retrospective about sbml on the occasion of the 10th anniversary of sbml

In the beginning...

1Saturday, July 7, 12

DR. KITANO DR. DOYLE

BIOLOGY


Bolouri.Hamid Bolouri.


HerbertAndrew

Mike


The ERATO Systems Biology Workbench

Hamid Bolouri

ERATO Kitano Systems Biology ProjectCalifornia Institute of Technology &

University of Hertfordshire, UK

Project PIs: Hiroaki Kitano and John Doyle

Software development team: Andrew Finney, Michael Hucka, Herbert Sauro

Collaborators:Adam Arkin (BioSpice), Dennis Bray (StochSim), Igor Goryanin (DBsolve), Les

Loew (VirtualCell), Pedro Mendes (Gepasi), Masaru Tomita (Ecell)

Acknowledgements: Mark Borisuk, Eric Mjolsness, Tau-Mu Yi

ICSB 2000


The ERATO Systems Biology Workbench

Hamid Bolouri

ERATO Kitano Systems Biology ProjectCalifornia Institute of Technology &

University of Hertfordshire, UK

Project PIs: Hiroaki Kitano and John Doyle

Software development team: Andrew Finney, Michael Hucka, Herbert Sauro

Collaborators:Adam Arkin (BioSpice), Dennis Bray (StochSim), Igor Goryanin (DBsolve), Les

Loew (VirtualCell), Pedro Mendes (Gepasi), Masaru Tomita (Ecell)

Acknowledgements: Mark Borisuk, Eric Mjolsness, Tau-Mu Yi

ICSB 2000

Shortly after, also CellML & ProMoT/DIVA


ERATO Systems Biology Workbench: driving principles

• Integrate, don’t reinvent!– integrate existing simulators– use standard application integration methods

• object oriented, XML, Java and related technologies

• Accommodate future tools– minimize need for ad hoc solutions

• object oriented, XML, Java and related technologies

– XML & API standards for future contributors

• Make sure contributors benefit– symmetric plug-in infrastructure– open source code infrastructure software– widen user-base, but protect IPR of contributors


The ensuing years...


SBedit(CellDesigner)

Jarnac

JDesigner

Gepasi


LibSBML

๏ Reads, writes, validates SBML

๏ Unit checking & conversion

๏ Written in portable C++

• Linux, Mac, Windows, FreeBSD

๏ C, C++, C#, Java, Octave, Perl, Python, Ruby, MATLAB

๏ Can use Expat, libxml2, or Xerces

๏ Open-source (LGPL)

๏ http://sbml.org/Software/libSBML

Original version 1.0.0 released Mar. 2003

Latest stable version: 4.2.0released Oct. 2010

Latest dev. version: 5.0.0-a1released Oct. 2010


SBML


Akira Funahashi

Sarah Keating

Ben Bornstein

Bruce Shapiro

Akija Jouraku


Nicolas Le Novère


SBML Editors, past and present

Andrew FinneyHerbert SauroHamid Bolouri

Nicolas Le NovèreSarah KeatingStefan Hoops

Frank BergmannSven SahleJim Schaff

Lucian SmithDarren Wilkinson

Mike Hucka (chair)17Saturday, July 7, 12

Where are we today?


Where are we today?(or, what have you done?)


Where are we today?(or, what have you done?)

(or, my God, what have you done?!)


Workshops to date: 27Apr. 2000 First general workshop Pasadena, California, USNov. 2000 Second general workshop Tokyo, Japan Jun. 2001 Third general workshop Pasadena, California, USDec. 2001 Fourth general workshop Pasadena, California, USJul. 2002 Fifth general workshop U. Hertfordshire, UK Dec. 2002 Sixth SBML Forum meeting Stockholm, SwedenMay 2003 Seventh SBML Forum meeting Ft. Lauderdale, Florida, USJul. 2003 First SBML Hackathon Blacksburg, Virginia, USNov. 2003 Eight SBML Forum meeting St. Louis, Missouri, USApr. 2004 Second SBML Hackathon Cambridge, UKOct. 2004 Ninth SBML Forum meeting Heidelberg, GermanyMay 2005 Third SBML Hackathon Tokyo, JapanOct. 2005 Tenth SBML Forum meeting Boston, Massachusetts, USApr. 2006 Fourth SBML Hackathon Nové Hrady, Czech RepublicOct. 2006 Eleventh SBML Forum meeting Tokyo, Japan Jun. 2007 Fifth SBML Hackathon Newcastle, UKSep. 2007 SBML Composition Workshop U. Connecticut, Connecticut, USOct. 2007 Twelfth SBML Forum meeting Long Beach, California, USDec. 2007 SBML Multi* Workshop Cambridge, UKApr. 2008 Sixth SBML Hackathon Stellenbosch, South AfricaAug. 2008 SBML Alt. Modeling Frameworks Cambridge, UKAug. 2008 Thirteenth SBML Forum meeting Göteborg, SwedenMar. 2009 Seventh SBML Hackathon Cambridge, UKMay 2010 SBML-NeuroML Interfacing Cambridge, UKMay 2010 SBML Annotations Pkg Workshop Cambridge, UKMay 2010 BioModels.net-SBML Hackathon U. Washington, Washington, USOct. 2011 COMBINE 2010 Edinburgh, UK


0

50

100

150

200

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Known software today: over 200

Count as of October 8: 201


Paper citations to date: 570

ISI Web of Science: 570Google Scholar: 1390

Citations to the original 2003 paper about SBML:


Real uses of SBML

2342 reactions2657 chemical species

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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Consensus yeast metabolic network model

Herrgård et al., Nature Biotech, 26:10, 2008


Real uses of SBML

30,965 reactions!36,263 species!

Aho et al., PLoS One. 2010 May 14;5(5):e10662.

RefRec: comprehensive yeast molecular interaction network reconstruction


Real uses of SBML

Comparing and clustering models in semanticSBML

(Slide from Wolfram Liebermeister)24Saturday, July 7, 12

SBRML


Lessons learned, lessons lost


What worked and is portable to other efforts?

๏ Actual stakeholders with real needs

• And make sure there’s more than one...

๏ Transparent and inclusive process

• Critical to legitimacy

๏ Continue to engage people

• Not getting responses? Find a new approach!

๏ Have independent organizers/shepherds/leaders

• Avoid even the appearance of bias or agenda

๏ Give credit freely

• “What goes around, comes around”


Lessons lost

๏ Over-dependence on one or small number of people

• Bottleneck to progress

• Fragile—whole effort at risk if they become unavailable, or difficult

๏ Inadequate balance between open process & making progress

• Democratic, open processes move slowly

• “Design by committee” is rarely good

• But you have to do it ...

๏ Inadequate testing before releasing/freezing

๏ Not sorting out intellectual property issues early on


Where do we go from here?


Remember why we’re doing this


We need to COMBINE our efforts


... and that is up to you.
