1. Affymetrix/BioCarta comparison & Java-based pathway analysis Michael Edmonson 2/26/2003

2. Goals Create programmable models of BioCarta pathway gene interaction networks Encode “rules” of known gene interactions in software Create association between available experimental assays (microarrays) and pathway elements Populate model with experimental data and compare with expected states

3. BioCarta pathway example

4. Basic uses of model Static state diagram Dynamic system

5. Static/state-based modeling Load model with static “snapshot” or state data taken from microarray experiment With data from normal tissues, use resulting state to validate model (is the data consistent with the rules of the model?) With cancerous data, see if state of the model can be explained by “broken” logic: detect breakdowns in normal gene function and attempt to backtrace failures to first causes

6. Dynamic modeling Integration of code with higher-level applications Model will be a working system whose state changes over a period of time\ Systematic/programmatic exploration of effects of arbitrary changes in the model’s state Explore interconnections between pathways

7. Source data

8. Fundamentals Functionality of model will be dictated by data used to populate it Need to connect BioCarta pathways with Affymetrix assays –Desirable to automatically maintain mapping as new data becomes available Web-based chip/pathway browsing tools

9. Available BioCarta data List of pathway names and genes contained within them Graphic-only pathway diagrams (no annotations of relationships between pathway elements) Not computable

10. Available Affymetrix data Existing database tables: Bob Clifford et al.:

11. New database tables: BioCarta derived from CGAP flatfile RFLP database on LPG server

12. New tables: BioCarta to Affy “pathway” bot keeps tables updated with each new UniGene build revisions needed: UniGene clustering issues, ambiguous probes, etc

13. Chip/pathway browser: affy2biocarta

14. affy2biocarta http://lpgfs.nci.nih.gov:82/perl/affy2biocarta Frontend to database; details how well pathways are covered by individual chips Searchable by gene, pathway or chip Master report for each pathway of best chip to use Ability to search for probes for missing genes on other chips

15. affy2biocarta: top-level

16. affy2biocarta: pathway selector

17. affy2biocarta: pathway/chip selector

18. affy2biocarta: gene detail Puzzlements: multiple sequences, missing entry

19. affy2biocarta: “missing” gene search Note probes were found on an earlier chip!

20. Omissions in chip revisions HG-U133A generally has the most complete pathway coverage However, for 45 genes in BioCarta pathways no matching probe accessions could be found Of these 45: –32 (71%) were found in Hs.127 (which predates 133 set) –36 (80%) were found on other chips

21. Multiple sequences/probes for same gene A single pathway element (gene) may have multiple probes/sequences representing it These states often do not all agree in expression data Relationship between probes and BioCarta elements needs clarification

22. Expression data with disagreements Often not a 1:1 relationship between Affymetrix probes and pathway entries...

23. Pathway interconnections Many genes appear in multiple pathways, a few appear in many Concept of “connectome”, a.k.a. “furball” Potential for indirect feedback from greater system (no pathway is an island) Difficult to explore in detail without database of connections

24. Genes in multiple pathways

25. Java modeling

26. Implementation: Java OOP Pathways are completely encapsulated in objects which can be embedded in higher-level programs –Programmatic control of node and connection states Simple classes representing elements in pathway and connections between them –Nodes, Connections, Complexes –ability to propagate signals around the network

27. Node A discrete component in the network: usually a gene but can be any event which can effect the system (contact inhibition, etc.) Each node has a state, which is currently binary (on or off) –Binary states resemble “present/absent” expression data, but this highlights contention/deadlocking problem Contains incoming and outgoing connections to other nodes in the network

28. Connection Object describing a link between nodes and the relationship between them abstract execute_action() method implemented by different connection types example: –LogicalConnection: state of source node determines state of destination node –SimpleActivator, SimpleBlocker Connections may be individually disabled to emulate non- functioning of upstream process

29. Complex Container for multiple discrete subelements Provides higher-order logic based on evaluation of components’ state; e.g. performing some action only when all subcomponents are considered active additional functionality beyond component parts

30. State change propagation Setting the state of a node propagates the effect of that change on downstream connections During propagation a list of initiating nodes is accumulated and passed along; propagation stops if an initiating node is encountered again (prevents infinite loops)

31. What’s Next State validation/sanity checking Diagnosis/backtracing of “broken” logic More subtle states and connection types (beyond a binary system) Improved probe/gene mappings Automated model instantiation from curated database Incorporation into higher-level programs