finding common ground between modelers and simulation software in systems biology

Finding common ground between modelersand simulation software in systems biology

Michael Hucka(On behalf of many people)

Senior Research FellowCalifornia Institute of Technology

Pasadena, California, USA

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So much is known, and yet, not nearly enough...3

Must weave solutions using different methods & tools

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Common side-effect: compatibility problems5

Models represent knowledge to be exchanged

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SBML

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Format for representing computational models

• Defines object model + rules for its use

- Serialized to XML

Neutral with respect to modeling framework

• ODE vs. stochastic vs. ...

A lingua franca for software

• Not procedural

SBML = Systems Biology Markup Language

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The reaction is central: a process occurring at a given rate

• Participants are pools of entities (species)

Models can further include:

• Other constants & variables

• Compartments

• Explicit math

• Discontinuous events

Basic SBML concepts are simple

naA + nbBf([A],[B],[P ],...)−−−−−−−−−−−−→npP

ncCf(...)−−−→ ndD + neE + nfF

...

• Unit definitions

• Annotations

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The reaction is central: a process occurring at a given rate

• Participants are pools of entities (species)

Models can further include:

• Other constants & variables

• Compartments

• Explicit math

• Discontinuous events

Basic SBML concepts are simple

naA + nbBf([A],[B],[P ],...)−−−−−−−−−−−−→npP

ncCf(...)−−−→ ndD + neE + nfF

...

Can be anything conceptually compatible

• Unit definitions

• Annotations

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Scope of SBML is not limited to metabolic models

Signaling pathway models Fernandez et al. (2006)

DARPP-32 Is a Robust Integrator of Dopamine and Glutamate Signals

PLoS Computational Biology

BioModels Database model#BIOMD0000000153

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Signaling pathway models

Conductance-based models

• “Rate rules” for temporal evolution of quantitative parameters

Hodgkin & Huxley (1952)

A quantitative description of membrane current and its application to conduction and excitation in nerve

J. Physiology 117:500–544


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Neural models

• “Events” for discontinuous changesin quantitative parameters

Izhikevich EM. (2003)

Simple model of spiking neurons.

IEEE Trans Neural Net.


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Neural models


Pharmacokinetic/dynamics models

• “Species” is not required to be abiochemical entity

Tham et al. (2008)

A pharmacodynamic model for the time course of tumor shrinkage by gemcitabine + carboplatin in non-small cell lung cancer patients

Clin. Cancer Res. 14


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Neural models


Pharmacokinetic/dynamics models

• “Species” is not required to be abiochemical entity

Infectious diseases

Munz et al. (2009 )

When zombies attack!: Mathematical modelling of an outbreak of zombie infection

Infectious Disease Modelling Research Progress, eds. Tchuenche et al., p. 133–150

BioModels Database model#MODEL1008060001

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0

50

100

150

200

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

(counted in middle of each year)

205 as of Nov. 28 ↓

Number of software systems supporting SBML

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2342 reactions

NATURE BIOTECHNOLOGY VOLUME 26 NUMBER 10 OCTOBER 2008 1155

of their parameters. Armed with such information, it is then possible to provide a stochastic or ordinary differential equation model of the entire metabolic network of interest. An attractive feature of metabolism, for the purposes of modeling, is that, in contrast to signaling pathways, metabo-lism is subject to direct thermodynamic and (in particular) stoichiometric constraints3. Our focus here is on the first two stages of the reconstruction process, especially as it pertains to the mapping of experimental metabo-lomics data onto metabolic network reconstructions.

Besides being an industrial workhorse for a variety of biotechnological products, S. cerevisiae is a highly developed model organism for biochemi-cal, genetic, pharmacological and post-genomic studies5. It is especially attractive because of the availability of its genome sequence6, a whole series of bar-coded deletion7,8 and other9 strains, extensive experimental ’omics data10–14 and the ability to grow it for extended periods under highly con-trolled conditions15. The very active scientific community that works on S. cerevisiae has a history of collaborative research projects that have led to substantial advances in our understanding of eukaryotic biology6,8,13,16,17. Furthermore, yeast metabolic physiology has been the subject of inten-sive study and most of the components of the yeast metabolic network are relatively well characterized. Taken together, these factors make yeast metabolism an attractive topic to test a community approach to build models for systems biology.

Several groups18–21 have reconstructed the metabolic network of yeast from genomic and literature data and made the reconstructions freely available. However, due to different approaches used to create them, as well as different interpretations of the literature, the existing reconstruc-tions have many differences. Additionally, the naming of metabolites and enzymes in the existing reconstructions was, at best, inconsistent, and there were no systematic annotations of the chemical species in the form of links to external databases that store chemical compound informa-tion. This lack of model annotation complicated the use of the models for data analysis and integration. Members of the yeast systems biology community therefore recognized that a single ‘consensus’ reconstruction and annotation of the metabolic network was highly desirable as a starting point for further investigations.

A crucial factor that enabled the building of a consensus network recon-struction is the ability to describe and exchange biochemical network

Genomic data allow the large-scale manual or semi-automated assembly of metabolic network reconstructions, which provide highly curated organism-specific knowledge bases. Although several genome-scale network reconstructions describe Saccharomyces cerevisiae metabolism, they differ in scope and content, and use different terminologies to describe the same chemical entities. This makes comparisons between them difficult and underscores the desirability of a consolidated metabolic network that collects and formalizes the ‘community knowledge’ of yeast metabolism. We describe how we have produced a consensus metabolic network reconstruction for S. cerevisiae. In drafting it, we placed special emphasis on referencing molecules to persistent databases or using database-independent forms, such as SMILES or InChI strings, as this permits their chemical structure to be represented unambiguously and in a manner that permits automated reasoning. The reconstruction is readily available via a publicly accessible database and in the Systems Biology Markup Language (http://www.comp-sys-bio.org/yeastnet). It can be maintained as a resource that serves as a common denominator for studying the systems biology of yeast. Similar strategies should benefit communities studying genome-scale metabolic networks of other organisms.

Accurate representation of biochemical, metabolic and signaling net-works by mathematical models is a central goal of integrative systems biology. This undertaking can be divided into four stages1. The first is a qualitative stage in which are listed all the reactions that are known to occur in the system or organism of interest; in the modern era, and especially for metabolic networks, these reaction lists are often derived in part from genomic annotations2,3 with curation based on literature (‘bibliomic’) data4. A second stage, again qualitative, adds known effectors, whereas the third and fourth stages—essentially amounting to molecular enzymology—include the known kinetic rate equations and the values

A consensus yeast metabolic network reconstruction obtained from a community approach to systems biologyMarkus J Herrgård1,19,20, Neil Swainston2,3,20, Paul Dobson3,4, Warwick B Dunn3,4, K Yalçin Arga5, Mikko Arvas6, Nils Blüthgen3,7, Simon Borger8, Roeland Costenoble9, Matthias Heinemann9, Michael Hucka10, Nicolas Le Novère11, Peter Li2,3, Wolfram Liebermeister8, Monica L Mo1, Ana Paula Oliveira12, Dina Petranovic12,19, Stephen Pettifer2,3, Evangelos Simeonidis3,7, Kieran Smallbone3,13, Irena Spasi!2,3, Dieter Weichart3,4, Roger Brent14, David S Broomhead3,13, Hans V Westerhoff3,7,15, Betül Kırdar5, Merja Penttilä6, Edda Klipp8, Bernhard Ø Palsson1, Uwe Sauer9, Stephen G Oliver3,16, Pedro Mendes2,3,17, Jens Nielsen12,18 & Douglas B Kell*3,4

*A list of affiliations appears at the end of the paper.

Published online 9 October 2008; doi:10.1038/nbt1492

P E R S P E C T I V E

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Model scale & complexity have been increasing16

30,965 reactions!Aho et al., PLoS One, May 14;5(5), 2010.

Today’s largest models are over 10x bigger!17

SBML continues to evolve

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SBML Level 3—A modular SBML

SBML Level 3 Core

Package X Package Y

Package Z

A package adds constructs & capabilities

Models declare which packages they use

• Applications tell users which packages they support

Package development can be decoupled

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Package Specification status

Graph layout Level 3 version defined; in review

Multicomponent species Level 3 version defined; in review

Hierarchical composition Level 3 specification under discussion

Groups Level 3 specification under discussion

Qualitative models Level 3 specification under discussion

Spatial geometry Level 3 specification under discussion

Arrays & sets Specification proposed

Distribution & ranges Specification proposed

Steady-state models Specification proposed

Graph rendering Specification proposed

Spatial diffusion Specification needed

Dynamic structures Specification needed

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Extends SBML species to represent:• Entities that can exist under

different states affecting their behaviors

• Entities that are complexes of other entities

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Models composed of submodels

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Grouping model entities together, for conceptual and annotation purposes

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Models in which entity variables are not quantities; e.g., boolean models

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2-D and 3-D geometry of physical objects (compartments & species)

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Is enough?

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Growing community, greater challenges22

Representationformat

Model Procedures Results

Minimal inforequirements

Semantics—

Mathematical

Other

SBRML

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annotations annotations annotations

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Annotations can answer questions:

• “What exactly is this entity you call X?”

• “What other identities does this entity have?”

• “What exactly is the process represented by equation ‘e17’?”

• “What role does constant ‘k3’ play in equation ‘e17’?”

• “What mathematical framework is being assumed?”

• “What organism is this in?”

• ... etc. ...

Multiple annotations on same entity are common

Annotations add semantics and connections

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For semantics of a model’s math

Human- & program-accessible

• Browser interface

• Web services

Math formulas in MathML

Systems Biology Ontology (SBO)

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<sbml ...> ... <listOfCompartments> <compartment id="cell" size="1e-15" /> </listOfCompartments> <listOfSpecies> <species compartment="cell" id="S1" initialAmount="1000" /> <species compartment="cell" id="S2" initialAmount="0" /> <listOfSpecies> <listOfParameters> <parameter id="k" value="0.005" sboTerm="SBO:0000339" /> <listOfParameters> <listOfReactions> <reaction id="r1" reversible="false"> <listOfReactants> <speciesReference species="S1" stoichiometry="2" sboTerm="SBO:0000010" /> </listOfReactants> <listOfProducts> <speciesReference species="S1" stoichiometry="2" sboTerm="SBO:0000011" /> </listOfProducts> <kineticLaw sboTerm="SBO:0000052"> <math> ... <math> ...</sbml>

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SBO:0000339

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SBO:0000339

“forward bimolecular rate constant, continuous case”

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Le Novère et al., Nature Biotech., 23(12), 2005.

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MIRIAM cross-references are simple triples

Data type identifier

Data item identifier

Annotation qualifier

Model element

Entity referenced

relationship qualifier(optional)

{ }(Required) (Required) (Optional)

URI chosen from agreed-upon list

Syntax & value space depends on data type

Format:

Controlled vocabulary term

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“Term #1.1.1.1 (alcohol dehydrogenase) in the Enzyme Commission’s Enzyme Nomenclature database”

⇒ urn:miriam:ec-code:1.1.1.1{URN scheme established

by the MIRIAM project

{Chosen by the creator of theentry in MIRIAM Resources

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http://www.ebi.ac.uk/miriam

MIRIAM Resources provides URI dictionary & resolver

Community-maintained

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<species metaid="metaid_0000009" id="species_3" compartment="c_1"> <annotation> <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bqbiol="http://biomodels.net/biology-qualifiers/" > <rdf:Description rdf:about="#metaid_0000009"> <bqbiol:is> <rdf:Bag> <rdf:li rdf:resource="urn:miriam:obo.chebi:CHEBI%3A15996"/> <rdf:li rdf:resource="urn:miriam:kegg.compound:C00044"/> </rdf:Bag> </bqbiol:is> </rdf:Description> </rdf:RDF> </annotation> </species>

SBML defines a syntax for annotations

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<rdf:Bag> <rdf:li rdf:resource="urn:miriam:obo.chebi:CHEBI%3A15996"/> <rdf:li rdf:resource="urn:miriam:kegg.compound:C00044"/> </rdf:Bag>

Data references

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<bqbiol:is>

</bqbiol:is>

Relationship qualifier

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Annotations permit inter-database linking

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Even more interesting capabilities are possible

http://www.semanticsbml.org

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MIRIAM identifiers now in use by many other projects

Data resources• BioModels Database (kinetic models)• PSI Consortium (protein interaction)• Reactome (pathways)• Pathway Commons (pathways)• SABIO-RK (reaction kinetics)• Yeast consensus model database• E-MeP (structural genomics)

Application software• ARCADIA• BioUML• COPASI• libAnnotationSBML• libSBML• Saint• SBML2BioPAX• SBML2LaTeX• SBMLeditor• semanticSBML• Snazer• SBW• The Virtual Cell

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Representationformat

Model Procedures Results

Minimal inforequirements

Semantics—

Mathematical

Other

SBRML

?

annotations annotations annotations

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SED-ML = Simulation Experiment Description ML

Application-independent format

Captures procedures, algorithms, parameter values

• Steps to go from model to output

libSedML project developing API library

<sbml ...> ... <listOfCompartments> <compartment id="cell" size="1e-15" /> </listOfCompartments> <listOfSpecies> <species compartment="cell" id="S1" initialAmount="1000" /> <species compartment="cell" id="S2" initialAmount="0" /> <listOfSpecies> <listOfParameters> <parameter id="k" value="0.005" sboTerm="SBO:0000339" /> <listOfParameters> <listOfReactions> <reaction id="r1" reversible="false"> <listOfReactants> <speciesReference species="S1" stoichiometry="2" sboTerm="SBO:0000010" /> ...

?

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Getting closer to the ideal

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People on SBML Team & BioModels Team

SBML Team BioModels.net TeamMichael Hucka Nicolas Le NovèreSarah Keating Camille Laibe

Frank Bergmann Nicolas RodriguezLucian Smith Nick Juty

Nicolas Rodriguez Lukas EndlerLinda Taddeo Vijayalakshmi ChelliahAkiya Joukarou Chen LiAkira Funahashi Harish Dharuri

Kimberley Begley Lu LiBruce Shapiro Enuo HeAndrew Finney Mélanie CourtotBen Bornstein Alexander Broicher

Ben Kovitz Arnaud HenryHamid Bolouri Marco DonizelliHerbert SauroJo Matthews

Maria Schilstra

VisionariesHiroaki Kitano

John Doyle

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National Institute of General Medical Sciences (USA)

European Molecular Biology Laboratory (EMBL)ELIXIR (UK)

Beckman Institute, Caltech (USA)

Keio University (Japan)

JST ERATO Kitano Symbiotic Systems Project (Japan) (to 2003)

National Science Foundation (USA)

International Joint Research Program of NEDO (Japan)

JST ERATO-SORST Program (Japan)

Japanese Ministry of Agriculture

Japanese Ministry of Educ., Culture, Sports, Science and Tech.

BBSRC (UK)

DARPA IPTO Bio-SPICE Bio-Computation Program (USA)

Air Force Office of Scientific Research (USA)

STRI, University of Hertfordshire (UK)

Molecular Sciences Institute (USA)

Agencies to thank for supporting SBML & BioModels.net39

Where to find out more

Thank you for listening!

SBML http://sbml.org

BioModels Database http://biomodels.net/biomodels

MIRIAM http://biomodels.net/miriam

MIASE http://biomodels.net/miase

SED-ML http://biomodels.net/sed-ml

SBO http://biomodels.net/sbo

KiSAO http://www.ebi.ac.uk/compneur-srv/kisao/

TEDDY http://www.ebi.ac.uk/compneur-srv/teddy/

SBRML http://tinyurl.com/sbrml

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