1 / 37

Marine bacteria – virus interaction in a chemostat

Marine bacteria – virus interaction in a chemostat. Jean-Christophe Poggiale. Laboratoire de Microbiologie, de Géochimie et d’Ecologie Marines (UMR CNRS 6117) Université de la Méditerranée Centre d’Océanologie de Marseille 13288 Marseille Cedex 09 France Jean-christophe.poggiale@univmed.fr

pixley
Télécharger la présentation

Marine bacteria – virus interaction in a chemostat

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Marine bacteria – virus interaction in a chemostat Jean-Christophe Poggiale Laboratoire de Microbiologie, de Géochimie et d’Ecologie Marines (UMR CNRS 6117) Université de la Méditerranée Centre d’Océanologie de Marseille 13288 Marseille Cedex 09 France Jean-christophe.poggiale@univmed.fr http://www.com.univ-mrs.fr/~poggiale/ Amsterdam – 24thJanuary 2008

  2. Aggregation of variables Andreasen, Iwasa, Levin, 1987, 1989 ? Amsterdam – 24thJanuary 2008

  3. Aggregation of variables P. Auger, R. Bravo de la Parra, J.-C. Poggiale, E. Sanchez, and T. Nguyen-Huu, 2008, « Aggregation of Variables and Applications to Population Dynamics » inStructured Population Models in Biology and Epidemiology Series: Lecture Notes in MathematicsSubseries: Mathematical Biosciences Subseries , Vol. 1936 Magal, Pierre; Ruan, Shigui (Eds.) , 345 p. Singular perturbation theory Discrete systems Delayed and partial differential equations Applications to population dynamics models Amsterdam – 24thJanuary 2008

  4. Aggregation of variables Individuals to populations : growth Mechanistic model (IBM) Population model Individual parameters Population parameters Amsterdam – 24thJanuary 2008

  5. Aggregation of variables Individuals to populations : growth Comparison between DEB model and logistic equation Comparison between DEB model and a Substrate-Structure model Amsterdam – 24thJanuary 2008

  6. ? ? Aggregation of variables Time scales and singular perturbation theory Amsterdam – 24thJanuary 2008

  7. Aggregation of variables Time scales and singular perturbation theory Amsterdam – 24thJanuary 2008

  8. Aggregation of variables The fundamental theorems : normal hyperbolicity theory Def. : The invariant manifold M0 is normally hyperbolic if the linearization of the previous system at each point of M0 has exactly k2 eigenvalues on the imaginary axis. Amsterdam – 24thJanuary 2008

  9. Theorem (Fenichel, 1971) : if is small enough, there exists a manifold M1 close and diffeomorphic to M0. Moreover, it is locally invariant under the flow, and differentiable. Aggregation of variables The fundamental theorems : normal hyperbolicity theory Theorem (Fenichel, 1971) : « the dynamics in the vicinity of the invariant manifold is close to the dynamics restricted on the manifolds ». Amsterdam – 24thJanuary 2008

  10. Alcalà de Henares - April 2005 Geometrical Singular Perturbation theory The fundamental theorems : normal hyperbolicity theory • Simple criteria for the normal hyperbolicity in concrete cases (Sakamoto, 1991) • Good behavior of the trajectories of the differential system in the vicinity of the perturbed invariant manifold. • Reduction of the dimension • Powerful method to analyze the bifurcations for the reduced system and link them with the bifurcations of the complete system • Intuitive ideas used everywhere (quasi-steady state assumption, adiabatic assumptions, time scale separation…)

  11. Marine bacteria – virus interaction in a chemostat • 3 state variables : S, I and V • If the «burst coefficient » increases then oscillations appear Amsterdam – 24thJanuary 2008

  12. Pseudoalteromonas sp. hbmmd.hboi.edu/ jpegs2/L261.jpg An experiment in a chemostat Amsterdam – 24thJanuary 2008

  13. D I Infected D D S Susceptible V Virus R Resistant D C Carbon substrate C0Reservoir D D Model description Amsterdam – 24thJanuary 2008

  14. Susceptibles Resistant Infected Amsterdam – 24thJanuary 2008

  15. Virus Carbon substrate Amsterdam – 24thJanuary 2008

  16. The Model Amsterdam – 24thJanuary 2008

  17. General property : itis a dissipative system (Compact) Result: the vector field defined by the model satisfies the following properties: - K+ is positively invariant - W is positively invariant - all trajectories initiated in K+ has its w–limit in W Amsterdam – 24thJanuary 2008

  18. Variables and parameters Unité de temps : 10 heures From Middelboe, 2000 Amsterdam – 24thJanuary 2008

  19. Comparison versus experimental data From Middelboe, 2000 Amsterdam – 24thJanuary 2008

  20. Fast Slow Time scales Amsterdam – 24thJanuary 2008

  21. Two fast variables and three slow variables Fastdynamics Amsterdam – 24thJanuary 2008

  22. The complete model FAST SLOW Amsterdam – 24thJanuary 2008

  23. While H>0, E1 is hyperbolically stable Equilibria While H>0, E2 is a saddle point Fastdynamics Amsterdam – 24thJanuary 2008

  24. Slow dynamics (GSP Theory) The Geometrical Singular Perturbation theory (e.g. Fenichel, 1971, Sakamoto, 1990, Tychonov, ) allows to conclude that the previous complete model can be reduced to the following 3D system, under the normal hyperbolicity condition : Normal hyperbolicity condition Amsterdam – 24thJanuary 2008

  25. Comparisonbetweencomplete and agregated model Amsterdam – 24thJanuary 2008

  26. Comparisonbetweencomplete and agregated model Amsterdam – 24thJanuary 2008

  27. S,I ? H {(0;0)}x{H}X{(C;R)} C,R Loss of Normal Hyperbolicity The reduced system well approximates the complete one Amsterdam – 24thJanuary 2008

  28. Normally stable invariant manifold Normally unstable invariant manifold Loss of Normal Hyperbolicity Amsterdam – 24thJanuary 2008

  29. Two ODE’s systems (H. Thieme, 1992) Loss of Normal Hyperbolicity Blow-up Fast Slow Amsterdam – 24thJanuary 2008

  30. Singular perturbation theory Fast Lotka-Volterra Model Amsterdam – 24thJanuary 2008

  31. Singular perturbation theory Asymptotic expansion of the invariant manifold with respect to the small parameter Amsterdam – 24thJanuary 2008

  32. Singular perturbation theory Centre perturbation Let be the duale form of the vector field defined by the previous system: Poincaré map x y s Amsterdam – 24thJanuary 2008

  33. Singular perturbation theory Centre perturbation Let be the duale form of the vector field defined by the previous system: Displacement map x y P(x) x s Amsterdam – 24thJanuary 2008

  34. Poincaré lemma: Application : Stockes theorem: Centre perturbation Amsterdam – 24thJanuary 2008

  35. Simulations Amsterdam – 24thJanuary 2008

  36. Summary • If the maximum ingestion rate of resistant population is larger to that of susceptible, the initial 5D system reduces to 2+1 equations. • In this case, the bifurcation diagram in the plan (C0;D) exhibits a transcritical curve. • If the ingestion rate of resistant is lower than that of susceptible, oscillations appear Amsterdam – 24thJanuary 2008

  37. CONCLUSIONS • Different time scales induced by the virus efficiency • The resistant population affects Beretta and Kuang conclusions. Amsterdam – 24thJanuary 2008

More Related