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M ODELLING AND A NALYSIS OF B IOCHEMICAL N ETWORKS WITH T IME P ETRI - PowerPoint PPT Presentation

CSP, C APUTH 2004 PN & Systems Biology M ODELLING AND A NALYSIS OF B IOCHEMICAL N ETWORKS WITH T IME P ETRI N ETS Louchka Popova-Zeugmann Humboldt University Berlin, Dep. of CS Monika Heiner Brandenburg University of Technology Cottbus,


  1. CSP, C APUTH 2004 PN & Systems Biology M ODELLING AND A NALYSIS OF B IOCHEMICAL N ETWORKS WITH T IME P ETRI N ETS Louchka Popova-Zeugmann Humboldt University Berlin, Dep. of CS Monika Heiner Brandenburg University of Technology Cottbus, Dep. of CS Ina Koch Technical University of Applied Sciences Berlin, Dep. of Bioinformatics popova@informatik.hu-berlin.de, monika.heiner@informatik.tu-cottbus.de, ina.koch@tfh-berlin.de September 2004

  2. F RAMEWORK PN & Systems Biology bionetworks knowledge quantitative modelling understanding animation quantitative model validation evaluation/simulation models quantitative behavior prediction popova@informatik.hu-berlin.de, monika.heiner@informatik.tu-cottbus.de, ina.koch@tfh-berlin.de September 2004

  3. F RAMEWORK PN & Systems Biology bionetworks knowledge qualitative modelling understanding invariants animation quantitative qualitative model validation model parameters evaluation/analysis models checking qualitative behavior prediction quantitative modelling understanding animation quantitative model validation evaluation/simulation models quantitative behavior prediction popova@informatik.hu-berlin.de, monika.heiner@informatik.tu-cottbus.de, ina.koch@tfh-berlin.de September 2004

  4. F RAMEWORK PN & Systems Biology bionetworks knowledge qualitative modelling understanding invariants animation quantitative qualitative model validation model parameters evaluation/analysis models checking qualitative behavior prediction quantitative modelling understanding animation quantitative model validation evaluation/simulation models quantitative behavior prediction popova@informatik.hu-berlin.de, monika.heiner@informatik.tu-cottbus.de, ina.koch@tfh-berlin.de September 2004

  5. B IONETWORKS , B ASICS PN & Systems Biology chemical reactions -> atomic actions -> Petri net transitions ❑ 2 NAD + + 2 H 2 O -> 2 NADH + 2 H + + O 2 NADH NAD + 2 2 input output 2 H + r1 compounds compounds 2 H 2 O O 2 popova@informatik.hu-berlin.de, monika.heiner@informatik.tu-cottbus.de, ina.koch@tfh-berlin.de September 2004

  6. B IONETWORKS , B ASICS PN & Systems Biology chemical reactions -> atomic actions -> Petri net transitions ❑ 2 NAD + + 2 H 2 O -> 2 NADH + 2 H + + O 2 NADH NAD + 2 2 input output 2 H + r1 compounds compounds 2 H 2 O O 2 chemical compounds -> Petri net places ❑ x y - primary compounds - metabolites - auxiliary compounds, - e. g. electron carrier A B r2 ubiquitous -> fusion nodes enzyme - catalyzing compounds - enzymes popova@informatik.hu-berlin.de, monika.heiner@informatik.tu-cottbus.de, ina.koch@tfh-berlin.de September 2004

  7. B IONETWORKS , B ASICS PN & Systems Biology chemical reactions -> atomic actions -> Petri net transitions ❑ 2 NAD + + 2 H 2 O -> 2 NADH + 2 H + + O 2 NADH NAD + 2 2 input output 2 H + r1 compounds compounds 2 H 2 O O 2 chemical compounds -> Petri net places ❑ x y - primary compounds - metabolites - auxiliary compounds, - e. g. electron carrier A B r2 ubiquitous -> fusion nodes enzyme - catalyzing compounds - enzymes stoichiometric relations -> Petri net arc multiplicities ❑ compounds distribution -> marking ❑ popova@informatik.hu-berlin.de, monika.heiner@informatik.tu-cottbus.de, ina.koch@tfh-berlin.de September 2004

  8. B IONETWORKS , I NTRO PN & Systems Biology A r1: A -> B r1 B popova@informatik.hu-berlin.de, monika.heiner@informatik.tu-cottbus.de, ina.koch@tfh-berlin.de September 2004

  9. B IONETWORKS , I NTRO PN & Systems Biology A r1: A -> B r1 r2: B -> C + D r2 r3: B -> D + E B r3 D C E -> alternative reactions popova@informatik.hu-berlin.de, monika.heiner@informatik.tu-cottbus.de, ina.koch@tfh-berlin.de September 2004

  10. B IONETWORKS , I NTRO PN & Systems Biology A r1: A -> B r1 r2: B -> C + D r2 r4 a r3: B -> D + E B r4: F -> B + a r3 D C F E r6: C + b -> G + c b b r7: D + b -> H + c r6 r7 c c H G -> concurrent reactions popova@informatik.hu-berlin.de, monika.heiner@informatik.tu-cottbus.de, ina.koch@tfh-berlin.de September 2004

  11. B IONETWORKS , I NTRO PN & Systems Biology A r1: A -> B r1 r2: B -> C + D r2 r4 a r3: B -> D + E B r4: F -> B + a r3 r5_rev r5: E + H <-> F D C F E r6: C + b -> G + c r5 b b r7: D + b -> H + c r6 r7 c c r8_rev r8: H <-> G H G r8 -> reversible reactions popova@informatik.hu-berlin.de, monika.heiner@informatik.tu-cottbus.de, ina.koch@tfh-berlin.de September 2004

  12. B IONETWORKS , I NTRO PN & Systems Biology A r1: A -> B r1 r2: B -> C + D r2 r4 a r3: B -> D + E B r4: F -> B + a r3 r5: E + H <-> F r5 D C F E r6: C + b -> G + c b b r7: D + b -> H + c r6 r7 c c r8: H <-> G r8 H G -> reversible reactions - hierarchical nodes popova@informatik.hu-berlin.de, monika.heiner@informatik.tu-cottbus.de, ina.koch@tfh-berlin.de September 2004

  13. B IONETWORKS , I NTRO PN & Systems Biology A r1: A -> B r1 r2: B -> C + D r2 r4 a r3: B -> D + E B r4: F -> B + a r3 r5: E + H <-> F r5 D C F E r6: C + b -> G + c b b r7: D + b -> H + c r6 r7 c c r8: H <-> G r8 H G r9: G + b -> K + c + d b a 29 28 r10: H + 28a + 29c -> 29b c r9 r10 c 2 29 r11 r11: d -> 2a d b K popova@informatik.hu-berlin.de, monika.heiner@informatik.tu-cottbus.de, ina.koch@tfh-berlin.de September 2004

  14. B IONETWORKS , I NTRO PN & Systems Biology A r1: A -> B r1 r2: B -> C + D r2 r4 a r3: B -> D + E B r4: F -> B + a r3 r5: E + H <-> F r5 D C F E r6: C + b -> G + c b b r7: D + b -> H + c r6 r7 c c r8: H <-> G r8 H G r9: G + b -> K + c + d b a 29 28 r10: H + 28a + 29c -> 29b c r9 r10 c 2 29 r11 r11: d -> 2a d b K popova@informatik.hu-berlin.de, monika.heiner@informatik.tu-cottbus.de, ina.koch@tfh-berlin.de September 2004

  15. B IONETWORKS , I NTRO PN & Systems Biology input compound A r1: A -> B r1 r2: B -> C + D r2 r4 a r3: B -> D + E B r4: F -> B + a r3 r5: E + H <-> F r5 D C F E r6: C + b -> G + c b b r7: D + b -> H + c r6 r7 c c r8: H <-> G r8 H G stoichiometric r9: G + b -> K + c + d b a relations 29 28 r10: H + 28a + 29c -> 29b c r9 r10 c 2 29 r11 r11: d -> 2a d b K fusion nodes - auxiliary compounds output compound popova@informatik.hu-berlin.de, monika.heiner@informatik.tu-cottbus.de, ina.koch@tfh-berlin.de September 2004

  16. B IONETWORKS , S UMMARY PN & Systems Biology networks of chemical reactions ❑ biologically interpreted Petri net ❑ -> partial order sequences of chemical reactions - transforming input into output compounds - respecting the given stoichiometric relations popova@informatik.hu-berlin.de, monika.heiner@informatik.tu-cottbus.de, ina.koch@tfh-berlin.de September 2004

  17. B IONETWORKS , S UMMARY PN & Systems Biology networks of chemical reactions ❑ biologically interpreted Petri net ❑ -> partial order sequences of chemical reactions - transforming input into output compounds - respecting the given stoichiometric relations network structure ❑ -> dense, apparently unstructured -> hard to read -> tend to grow fast typical (structural) properties ❑ INA ORD HOM NBM PUR CSV SCF CON SC Ft0 tF0 Fp0 pF0 MG SM FC EFC ES N N N Y N N Y N N N Y Y N N N N N DTP CPI CTI B SB REV DSt BSt DTr DCF L LV L&S N N N Y Y ? ? ? ? ? N ? N popova@informatik.hu-berlin.de, monika.heiner@informatik.tu-cottbus.de, ina.koch@tfh-berlin.de September 2004

  18. B IONETWORKS , S UMMARY PN & Systems Biology networks of chemical reactions ❑ biologically interpreted Petri net ❑ -> partial order sequences of chemical reactions - transforming input into output compounds - respecting the given stoichiometric relations network structure ❑ -> dense, apparently unstructured -> hard to read -> tend to grow fast typical (structural) properties ❑ INA ORD HOM NBM PUR CSV SCF CON SC Ft0 tF0 Fp0 pF0 MG SM FC EFC ES N N N Y N N Y N N N Y Y N N N N N DTP CPI CTI B SB REV DSt BSt DTr DCF L LV L&S N N N Y Y ? ? ? ? ? N ? N popova@informatik.hu-berlin.de, monika.heiner@informatik.tu-cottbus.de, ina.koch@tfh-berlin.de September 2004

  19. B IONETWORKS N EED E NVIRONMENT B EHAVIOR PN & Systems Biology to animate the model ❑ A -> infinite substance flow r1 -> deeper insights r2 r4 a B to validate the model ❑ -> consistency criteria r3 r5 steady flow ❑ D C F E -> input substances b b r6 r7 -> output substances c c r8 H G auxiliary substances ❑ b a -> as much as necessary 29 28 c r9 r10 c 2 29 r11 minimal assumptions d ❑ b K popova@informatik.hu-berlin.de, monika.heiner@informatik.tu-cottbus.de, ina.koch@tfh-berlin.de September 2004

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