1. Jianlin Cheng Institute for Genomics and Bioinformatics School of Information and Computer Science University of California Irvine Sigmoid: A Systems Biology Infrastructure for the Simulation, Visualization, and Storage of Biological Networks

2. Biological Networks (Pathways) – Systems Biology Modeling networks of molecular reactions Metabolic pathways Signal transduction pathways Transcription regulatory pathways MAPKinase Pathway

3. Goals of Sigmoid Biology Mathematics Computing Hypotheses Storage Simulation Visualization Inference

4. Architecture of Sigmoid Four Main Modules 3-tier Architecture (Model – View – Controller) IV. Pathway Visualization and GUI I. Pathway Representation Storage Database II. Pathway Simulation Inference Engine Database Access Model Translation Front End Middle Layer Backend Biologists III.

5. Module I : Representation and Storage of Pathway Database Reactant BioComplexMolecule Protein Multimer Affinity Derived complex Amino Acid SeqDNARNAPeptideSmall Molecule Known Protein Complex Y2H Dimer High Through Put ORF Complex Lipid Protein Hypothetical Protein Documented Protein Gene ORF Reactant Hierarchy

6. Reaction Hierarchy

7. Implementation of Biological Pathway Database UML (Universal Modeling Language) schema. OJB (Object Relation Bridge) Postgres relational database. Java

8. Module II : Simulation Engine Law of Mass Action

9. Generate Mathematical Model for Pathway (Cellerator) Shapiro, BE, Levchenko, A, Meyerowitz, EM, Wold, BJ, and Mjolsness, ED, Bioinformatics

10. Module III : Distributed and Web- based Computing (Middleware) Support distributed, web-based computing and resource sharing. Pathway/model objects can be transferred across internet between database, GUI and computation engine via SOAP. Java pathway objects need to be translated into mathematica commands recoginized by simulation engine (Cellerator).

11. Design of Intelligent Middleware

12. Translate Pathway into Cellerator Commands GeneratedReaction={List[Overscript[RightArrowLe ftArrow[aDHIV,aKIV],DAD],kfDADaDHIV,kr DADaDHIVnotsame,kcat$DAD$aDHIV]} {myODEs, myVars} = interpret[GeneratedReaction] Lamda = 100 Omega = 1 myKConstants = {KmDADaDHIV=500;kcat$DAD$aDHIV=1000; kfDADaDHIV- >Kf[KmDADaDHIV,kcat$DAD$aDHIV,Lamda ],krDADaDHIVnotsame- >Kr[kcat$DAD$aDHIV,Lamda]} myICs = {aDHIV[0]==1000,aKIV[0]==0,DAD[0]==10,$C omplex$aDHIV$DAD$[0]== 0} tmax = 10 mySolution = NDSolve[Join[myODEs/.myKConstants, myICs], myVars, {t, 0, tmax},AccuracyGoal->2, PrecisionGoal->2, MaxSteps->3000] Plot[aDHIV[t]/.mySolution,{t,0,tmax}, PlotLabel- >aDHIV,PlotRange->All] Plot[aKIV[t]/.mySolution,{t,0,tmax}, PlotLabel- >aKIV,PlotRange->All] aDHIVaKIV DAD(kf,kr)

13. Module IV : Visualization and GUI

14. A Simulation Example

15. Acknowledgements Pierre Baldi Mike Sweredoski Arlo Randall Gianluca Pollastri Alessandro Vullo Hiroto Saigo Chin-Rang Yang Lucas Scharenbroich Trent Su Peter Hebden Eric Mjolsness