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Computational Cell Biology Summer Course

Using of XPP, AUTO and VCell for qualitative analysis of building blocks of hemostatic system by Alexey Tokarev. Computational Cell Biology Summer Course. Cold Spring Harbor Laboratory – 2012. Hemostasis = platelet + plasma subsystems. Plug / thrombus growth. Blood flow.

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Computational Cell Biology Summer Course

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  1. Using of XPP, AUTO and VCell for qualitative analysis of building blocks of hemostatic system by Alexey Tokarev Computational Cell Biology Summer Course Cold Spring Harbor Laboratory – 2012

  2. Hemostasis = platelet + plasma subsystems Plug/ thrombus growth Blood flow • Platelet activation, secretion, aggregation Falati et al (2002) platelets fibrin platelets+fibrin • Project goal: • analyze the behavior of different components (building blocks) of hemostatic system using VCell, XPP and AUTO • Tasks: • Run the best of published models of platelet signaling in VCell and reproduce Ca2+ oscillations • Investigate oscillating and steady regimes of functioning of IP3 receptor using XPP/AUTO • Study travelling wave solutions of the reduced model of blood coagulation using XPP/AUTO • Plasma clotting Fibrin mesh. SEM made by Jean-Claude Bordet (Lyon, France). Ohlmann (2000)

  3. Part I. Quantitative model of platelet activation Purvis et al, Blood, 2008 • Model summary: • 5 compartments • 70 species • 77 reactions • 132 kinetic parameters • xml file obtained from the author • Two-week efforts to run this model in VCell (thanks to Ion and Olena for help!) • Failure to reproduce published results.

  4. Part II. IP3-receptor model for Ca2+ oscillations DeYoung and Keizer, PNAS, 1992; Li and Rinzel, J.Theor.Biol., 1994 Full model: 1+8-1=8 equations τ(IP3) << τ(Ca,act) << τ(Ca,inact) Reduced model: 2 equations h=x000+x100+x010+x110

  5. h – Ca2+ phase plane, 2-variables model of IP3R [IP3]=0.3 uM [IP3]=0.5 uM h [Ca2+]I, uM h [Ca2+]I, uM

  6. Bifurcation diagrams of the full and reduced models Full (9 variables) Reduced (2 variables) • Conclusions: • full IP3R model may be too redundant for modeling of signal transduction in a cell • (general): robust properties of cellular building blocks are governed by time hierarchy of processes and thus can be described by low-dimensional models Full (dashed) and reduced (solid) models. Li and Rinzel, JTB, 1994 Marco is acknowledged for very helpful discussion

  7. Properties of IP3 receptor module in the platelet activation model of Lenoci et al. (Mol. BioSyst., 2011) h 2-variables IP3R model,[IP3]=0.1, 1, 10 uM [IP3] [IP3] [Ca2+]I, uM Modification of IP3R parameters vs. DeYoung and Keizer: forward binding rates are 10 times faster, dissociation constants are 2 times smaller [Ca2+]ss Conclusion: no IP3R-dependent Ca2+ oscillations possible at all [IP3] [IP3]

  8. Investigation of cell volume effect on Ca2+ oscillations ODE, Vcyt=10 um3 Stochastic… …under construction…

  9. Part III. Travelling wave solution in the mathematical model of blood clotting Reduced model: (full model) N. Dashkevich, M. Ovanesov, A. Balandina, S. Karamzin, P. Shestakov, N. Soshitova, A. Tokarev, M. Panteleev, F. Ataullakhanov. Thrombin activity propagates in space during blood coagulation as an excitation wave. To appear in Biophys. J. Zarnitsina et al., Chaos, 2001

  10. Phase diagram for c=3: Test case 1: moving front solution of the Fisher-KPP (Kolmogorov-Petrovskii-Piskunov) equation V Fisher, Ann. Eugenics, 1937; Kolmogorov, Petrovskii, Piscounov. Bull.Mocsow Univ., Math.Mech., 1937 Stable manifolds of f.p.(1,0) u’t=u’’xx + u(1-u) u(+∞)=0, u(-∞)=1 c – velocity of the moving front ξ=x-ct U(ξ)=u(x,t) -cU’=U’’+U(1-U) U’=V V’=-cV-U(1-U) (U,V)(+∞)=(0,0), (U,V)(-∞)=(1,0) Unstable manifolds of f.p.(1,0) U V heteroclinic orbit U U solution Solutions exist for every c>2 ξ

  11. Test case 2: moving front solution of the FitzHugh-Nagumo (FHN) equation c = 0 V ut = uxx + f(u), f(u)=u(1-u)(u-a) u(+∞)=0, u(-∞)=1 The solution exits at single unique c Stable manifolds of f.p.(1,0) Unstable manifolds of f.p.(1,0) U V c = 1, 0.5, 0.3535, 0 heteroclinic orbit AUTO U Exact heteroclinic trajectory Exact solution c=0.3535 U solution ξ

  12. Test case 3: finding the exact homoclinic trajectory in AUTO v u'=v v'=-cv+u(1-u) +auv u u ξ Autowave solution u(x,t)

  13. Conclusions / advices / hopes • Importing/using of the non-proved sbml models may appear to be the waste of your time • Always think how to reduce your complex model • Using AUTO one can find the steady autowave solution of coupled PDEs (if it exists). Hope this will help in studying the plasma coagulation system 

  14. Thank you for your attention!

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