BioKC: a collaborative platform for systems biology model curation and annotation

Curation of biomedical knowledge into standardised and inter-operable systems biology models is essential for studying complex biological processes. However, systems-level curation is a laborious manual process, especially when facing ever increasing growth of domain literature. Currently, these systems-level curation efforts concentrate around dedicated pathway databases, with a limited input from the research community. The demand for systems biology knowledge increases with new findings demonstrating elaborate relationships between multiple molecules, pathways and cells. This new challenge calls for novel collaborative tools and platforms allowing to improve the quality and the output of the curation process. In particular, in the current systems biology environment, curation tools lack reviewing features and are not well suited for an open, community-based curation workflows. An important concern is the complexity of the curation process and the limitations of the tools supporting it. Currently, systems-level curation combines model-building with diagram layout design. However, diagram editing tools offer limited annotation features. On the other hand, text-oriented tools have insufficient capabilities representing and annotating relationships between biological entities. Separating model curation and annotation from diagram editing enables iterative and distributed building of annotated models. Here, we present BioKC (Biological Knowledge Curation), a web-based collaborative platform for the curation and annotation of biomedical knowledge following the standard data model from Systems Biology Markup Language (SBML).

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SBML-PET: a Systems Biology Markup Language-based parameter estimation tool

Bioinformatics ◽

10.1093/bioinformatics/btl443 ◽

2006 ◽

Vol 22 (21) ◽

pp. 2704-2705 ◽

Cited By ~ 50

Author(s):

Z. Zi ◽

E. Klipp

Keyword(s):

Parameter Estimation ◽

Systems Biology ◽

Markup Language ◽

Systems Biology Markup Language

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Systems biology markup language: Level 2 and beyond

Biochemical Society Transactions ◽

10.1042/bst0311472 ◽

2003 ◽

Vol 31 (6) ◽

pp. 1472-1473 ◽

Cited By ~ 105

Author(s):

A. Finney ◽

M. Hucka

Keyword(s):

Systems Biology ◽

Computational Models ◽

Chemical Species ◽

Biochemical Networks ◽

Markup Language ◽

Exchange Format ◽

Systems Biology Markup Language ◽

Level 3 ◽

Level 1 ◽

Level 2

The SBML (systems biology markup language) is a standard exchange format for computational models of biochemical networks. We continue developing SBML collaboratively with the modelling community to meet their evolving needs. The recently introduced SBML Level 2 includes several enhancements to the original Level 1, and features under development for SBML Level 3 include model composition, multistate chemical species and diagrams.

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A standard-enabled workflow for synthetic biology

Biochemical Society Transactions ◽

10.1042/bst20160347 ◽

2017 ◽

Vol 45 (3) ◽

pp. 793-803 ◽

Cited By ~ 24

Author(s):

Chris J. Myers ◽

Jacob Beal ◽

Thomas E. Gorochowski ◽

Hiroyuki Kuwahara ◽

Curtis Madsen ◽

...

Keyword(s):

Systems Biology ◽

Synthetic Biology ◽

Markup Language ◽

Design Tools ◽

Data Standards ◽

Data Repositories ◽

Simulation Tools ◽

Systems Biology Markup Language ◽

Visualization Tools ◽

Synthetic Biology Open Language

A synthetic biology workflow is composed of data repositories that provide information about genetic parts, sequence-level design tools to compose these parts into circuits, visualization tools to depict these designs, genetic design tools to select parts to create systems, and modeling and simulation tools to evaluate alternative design choices. Data standards enable the ready exchange of information within such a workflow, allowing repositories and tools to be connected from a diversity of sources. The present paper describes one such workflow that utilizes, among others, the Synthetic Biology Open Language (SBOL) to describe genetic designs, the Systems Biology Markup Language to model these designs, and SBOL Visual to visualize these designs. We describe how a standard-enabled workflow can be used to produce types of design information, including multiple repositories and software tools exchanging information using a variety of data standards. Recently, the ACS Synthetic Biology journal has recommended the use of SBOL in their publications.

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The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 1 Core (Release 1 Candidate)

Nature Precedings ◽

10.1038/npre.2010.4123.1 ◽

2010 ◽

Cited By ~ 10

Author(s):

Michael Hucka ◽

Frank Bergmann ◽

Stefan Hoops ◽

Sarah Keating ◽

Sven Sahle ◽

...

Keyword(s):

Systems Biology ◽

Markup Language ◽

Systems Biology Markup Language ◽

Level 3 ◽

Language Specification

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cd2sbgnml: bidirectional conversion between CellDesigner and SBGN formats

Bioinformatics ◽

10.1093/bioinformatics/btz969 ◽

2020 ◽

Vol 36 (8) ◽

pp. 2620-2622 ◽

Cited By ~ 3

Author(s):

Irina Balaur ◽

Ludovic Roy ◽

Alexander Mazein ◽

S Gökberk Karaca ◽

Ugur Dogrusoz ◽

...

Keyword(s):

Systems Biology ◽

Web Service ◽

Large Scale ◽

Supplementary Information ◽

Markup Language ◽

File Format ◽

Signalling Network ◽

Lesser General Public License ◽

Systems Biology Markup Language ◽

General Public License

Abstract Motivation CellDesigner is a well-established biological map editor used in many large-scale scientific efforts. However, the interoperability between the Systems Biology Graphical Notation (SBGN) Markup Language (SBGN-ML) and the CellDesigner’s proprietary Systems Biology Markup Language (SBML) extension formats remains a challenge due to the proprietary extensions used in CellDesigner files. Results We introduce a library named cd2sbgnml and an associated web service for bidirectional conversion between CellDesigner’s proprietary SBML extension and SBGN-ML formats. We discuss the functionality of the cd2sbgnml converter, which was successfully used for the translation of comprehensive large-scale diagrams such as the RECON Human Metabolic network and the complete Atlas of Cancer Signalling Network, from the CellDesigner file format into SBGN-ML. Availability and implementation The cd2sbgnml conversion library and the web service were developed in Java, and distributed under the GNU Lesser General Public License v3.0. The sources along with a set of examples are available on GitHub (https://github.com/sbgn/cd2sbgnml and https://github.com/sbgn/cd2sbgnml-webservice, respectively). Supplementary information Supplementary data are available at Bioinformatics online.

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Systems Biology Markup Language (SBML) Level 2: Structures and Facilities for Model Definitions

Nature Precedings ◽

10.1038/npre.2007.58.1 ◽

2007 ◽

Cited By ~ 1

Author(s):

Michael Hucka ◽

Andrew Finney ◽

Stefan Hoops ◽

Sarah Keating ◽

Nicolas Le Novere

Keyword(s):

Systems Biology ◽

Markup Language ◽

Systems Biology Markup Language ◽

Level 2

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Human Systems Biology and Metabolic Modelling: A Review—From Disease Metabolism to Precision Medicine

BioMed Research International ◽

10.1155/2019/8304260 ◽

2019 ◽

Vol 2019 ◽

pp. 1-16 ◽

Cited By ~ 12

Author(s):

Claudio Angione

Keyword(s):

Systems Biology ◽

Precision Medicine ◽

Model Building ◽

Research Field ◽

Point Of View ◽

Patient Specific ◽

Metabolic Modelling ◽

Metabolic Systems ◽

A Cell ◽

Metabolic Models

In cell and molecular biology, metabolism is the only system that can be fully simulated at genome scale. Metabolic systems biology offers powerful abstraction tools to simulate all known metabolic reactions in a cell, therefore providing a snapshot that is close to its observable phenotype. In this review, we cover the 15 years of human metabolic modelling. We show that, although the past five years have not experienced large improvements in the size of the gene and metabolite sets in human metabolic models, their accuracy is rapidly increasing. We also describe how condition-, tissue-, and patient-specific metabolic models shed light on cell-specific changes occurring in the metabolic network, therefore predicting biomarkers of disease metabolism. We finally discuss current challenges and future promising directions for this research field, including machine/deep learning and precision medicine. In the omics era, profiling patients and biological processes from a multiomic point of view is becoming more common and less expensive. Starting from multiomic data collected from patients and N-of-1 trials where individual patients constitute different case studies, methods for model-building and data integration are being used to generate patient-specific models. Coupled with state-of-the-art machine learning methods, this will allow characterizing each patient’s disease phenotype and delivering precision medicine solutions, therefore leading to preventative medicine, reduced treatment, andin silicoclinical trials.

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