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Jianlin Cheng Institute for Genomics and Bioinformatics School of Information and Computer Science

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.

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Jianlin Cheng Institute for Genomics and Bioinformatics School of Information and Computer Science

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  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 • Storage • Simulation • Visualization • Inference Mathematics Hypotheses Computing

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

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

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

  7. Module II: Simulation Engine Law of Mass Action

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

  9. 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).

  10. Design of Intelligent Middleware

  11. Translate Pathway into Cellerator Commands DAD(kf,kr) aDHIV aKIV GeneratedReaction={List[Overscript[RightArrowLeftArrow[aDHIV,aKIV],DAD],kfDADaDHIV,krDADaDHIVnotsame,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,$Complex$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]

  12. Module IV: Visualization and GUI

  13. A Simulation Example

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

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