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Creating a new language to support open innovation
The document discusses the creation and importance of the Systems Biology Markup Language (SBML) in facilitating the communication and reproducibility of computational models in biological research. It highlights shared efforts with MIRIAM and SED-ML to improve model annotations and integrate systems biology standards through community collaboration. Additionally, it outlines the evolution and future development of SBML while reflecting on lessons learned from past standardization efforts.
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Creating a new language to support open innovation
- 1. Creating a newlanguage to support open innovation Michael Hucka, Ph.D. Department of Computing + Mathematical Sciences California Institute of Technology Pasadena, CA, USA BioBriefing – BioMelbourne Network, Australia, August 2013 Email: mhucka@caltech.edu Twitter: @mhucka
- 2. Outline Background and introduction TheSystems Biology Markup Language (SBML) Complementary efforts: MIRIAM and SED-ML COMBINE: the Computational Modeling in Biology Network Conclusion
- 3. Outline Background and introduction TheSystems Biology Markup Language (SBML) Complementary efforts: MIRIAM and SED-ML COMBINE: the Computational Modeling in Biology Network Conclusion
- 4. Research today: experimentation,computation, cogitation
- 5. “ The natureof systems biology” Bruggeman & Westerhoff, Trends Microbiol. 15 (2007).
- 6. Large-scale integrative modelsare growing
- 7. Many models havetraditionally 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 communicate the model in a paper?
- 8. Experiences from BioModelsDatabase BioModels Database: • Public database of published computational models in biology • Many models are curated – i.e., made to work & annotated - If not available in electronic form, they encode it from the paper Their experiences? • Vast majority of models encoded directly from the publication did not work as published - Often (not always) due to common errors – typos, omissions • Success rate improved in recent years thanks to more people providing their models in electronic formats More is needed to make computational results reproducible
- 9. Experiences from BioModelsDatabase BioModels Database: • Public database of published computational models in biology • Many models are curated – i.e., made to work & annotated - If not available in electronic form, they encode it from the paper Their experiences? • Vast majority of models encoded directly from the publication did not work as published - Often (not always) due to common errors – typos, omissions • Success rate improved in recent years thanks to more people providing their models in electronic formats More is needed to make computational results reproducible http://biomodels.net/biomodels
- 10. Experiences from BioModelsDatabase BioModels Database: • Public database of published computational models in biology • Many models are curated – i.e., made to work & annotated - If not available in electronic form, they encode it from the paper Their experiences? • Vast majority of models encoded directly from the publication did not work as published - Often (not always) due to common errors – typos, omissions • Success rate improved in recent years thanks to more people providing their models in electronic formats More is needed to make computational results reproducible
- 11. Is it enoughto 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, verify the computational results, build on them, etc. • Opinion: you should always do this in any case
- 12. Is it enoughto 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 the same software? • What if they don’t want to use that software? • What if they want to use a different conceptual framework? • And how will people be able to relate the model to other work?
- 13. Different tools differentinterfaces & languages
- 14. Outline Background and introduction TheSystems Biology Markup Language (SBML) Complementary efforts: MIRIAM and SED-ML COMBINE: the Computational Modeling in Biology Network Conclusion
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- 16. Format for representingcomputational models of biological processes • Data structures + usage principles + serialization to XML • (Mostly) Declarative, not procedural—not a scripting language Neutral with respect to modeling framework • E.g., ODE, stochastic systems, etc. Important: software reads/writes SBML, not humans SBML = Systems Biology Markup Language
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- 18. The process iscentral • Literally called a“reaction”in SBML • Participants are pools of entities (biochemical species) Models can further include: • Compartments • Other constants & variables • Discontinuous events • Other, explicit math Core SBML concepts are fairly simple • Unit definitions • Annotations
- 19. SBML is nowwidely used Dozens of journals accept models in SBML format 100’s of software tools available today 1000’s of models available in SBML format today 0 100 200 300 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 254+ today
- 20. Contents of BioModelsDatabase Contents today: • 142,000+ pathway models (converted from KEGG) • 460+ hand-curated quantitative models • 460+ non-curated quantitative models 8% 2% 3% 6% 6% 7% 8% 9% 24% 27% signal transduction metabolic process multicelullar organismal process rhythmic process cell cycle homeostatic process response to stimulus cell death localization others (e.g., developmental process) Database data from 2013
- 21. Free software libraries– 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
- 22. Free software libraries– 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
- 23. Evolution of SBMLcontinues Today: SBML Level 3 • Level 3 Core provides framework for common models • Level 3 packages add additional constructs to the Core
- 24. Level 3 packageWhat it enables Hierarchical model composition Models containing submodels ✔ Flux balance constraints Constraint-based models ✔ Qualitative models Petri net models, Boolean models ✔ Graph layout Diagrams of models ✔ Multicomponent/state species Entities w/ structure; also rule-based models draft Spatial Nonhomogeneous spatial models draft Graph rendering Diagrams of models draft Groups Arbitrary grouping of components draft Distributions Numerical values as statistical distributions in dev Arrays & sets Arrays or sets of entities in dev Dynamic structures Creation & destruction of components in dev Annotations Richer annotation syntax Status
- 25. NationalInstituteofGeneralMedicalSciences(USA) European Molecular BiologyLaboratory (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) SBML funding sources over the past 13+ years
- 26. Outline Background and introduction TheSystems Biology Markup Language (SBML) Complementary efforts: MIRIAM and SED-ML COMBINE: the Computational Modeling in Biology Network Conclusion
- 27. Mathematical semantics Biological semantics Visualinterpretation Discrete stochastic entities Continuous lumped parameter State transition Meanfieldapproximation Model type Model creation Model annotation Model analysis Numericalresults Model life-cycle Model representation level COMBINE efforts cover different facets of modeling ... ConceptduetoNicolasLeNovère
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- 31. Raw models aloneare insufficient Need standard schemes for machine-readable annotations • Identify entities • Mathematical semantics • Links to other data resources • Authorship & pub. info Modelerswanttousetheirownconventions Low info content No standard identifiers
- 32. Addresses 2 generalareas of annotation needs: MIRIAM is not specific to SBML MIRIAM(MinimumInformationRequestedIntheAnnotationofModels) Requirements for reference correspondence Scheme for encoding annotations Annotations for attributing model creators & sources Annotations for referring to external data resources
- 33. Addresses 2 generalareas of annotation needs: MIRIAM is not specific to SBML MIRIAM(MinimumInformationRequestedIntheAnnotationofModels) Requirements for reference correspondence Scheme for encoding annotations Annotations for attributing model creators & sources Annotations for referring to external data resources Annotations for referring to external data resources
- 34. Example of aproblem that can be solved with annotations http://www.ebi.ac.uk/chebi Low info content
- 35. Example of aproblem that can be solved with annotations http://www.ebi.ac.uk/chebi Low info content Known by different names – do you want to write all of them into your model? salicylic acid
- 36. MIRIAM annotations forexternal 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 data
- 37. How do wecreate globally unique identifiers consistently? Long story short—developed by the Le Novère group at the EBI • Resource identifiers (URIs) combine 2 parts: • There’s a registry for namespaces: MIRIAM Registry - Allows people & software to use same namespace identifiers • There’s a URI resolution service: MIRIAM Resources & identifiers.org - Allows people & software to take a given identifier and figure out what it points to namespace entity identifier { { Identifies a dataset Identifies a datum within the dataset
- 38. Another problem: softwarecan’t read figure legends ? BIOMD0000000319 in BioModels Database Decroly & Goldbeter, PNAS, 1982
- 39. SED-ML = SimulationExperiment Description ML Application-independent format •Captures procedures, algorithms, parameter values Can be used for •Simulation experiments encoding parametrizations & perturbations •Simulations using more than one model and/or method •Data manipulations to produce plot(s) http://sedml.org Simulation Model Task Data generators Reports
- 40. Efforts like SED-MLimprove reproducibility of publications Waltemath et al., BMC Sys Bio 5, 2011.
- 41. Outline Background and introduction TheSystems Biology Markup Language (SBML) Complementary efforts: MIRIAM and SED-ML COMBINE: the Computational Modeling in Biology Network Conclusion
- 42. Need interoperable formats,but developing them is not easy Need people with diverse set of knowledge & skills • Scientific needs • Technical implementation skills • Practical experience Need manage multiple phases of a standardization effort • Creation • Evolution • Support
- 43. Need interoperable formats,but developing them is not easy Need people with diverse set of knowledge & skills • Scientific needs • Technical implementation skills • Practical experience Need manage multiple phases of a standardization effort • Creation • Evolution • Support } This is just for the specification of the standards, to say nothing of the necessary software and other infrastructure!
- 44. Realizations about thestate of affairs in late-2000’s • Many standardization efforts overlapped, but lacked coordination • Efforts were inventing their own processes from scratch • Many individual meetings meant more travel for many people • Limited and fragile funding didn’t support solid, coherent base COMBINE = Computational Modeling in Biology Network • Coordinate standards development • Develop common procedures & tools (but not impose them!) • Coordinate meetings • Provide a recognized voice Motivations for the creation of COMBINE
- 45. Standardization efforts representedin COMBINE today BioPAX Qualifiers GPML COMBINE Standards Associated Standardization Efforts Related Standardization Efforts
- 46. Those are theproducts of successful, open collaborations!
- 47. Examples of communityorganization Two main annual meetings, plus ad hoc workshops • COMBINE meeting: status updates, presentations, outreach - Next COMBINE: Paris, Sep 16–20, 2013 • HARMONY: Hackathon on Resources for Modeling in Biology - Software development, interoperability hacking COMBINE 2012, TorontoCOMBINE 2011, Heidelberg
- 48. What motivates peopleto do this? Solving a problem for yourself/your closed group is easier and quicker • So what are these people getting out of it? Some advantages of an open, community-oriented approach: • Finding better solutions they wouldn’t find alone - Arguments Discussions leads to realizations & better solutions • Contributions to science – publications, peer recognition • Support of a standard makes their software more desirable • Sense of community involvement Some admitted disadvantages: • Agreement takes time – progress can be very slow • Solutions may include features you didn’t plan on, or need
- 49. COMBINE is opento all—and COMBINE needs you! http://co.mbine.org Current coordinators: • Nicolas Le Novère, Mike Hucka, Falk Schreiber, Gary Bader
- 50. Outline Background and introduction TheSystems Biology Markup Language (SBML) Complementary efforts: MIRIAM and SED-ML COMBINE: the Computational Modeling in Biology Network Conclusion
- 51. Time it well •Too early and too late are bad Start with actual stakeholders • Address real needs, not perceived ones Start with small team of dedicated developers • Can work faster, more focused; also avoids“designed-by-committee” Engage people constantly, in many ways • Electronic forums, email, electronic voting, surveys, hackathons Make the results free and open-source • Makes people comfortable knowing it will always be available Be creative about seeking funding Some things we (maybe?) got right with SBML
- 52. Not waiting forimplementations before freezing specifications • Sometimes finalized specification before implementations tested it - Especially bad when we failed to do a good job ‣ E.g.,“forward thinking”features, or“elegant”designs Not formalizing the development process sufficiently • Especially early in the history, did not have a very open process Not resolving intellectual property issues from the beginning • Industrial users ask“who has the right to give any rights to this?” Some things we certainly got wrong
- 53. Nicolas Le Novère,Henning Hermjakob, Camille Laibe, Chen Li, Lukas Endler, Nico Rodriguez, Marco Donizelli,Viji Chelliah, Mélanie Courtot, Harish Dharuri Attendees at SBML 10th Anniversary Symposium, Edinburgh, 2010 John C. Doyle, Hiroaki Kitano Mike Hucka, Sarah Keating, Frank Bergmann, Lucian Smith, Andrew Finney, Herbert Sauro, Hamid Bolouri, Ben Bornstein, Bruce Shapiro, Akira Funahashi, Akiya Juraku, Ben Kovitz OriginalPI’s: SBMLTeam: SBMLEditors: BioModelsDB: Mike Hucka, Nicolas Le Novère, Sarah Keating, Frank Bergmann, Lucian Smith, Chris Myers, Stefan Hoops, Sven Sahle, James Schaff, DarrenWilkinson And a huge thanks to many others in the COMBINE community This work was made possible thanks to a great community
- 54. SBML http://sbml.org BioModels Databasehttp://biomodels.net/biomodels MIRIAM http://biomodels.net/miriam identifiers.org http://identifiers.org SED-ML http://biomodels.net/sed-ml SBO http://biomodels.net/sbo SBGN http://sbgn.org COMBINE http://co.mbine.org URLs
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