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Standards for systems biology

Nature Reviews Genetics volume 7, pages 593–605 (2006)Cite this article

Key Points

  • Several standards are contributing to advancement of knowledge in biology, however most standardization initiatives are still in the investment stage for biologists.

  • Developing a complete and self-contained standard in biology involves four steps: conceptual model design, model formalization, development of a data exchange format and implementation of the supporting tools.

  • In life sciences, standards development typically is done by grass roots movements, and it is difficult to persuade funding agencies to fund such activities.

  • Although it might be faster for a single organization to develop its own standards, a bottom-up community consensus approach is key to the long-term acceptance and usefulness of standards.

  • Developing and deploying a standard creates an overhead, which can be expensive. Standards related to a particular technology have a life span that is no longer than the technology itself and there is only a limited period of time in which the overheads can be paid off.

  • The body of biological knowledge is incomplete and expanding rapidly; therefore, standards that describe biological knowledge have to be flexible, and a mechanism of change must be a part of the standard.

  • To avoid proliferation of standards, common features of existing standards should be re-used wherever possible. Simplicity, but not oversimplification, is the key to success.

Abstract

High-throughput technologies are generating large amounts of complex data that have to be stored in databases, communicated to various data analysis tools and interpreted by scientists. Data representation and communication standards are needed to implement these steps efficiently. Here we give a classification of various standards related to systems biology and discuss various aspects of standardization in life sciences in general. Why are some standards more successful than others, what are the prerequisites for a standard to succeed and what are the possible pitfalls?

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Figure 1: Development of components of data exchange standards: the MGED example.
Figure 2: Proteomics standards.

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Acknowledgements

We would like to thank M. Ashburner, C. Brooksbank, H. Hermjakob and N. Le Novere for reading the manuscript and providing valuable comments. The work on this survey was partly funded by the MolPAGE grant from the European Commission and a grant from the US National Human Genome Research Institute and National Institute of Biomedical Imaging and Bioengineering.

Author information

Authors and Affiliations

  1. European Bioinformatics Institute, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK

    Alvis Brazma, Maria Krestyaninova & Ugis Sarkans

Authors
  1. Alvis Brazma
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  2. Maria Krestyaninova
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  3. Ugis Sarkans
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Corresponding author

Correspondence to Alvis Brazma.

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Related links

Related links

FURTHER INFORMATION

ArrayExpress

BioModels Database

BioPAX

CellML

Extensible Markup Language

Gene Expression Omnibus

Gene Ontology

Human Genome Organisation

Human Proteome Organisation

Life Sciences Identifier

MIAME and MAGE-OM Terms Explained web site

MIAME

Microarray and Gene Expression

Microarray Gene Expression Data Society web site

MISFISHIE Standard Working Group web page

MolPAGE

mzData

Open Biological Ontology

Proteomics Standards Initiative — Molecular Interactions

Stanford Microarray Database

Summary Report — W3C Workshop on Semantic Web for Life Sciences

Systems Biology Markup Language

The Law of Standard

UniProtKB

W3C Semantic Web Health Care and Life Sciences Interest Group web site

Glossary

Domain

A field of study.

Conceptual model

In information engineering, a model that is meant to facilitate human communication; it does not need to be absolutely precise, as opposed to a formal model that has strictly defined semantics.

Data exchange format

A file or message format that is formally defined so that software can be built that 'knows' where to find various pieces of information.

Ontology

A model that describes a domain and can be used to reason about objects and relationships between them.

Tool

In softwa re engineering, a program or set of programs that enables a certain task(s).

Directed acyclic graph

A graph consisting of nodes and edges, where edges have direction (that is, can be traversed only one way), and it is not possible to find a set of edges that form a closed loop.

Diagram

A visual representation of concepts and relationships, used in information engineering to facilitate human communication.

Semantics

The meaning of something; in computer science, it is usually used in opposition to syntax (that is, format).

Reporting requirements

An agreed set of information items that needs to be provided for meaningful information communication (reporting).

Metabolomics

The study of metabolite profiles in individual cells and cell types.

Metabonomics

The study of systemic response to the pathophysiological stimuli and regulation of function in the whole organism through analysis of biofluids and tissues.

Visual language

In computer science and computer engineering, an agreed set of conventions for drawing diagrams that formally describe a model or a program.

Class

A concept used in ontology engineering and model building for referring to a set of objects with similar properties.

Graph

A visual representation of information in the form of edges (lines) and nodes (connection points). In biology, graphs can be represented as boxes (nodes) and lines between boxes.

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Brazma, A., Krestyaninova, M. & Sarkans, U. Standards for systems biology. Nat Rev Genet 7, 593–605 (2006). https://doi.org/10.1038/nrg1922

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