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Cellerator: A System for Simulating Biochemical Reaction Networks

Cellerator: A System for Simulating Biochemical Reaction Networks. Bruce E Shapiro . Jet Propulsion Laboratory California Institute of Technology. bshapiro@jpl.nasa.gov. Part of a Biochemical Network.

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Cellerator: A System for Simulating Biochemical Reaction Networks

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  1. Cellerator: A System for Simulating Biochemical Reaction Networks Bruce E Shapiro Jet Propulsion Laboratory California Institute of Technology bshapiro@jpl.nasa.gov

  2. Part of a Biochemical Network From: Kohn (1999) Molecular interaction map of the mammalian cell cycle control and DNA repair systems. Mol Biol Cell 10:2703-2734

  3. Biochemical Networks Are... • Complex • Mutually interacting • Large • Number of reactions grows exponentially with number of states • Best understood pictorially • Best described quantitatively by a large system of differential equations (ODEs) Need to translate pictures to ODEs

  4. Online network databases exist ... http://www.genome.ad.jp/kegg/

  5. ... but mathematical simulations of these networks are hopelessly naive...

  6. A B C Input Canonical Form Biochemical Notation Output Canonical Form System of ODEs Activity (e.g., Cell Division) Solver Concentrations vs. Time

  7. Caltech ERATO* Simulator Architecture A GUI and Modeling meta-language C B Text Transfer Protocol XML based protocol Application Application Application Application *Exploratory Research for Advanced Technology (Japan Science & Technology Corporation) http://www.systems-biology.org Application

  8. A simpler network for cell division C=Cyclin: enzyme that gets things going M=MPF promoting factor. M>Threshold induces cell division X=Cyclin Protease: enzyme that breaks down C Goldbeter, A (1991) A minimal cascade model for the mitotic oscillator involving cyclin and cdc2 kinase. PNAS 88:9107-9111

  9. Equations and prections of Goldbeter Mitotic Oscillator

  10. Cellerator canonical form for input Reactions are input with a biochemical based notation Prints out ODES STN = {{reaction, rate-constants}, {reaction, rate-constants},…}; interpret[STN]; Simulation = predictTimeCourse[STN, options]; Returns tables of values as a function of time, with optional plots

  11. Cellerator input/output for Goldbeter Mitotic Oscillator

  12. Simple Cooperative Conversion Creation, Degradation Enzymatic Reversible Enzymatic Transcription (Gene  RNA) Post-transcriptional Processing Translation (RNA  Protein) Diffusion and more ... The Basis of Cellerator: Chemical Reactions

  13. Translation of Biochemical Formula to ODE rate constant Concentrations • Law of Mass Action • Two-way Reaction • Complex reactions built from simple reactions is described by Similar ODE’s can be written for B and C is described by

  14. Enzyme Kinetic (Catalytic) Reaction • Enzyme Ecatalyzes the production of product P from substrate (source) S • Write more compactly as 3 Reactions written two different ways Explicit Cellerator syntax for this set of reactions Hidden Rate constants

  15. Two-way catalytic reaction • A second enzyme F catalyzes the reverse reaction • Total of Six Elementary Reactions • Write more compactly as Explicit Rate constants Hidden Cellerator syntax for this set of reactions

  16. Canonical Forms for Translation: Chemical reactions • Input Canonical Form for Chemical Reaction • Output Canonical Form: Terms in an ODE

  17. Cellerator Arrows: Law of Mass Action

  18. Cellerator Arrows: Catalytic Reactions

  19. Cellerator Arrows: Transcriptional Regulation

  20. INPUT OUTPUT MAP Kinase Cascade

  21. MAP Kinase in Scaffold

  22. The combinatoric explosion

  23. IP3 Calcium Receptor

  24. IP3 Calcium Receptor (continued)

  25. Repressilator

  26. Repressilator

  27. Object Oriented Implementation:“Domains” and “Fields” • Domain: object • Field: function that maps domains to R • Field of Domains: maps domain elements to domains • Example • graphDomain: represents tissue • node Domains: cells • neighbors[g,n] returns a list of nodeDomains that are neighbors of node n n in graph g

  28. Multicellular Organisms

  29. Myogenesis: Collaboration with Laboratory Dr. Barbara Wold (Chris Hart), Caltech

  30. Plant Growth: Collaboration with Laboratory Dr. Elliot Meyerowitz, Caltech

  31. Secondary Leukemia: Collaboration with City of Hope National Medical Center (NASA/BSRP) Focus: Pathogenesis of myelodysplasia & acute myeloid leukemia following high-dose chemo/radiotherapy and autologous peripheral blood stem cell transplantation for treatment of Hodgkin’s disease and non-Hodgkin’s lymphoma

  32. JPL Collaborations using Cellerator • Effects of microgravity during space flight on bone and muscle development (Caltech, JSC, and UCI) • Development of childhood leukemias (Caltech, Children’s Hospital of LA, and UC, Irvine) • Description of “core” signal transduction units (Johns Hopikins) • Improving algorithms for micro-array data analysis (Caltech, Harvey Mudd) • Systems Biology Workbench (Caltech, JST/Erato)

  33. Acknowledgements • Eric Mjolsness* - UC, Irivine • Andre Levchenko* - Johns Hopkins University • Barbara Wold - Caltech • Elliot Meyerowitz - Caltech * Original Developers

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