SBML (the Systems Biology Markup Language), model databases, and other resources

SBML (the Systems Biology Markup Language),
model databases, and other resources
Michael Hucka, Ph.D.
Department of Computing + Mathematical Sciences
California Institute of Technology
Pasadena, CA, USA
Email: mhucka@caltech.edu Twitter: @mhucka

CCB 2012, August 2012, Cold Spring Harbor Laboratory, NY, USA

General background and motivations
Brief summary of SBML features
Outline

A selection of resources for the SBML-oriented modeler
Annotations, connections and semantics
Current and upcoming developments in community standards
Closing

Research today: experimentation, computation, cogitation

The many roles of computation in biological research
Instrument/device control, data management, data processing,
database applications, statistical analysis, pattern matching, image
processing, text mining, chemical structure prediction, genomic
sequence analysis, proteomics, other *omics, molecular modeling,
molecular dynamics, kinetic simulation, simulated evolution,
phylogenetics, ... (to name only a subset)!
Focus here: modeling and simulation

What are the outcomes of modeling and simulation?
Usually, there are at least two scientiﬁc outcomes:
• One or more models (+ associated claims about their behaviors)
• Publication of the results (in some form)

Models come
in many forms

Models are results
Models serve as statements of our current understanding of the
phenomena being studied*
• A computational model documents your theory in a concrete form
Model can—
• Reduce ambiguity in communication
• Oﬀer a concrete framework for adding new data and theories
• Support direct evaluation of relationships between theories

Bower & Bolouri, Computational modeling of genetic and biochemical networks, MIT Press, 2001

But only if the modeling results are reproducible

Is it enough to describe the model & equations in a paper?
Many models have traditionally been published this way
Problems:
• Errors in printing
• Missing information
• Dependencies on
implementation
• Outright errors
• Can be a huge
eﬀort to recreate

Is it enough to make your (software X) script available?
It’s vital for good science:
• Someone with access to the same software can try to run it,
understand it, verify the computational results, build on them, etc.
• Opinion: you should always do this in any case

Is it enough to make your (software X) code available?
It’s vital for good science—
• Someone with access to the same software can try to run it,
understand it, build on it, etc.
• Opinion: you should always do this in any case
But it’s still not ideal for communication of scientiﬁc results:
• What if they don’t have access to that software?
• And anyway, how will people ﬁnd the model?
• And how will people be able to relate the model to other work?

Diﬀerent tools diﬀerent interfaces & languages

Communication is better with interoperable data formats

SB
ML
:a
fo lin
rs g
of ua
tw fr
ar an
e ca

SBML = Systems Biology Markup Language
Format for representing computational models of biological processes
• Data structures + usage principles + serialization to XML
Neutral with respect to modeling framework
• E.g., ODE, stochastic systems, etc.

Development started in 2000, with ﬁrst speciﬁcation distributed in 2001

The process is central
• Called a “reaction” in SBML
• Participants are pools of entities (species)
Models can further include:
• Other constants & variables • Unit deﬁnitions
• Compartments • Annotations
• Explicit math
• Discontinuous events

Basic SBML concepts are fairly simple

Well-stirred compartments

c

n

Species pools are located in compartments
c
protein A protein B

n

gene mRNAn mRNAc

Reactions can involve any species anywhere

c
protein A protein B

n

gene mRNAn mRNAc

Reactions can cross compartment boundaries

c
protein A protein B

n

gene mRNAn mRNAc

Reaction/process rates can be (almost) arbitrary formulas

c
protein A f1(x) protein B

n

f5(x) f2(x)

gene f4(x) mRNAn f3(x) mRNAc

“Rules”: equations expressing relationships in addition to reaction sys.

g1(x) c
g2(x) protein A f1(x) protein B
.
.
. n

f5(x) f2(x)


“Events”: discontinuous actions triggered by system conditions

g1(x) c
g2(x) protein A f1(x) protein B
.
.
. n

f5(x) f2(x)


Event1: when (...condition...), Event2: when (...condition...), ...
do (...assignments...) do (...assignments...)

Annotations: machine-readable semantics and links to other resources

“This is identiﬁed “This is an enzymatic
c
g1(x)by GO id # ...” reaction with EC # ...”
g2(x)
. protein A f1(x) protein B
.
“This is a transport
. n
into the nucleus ...” “This compartment
represents the nucleus ...”
f5(x) f2(x)

“This event
represents ...”
Event1: when (...condition...), Event2: when (...condition...), ...
do (...assignments...) do (...assignments...)

Today: spatially homogeneous models
• Metabolic network models Find
BioM
exam
ples
in
• Signaling pathway models
http:
odels
Data
base
• Conductance-based models //bio
mod
els.ne
t/bio
• Neural models models
• Pharmacokinetic/dynamics models
• Infectious diseases

Coming: SBML Level 3 packages to support other types
• E.g.: Spatially inhomogeneous models, also qualitative/logical

Scope of SBML encompasses many types of models

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
Many signiﬁcant and popular models are in SBML form 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

SBML Level 1 SBML Level 2 SBML Level 3
predefined math functions user-defined functions user-defined functions

text-string math notation MathML subset MathML subset

reserved namespaces for no reserved namespaces no reserved namespaces
annotations for annotations for annotations

no controlled annotation RDF-based controlled RDF-based controlled
scheme annotation scheme annotation scheme

no discrete events discrete events discrete events

default values defined default values defined no default values

monolithic monolithic modular

You want models? We got models.

BioModels Database
Stores & serves quantitative models of biological interest
• Free, public resource
• Models must be described in peer-reviewed publication(s)
Hundreds of models are curated by hand
Imports & exports models in several formats

Figure courtesy of Camille Laibe

BioModels Database

http://biomodels.net/biomodels

Contents of BioModels Database
Contents today:
• 142,000+ pathway models (converted from KEGG)
• 400+ hand-curated quantitative models
signal transduction 9%
metabolic process 3%
3% 25%
multicelullar organismal process 5%
rhythmic process
cell cycle 6%
homeostatic process
response to stimulus 8%
cell death
9% 23%
localization
others (e.g., developmental process) 9%

• 400+ non-curated quantitative models
Database data from 2012-08-10

How can you check that a given SBML ﬁle is valid?

The Online SBML Validator

Find it
here

http://sbml.org/Facilities/Validator

Where can you ﬁnd more software?

Find software in the SBML Software Guide

Find software in the SBML Software Guide

Find SBML software

Results of 2011 survey of SBML-compatible software
Question: Which of the following categories best describe your software?
(Check all that apply.)

Simulation software 42

Analysis s/w (in addition, or instead of, simulation) 40

Creation/model development software 31

Visualization/display/formatting software 31

Utility software (e.g., format conversion) 23

Data integration and management software 16

Repository or database 14

Framework or library (for use in developing s/w) 13

S/w for interactive env. (e.g., MATLAB, R, ...) 13

Annotation software 11
0 20 40 60 80

Out of 81 responses

What about libraries for writing SBML-compatible software?

libSBML
Reads, writes, validates SBML
Can check & convert units
Written in portable C++
Runs on Linux, Mac, Windows
APIs for C, C++, C#, Java, Octave,
Perl, Python, R, Ruby, MATLAB
Well documented API
Open-source (LGPL)

http://sbml.org/Software/libSBML

JSBML
Pure Java implementation
API is compatible with libSBML but
more Java-like
Functionality is subset of libSBML
Open source (LGPL)

http://sbml.org/Software/JSBML

How can you stay informed of new developments?

Resources for news, questions and discussions

Front-page news


Twitter & RSS feeds


Mailing lists/forums


SBML itself provides syntax and only limited semantics


No standard
identiﬁers


Low info
content

No standard
identiﬁers

Raw models alone are insuﬃcient
Need standard schemes for
Low info machine-readable annotations
content
• Identify entities
• Mathematical semantics
• Links to other data resources
• Authorship & pub. info

No standard
identiﬁers

Element in Entity elsewhere
the model (e.g., in a database)
relationship qualiﬁer
(optional)

Annotations at their simplest

Annotations add meaning and connections
Annotations can answer questions:
• “What exactly is the process represented by equation ‘r17’?”
• “What other identities (synonyms) does this entity have?”
• “What role does constant ‘k3’ play in equation ‘r17’?”
• “What organism are we talking about?”
• ... etc. ...
Multiple annotations on same entity are common

SBML supports two annotation schemes
SBO (Systems Biology Ontology)
• For mathematical semantics

• One SBML object ← one SBO term
• Short, compact, tightly coupled but limited scope
MIRIAM (Minimum Information Requested In the Annotation of Models)
• For any kind of annotation

• One SBML object ← multiple MIRIAM annotations
• Larger, more free-form, wider scope
Both are externalized and independent of SBML

Systems Biology Ontology (SBO)

http://biomodels.net/sbo

<sbml ...>
...
<listOfSpecies>
<listOfSpecies>
<listOfParameters>
SBO:0000339
<listOfParameters>
<listOfReactions>
<listOfReactants>
</listOfReactants>
<listOfProducts>
</listOfProducts>
<math>
...
<math>
...
</sbml> “forward bimolecular rate constant, continuous case”

Software can use SBO terms to help you work with models

semanticSBML

SBMLsqueezer

MIRIAM (Minimum Information Requested In the Annotation of Models)

Addresses 2 general areas of annotation needs:

Requirements for Scheme for encoding
reference correspondence annotations

Annotations for Annotations for
attributing model referring to external
creators & sources data resources

MIRIAM is not speciﬁc to SBML

Goal: permit tracing model’s origins & people involved in its creation
Minimal info required:
• Name for the model
• Citation for a description of what is being modeled & its author
• Contact info for the model creator(s)
• Creation date & time
• Last modiﬁcation date & time
• Statement of the model’s terms of distribution
- Speciﬁc terms not mandated, just a statement of the terms

Annotations for attributing model creators and sources

Annotations for external references
Goal: link model constituents to corresponding entities in
bioinformatics resources (e.g., databases, controlled vocabularies)
• Supports:
- Precise identiﬁcation of model constituents
- Discovery of models that concern the same thing
- Comparison of model constituents between diﬀerent models
MIRIAM approach avoids putting data content directly in the model;
instead, it points at external resources that contain the knowledge.

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

Low info
content

Why might you care?

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

salicylic acid

Known by different names –
Low info you want to write all of
do
content them into your model?

Why might you care?

Identifying resources has its own challenges
For linking to data, need:
• Globally unique, unambiguous identifiers
• ... that are persistent despite resource changes (e.g., changed URLs)
• ... that are maintained by the community
Problem: different resources have different identification schemes
• E.g.: entity “16480”
- In ChEBI: entry 16480 is nitrous oxide
- In PubMed: entry 16480 is the 1977 paper “Effect of gallstone-
dissolution therapy on human liver structure”
- In PubChem: entry 16480 is 1-chloro-4-isothiocyanatobenzene

How do we create globally unique identifiers consistently?
Long story short:
• Create unique resource identifiers (URIs) by combining 2 parts:

namespace entity identifier

{
{
Identifies a dataset Identifies a datum
within the dataset

• Create registry for namespaces
- Allows people & software to use same namespace identifiers
• Create service for URI resolution
- Allows people & software to take a given resource identifier and
figure out what it points to

Resolving resource identifiers
MIRIAM Registry supports the creation of globally unique identifiers
• Example MIRIAM identifier:
urn:miriam:ec-code:1.1.1.1
• Provides various data about the
resource, including alternate servers
• Provides web services

identifiers.org is layered on top of that and provides resolvable URIs
• Can type it in a web browser!
• Example identifiers.org URI:
http://identifiers.org/ec-code/1.1.1.1

BioModels Database: example of using the annotations

Annotations enable many interesting possibilities
Annotations interesting possibilities
semanticSBML

Figure courtesy of Wolfram Leibermeister

Summary: why care about standard ways of writing annotations?
Structured, machine-readable annotations increase your model’s utility
• Allow more precise identiﬁcation of model components
- Understand model structure
- Search/discover models
- Compare models
• Adds a semantic layer—integrates knowledge into the model
- Helps recipients understand the underlying biology
- Allows for better reuse of models
- Supports conversion of models from one form to another

Model representation level

Concept due to Nicolas Le Novère
Visual interpretation

Biological semantics
Dis
Co cre
nti te Mathematical semantics
nuo s toc
us ha
lum sti
pe ce
Me dp nti
tie tion
an Sta ara s lc rea ion
fie te me ode tat
ld
ap tra ter M la nno
pro ns
itio ode al ysis
xim n M
de l an ults
Mo res
ati
on erical
Num

Model type Model life-cycle

Major dimensions of a computational model

What about other kinds of models?

SBML Level 3: Supporting more categories of models

Package W

Package X Package Y Package Z

SBML Level 3 Core
(dependencies)

An SBML Level 3 package adds constructs & capabilities
Models declare which packages they use
• Applications tell users which packages they support
Package development can be decoupled

Level 3 package What it enables
Hierarchical composition Models containing submodels
Flux balance constraints Flux balance analysis models
Qualitative models Petri net models, Boolean models
Spatial Nonhomogeneous spatial models
Multicomponent species Entities with structure & state; rule-based models
Graph layout Diagrams of models
Graph rendering Diagrams of models
Distribution & ranges Nonscalar values
Annotations Richer annotation syntax
Groups Arbitrary grouping of model components
Dynamic structures Creation & destruction of model components
Arrays & sets Arrays or sets of entities

How can we capture the simulation/analysis procedures?

Decroly & Goldbeter, PNAS, 1982

?

BIOMD0000000319 in BioModels Database

Software can’t read ﬁgure legends

SED-ML = Simulation Experiment Description ML
Application-independent format to capture procedures, algorithms,
parameter values
• Neutral format for encoding the steps to go from model to output
Can be used for
• Simulation experiments encoding parametrizations & perturbations

• Simulations using more than one model
• Simulations using more than one method
• Data manipulations to produce plot(s)
libSedML project developing API library

http://www.biomodels.net/sedml

Graphical representation of models
Today: broad variation in graphical notation used in biological diagrams
• Between authors, between journals, even people in same group
However, standard notations would offer benefits:
• Consistency = easier to read diagrams with less ambiguity
• Software support: verification of correctness, translation to math

SBGN = Systems Biology Graphical Notation
Goal: standardize the graphical notation in diagrams of biological processes
• Community-based development, à la SBML
Many groups participating
3 sublanguages to describe diﬀerent facets of a model

http://sbgn.org

Attendees at SBML 10th Anniversary Symposium, Edinburgh, 2010

Such standards are the work of a great community

Get involved and make things better!
COMBINE (Computational Modeling in Biology Network)
• SBML, SBGN, BioPAX, SED-ML, CellML, NeuroML

http://co.mbine.org

Upcoming meeting: August 15–19 in Toronto, Canada
• Right before ICSB (International Conference on Systems Biology)

SBML http://sbml.org

BioModels Database http://biomodels.net/biomodels

COMBINE http://co.mbine.org

identiﬁers.org http://identiﬁers.org
URLs

MIRIAM http://biomodels.net/miriam

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

SBO http://biomodels.net/sbo

SBGN http://sbgn.org

I’d like your feedback!
You can use this anonymous form:
http://tinyurl.com/mhuckafeedback

SBML was made possible thanks to funding from:
National Institute of General Medical Sciences (USA)
European Molecular Biology Laboratory (EMBL)
JST ERATO Kitano Symbiotic Systems Project (Japan) (to 2003)
JST ERATO-SORST Program (Japan)
ELIXIR (UK)
Beckman Institute, Caltech (USA)
Keio University (Japan)
International Joint Research Program of NEDO (Japan)
Japanese Ministry of Agriculture
Japanese Ministry of Educ., Culture, Sports, Science and Tech.
BBSRC (UK)
National Science Foundation (USA)
DARPA IPTO Bio-SPICE Bio-Computation Program (USA)
Air Force Office of Scientific Research (USA)
STRI, University of Hertfordshire (UK)
Molecular Sciences Institute (USA)

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