Publications

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, Martin 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

Journal: Methods Mol Biol

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

Journal: Encyclopedia of Bioinformatics and Computational Biology

Abstract

[This corrects the article DOI: 10.4056/sigs.5279417.].

Authors: D. Waltemath, F. T. Bergmann, C. Chaouiya, T. Czauderna, P. Gleeson, C. Goble, Martin Golebiewski, M. Hucka, N. Juty, O. Krebs, N. Le Novere, H. Mi, I. I. Moraru, C. J. Myers, D. Nickerson, B. G. Olivier, N. Rodriguez, F. Schreiber, L. Smith, F. Zhang, E. Bonnet

Date Published: 9th Aug 2018

Journal: Stand Genomic Sci

Abstract (Expand)

Standards are essential to the advancement of Systems and Synthetic Biology. COMBINE provides a formal body and a centralised platform to help develop and disseminate relevant standards and related resources. The regular special issue of the Journal of Integrative Bioinformatics aims to support the exchange, distribution and archiving of these standards by providing unified, easily citable access. This paper provides an overview of existing COMBINE standards and presents developments of the last year.

Authors: F. Schreiber, G. D. Bader, P. Gleeson, Martin Golebiewski, M. Hucka, S. M. Keating, N. L. Novere, C. Myers, D. Nickerson, B. Sommer, D. Waltemath

Date Published: 30th Mar 2018

Journal: J Integr Bioinform

Abstract (Expand)

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the COmputational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation.

Authors: M. L. Neal, Matthias König, D. Nickerson, G. Misirli, R. Kalbasi, A. Drager, K. Atalag, V. Chelliah, M. T. Cooling, D. L. Cook, S. Crook, M. de Alba, S. H. Friedman, A. Garny, J. H. Gennari, P. Gleeson, Martin Golebiewski, M. Hucka, N. Juty, C. Myers, B. G. Olivier, H. M. Sauro, M. Scharm, J. L. Snoep, V. Toure, A. Wipat, O. Wolkenhauer, D. Waltemath

Date Published: 22nd Jan 2018

Journal: Brief Bioinform

Abstract (Expand)

Standards for data exchange are critical to the development of any field. They enable researchers and practitioners to transport information reliably, to apply a variety of tools to their problems, and to reproduce scientific results. Over the past two decades, a range of standards have been developed to facilitate the exchange and reuse of information in the domain of representation and modeling of biological systems. These standards are complementary, so the interactions between their developers increased over time. By the end of the last decade, the community of researchers decided that more interoperability is required between the standards, and that common development is needed to make better use of effort, time, and money devoted to this activity. The COmputational MOdeling in Biology NEtwork (COMBINE) was created to enable the sharing of resources, tools, and other infrastructure. This paper provides a brief history of this endeavor and the challenges that remain.

Authors: Chris J. Myers, Gary Bader, Padraig Gleeson, Martin Golebiewski, Michael Hucka, Nicolas Le Novere, David P. Nickerson, Falk Schreiber, Dagmar Waltemath

Date Published: 1st Dec 2017

Journal: 2017 Winter Simulation Conference (WSC)

Abstract

Not specified

Authors: Wolfgang Müller, Meik Bittkowski, Martin Golebiewski, Renate Kania, Maja Rey, Andreas Weidemann, Ulrike Wittig

Date Published: 1st Mar 2017

Journal: Datenbank Spektrum

Abstract (Expand)

Standards are essential to the advancement of science and technology. In systems and synthetic biology, numerous standards and associated tools have been developed over the last 16 years. This special issue of the Journal of Integrative Bioinformatics aims to support the exchange, distribution and archiving of these standards, as well as to provide centralised and easily citable access to them.

Authors: F. Schreiber, G. D. Bader, P. Gleeson, Martin Golebiewski, M. Hucka, N. Le Novere, C. Myers, D. Nickerson, B. Sommer, D. Walthemath

Date Published: 12th Feb 2017

Journal: J Integr Bioinform

Abstract (Expand)

The FAIRDOMHub is a repository for publishing FAIR (Findable, Accessible, Interoperable and Reusable) Data, Operating procedures and Models (https://fairdomhub.org/) for the Systems Biology community. It is a web-accessible repository for storing and sharing systems biology research assets. It enables researchers to organize, share and publish data, models and protocols, interlink them in the context of the systems biology investigations that produced them, and to interrogate them via API interfaces. By using the FAIRDOMHub, researchers can achieve more effective exchange with geographically distributed collaborators during projects, ensure results are sustained and preserved and generate reproducible publications that adhere to the FAIR guiding principles of data stewardship.

Authors: K. Wolstencroft, Olga Krebs, J. L. Snoep, N. J. Stanford, F. Bacall, Martin Golebiewski, R. Kuzyakiv, Q. Nguyen, S. Owen, S. Soiland-Reyes, J. Straszewski, D. D. van Niekerk, A. R. Williams, L. Malmstrom, B. Rinn, Wolfgang Müller, C. Goble

Date Published: 3rd Dec 2016

Journal: Nucleic Acids Res

Abstract (Expand)

Reconstructing and understanding the Human Physiome virtually is a complex mathematical problem, and a highly demanding computational challenge. Mathematical models spanning from the molecular level through to whole populations of individuals must be integrated, then personalized. This requires interoperability with multiple disparate and geographically separated data sources, and myriad computational software tools. Extracting and producing knowledge from such sources, even when the databases and software are readily available, is a challenging task. Despite the difficulties, researchers must frequently perform these tasks so that available knowledge can be continually integrated into the common framework required to realize the Human Physiome. Software and infrastructures that support the communities that generate these, together with their underlying standards to format, describe and interlink the corresponding data and computer models, are pivotal to the Human Physiome being realized. They provide the foundations for integrating, exchanging and re-using data and models efficiently, and correctly, while also supporting the dissemination of growing knowledge in these forms. In this paper, we explore the standards, software tooling, repositories and infrastructures that support this work, and detail what makes them vital to realizing the Human Physiome.

Authors: D. Nickerson, K. Atalag, B. de Bono, J. Geiger, C. Goble, S. Hollmann, J. Lonien, Wolfgang Müller, B. Regierer, N. J. Stanford, Martin Golebiewski, P. Hunter

Date Published: 7th Apr 2016

Journal: Interface Focus

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