-information, have been difficult to access. We present PK-DB (https://pk-db.com), an open database for pharmacokinetics information from clinical trials including pre-clinical research. PK-DB provides curated information on (i) characteristics of studied patient cohorts and subjects (e.g. age, bodyweight, smoking status); (ii) applied interventions (e.g. dosing, substance, route of application); (iii) measured pharmacokinetic time-courses; (iv) pharmacokinetic parameters (e.g. clearance, half-life, area under the curve). Key features are the representation of experimental errors, the normalization of measurement units, annotation of information to biological ontologies, calculation of pharmacokinetic parameters from concentration-time profiles, a workflow for collaborative data curation, strong validation rules on the data, computational access via a REST API as well as human access via a web interface. PK-DB enables meta-analysis based on data from multiple studies and data integration with computational models. A special focus lies on meta-data relevant for individualized and stratified computational modeling with methods like physiologically based pharmacokinetic (PBPK), pharmacokinetic/pharmacodynamic (PK/DB), or population pharmacokinetic (pop PK) modeling.

Authors: Jan Grzegorzewski, Janosch Brandhorst, Dimitra Eleftheriadou, Kathleen Green, Matthias König

Date Published: 9th Sep 2019

Publication Type: Unpublished

Abstract (Expand)

This special issue of the Journal of Integrative Bioinformatics presents an overview of COMBINE standards and their latest specifications. The standards cover representation formats for computational modeling in synthetic and systems biology and include BioPAX, CellML, NeuroML, SBML, SBGN, SBOL and SED-ML. The articles in this issue contain updated specifications of SBGN Process Description Level 1 Version 2, SBML Level 3 Core Version 2 Release 2, SBOL Version 2.3.0, and SBOL Visual Version 2.1.

Authors: Falk Schreiber, Björn Sommer, Gary D. Bader, Padraig Gleeson, Martin Golebiewski, Michael Hucka, Sarah M. Keating, Matthias König, Chris Myers, David Nickerson, Dagmar Waltemath

Date Published: 26th Jun 2019

Publication Type: Not specified

Powered by
(v.1.15.2)
Copyright © 2008 - 2024 The University of Manchester and HITS gGmbH