The document discusses Systems Biology Markup Language (SBML), a format for representing computational models of biological processes. SBML allows models to be exchanged between different software applications and defines concepts like species, compartments, reactions, and parameters. It aims to serve as a common language for software in systems biology. The document outlines some basic SBML concepts and notes that the scope of SBML is not limited to metabolic models, but can also represent signaling pathways, neural models, pharmacokinetic models, and other types of models. It discusses how SBML continues to evolve through new Levels and Packages to support additional model constructs and capabilities.

1. Finding common ground between modelers
and simulation software in systems biology
Michael Hucka
(On behalf of many people)
Senior Research Fellow
California Institute of Technology
Pasadena, California, USA
2

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

3. Must weave solutions using different methods & tools
4

4. Common side-effect: compatibility problems
5

5. Models represent knowledge to be exchanged
6

6. SBML
7

7. SBML = Systems Biology Markup Language
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
8

8. Basic SBML concepts are simple
The reaction is central: a process occurring at a given rate
• Participants are pools of entities (species)
f ([A],[B],[P ],...)
na A + nb B − − − − − − → np P
−−−−−−
f (...)
nc C −−
−→ nd D + ne E + nf F
.
.
.
Models can further include:
• Other constants & variables • Unit definitions
• Compartments • Annotations
• Explicit math
• Discontinuous events
9

9. Basic SBML concepts are simple
The reaction is central: a process occurring at a given rate
• Participants are pools of entities (species) Can be anything
conceptually
f ([A],[B],[P ],...)
na A + nb B − − − − − − → np P
−−−−−− compatible
f (...)
nc C −−
−→ nd D + ne E + nf F
.
.
.
Models can further include:
• Other constants & variables • Unit definitions
• Compartments • Annotations
• Explicit math
• Discontinuous events
9

10. 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
Scope of SBML is not limited to metabolic models
10

11. Signaling pathway models Hodgkin & Huxley (1952)
A quantitative description of
Conductance-based models membrane current and its
•
application to conduction and
“Rate rules” for temporal evolution excitation in nerve
of quantitative parameters J. Physiology 117:500–544
BioModels Database model
#BIOMD0000000020
Scope of SBML is not limited to metabolic models
11

12. Signaling pathway models Izhikevich EM. (2003)
Simple model of spiking neurons.
Conductance-based models IEEE Trans Neural Net.
• “Rate rules” for temporal evolution
of quantitative parameters
BioModels Database model
#BIOMD0000000127
Neural models
• “Events” for discontinuous changes
in quantitative parameters
Scope of SBML is not limited to metabolic models
12

13. Signaling pathway models Tham et al. (2008)
A pharmacodynamic model for
Conductance-based models the time course of tumor
shrinkage by gemcitabine +
• “Rate rules” for temporal evolution
of quantitative parameters
carboplatin in non-small cell lung
cancer patients
Clin. Cancer Res. 14
Neural models BioModels Database model
• “Events” for discontinuous changes
in quantitative parameters
#BIOMD0000000234
Pharmacokinetic/dynamics models
• “Species” is not required to be a
biochemical entity
Scope of SBML is not limited to metabolic models
13

14. Signaling pathway models Munz et al. (2009 )
Conductance-based models When zombies attack!:
Mathematical modelling of an
• “Rate rules” for temporal evolution
of quantitative parameters
outbreak of zombie infection
Infectious Disease Modelling
Research Progress, eds.
Tchuenche et al., p. 133–150
Neural models
• “Events” for discontinuous changes
in quantitative parameters
BioModels Database model
#MODEL1008060001
Pharmacokinetic/dynamics models
• “Species” is not required to be a
biochemical entity
Infectious diseases
Scope of SBML is not limited to metabolic models
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15. Number of software systems supporting SBML
205 as of Nov. 28 ↓
200
150
100
50
0
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
(counted in middle of each year)
15

16. Herrgård et al., Nature Biotech., 26:10, 2008 2342 reactions
A consensus yeast metabolic network reconstruction
© 2008 Nature Publishing Group http://www.nature.com/naturebiotechnology
obtained from a community approach to systems
biology
Markus 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 Spasić2,3, Dieter Weichart3,4,
Roger Brent14, David S Broomhead3,13, Hans V Westerhoff 3,7,15, 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
Genomic data allow the large-scale manual or semi-automated of their parameters. Armed with such information, it is then possible to
assembly of metabolic network reconstructions, which provide provide a stochastic or ordinary differential equation model of the entire
highly curated organism-specific knowledge bases. Although metabolic network of interest. An attractive feature of metabolism, for the
several genome-scale network reconstructions describe purposes of modeling, is that, in contrast to signaling pathways, metabo-
Saccharomyces cerevisiae metabolism, they differ in scope lism is subject to direct thermodynamic and (in particular) stoichiometric
and content, and use different terminologies to describe the constraints3. Our focus here is on the first two stages of the reconstruction
same chemical entities. This makes comparisons between them process, especially as it pertains to the mapping of experimental metabo-
difficult and underscores the desirability of a consolidated lomics data onto metabolic network reconstructions.
metabolic network that collects and formalizes the ‘community Besides being an industrial workhorse for a variety of biotechnological
knowledge’ of yeast metabolism. We describe how we have products, S. cerevisiae is a highly developed model organism for biochemi-
produced a consensus metabolic network reconstruction cal, genetic, pharmacological and post-genomic studies5. It is especially
for S. cerevisiae. In drafting it, we placed special emphasis attractive because of the availability of its genome sequence6, a whole series
on referencing molecules to persistent databases or using of bar-coded deletion7,8 and other9 strains, extensive experimental ’omics
database-independent forms, such as SMILES or InChI strings, data10–14 and the ability to grow it for extended periods under highly con-
as this permits their chemical structure to be represented trolled conditions15. The very active scientific community that works on
unambiguously and in a manner that permits automated S. cerevisiae has a history of collaborative research projects that have led to
reasoning. The reconstruction is readily available via a publicly substantial advances in our understanding of eukaryotic biology6,8,13,16,17.
Model scale & complexity have been increasing
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
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
for studying the systems biology of yeast. Similar strategies metabolism an attractive topic to test a community approach to build 16

17. Aho et al., PLoS One, May 14;5(5), 2010. 30,965 reactions!
Today’s largest models are over 10x bigger!
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18. SBML continues to evolve
18

19. SBML Level 3—A modular SBML
Package Z
Package X Package Y
SBML Level 3 Core
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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20. 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
20

21. Package Specification status
Graph layout Level 3 version defined; in review
Multicomponent species Level 3 version species to represent:
Extends SBML defined; in review
• Entities that can exist under
Hierarchical composition Level 3 specification under discussion
different states affecting their
Groups behaviors
Level 3 specification under discussion
• Entities that are complexes of
Qualitative models Level 3 specification under discussion
other entities
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
20

22. Package Specification status
Graph layout Level 3 version defined; in review
Multicomponent species Level 3 version defined; in review
Hierarchical composition Models composed of submodels
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
20

23. 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
Grouping model entities together,
Groups Level 3 specification under discussion
for conceptual and annotation
Qualitative models Level 3 specification under discussion
purposes
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
20

24. 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
Models in which entity variables are
Qualitative models Levelquantities; e.g., boolean models
not 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
20

25. 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
2-D and 3-D geometry of physical
Spatial geometry Level 3 specification under discussion
objects (compartments & species)
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
20

26. 21

27. Is enough?
21

28. Growing community, greater challenges
22

29. Model Procedures Results
Representation
format SBRML
Minimal info
?
requirements
Semantics—
Mathematical
Other
annotations annotations annotations
23

30. Model Procedures Results
Representation
format SBRML
Minimal info
?
requirements
Semantics—
Mathematical
Other
annotations annotations annotations
23

31. Annotations add semantics and connections
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
24

32. Systems Biology Ontology (SBO)
For semantics of a model’s math
Human- & program-accessible
• Browser interface
• Web services
Math formulas in MathML
25

33. Systems Biology Ontology (SBO)
For semantics of a model’s math
Human- & program-accessible
• Browser interface
• Web services
Math formulas in MathML
25

34. Systems Biology Ontology (SBO)
For semantics of a model’s math
Human- & program-accessible
• Browser interface
• Web services
Math formulas in MathML
25

35. <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>
26

36. <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" />
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>
26

37. <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" />
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> “forward bimolecular rate constant, continuous case”
26

38. Le Novère et al., Nature Biotech., 23(12), 2005.
27

39. Model Entity
element referenced
relationship qualifier
(optional)
MIRIAM cross-references are simple triples
{ Data type
identifier
Data item
identifier
Annotation
qualifier }
(Required) (Required) (Optional)
Format:
URI chosen from Syntax & value space Controlled
agreed-upon list depends on data type vocabulary term
28

40. “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 Chosen by the creator of the
by the MIRIAM project entry in MIRIAM Resources
29

41. MIRIAM Resources provides URI dictionary & resolver
http://www.ebi.ac.uk/miriam Community-maintained
30

42. MIRIAM Resources provides URI dictionary & resolver
http://www.ebi.ac.uk/miriam Community-maintained
30

43. SBML defines a syntax for annotations
<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>
31

44. SBML defines a syntax for annotations
<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> Data references
<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>
31

45. SBML defines a syntax for annotations
<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> Relationship qualifier
<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>
31

46. Annotations permit inter-database linking
32

47. Annotations permit inter-database linking
32

48. Even more interesting capabilities are possible
http://www.semanticsbml.org
33

49. MIRIAM identifiers now in use by many other projects
Data resources Application software
• BioModels Database (kinetic models) • ARCADIA
• PSI Consortium (protein interaction) • BioUML
• Reactome (pathways) • COPASI
• Pathway Commons (pathways) • libAnnotationSBML
• libSBML
• SABIO-RK (reaction kinetics)
• Saint
• Yeast consensus model database
• SBML2BioPAX
• E-MeP (structural genomics) • SBML2LaTeX
• SBMLeditor
• semanticSBML
• Snazer
• SBW
• The Virtual Cell
34

50. Model Procedures Results
Representation
format SBRML
Minimal info
?
requirements
Semantics—
Mathematical
Other
annotations annotations annotations
35

51. Model Procedures Results
Representation
format SBRML
Minimal info
?
requirements
Semantics—
Mathematical
Other
annotations annotations annotations
35

52. <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" />
...
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
36

53. Getting closer to the ideal
37

54. People on SBML Team & BioModels Team
SBML Team BioModels.net Team
Michael Hucka Nicolas Le Novère
Sarah Keating Camille Laibe
Frank Bergmann Nicolas Rodriguez
Lucian Smith Nick Juty
Nicolas Rodriguez Lukas Endler
Linda Taddeo Vijayalakshmi Chelliah
Akiya Joukarou Chen Li
Akira Funahashi Harish Dharuri
Kimberley Begley Lu Li
Bruce Shapiro Enuo He
Andrew Finney Mélanie Courtot
Ben Bornstein Alexander Broicher
Ben Kovitz Arnaud Henry
Hamid Bolouri Marco Donizelli
Herbert Sauro Visionaries
Jo Matthews Hiroaki Kitano
Maria Schilstra John Doyle
38

55. 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.net
39

56. Where to find out more
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
Thank you for listening!
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