Integrative information management for systems biology

Integrative Information Management for Systems Biology
Neil Swainston
Manchester Centre for Integrative Systems Biology
Data Integration in the Life Sciences, Gothenburg, Sweden
27 August 2010

The MCISB
Pioneer the development of new experimental and computational technologies in systems biology
Currently employs 9.5 multidisciplinary people
Mathmaticians, informaticians, experimentalists, etc.
All share same office , lab
Develop kinetic models of yeast metabolism

Metabolism

Models
Genome-scale SBML model of yeast metabolism
Not kinetic / quantitative!
Annotated model
All >2000 molecules have unique database references
MIRIAM standards have been followed ( RDF )
Should be entirely unambiguous for third party users
Should be usable in third party tools
Should allow experimental data to be imported easily
Herrgård MJ, Swainston N, et al. A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nat Biotechnol . 2008, 26 , 1155-60.

Bottom-up systems biology
Steps in kinetic modeling:
Identify the pathway or portion of a network that is to be modeled
Associate the model with functions and parameter values that represent its dynamic behavior, either from databases or experimentation
Analyze and/or simulate the resulting model to understand its properties

Bottom-up systems biology
In common practice, model construction is a manual process , in which a modeler associates a model with experimental data for simulation
Such an approach can give rise to good quality models, but is more a cottage industry than as a highly scalable production process
Can this be automated ?

Automation of the process
Experimental data is captured from instruments , and subject to primary analyses
Experimental data and results of the primary analyses are archived in experimental data repositories
The information required for modeling is extracted from the experimental data resources and stored in a Key Results Database (KRDB)
A workflow obtains qualitative model information , represented using SBML, parameterizes this model with results in the KRDB, and analyses / simulates the resulting quantitative model

Enzyme kinetics Quantitative metabolomics Quantitative proteomics SBML Model Parameters (K M , k cat ) Variables (metabolite, protein concentrations) PRIDE XML MeMo SABIO-RK Web service KRDB Web service

From instrument to result
Raw data typically needs analysed before use
Experimental data is often managed in an ad hoc way
Experimentalists are not keen to spend time on data curation for archiving or sharing
Try to capture necessary metadata as part of primary data analysis

Requirements
The experimental techniques share requirements:
perform analyses on the raw experimental data to derive the secondary quantitative parameters required in the model
store the raw experimental data along with relevant metadata and the derived parameters , thus providing the facility to trace back and reanalyze raw data should this be required
Where possible, existing data standards and tools are reused, although in practice data standards tend to lag behind technique development, and tools tend to lag behind standards

Data capture
Software wizards have been developed that step experimentalists through the analysis of primary data
QconCAT PrideWizard for proteomics
KineticsWizard for enzyme kinetics
Metadata collected along the way, as unobtrusively as possible
Heavily reliant on database web services

KineticsWizard

QconCAT PrideWizard

QconCAT PrideWizard eXist database PRIDE XML Identify QconCAT Pride Wizard Quantify Format Upload Web / web service Browser Mascot PRIDE XML PRIDE Converter mzData Pride

Web interfaces

From instrument to result
All laboratories carry out primary analyses of experimental data
All laboratories carry out some form of secondary analyses based on primary results
Many laboratories struggle to manage the results of these processes in a systematic manner
We see the key to obtaining manageable results as being to integrate data capture and management with necessary analyses

But…
… MCISB has to manage “only” three types of experiment
Proteomics, metabolomics, enzyme kinetics
Informatics team share office with experimentalists and modellers
We’ve been doing this for years…
Lots of time, lots of people, lots of resource
Infrastructure development is part of our remit

And…
… many projects are far more diverse
Informatics team separated from experimentalists, who are separated from modellers
Less informatics resource
Heavyweight approach of MCISB ( bespoke tools for each experiment) not always applicable…

So…
… lightweight approach may be more suitable
Store only secondary data necessary for modelling
Not raw data
Key Results Database (KRDB)
More modeller -focussed

Key Results Database
Who , what , some how and why ?
Measure “something” under “some conditions”
Measurements are generally a number but may be some other artifact
Conditions may apply across entire experiment (Static Factors)
Conditions may change across measurements (Variable Factors)
Measurements may take place at a certain time

KRDB structure

KRDB web interface

Deployed in Liverpool, MCISB, UCD
Easily extensible interface
eXist “lets it all hang out” as RESTful web services

Modelling infrastructure

Taverna http://taverna.sourceforge.net

Taverna

Modelling life-cycle workflows

Qualitative model construction Input: list of ORFs Output: SBML file 1. Get reaction info 3. Create species 2. Create compartments 4. Create reactions

Qualitative model construction

Qual to quan: parameterisation
Data requirements
Qualitative SBML model
Starting concentrations for enzymes and source metabolites
Enzyme kinetics data
SABIO-RK database web service

Qual to quan: parameterisation

Model parameterisation

Model calibration
Optional modification of parameters in reaction kinetics until the output of the model produces results similar to those obtained from experimentation
Data requirements
Parameterised SBML model
Experimental data
Metabolite concentrations from KRDB
Calibration by COPASI web service

COPASI web service Design and Architecture of Web Services for Simulation of Biochemical Systems. Dada JO, Mendes P. Data Integration in the Life Sciences, Manchester, UK (2009).

Model calibration

Model simulation
The running of a parameterized (and calibrated?) model using a specified simulation operation

Model simulation

SBRML
Simulation results are data too, and are represented in our case in SBRML
Systems Biology Results Markup Language
Developed by Joseph Dada, et al. (Manchester)
Structured format for representing simulation results
And experimental data ?
Dada JO, et al . SBRML: a markup language for associating systems biology data with models. Bioinformatics 2010, 26 , 932-938.

Model simulation

Conclusion
Classically, systems biology has been a cottage industry
Experimental results are selected for use in modelling in an ad hoc manner
Modellers develop and refine models using a time consuming and partially documented process

Conclusion
Large scale experimentation should lead to more systematic behaviour
Data integration to support the construction and parameterisation of models
Large scale computational experimentation to support the comparison of models and their results

Thanks…

Integrative Information Management for Systems Biology
Neil Swainston
Manchester Centre for Integrative Systems Biology
Data Integration in the Life Sciences, Gothenburg, Sweden
27 August 2010

Integrative information management for systems biology

by Neil Swainston

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