1. MDL Meeting Notes 2002-09-25 and 2002-09-26 at MIT

2. Participants Drew Endy (MIT), Tom Knight (MIT), George Church (Harvard), Oleg Sokolsky (UPenn), Daniel Zak (U.Delaware/ThomasJeffferson U), Birgit Schoeberl (MIT), Jim Collins (B.U.), Jonathan Webb (BBN), Cliff Shaffer (VT), Peter Finin (UPenn), Michael Hucka (Caltech), Andrew Finney (Caltech), Chris Cox (UTenn), Jesse S.A. Bridgewater (UCLA), Dennis A Dean (Harvard), Katherine Gurdlier (BMU / Harvard), Jordan Fielder (MITRE), Olivia Tate (MITRE), David Beckwith (UCLA), Daniel Segre (Harvard), Wayne Rindone (Harvard), Sriram Kosuri (MIT), Clare Thiem (AFRL), Tom Garvey (SRI), Patrick Lincoln (SRI), Leon Chan (MIT), Arch Owen (BBN), Bud Misra (NYU)

3. Summary - Key Talking Points 1. Model Type Examples 2. Model Definition Language Specification, Requirements, & Vocabulary 3. BioSPICE System Integration

4. How to Get Things Moving u Group agreement on deadlines Use cases/MDL specification & requirements: –Oct 15 first draft –Halloween draft to MDL group Model types: –Halloween 12 different model types u Volunteer to be chief annoying person on model types: Cliff Shaffer u Volunteer to be chief annoying person on use cases/MDL requirements: Patrick Lincoln

5. Lesson from the Past u Model definitions need to support: Search –BLAST, etc. Merging –Dock two modeled components together Checking –Connect with other datatypes, compare independently generated models

6. Model Types - Discussion u Chemical Kinetic Normal (Tyson/Shaffer/Kumar) Fine-grained (Kosuri/Arkin) Complexes (Lok/Finney/Faeder) Markov models (Cox)…abstraction is research u 3D PDE (Arkin/Lowe/Przekwas) Ray Tracing (Bartol/Stiles) u Flux Balance (Segres) u Static Graph (Karp) u Boolean/Logic (Lincoln) u Bayesian inferred models (Gifford, Liao, Lauffenberger) u Diagramatic notations (Hucka, Schoeberl, Kohn) u Other

7. Model Types - Points of Contact 1. Chemical Kinetics Endy Normal/ODE (Tyson, Others), Fine-grained/Stoch (Cox), Complexes (Endy) 2. Static / graph / Flux balance Church, Lincoln, Lok?, Hucka 3. Automata (cell movement) Schoeberl, Kaiser 4. MarkovCox? 5. 2d/3d PDE diffusionVirtual Cell, Finney? 6. S-system (power law format ODE)Mishra 7. Hybrid systemsLincoln, Sokolsky 8. 4D: structural/cell dynamicsChurch 9. Boolean, Digital, Discrete eventHucka 10. Shape/4d ray trace Endy Mcell? 11. Electrophysiology ODEZak 12. Delay differential equationsZak BioSPICE.org sandbox folders Lincoln

8. Model Types - By Halloween 1. Chemical Kinetics Endy (SBML) Normal/ODE (Tyson, Others), Fine-grained/Stoch (Cox), complexes (Endy) 2. Static / graph / Flux balance Church, Lincoln, Lok?, Hucka (SMBL) 3. Automata (cell movement) Schoeberl, Kaiser (SBML) 4. MarkovBeckwith, Cox? (no) 5. 2d/3d PDE diffusionVirtual Cell, Arkin, Finney? (no) 6. S-system (power law format ODE)Mishra (SBML composition?) 7. Hybrid systemsLincoln, Sokolsky (no…?) 8. 4D: structural/cell dynamicsChurch (no) 9. Boolean, Digital, Discrete eventHucka(SBML) 10. Shape/4d ray trace Endy, Mcell? (no) 11. Electrophysiology ODEZak (no…VCell?) 12. Delay differential equationsZak (SBML) (SBML) == SBML does/will handle, (no) == SBML does not handle

9. Working Groups - discussion u Existing BioSPICE working groups: DB, EWG, MDL u System-level integration (needed now) Identify overall map, components that make it up System-level Includes communication architecture (APIs) E.g., “this is the API for a simulator” Do it by layering, identify common functionality Extensible interfaces u Model builder GUIs (community exists already) u Simulators (community exists already?) Multiple simulators operating simultaneously, validation Have affinity group meeting at Dec 02 PI meeting (Garvey) u Non-simulator analysis tools Have affinity group meeting at Dec 02 PI meeting (Garvey) u Data modeling (Resources? Do this in future)

10. BioSPICE Components u Model building GUIs Reads and writes MDL u Analysis tools Model analysis –Simulators Reads MDL, parameter values, configuration –Bifurcation, reachability, sensitivity analysis, elementary modes Data analysis –Compare experimental data with output of simultators, parameter estimation, confidence u Control systems Coordinate multiple simulators, multiple models Version control, multiple runs, Chicago Chimera, make u Databases – connections u Lab notebook u End-to-end use cases: Hucka

11. Data Modeling: Added Working Group? u From raw data to mechanism, rate terms and parameter estimates. u Metadata (model points back to dataset) Version, author, … u Bayesian inference of models

12. Notes on Model Definition Language Specification, Requirements, & Vocabulary

13. MDL Discussion -- Cliff’s List u What goes in a model definition language Metadata scheme Graphical layout Geometry / topology Variables (species, things with names) Bits of computation –Named functions for use in rate laws States Control (state transitions, compartment…) Set of constraints Objective function –Might be a bit of computation

14. What is Needed u Need group that looks at components and how they fit together u Interagent coordination language: OAA is much improved, particularly performance u Inputs to simulator? u But don’t need BioSPICE working groups when there are already healthy communities Model building (user interfaces), simulators

15. u Map out the space What are the gaps? u System integration u Overall functionality u The art of how they fit together

16. What MDL is Not u Not data model u Not process model u Not integration language for components u Not ontology for biology, biochemistry But we will produce a controlled vocabulary But we may require an ontology

17. MDL definition u Comes from use cases 1.Model development 2.Model creation / editing / evolution 3.Domain extensibility 4.Simulation (numeric) 5.Analysis (symbolic) 6.Models as a useable tool for browsing, etc Personal organization 7.Models a presentation or communication mechanism Whiteboard 8.Producing a formal representation is useful MDL can play a role in that intellectual activity u End of October

18. MDL Use Cases 1. Model development Model creation 2. Analysis Symbolic and numeric (Simulation) Model validation Wet-lab experiment design Models as a useful organization tool for browsing, etc Personal organization Models a presentation or communication mechanism Whiteboard Domain extensibility Editing and evolution of models Producing a formal representation is useful in itself MDL can play a role in that intellectual activity 1. Model discovery (Regulography)

19. Action Items: First draft Oct 15 u Webb and Hucka Use case guide and example u Shaffer (Full Tyson example) u Zak sensitivity analysis, experimental design u Bridgewater (Simulation) (coupled represselators) u Dennis (brainstorming, collaboration) u Experimental design, model validation (Cox) u Model discovery, regulography (Beckwith) u Model creation (Finney, Bridgewater) u Mode switching, hybrid control need to be in MDL

20. Model Development Flow 1. Experimental design 2. Experimental data processing Array sw, expression data management 3. Identification of players Genomics sw, clustering sw 4. Annotation vis-à-vis known info Genomic 5. Relationship inference Bayesian 6. Generation of qualitative model Model induction 7. Generation of quantitative model Optimization 8. Simulation / analysis / prediction Matlab, XS, BioReactor, Sensitivity analysis 9. Publication of model

21. Collaboration u Diversity of Discipline, Organization, Location –MDL needs: defined vocabulary, mappings –MDL needs: ability to difference –MDL needs: version control annotation?