1. The Berkeley BioSPICE Conceptual Framework Representing formal and informal knowledge with varying degrees of detail Mostly by Alex Gilman The BioSPICE Tool: A cast of thousands of meese.

2. But first: People in my group in AFCS Experiments and modeling of multiple signal processing in immune cells (Keith Erickson) Experiments and theory of mechanochemical components of signal transduction ( Matt Onsum) Microfluidic devices for spatiotemporal application of ligands to single cells (Shilpa Shroff) Functional genomic data analysis of ligand/response compendia (Pat Flaherty) Tools for knowledge capture and analysis (Alex Gilman)

3. The Berkeley BioSPICE team Alex Ken Koster Janet Jacobsen Keith Keller TingTing Zhang Hai Phan Kevin Keck

4. Some dogma Biology is not done yet Not everything must be formalized Everyone has a different take There is more to life than reactions Differential equations are not everything Using models should be child’s play

5. Goals of the framework Facilitate expressing informal knowledge in a structured way Represent the known and “hypothetical” in the same way Allow arbitrary scope Allow deepening and formalization

6. Informal knowledge How to strike a balance between the ambiguity of a diagram… Diagrams are nice but ambiguous And the relative precision of full sentences ? “…later, a hypothetical  F -dependent gene product (X) initiates the destruction of both SigE and SpoIIGA in the forespore….” Ju, et.al. J. Bact. 1998 180:1673

7. Informal knowledge Easy! Put labels on the diagram.

8. Informal knowledge Entity-Process-Role abstraction: Entity: is an entity is an entity Process: a named relationship between entities Role: a label for how an entity relates to other entities What can be a process? Anything…

10. Source: Gil Sambrano, AFCS

13. Increasingly detailed versions of processes Gene Effector Product Gene Product Gene Repressor Product  factor Transcriptional enhancer Gene Product Activator Repressor

14. Process collections Families Refinement hierarchies (a simple ontology)

15. Mechanisms: composing processes Still being implemented…

16. Formal knowledge Model: an expression of formal knowledge –Differential equation –Logical statements –Executable code

17. Formal knowledge Model assignment: a model must “understand” the roles of its process.

18. Formal knowledge Model assignment: a model must “understand” the roles of its process.

19. Model reuse - Generalized models: up the refinement hierarchy - Isomorphic processes (examples): 1-to-1 mapping between roles

20. Example model output k k X_2 k X_2 2 k X_3 function output = run_simulation % x(1): Molecule 1 (mmol L^-1 (mMolar)) % x(2): Molecule 2 (mmol L^-1 (mMolar)) Ti = 0.0; Tf = 10.0; %------------------------------------------------------ % initial conditions %------------------------------------------------------ x = zeros(2, 1); x(1) = 0.0; x(2) = 0.0; [T x] = ode45(@model, [Ti Tf], x); plot(T, x) output = [T x]; %------------------------------------------------------ function dx = model(t, x) dx = zeros(2,1); %------------------------------------------------------ % parameters k_0 = 3.2; % mmol^-1 L sec^-1 k_2 = 0.6; % sec^-1 k_1 = 1.0; % mmol L^-1 sec^-1 (mMolar_per_second) k_3 = 0.4; % sec^-1 %------------------------------------------------------ dx(1) = k_1 - k_2*x(1) - 2*k_0*x(1)^2; dx(2) = k_0*x(1)^2 - k_3*x(2);

21. Model suites

23. Archiving & searching Relational schema Query capabilities not yet well developed –Eg. how to search for phosphorylations – represented as “reaction” processes? –Searching by metadata –Searching by role or process name or label –Entity participation –Modeled or not; which models Relationship to a real database of primary and secondary data!

24. Drawbacks Not done!! Some habitual connections not available Process collection requires careful management (what doesn’t?) Models are labor-intensive Tools are NOT web-based but are network aware.

25. Conclusions for Pathway Builder Extremely simple and general representation Works for any scope Open and extensible “Smooth” transition from informal to formal knowledge Easy to use models contributed by others Slightly less easy but possible to collaborate on models. Still not ready for prime time…

26. DARPA BioCOMP BioSPICE Release 3.0 May 14, 2003, Ft. Lauderdale, FL Many tools from multiple investigators Integrated “dashboard” Integrated by SRI and LBNL New API for making modules. http://biospice.org Still very beta….sorry.

27. BioSPICE Product Data Warehouse Model Builder Simulation Engine Data Display Lab Notebook Run Manager Post Processing Application-level model API? Experimental Data Analysis Model Analysis MDL EWG MDL

28. SEPDTF Branding of Netbeans

29. API definitions from SEPDTF and interchange formats from EWG and MDL allow actual workflow and project management tools to work.

31. BioSPICE Dashboard: Work Flow Editor Basic work flow development area. Components added by using the add button. Work flow started by using Workflow button menu items Display of available local and distributed services. OAA is one of several possible discovery and remote service invocation mechanisms.

32. BioSPICE Dashboard – Simple Work Flow Simple work flow consisting of SBML input document, Exact Stochastic Simulator agent (ESS) and grapher module output Start work flow Output graph of time-series date representing concentration changes over time

33. Additional Capabilities External ProcessesExternal Web Sites Additional capabilities can be added by modifying a simple text properties file from which the menu is created.

34. NOT JUST SIMULATION Pathway homolog finder registers itself as an analysis of a pathway with the output of a pathway…

37. Examples We’re connecting a number of important analytical tools into BioSPICE Our functional genomic analyzers and visualizers Andry Rzhetsky’s NLP literature mining for entity- process relationships Various simulators from logical analysis to 3D simulation.

38. A complicated sketch of Initiation population [P 1 ] [P 2 ]

39. Short Demo?