advances in geometric singular perturbation theory (gspt) [0.4cm] · 2019-07-22 · andrea braides:...
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Advances in Geometric SingularPerturbation Theory (GSPT)
Peter Szmolyan
Vienna University of Technology
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Overview
0. Introduction
1. Scaling, perturbation methods, and singularlimits
2. Examples
3. slow-fast systems
4. Geometric singular perturbation theory
5. A glimpse of applications
6. Glycolytic oscillator
7. Mitotic oscillator
8. Extensions, outlook, plan?
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0. Introduction
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What is the summer school about?
Multiscale phenomena in geometry and dynamics
Andrea Braides: Geometric Flows on Lattices
Daniel Grieser: Scales, blow-up and quasimodeconstructions
Tere M-Seara: Exponentially small splitting ofseparatrices: examples and techniques
P. Sz.: Advances in Geometric SingularPerturbation Theory
keywords: analysis and its applications, dynamicalsystems and ODEs, geometry, PDEs
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Why is geometry included?
obvious: ODE, dynamical systems
less obvious: PDE, multiscale
Main messages:
“getting the geometry right, helps”
transform “difficult equations” in “simplegeometries” to “easy equations” in“complicated geometries” (D. Grieser)
difficult = singular, multiscale
easy = regular, good properties for analysis,covered by existing theory
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How can this field be approached
useful viewpoint...
there is theory, even proofs; but...
view it as a toolbox...
best learned through examples...
adapt and extend it to new problems...
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1. Scaling, perturbation methods, and singular limits
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Scaling and perturbation arguments are crucial inapplied mathematics
context: ODE or PDE models
processes on very different scales areapproximately decoupled
neglecting couplings gives simpler models
simple theories are limits of more generaltheories
simple models must be coupled to approximatefull problem
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Rescaling makes hidden details visible
“macro” - state: U(X,T )
“micro” - state: u(x, t) ε→ 0, δ → 0
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Rescaling makes hidden details visible
“macro” - state: U(X,T )
scalings for u? u = εαδβU matching
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This can lead to regular perturbation problems
full problem: F (u, ε) = 0, solution uε, ε 1
limit problem: F (u, 0) = 0, solution u0
regular perturbation:
1 uε → u0 smoothly
uε = u0 + εu1 + ε2u2 + · · ·
2 convergent expansion
3 implicit function theorem
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Typically this leads to singular perturbation problems
full problem: F (u, ε) = 0, solution uε, ε 1
limit problem: F (u, 0) = 0, solution u0
singular perturbation:
1 u0 may develop singularities2 no smooth approximation by a single limit
problem
3 several scalings with different limit problemsare needed
4 approximation by matched asymptoticexpansions
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2. Examples
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Example 1: singularly perturbed second order ODE
singularly perturbed second order ODE
εu+ p(t)u+ q(t)u = f(t)
initial - or boundary value problem
limit problem
p(t)u+ q(t)u = f(t)
first order ODE; simpler, but cannot satisfy allinitial or boundary conditions
⇒ boundary layers, internal layers
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Rescaling gives another limit problem
t ∈ [0, T ], p(0) = λ > 0 ⇒ boundary layernear t = 0, fast scale τ := t
ε
u′′ + p(ετ)u′ + εq(ετ)u = εf(εt)
limit problem, first order ODE, simpler!
u′′ + p(0)u′ = 0, u(τ) ∼ e−λτ = e−λtε
∃ exponential decaying components, matchedasymptotic expansion
u(t, ε) = u0(t) + u0(τ) +O(ε)
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A boundary layer occurs...
http://www.scholarpedia.org/article/Singular−perturbation−theory
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Example 2: slow-fast systems
singularly perturbed systems of ODEs in standardform
εx = f(x, y, ε)
y = g(x, y, ε)0 ≤ ε 1
x ∈ Rm fast , y ∈ Rn slow, t ∈ R time
mathematics: interesting and accessible dynamics
applications: biology, chemistry, electricalengineering, mechanics,...
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Example 3: systems of singularly perturbed reactiondiffusion equations
ut = ε2∆u+ f(u, v)
δvt = ∆v + g(u, v)
ε ∈ (0,∞) different speeds of diffusion
δ ∈ (0,∞) different reaction speeds
pattern formation: travelling waves, spikes,spiral waves,...
existence, stability, bifurcations,...
stationary case in 1-d ⇒ back to slow-fastsystems
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Example 4: vanishing viscosity for hyperbolicconservation laws
u ∈ Rn, x ∈ R, t > 0, (also x ∈ Rd)
ut + f(u)x = ε(B(u)ux)x
flux f : Rn → Rn smooth; diffusion matrix B(u)smooth, positiv (semi)definit, ε 1
limit problem: hyperbolic conservation law
ut + f(u)x = 0
solutions develop singularities (shocks) in finite time
many deep results but also many open questions!
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Example 5: Navier Stokes equations for largeReynolds number
velocity u ∈ R3, pressure p ∈ R, x ∈ Rd,d = 2, 3, t ∈ R, Reynolds number Re = UL/ν,ε := 1/Re 1
ut + (u · ∇)u+∇p = ε∆u
∇ · u = 0
limit problem: Euler equations
ut + (u · ∇)u+∇p = 0
∇ · u = 0
many deep results but also many open questions!
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L. Prandtl created singular perturbation theory toexplain boundary layers of fluids near walls (1904)
x ∈ Ω ⊂ Rd, boundary conditions at ∂Ω
Navier Stokes: u |∂Ω= 0, no slip at ∂Ω
Euler equations: u · n |∂Ω= 0, no flow through ∂Ω
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Reality is much more multiscale!
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Reality is much more multiscale!
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Example 6: (semi)classical limit of Schrodingerequation and eigenvalue asymptotics
wave function ψ(t, x), potential V (x)
iεψt = −ε2∆ψ + V (x)ψ
limit ε→ 0 corresponds to
Quantum mechanics → classical mechanics
eigenvalue problem: energy E, asymptotics E →∞
−ε2∆ψ + V (x)ψ = Eψ
many deep results but also many open questions!
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Schrodinger equation was (is!) very influential fordevelopment of singular perturbation theoy
eigenvalue problem in 1-d, eigenvalue: energy E
ε2ψxx = (V (x)− E)ψ, ψ(±∞) = 0
layer behaviour for V (x) > E, classicallyforbidden region
fast oscillations for V (x) < E, classicallyallowed region
turning points at V (x) = E !
classical approach: WKB method
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EVP for 1-d Schrodinger equation is a slow-fastproblem
ε2ψxx = (V (x)− E)ψ, ψ(±∞) = 0
define
u :=εψxψ
u satisfies slow-fast Riccati equation
εu = V (x)− E − u2
x = 1
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Singular limits are important and interesting - alsofrom the dynamical systems and geometric point ofview
shift from finding approximate solutions toqualitative understanding of patterns anddynamics
difficult for numerics: stiffness, resolving smallscales expensive
accessible to formal and rigorous analysis
analysis: difficulties but also advantages
getting the geometry right helps
lots of things to discover - even in fairly simpleproblems
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In many applications ε is not given a priori
perturbation parameters are found by:
scaling
nondimensionalization
singular dependence on several parameters ispossible
numerical simulation
intuition
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3. Slow-fast systems are very common in biologyand chemistry
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Important biological processes are periodic on verydifferent time scales
Rhythm Period
Neural rhythms 0.01 - 1 sCardiac rhythm 1 sCalzium-oszillations 1 s – minBiochemical oscillations 1 min – 20 minMitotic cycle 10 min – 24 hHormonal rhythms 10 min – 24 hCircadian rhythm 24 hOvarian cycle 28 daysAnnual rhythms 1 yearEcological oscillations years
A. Goldbeter (1996)
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Most of these processes show slow-fast dynamics
γ- and β - oscillations in human brain38 Hz and 42Hz, units: 100 ms, 1mV
mixed mode oscillations and delay effects
mechanisms? classification? noise?
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Modelling of processes on very different time scalesleads to slow-fast dynamical systems
slow processes coupled to fast processes
singularly perturbed systems of ODEs
εx = f(x, y, ε)
y = g(x, y, ε)0 ≤ ε 1
x ∈ Rn fast , y ∈ Rm slow, t ∈ R time
standard form, global splitting
particularly relevant in biology
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Singularly perturbed (slow-fast) ODEs in standardform require (at least) two scalings
εx = f(x, y, ε)y = g(x, y, ε)
(1)
x fast, y slow, ε 1, slow time scale t,
transform to fast time scale τ := t/ε
x′ = f(x, y, ε)y′ = εg(x, y, ε)
(2)
Syst. (1) and Syst. (2) equivalent for ε > 0
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There are two distinct limiting systems for ε = 0
reduced problem0 = f(x, y, 0)y = g(x, y, 0)
layer problemx′ = f(x, y, 0)y′ = 0
critical manifold S := f(x, y, 0) = 0
reduced problem dynamical system on S
x slaved to y through constraint f(x, y, 0) = 0
S “manifold” of equilibria for layer problem
y acts as parameter in layer problem
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Two possible interpretations for ε 1
main interest in slow process:
x fast process, which should be eliminated toobtain simpler model for y
effects of fast processes?
main interest in fast process:
y slowly varying parameter
effect of slow changes in y on dynamics of x
simplest case: x′ = f(x, y), y′ = ε, y ∈ R
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In “good” situations (pieces of) critical manifold Spersists as a slow manifold Sε
Sε is invariant manifold
Sε is O(ε) close to S
Sε depends smoothly on O(ε)
Sε inherits stablity properties from S
flow on Sε close to flow on S
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The classical example: Van der Pol oscillator
εx = y − x3
3 + xy = a− x parameter a
reduced problem S : y = x3
3 − x, x ∈ R
0 = y − x3
3 + xy = a− x
layer problem
x′ = y − x3
3 + xy′ = 0
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Van der Pol oscillator has folded critical manifold Slayer problem: x′ = y − x3
3+ x
S attracting for x < −1 and x > 1
S repelling for −1 < x < 1
fold points at x = −1 and x = 1
reduced problem: y = x3
3− x ⇒ y = (x2 − 1)x = a− x
equilibrium at x = a
y > 0, x < a, y < 0, x > a
singular at x = ±1, except for a = ±1!
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For −1 < a < 1 relaxation oscillations occur
singular cycle for ε = 0 relaxation oscillationfor 0 < ε 1
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Close to a = ±1 interesting bifurcations occur
relaxation oscillations for −1 < a < 1
excitability for a < −1 and a > 1
canards and canard cycles for special values of aclose to a = ±1
for 0 < ε 1
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In higher dimensions slow-fast systems can be morecomplicated
a “terrible” problem: Olsen model
a “good” problem: 3-d and 2-d Autocataltor
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Olsen model describes oxidization of NicotinamideAdenine Dinucleotide (NADH)
A = k7 − k9A− k3ABY
B = k8 − k1BX − k3ABY
X = k1BX − 2k2X2 + 3k3ABY − k4X + k6
Y = 2k2X2 − k3ABY − k5Y
A oxygen, B NADH, X, Y intermediate products
Reaction rates: k1 = 0.16, 0.35, 0.41
k2 = 250, k3 = 0.035, k4 = 20, k5 = 5.35,k6 = 10−5, k7 = 0.8, k8 = 0.825, k9 = 0.1
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The Olsen model has complicated dynamics
a) k1 = 0.16, b) k1 = 0.35, c) k1 = 0.41
slow-fast dynamics: a) and b) mixed-modeoscillations or chaotic, c) relaxation oscillations
Goal: understand mechanisms of these patterns andbifurcations, very sensitive parameter dependence
C. Kuehn, P. Sz., J. Nonlinear Science (2015)
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Visualization in phase space shows more details
slow dynamics in A, B close to X, Y ≈ 0,
fast dynamics in A,B,X, Y away from X, Y ≈ 0,
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Scaling A, B, X, and Y gives a slow-fast system
a = θ − αa− abyb = ν(1− bx− aby)
ε2x = bx− x2 + 3aby − βx+ δ
ε2y = x2 − y − aby
ν ≈ 10−1, θ, α, β ≈ 1, ε ≈ 10−2, δ ≈ 10−5
ε, ν determine time scales:a, b slow variables x2, y2 fast variables;ν 1 ⇒ b is slower than a
α ∼ k1, δ ∼ k6, bifurcation parameters
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Finding the scaling is not easy
A =k1k5
k3
√2k2k8
a, B =
√2k2k8
k1b
X =k8
2k2x, Y =
k8
k5y
T =k1k5
k3k8
√2k2k8
t
phase space: a, b, x, y ≥ 0
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Olsen model has a complicated critical manifold
a = θ − αa− abyb = ν(1− bx− aby)
ε2x = bx− x2 + 3aby − βx+ δ
ε2y = x2 − y − aby
complicated critical manifold S
bx− x2 + 3aby − βx+ δ = 0
x2 − y − aby = 0
further complications:
impact of δ 6= 0 versus δ = 0
for x, y large different scaling needed
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Good scaling is a bit like magic
large terms dominate small terms
finding a good scaling is nontrivial!
what is a good scaling?
nonlinear problem ⇒ good scaling depends onposition in phase spaceoften there exist several good scalings
x′ = −x+ εx+ εx2, x ∈ R, ε 1
x = O(1) =⇒ x′ = −x+O(ε)
x = O(ε−1), x =X
ε=⇒ X ′ = −X +X2 +O(ε)
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4. Geometric Singular Perturbation Theory
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Singularly perturbed (slow-fast) ODEs in standardform require (at least) two scalings
εx = f(x, y, ε)y = g(x, y, ε)
x fast, y slow, ε 1, slow time scale t,
transform to fast time scale τ := t/ε
x′ = f(x, y, ε)y′ = εg(x, y, ε)
systems equivalent for ε > 0
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There are two distinct limiting systems for ε = 0
reduced problem0 = f(x, y, 0)y = g(x, y, 0)
layer problemx′ = f(x, y, 0)y′ = 0
critical manifold S := f(x, y, 0) = 0
reduced problem is a dynamical system on S.
S is a “manifold” of equilibria for layer problem.
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Large pieces of critical manifold S can be describedas a graph
Solve f(x, y, 0) = 0 by implicit function theorem for
x = h(y)
when∂f
∂x(x, y, 0) regular
Reduced problem is essentially
y = g(h(y), y, 0)
lifted to S via x = h(y)
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Spectrum of linearization of layer problemdetermines stability of S
(x0, y0) ∈ S, x0 equilibrium of x′ = f(x, y0, 0)
linearization A0 :=∂f
∂x(x0, y0, 0), spectrum σ
splits according to
Reλs < 0, Reλc = 0, Reλu > 0
inσ = σs ∪ σc ∪ σu
with stable, center, and unstable eigenspaces Es,Ec, and Eu
Rm = Es ⊕ Ec ⊕ Eu
x0 hyperbolic iff Ec = 0
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Invariant manifold theory provides nonlinear analogsto stable-, center-, unstable spaces
stable and unstable manifolds W s(x0) andW u(x0) at hyperbolic equilibria x0
center-stable, center- and center-unstablemanifolds at non-hyperbolic equilibria x0
stable- and unstable manifolds are unique androbust under perturbation
center manifolds more not unique; more delicate
local bifurcations take place within centermanifolds
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Individual points of a critical manifolds are nothyperbolic
Assume that S0 ⊂ S is given as a graph x = h(y)Linearization of layer problem at point(h(y), y) ∈ S)
x′ = f(x, y, 0)y′ = 0
is A :=
(fx fy0 0
)∃ trivial eigenvalue λ =0, multiplicity (at least) n;eigenspace is tangent space of SProof: f(h(y), y) = 0 differentiate
fxhy + fy = 0 ⇒
(hy
In×n
)⊂ ker(A)
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Pieces of critical manifolds can be normallyhyperbolic
Assume that S0 ⊂ S is given as a graph x = h(y)
Definition: S0 is normally hyperbolic iff:
1) linearization A no eigenvalues Reλ = 0, excepttrivial eigenvalue λ = 0 with multiplicity n
2) S0 is compact
1) holds iff fx |S0has no eigenvalues Reλ = 0
2) is a uniformity condition
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GSPT based on invariant manifold theory allows togo from ε = 0 to 0 < ε 1
Theorem: S0 ⊂ S normally hyperbolic ⇒ S0
perturbs smoothly to slow manifold Sε for ε smallN. Fenichel (1979)
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The dynamics on the slow manifold is a smoothperturbation of the reduced problem
critical manifold S0: graph x = h0(y)
reduced problem: y = g(h0(y), y, 0)
slow manifold Sε: graph x = h(y, ε), smoothexpansion:
h(y, ε) = h0(y) + εh1(y) + ε2h2(y) + · · ·
slow dynamics on Sε
y = g(h(y, ε), y, ε) = g(h0(y), y, 0) +O(ε)
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Slow manifold has stable and unstable manifolds
W s(Sε) and W u(Sε), smooth dependence on ε
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In many applications critical manifolds are morecomplicated
S has several normally hyperbolic branchesseparated by non-hyberbolic submanifolds, e.g.foldsS has bifurcation points or singularitieslayer problem allows jumps (fast transitions)between these branchesparameters µ ∈ Rp can be included as “trivial”slow variables (with nontrivial effects!)
εx = f(x, y, µ, ε)y = g(x, y, µ, ε)µ = 0
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5. A glimpse of applications and things to come
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Fenichel’s normally hyperbolic GSPT explains manyphenomena and has many applications
nerve pulses Jones (1986, 1990)
pulses and other patterns in reaction diffusionequations A. Doelman, B. Gardner, T. Kaper, B.
Sandstede, S. Schecter, A. Scheel,...(1990,...)
detonation waves Gasser + Sz. (1993)
viscous shock waves Freistuhler + Sz. (2002, 2010)
issues: existence, stability and bifurcations
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Fenichel theory can be used for three types ofproblems
reduction to a single normally hyperbolic slowmanifold (often attracting); stucturally stableproperties of reduced flow persist.
connections between invariant objects containedin two different normally hyberbolic slowmanifolds, i.e. heteroclinic orbits; needstransversality arguments
connections involving additional passages closeto slow manifolds of saddle type; needstransversality and “Exchange Lemma”
C. Jones, N. Kopell, T. Kaper,...
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Singular heteroclinic orbits perturb to heteroclinicorbits for 0 < ε 1
Sz. (1991)
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Pulse propagation in Fitzhugh-Nagumo equation
ut = uxx + f(u)− w
vt = ε(u− γw)
w = f(u) S-shaped f(u) = u(u− a)(1− u)
Reststate (u, v) = (0, 0)
travelling wave (u,w)(x, t) = (u,w)(x+ ct︸ ︷︷ ︸:=τ
),
speed c
limτ→±∞(u,w)(τ) = (0, 0)
existence, stability
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Pulse is homoclinic orbit of slow-fast system
u′ = v
v′ = cv − f(u) + w
w′ = ε(u− γw)/c
equilibrium (0, 0, 0), hyperbolic
one-dimensional unstable manifold W u
two-dimensional stable manifold W s
pulse: homoclinc orbit ω ⊂ W u ∩W s
homoclinic ∃ ⇔ c = c(ε)
u, v fast, w slow, add c′ = 0 as slow
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Reduced problem and layer problem
0 = v
0 = cv − f(u) + w
w = (u− γw)/c
one dimensional S w = f(u), v = 0
u′ = v
v′ = cv − f(u) + w
w′ = 0
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Singular homoclinic orbit of travelling wave problemfor Fitzhugh-Nagumo equation
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Particularly successful in “low” dimensions
m = 1, n = 1 one slow and one fast variable
m = 2, n = 1, two fast variables and one slowvariable
m = 1, n = 2, one fast variable and two slowvariables
m = 2, n = 2, two fast variables and two slowvariables
m large n = 1, 2, 3 global reduction to singlecritical manifold
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Singularities of S cause loss of normal hyperbolicity
singularities of S: folds,bifurcation points, poles,...
⇒ ∂f∂x singular!
loss of normal hyperbolicityFenichel theory does notapply!
Blow-up method:“clever” rescalings near singularities of S
singularities are “blown-up” to spheres,cylinders, etc.
Dumortier + Roussarie (1996), Krupa + Sz. (2001)
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The 3-d autocatalator has complicated slow-fastdynamics
V. Petrov, S. Scott, K. Showalter (1992), A. Milik + Sz. (1998)
a = µ+ c− a− ab2
εb = a− b+ ab2
c = b− c
2-dim folded criticalmanifold S
a− b+ ab2 = 0
S = Sa ∪ pf ∪ SrSa attracting
Sr repelling
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Mixed mode oscillations are complicated periodicsolutions containing large and small oscillations
a = µ+ c− a− ab2
εb = a− b+ ab2
c = b− c
12 periodische Losung
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Intersection of attracting and repelling slowmanifolds generates canards und mixed modeoscillations
A. Milik + Sz., Int. J. Bif. Chaos (2001)
a = µ+ c− a− ab2
εb = a− b+ ab2
c = b− c
12 periodische Losung
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Details of generic planar fold point are fairlycomplicated
normal hyperbolicity breaks down at fold point
reduced flow singular at fold pint
important for relaxation oscillations
classical problem, many approaches and results
very complicated asymptotic expansions: powersof ε1/3 and also ln ε terms!
blow-up methodDumortier + Roussarie (1996), Krupa + Sz. (2001)
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Fold point: (0, 0) nonhyperbolic, blow-up method
Krupa, Sz. (2001)x′ = −y + x2 + · · ·y′ = −ε+ · · ·
asymptotics of Sa,ε ∩ Σout
map: π : Σin → Σout contraction, rate e−C/ε
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One has to consider the extended system
x′ = f(x, y, ε)
y′ = εg(x, y, ε)
ε′ = 0
defining conditions of generic fold at origin(x, y, ε) = (0, 0, 0)
f = 0, fx = 0, origin non-hyperbolic
g(0, 0, 0) 6= 0, reduced flow nondegenerate
fxx 6= 0, fy 6= 0
⇒ saddle node bifurcation in f = 0
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It is straightforward to transform to normal form
x′ = −y + x2 +O(ε, xy, y2, x3)
y′ = ε(−1 +O(x, y, ε))
ε′ = 0
(0, 0, 0) is a very degenerate equilibrium
eigenvalue λ = 0, multiplicity three
blow-up the singularity
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Blow-up corresponds to using (weighted) sphericalcoordinates for (x, y, ε)
x = rx, y = r2y, ε = r3ε
(x, y, ε) ∈ S2, r ∈ R
singularity at origin is blown up to sphere r = 0
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Blow-up makes hidden details visible and accessibleto analysis
blow-up is
clever rescalingzooming into singularityand compactification (of things that arepushed to “infinity” by zooming in)
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Blow-up of layer problem shows some details
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More details live on the sphere
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The full dynamics of the blown up fold point
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Many details have to be filled in
computations in suitable charts
charts correspond to asymptotic regimes
gain hyperbolicity
gain transversality
invariant manifold theory, center manifolds,Fenichel theory
local and global bifurcations
special functions: here Airy equation
regular perturbation arguments
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Many applications need GSPT beyond the standardform
no global separation into slow and fast variables
loss of normal hyperbolicity
dynamics on more than two distinct time-scales
several scaling regimes with different limitingproblems are needed
singular or non-uniform dependence on severalparameters
lack of smoothness
Motivation: applications from biology, chemistry,and mechanics
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6. Glycolytic oscillator
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Singular dependence on two parameters in a modelof glycolytic oscillations
existence of a complicated limit cycle
desingularization by GSPT + blow up method
many time scales
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Glycolysis is a complicated enzyme reaction:
sugar → water + CO2 + energy
subprocess: glucose α → pyruvat β + energy
α = µ− φ(α, β)
β = λφ(α, β)− βφ(α, β) =
α2β2
L+ α2β2
L, λ 1, 0 < µ < 1
L. Segel, A. Goldbeter, Scaling in biochemical kinetics:
dissection of a relaxation oscillator, J. Math. Bio. (1994)√λ/L 1/
√λ 1
formally=⇒ periodic solution
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Numerical simulation for L = 5× 106, λ = 40, µ = 0.15shows limit cycle
L large, λ fixed: classical relaxation oscillationsL, λ both large: more complicated
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For λ, L→∞ the variables α, β are large ⇒ rescaling
ε :=√λ/L, δ := 1/
√λ, a := εα, b = δ2β
a′ = ε
(µ− a2b2
δ2 + a2b2
)b′ =
a2b2
δ2 + a2b2− b+ δ2
a slow, b fast with respect to ε
Goldbeter-Segel condition: ε δ 1
ε→ 0 “standard” , δ → 0 “singular” ?
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Critical manifold has two folds for δ > 0, ε = 0
Sδ = (a, b) : a2b2(1− b) + δ2(a2b2− b+ δ2) = 0
=⇒ relaxation oscillations ∃ for δ > 0 and ε 1
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Critical manifold Sδ is singular for δ → 0
Sδ : a2b2(1− b) + δ2(a2b2 − b+ δ2) = 0
S0 : a2b2(1− b) = 0
a = 0, b = 0non-hyperbolic
b = 1 hyperbolic
desingularization: blow-up (with respect to δ)
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Consider the extended system in (a, b, δ) space
vector field Xε
a′ = εf(a, b, δ)
b′ = g(a, b, δ)
δ′ = 0
(ε, δ) = (0, 0) degenerate
lines la and lb of non-hyperbolic equilbria
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Line lb is desingularized by first blow-up
Xε Xε
a = ra, δ = rδ, (a, δ) ∈ S1, r ∈ R, b = b ∈ R
line lb → surface of cylinder r = 0
line la is still degenerate
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Line la is desingularized by second blow-up
b = ρb, δ = ρ2δ, (b, δ) ∈ S1, ρ ∈ R, a = a ∈ R
vector field Xε is desingularized with respect to δ
for ε = 0 ∃ smooth critical manifold S
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GSPT is applicable with respect to ε uniformly withrespect to δ
Theorem: ε δ 1 =⇒ ∃ periodic solution
I. Kosiuk + Sz., SIAM J. Appl. Dyn. Systems (2011)
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7. Mitotic Oscillator
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Singular behaviour in a model of the cell cycle
existence of a complicated limit cycle
singular behaviour as ε→ 0
very different from standard form
desingularization by GSPT + blow-up
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The mitotic oscillator is a simple model related tothe dynamics of the cell-cycle
Paul Nurse, Lee Hartwell,Tim Hunt
Nobel-Prize in medicine (2001)
cell-cycle: periodic sequenceof cell divisions
crucial players:Cyclin, Cyclin-dependentkinase (Cdk)driven by an oscillator?
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The mitotic oscillator has the following components
Cyklin Cactive Cdk Minactive Cdk M+
active C-protease Xinactive C-protease X+
C activates M+ −→MM activates X+ −→ XX degrades C
A. Goldbeter, PNAS (1991); more realistic larger modelscontain subsystems similar to the Goldbeter model.
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Dynamics is governed by Michaelis-Menten kinetics
cyklin C ≥ 0
aktiveCdk M ≥ 0
active Cyklin-protease X ≥ 0
C = vi − vdXC
Kd + C− kdC
M = V1C
Kc + C
1−MK1 + 1−M
− V2M
K2 +M
X = V3M1−X
K3 + 1−X− V4
X
K4 +X
Michaelis constants Kj
small Michaelis constants
ε 1
X
ε+X=
≈ 1, X = O(1)x
1+x , X = εx
≈ 0, X = o(ε)
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The mitotic oscillator has a periodic solution
C =1
4(1−X − C)
M =6C
1 + 2C
1−Mε+ 1−M
− 3
2
M
ε+M
X = M1−X
ε+ 1−X− 7
10
X
ε+X
parameter
kd = 0.25vi = 0.25Kc = 0.5Kd = 0V1 = 3V2 = 1.5V3 = 1V4 = 0.7
ε = 10−3
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The mitotic oscillator has a periodic solution
C =1
4(1−X − C)
M =6C
1 + 2C
1−Mε+ 1−M
− 3
2
M
ε+M
X = M1−X
ε+ 1−X− 7
10
X
ε+X
parameter
kd = 0.25vi = 0.25Kc = 0.5Kd = 0V1 = 3V2 = 1.5V3 = 1V4 = 0.7
ε = 10−3
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The periodic orbit lies in the cube [0, 1]3 ⊂ R3
partly very close toM = 0, X = 0, M = 1,X = 0
Theorem: ε 1⇒exists periodic orbit ΓεKosiuk + Sz. (2011)
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The periodic orbit lies in the cube [0, 1]3 ⊂ R3
partly very close toM = 0, X = 0, M = 1,X = 0
Theorem: ε 1⇒exists periodic orbit ΓεKosiuk + Sz. (2011)
singularperturbation?
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The periodic orbit lies in the cube [0, 1]3 ⊂ R3
partly very close toM = 0, X = 0, M = 1,X = 0
Theorem: ε 1⇒exists periodic orbit ΓεKosiuk + Sz.
J. Math. Bio. (2016)
singularperturbation? no
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The periodic orbit lies in the cube [0, 1]3 ⊂ R3
partly very close toM = 0, X = 0, M = 1,X = 0
Theorem: ε 1⇒exists periodic orbit ΓεKosiuk + Sz.
J. Math. Bio. (2016)
singularperturbation?at least not instandard form!
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Proof uses GSPT and blow-up
Sliding on sides corresponds to slow motion oncritical manifolds M = 0 and X = 0
M = 0 X = 0
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Proof uses GSPT and blow-up
Sliding on edge corresponds to slow motion onone-dimensional critical manifold in blown-up edge.
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8. Conclusion, outlook and program
overview and two case studies
identify fastest time-scale and correspondingscale of dependent variables, rescale
often the limiting problem has a (partially)non-hyperbolic critical manifold
use (repeated) blow-ups to desingularize
identify relevant singular dynamics
carry out perturbation analysis
approach useful in other multi-parametersingular perturbation problems