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107 Publications visible to you, out of a total of 107
Promoting coordinated development of community-based information standards for modeling in biology: the COMBINE initiative

Abstract (Expand)

The Computational Modeling in Biology Network (COMBINE) is a consortium of groups involved in the development of open community standards and formats used in computational modeling in biology. COMBINE’s … aim is to act as a coordinator, facilitator, and resource for different standardization efforts whose domains of use cover related areas of the computational biology space. In this perspective article, we summarize COMBINE, its general organization, and the community standards and other efforts involved in it. Our goals are to help guide readers toward standards that may be suitable for their research activities, as well as to direct interested readers to relevant communities where they can best expect to receive assistance in how to develop interoperable computational models.

Authors: Michael Hucka, David Phillip Nickerson, Gary Bader, Frank T Bergmann, Jonathan Cooper, Emek Demir, Alan Garny, Martin Golebiewski, Chris John Myers, Falk Schreiber, Dagmar Waltemath, Nicolas Le Novère

Date Published: 24th Feb 2015

Publication Type: Journal

COMBINE archive and OMEX format: one file to share all information to reproduce a modeling project

Abstract

Not specified

Authors: Frank T Bergmann, Richard Adams, Stuart Moodie, Jonathan Cooper, Mihai Glont, Martin Golebiewski, Michael Hucka, Camille Laibe, Andrew K Miller, David P Nickerson, Brett G Olivier, Nicolas Rodriguez, Herbert M Sauro, Martin Scharm, Stian Soiland-Reyes, Dagmar Waltemath, Florent Yvon, Nicolas Le Novère

Date Published: 1st Dec 2014

Publication Type: Journal

Data extraction for the reaction kinetics database SABIO-RK

Abstract (Expand)

SABIO-RK (http://sabio.h-its.org/) is a web-accessible, manually curated database that has been established as a resource for biochemical reactions and their kinetic properties with a focus on supporting … the computational modeling to create models of biochemical reaction networks. SABIO-RK data are mainly extracted from literature but also directly submitted from lab experiments. In most cases the information in the literature is distributed across the whole publication, insufficiently structured and often described without standard terminology. Therefore the manual extraction of knowledge from the literature requires biological experts to understand the paper and interpret the data. The database offers the literature data in a structured format including annotations to controlled vocabularies, ontologies and external databases which supports modellers, as well as experimentalists, in the very time consuming process of collecting information from different publications. Here we describe the data extraction and curation efforts needed for SABIO-RK and give recommendations for publishing kinetic data in a complete and structured manner.

Authors: Ulrike Wittig, Renate Kania, Meik Bittkowski, Elina Wetsch, Lei Shi, Lenneke Jong, Martin Golebiewski, Maja Rey, Andreas Weidemann, Isabel Rojas, Wolfgang Müller

Date Published: 1st May 2014

Publication Type: Journal

Meeting report from the fourth meeting of the Computational Modeling in Biology Network (COMBINE)

Abstract

Not specified

Authors: Dagmar Waltemath, Frank T. Bergmann, Claudine Chaouiya, Tobias Czauderna, Padraig Gleeson, Carole Goble, Martin Golebiewski, Michael Hucka, Nick Juty, Olga Krebs, Nicolas Le Novère, Huaiyu Mi, Ion I. Moraru, Chris J. Myers, David Nickerson, Brett G. Olivier, Nicolas Rodriguez, Falk Schreiber, Lucian Smith, Fengkai Zhang, Eric Bonnet

Date Published: 15th Mar 2014

Publication Type: Journal

Path2Models: large-scale generation of computational models from biochemical pathway maps

Abstract

Not specified

Authors: Finja Büchel, Nicolas Rodriguez, Neil Swainston, Clemens Wrzodek, Tobias Czauderna, Roland Keller, Florian Mittag, Michael Schubert, Mihai Glont, Martin Golebiewski, Martijn van Iersel, Sarah Keating, Matthias Rall, Michael Wybrow, Henning Hermjakob, Michael Hucka, Douglas B Kell, Wolfgang Müller, Pedro Mendes, Andreas Zell, Claudine Chaouiya, Julio Saez-Rodriguez, Falk Schreiber, Camille Laibe, Andreas Dräger, Nicolas Le Novère

Date Published: 1st Dec 2013

Publication Type: Journal

Challenges for an enzymatic reaction kinetics database.

Abstract (Expand)

The scientific literature contains a tremendous amount of kinetic data describing the dynamic behaviour of biochemical reactions over time. These data are needed for computational modelling to create … models of biochemical reaction networks and to obtain a better understanding of the processes in living cells. To extract the knowledge from the literature, biocurators are required to understand a paper and interpret the data. For modellers, as well as experimentalists, this process is very time consuming because the information is distributed across the publication and, in most cases, is insufficiently structured and often described without standard terminology. In recent years, biological databases for different data types have been developed. The advantages of these databases lie in their unified structure, searchability and the potential for augmented analysis by software, which supports the modelling process. We have developed the SABIO-RK database for biochemical reaction kinetics. In the present review, we describe the challenges for database developers and curators, beginning with an analysis of relevant publications up to the export of database information in a standardized format. The aim of the present review is to draw the experimentalist's attention to the problem (from a data integration point of view) of incompletely and imprecisely written publications. We describe how to lower the barrier to curators and improve this situation. At the same time, we are aware that curating experimental data takes time. There is a community concerned with making the task of publishing data with the proper structure and annotation to ontologies much easier. In this respect, we highlight some useful initiatives and tools.

Authors: U. Wittig, M. Rey, R. Kania, M. Bittkowski, L. Shi, M. Golebiewski, A. Weidemann, W. Muller, I. Rojas

Date Published: 30th Oct 2013

Publication Type: Journal

Excemplify: a flexible template based solution, parsing and managing data in spreadsheets for experimentalists.

Abstract (Expand)

In systems biology, quantitative experimental data is the basis of building mathematical models. In most of the cases, they are stored in Excel files and hosted locally. To have a public database for … collecting, retrieving and citing experimental raw data as well as experimental conditions is important for both experimentalists and modelers. However, the great effort needed in the data handling procedure and in the data submission procedure becomes the crucial limitation for experimentalists to contribute to a database, thereby impeding the database to deliver its benefit. Moreover, manual copy and paste operations which are commonly used in those procedures increase the chance of making mistakes. Excemplify, a web-based application, proposes a flexible and adaptable template-based solution to solve these problems. Comparing to the normal template based uploading approach, which is supported by some public databases, rather than predefining a format that is potentiall impractical, Excemplify allows users to create their own experiment-specific content templates in different experiment stages and to build corresponding knowledge bases for parsing. Utilizing the embedded knowledge of used templates, Excemplify is able to parse experimental data from the initial setup stage and generate following stages spreadsheets automatically. The proposed solution standardizes the flows of data traveling according to the standard procedures of applying the experiment, cuts down the amount of manual effort and reduces the chance of mistakes caused by manual data handling. In addition, it maintains the context of meta-data from the initial preparation manuscript and improves the data consistency. It interoperates and complements RightField and SEEK as well.

Authors: L. Shi, L. Jong, U. Wittig, P. Lucarelli, M. Stepath, S. Mueller, L. A. D'Alessandro, U. Klingmuller, W. Muller

Date Published: 4th Apr 2013

Publication Type: Journal

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