Data Standards for Systems Biology

Neil SwainstonManchester Centre for Integrative Systems Biology

neil.swainston@manchester.ac.uk

Introduction

• Experimental standards• Proteomics• Metabolomics• Enzyme kinetics

• Modelling standards• Models• Simulations• Results

Why do we need standards?

• Aids researchers by facilitating management of experimental data

• Facilitates open-source software development and interoperability

• Allows data to be shared• Increasingly becoming a requirement for journal

submissions

When are standards developed?

• Standards generally are generated organically

• Not for pioneers

• When an experimental technique becomes established

• Need for a standard becomes obvious

Who develops standards?

• Usually two or more academic groups• Commercial providers often less enthusiastic

• Often formed by a Working Group• Proteome Standards Initiative• Metabolomics Standards Initiative

• “Minimum information required” specification provided

• Followed by data schema, XML standard

MCISB project overview

Enzyme kineticsQuantitativemetabolomics

Quantitativeproteomics

Model

Parameters(KM, Kcat)

Variables(metabolite, proteinconcentrations)

PRIDE XML MeMo SABIO-RK

Web serviceWeb serviceWeb service

MeMo-RK

Web service

Proteomics

• We wish to store:

• Raw experimental mass spectrometry data

• Protein / peptide identifications

• Protein / peptide quantitations

• Metadata (instrument, search algorithm, user, etc.)

Mass spectrometry data

• How do we represent the following?

Mass spectrometry data

• The simple approach:

Mass spectrometry data

• The simple approach does provide a list of masses and intensities, but…• What instrument was used?• Who ran the instrument?• What sample was used?• …etc.

• The simple approach lacks metadata

• Many simple approaches (formats) exist

Mass spectrometry data

• The less simple approach: mzData• Developed by the Proteome Standards Initiative, 2005• Put together by Working Group of academics and

commercial parties• Regular meetings, both real and virtual

• Goal: unify the existing “simple” formats into one• Support “tagging” with metadata

mzData

• http://www.psidev.info/index.php?q=node/80#mzdata

• XML format, includes…• Peak lists (mz / intensities)• Experimental protocols• Admin (Who? When?)• Instrument details• etc.

Controlled vocabularies

• Use of free text is “dangerous”• Non-standard, ambiguous terms• Difficult to match / compare

• Controlled vocabularies• Collection of standardised terms• Organised into vocabularies or ontologies• Ontologies contain controlled terms and relationships

between them (predicates)

Controlled vocabularies

• Ontology Lookup Service, EBI

mzData

Proteomics data

• Proteomics data is not solely mass spectrometry data• Sample preparation protocol?• Peptide / protein identifications?• Post-translational modifications• Identification scores?

• To support this, an extension is required• Extension based on defined set of “minimum

requirements”• MIAPE

MIAPE

PRIDE

• Proteomics identifications database– Both a format and a database

– Centralised, standards compliant, open source, public data repository for proteomics data

– Query, submit and retrieve proteomics data in standardized XML formats

– Public version housed at the EBI– http://www.ebi.ac.uk/pride/

PRIDE

• Peptide / protein identifications

PRIDE Converter

• User interface

• Usable by biologists

• Interfaces with Ontology Lookup Service

• Developed by EBI

• Automatic upload to PRIDE database

PRIDE database

Future directions

• PRIDE does NOT hold:• Protein and peptide quantitations

• New approaches being developed• mzML – mass spectrometry format, enhancement of

mzData, including support for richer datasets

• mzIdentML – storage of protein and peptide identifications

• mzQuantML – storage of protein and peptides quantitations

Metabolomics

• We wish to store:

• Raw experimental mass spectrometry (and NMR) data

• Metabolite identifications

• Metabolite quantitations

• Metadata (instrument, search algorithm, user, etc.)

Metabolomics

• Data standard does NOT currently exist• Core Information for Metabolomics Reporting

• Metabolites Standard Initiative (MSI)• http://msi-workgroups.sourceforge.net/

• MetaboLights being developed at EBI• Not many details as yet

• In the mean time…• MCISB has developed its own repository

MeMo

• Metabolomics Model database

• Designed initially for metabolomics data• SQL / XML hybrid approach

• Holds:– Experimental meta-data (submitter, lab, date)– Sample meta-data (including biological source)– Instrumentation meta-data– Mass spectra– Metabolite identifications

MeMo

MeMo web interface

Enzyme kinetics

• How fast does a given reaction occur?

Enzyme

A B

• Determination of kinetic constants which define the kinetics of the reaction

• Experimental approach: perform kinetic assays

Enzyme kinetics

• Many approaches:– Absorbance– Fluorescence– others

• Currently concentrating on absorbance assays on BMG NOVOstar instrument

• Requirement: determination of KM and kcat for a given reaction under particular conditions (pH and temperature)

Enzyme kinetics: Michaelis-Menten

• Traditionally, for each assay, initial rate, v is determined

Enzyme kinetics: Michaelis-Menten

• Performing this at various substrate concentrations allows KM and Vmax to be determined:

STRENDA guidelines

• Standards for Reporting Enzymology Data• http://www.beilstein-institut.de/en/projects/strenda/

• Specifies…• Reactants / products• Enzyme (wild-type, modified, purification, expressed

in• Experimental conditions (pH, temperature, buffer)• Instrument, experiment type• Submitter (contact details)

SABIO-RK

• http://sabio.villa-bosch.de/

• Comprehensive collection of enzyme kinetic constants

• Adheres to STRENDA recommendation

• Harvested from literature

• Searchable web interface

SABIO-RK

SABIO-RK

SABIO-RK

BRENDA

• http://www.brenda-enzymes.org/

• Even more comprehensive

• Slightly less well-curated

• Again, searchable web interface

BRENDA

Other experimental standards

• MIBBI: Minimum Information for Biological and Biomedical Investigations• http://mibbi.org/

• Over thirty recommendations for a range of experimental techniques

Modelling standards

MCISB project overview

Enzyme kineticsQuantitativemetabolomics

Quantitativeproteomics

Model

Parameters(KM, Kcat)

Variables(metabolite, proteinconcentrations)

PRIDE XML MeMo SABIO-RK

Web serviceWeb serviceWeb service

MeMo-RK

Web service

MCISB project overview

Enzyme kineticsQuantitativemetabolomics

Quantitativeproteomics

Model

Parameters(KM, Kcat)

Variables(metabolite, proteinconcentrations)

PRIDE XML MeMo SABIO-RK

Web serviceWeb serviceWeb service

MeMo-RK

Web service

Modelling

• What is a model?

• “An analytic or computational model proposes specific testable hypotheses about a biological system”

• Mathematical / computational representation of a biological system

• May allows computational simulations of the system

Pathway databases

• Building a model often starts with a topological description of a pathway or pathways

• What reacts with what?

• A number of existing data resources• Biochemical knowledge, curated from literature

KEGG

KEGG

Metabolite

Enzyme

Reaction

MetaCyc

Reactome

Simulation tools

• The systems biology community has developed a strong software infrastructure

• Many tools exist, including simulators• Several hundred

• How do we link pathway databases to these simulators?

• A standard: SBML• Systems Biology Markup Language• Recently celebrated its 10th birthday

SBML

• XML markup language describing models

• Contains concepts such as…• compartments• species (metabolites, enzymes, RNA, etc.)• reactions

• Similar to pathway databases• KEGG2SBML tool exists for converting KEGG pathway

maps to SBML files

Mathematical SBML

• Also contains concepts allowing simulations• Many of these driven by experimental work

• Specification of metabolite and enzyme concentrations

• Specification of kinetic laws and kinetic parameters

• Parameterised model = pathways + experimental data

SBML

SBML data resources

• Biomodels.net• http://www.ebi.ac.uk/biomodels-main/• Curated collection of biochemical models at EBI

• JWS Online• http://jjj.mib.ac.uk/• Also curated• BUT also includes an online simulator• You’ll learn more next month…

SBML tools

• Hundreds of ‘em (205)• http://sbml.org/SBML_Software_Guide

• Different goals• Whole cell / single pathway• Deterministic / stochastic simulators• Different platforms / programming languages

• Matrix exists, describing capabilities of each tool• http://sbml.org/SBML_Software_Guide/

SBML_Software_Matrix

Making SBML models: CellDesigner

Other model representations

• CellML• http://www.cellml.org/• Larger scale modelling• Inter-cellular, used in whole organ modelling

• BioPAX• http://www.biopax.org/• Similar goals to SBML

• Overlap between “competing” representations is being reduced• Regular “COMBINE” meetings

MIRIAM

• Minimum Information Required in the Annotation of Models• http://www.ebi.ac.uk/miriam/

• Set of guidelines describing how to make models reusable• Specify model creator contact details• Ensure consistent annotation of terms with database

resources• e.g. use UniProt identifiers for unambigous

identification of enzymes

SBML visualisation: SBGN

• Until recently, no standardised way of viewing models• Systems Biology Graphical Notation• Attempts to generate standard “wiring-diagram” for

biological representations

Model simulation

Model simulation

• Many simulators exist

• How do we tell a simulator what to simulate?• Simulation Experiment Description Markup Language

(SED-ML)

• Contains concepts…• Model (what to run the simulation on)• Simulation (define what to simulate, duration, step-

size)• Data generation (post-processing normalisation)• Output (2D plot, 3D plot)

Simulation results: SBRML

• Simulation results are data too, and are represented by SBRML• Systems Biology Results Markup Language• Developed by Joseph Dada, et al. (Manchester)

• Structured format for representing simulation results

• Dada JO, et al. SBRML: a markup language for associating systems biology data with models. Bioinformatics 2010, 26, 932-938.

SBRML

Conclusion

• Data standards greatly facilitate computational systems biology

• Standards exist (and are being continually developed) for both experimental and modelling data

• Provides a framework for data sharing and open-source software tool development

Data Standards for Systems Biology

Neil SwainstonManchester Centre for Integrative Systems Biology

neil.swainston@manchester.ac.uk