Adaptive evolution : a population approach Benoît Perthame

Transcription

1 Adaptive evolution : a population approach Benoît Perthame

2 Adaptive dynamic : selection principle d dt n(x, t) = n(x, t)r( x, ϱ(t) ), ϱ(t) = R d n(x, t)dx. given x, n(x) = ϱ δ(x x), R( x, ϱ) = 0, ϱ( x). They are stable by perturbation of the weight ϱ (strong topology) d ϱ(t) = ϱ(t)r( x, ϱ(t)). dt But they are unstable by approximation in measures (weak topology), and by mutation (structural)...

3 Adaptive dynamic : mutations Off-springs undergo small mutations that change slightly the trait t n(x, t) n = n(x, t)r( x, ϱ(t) ), ϱ(t) = R d n(x, t)dx. t n(x, t) = n(x, t)r( x, ϱ(t) ) + ϱ(t) = R d n(x, t)dx. M(x, y)b(y)n(y, t)dy,

4 Adaptive dynamic : mutations Off-springs undergo small mutations that change slightly the trait t n(x, t) n = n(x, t)r( x, ϱ(t) ), ϱ(t) = R d n(x, t)dx. We assume that mutations are RARE and introduce a scale ε for small mutations ε t n(x, t) ε2 n = n(x, t)r ( x, ϱ(t) ), ϱ(t) = R d n(x, t)dx.

5 Adaptive dynamic : mutations Off-springs undergo small mutations that change slightly the trait t n(x, t) n = n(x, t)r( x, ϱ(t) ), ϱ(t) = R d n(x, t)dx. We assume that mutations are RARE and introduce a scale ε for small mutations ε t n ε(x, t) ε 2 n ε = n ε (x, t)r ( x, ϱ ε (t) ), ϱ ε (t) = R d n ε (x, t)dx.

6 Population model of adaptive dynamics : mutations This is not far from Fisher/KPP equation for invasion fronts/chemical reaction ε t n ε(x, t) ε 2 n ε = n ε (x, t) ( 1 n ε (x, t) ),. {n ε = 1} {n ε = 0} WKB, large deviations, level sets, geometric motion G. Barles, L. C. Evans, W. Fleming, P. E. Souganidis, S. Osher, J. Sethian...

7 Population model of adaptive dynamics : mutations This is not far from Fisher/KPP equation for invasion fronts/chemical reaction ε t n ε(x, t) ε 2 n ε = n ε (x, t) ( 1 n ε (x, t) ),. {n ε = 1} {n ε = 0} in the limit n(x, t)(1 n(x, t) = 0.

8 Population model of adaptive dynamics : mutations The situation is very different for the nonlocal equation ε t n ε(x, t) ε 2 n ε = n ε (x, t)r ( x, ϱ ε (t) ), ϱ ε (t) = R d n ε(x, t)dx.

9 Population model of adaptive dynamics : mutations The situation is very different for the nonlocal equation ε t n ε(x, t) ε 2 n ε = n ε (x, t)r ( x, ϱ ε (t) ), ϱ ε (t) = n ε(x, t)dx. R d In the limit one can expect 0 = n(x, t)r ( x, ϱ(t) ), n(x, t) = ϱδ Γ(t), Γ(t) { R(, ϱ(t)) = 0 }.

10 Asymptotic method Question. What tools to describe Dirac concentrations in PDEs? n ε (x) = ϱ (2πε) d/2e x x 2 /(2ε) ε 0 ϱδ(x x) n ε (x) = e ( x x 2 +ε ln O(ε))/(2ε) ε 0 ϱδ(x x) More generally (Hopf-Cole/WKB) with the conditions n ε (x) = e ϕ ε(x)/ε ε 0 ϱδ(x x) ϕ ε ε 0 ϕ, max x ϕ(x) = 0 = ϕ( x)

11 Asymptotic method Theorem Suppose x R, R x > 0, R ϱ < 0. Then, for subsequences n ε (x, t) ϱ(t)δ(x = x(t)), ϱ ε ϱ(t) = n(x, t)dx, εk 0 εk 0 Can one give a law for the dynamics of x(t)?

12 Asymptotic method Theorem Suppose x R, R x > 0, R ϱ < 0. Then, for subsequences n ε (x, t) ϱ(t)δ(x = x(t)), ϱ ε ϱ(t) = n(x, t)dx, εk 0 εk 0 and the fittest trait x(t) is characterised by the Eikonal equation with constraints t ϕ(x, t) = R( x, ϱ(t) ) + ϕ(x, t) 2 max x ϕ(x, t) = 0 = ϕ(t, x(t)) Definition This situation is called monomorphism Difficulty Solutions to H.-J. eq. are not smooth

13 Asymptotic method Theorem Suppose x R, R x > 0, R ϱ < 0. Then, for subsequences n ε (x, t) ϱ(t)δ(x = x(t)), ϱ ε ϱ(t) = n(x, t)dx, εk 0 εk 0 and the fittest trait x(t) is characterised by the Eikonal equation with constraints However t ϕ(x, t) = R( x, ϱ(t) ) + ϕ(x, t) 2 max x ϕ(x, t) = 0 = ϕ(t, x(t)) t ϕ( x(t), t ) = 0 = x ϕ( x(t), t ) R ( x(t), ϱ(t) ) = 0 (Pessimism Principle)

14 Asymptotic method This problem should be understood as follows max x ϱ(t) ϕ(x, t) = 0, t is a constraint, is a Lagrange multiplier.. This is not an obstacle problem! 0!!"! -0.05!!"# -0.1!!"$ -0.15!!"% -0.2!!"& -0.25!!"' -0.3!!"( !!")!"!!"#!"$!"%!"&!"'!"(!")!"*!"+ #"!

15 Asymptotic method Theorem In R d, set n ε (x, t) = e ϕ ε(x,t)/ε. After extraction, ϕ ε εk 0 ϕ (locally uniformly), ϱ ε(t) εk 0 ϱ(t) And n ε (x, t) εk 0 t ϕ(x, t) = R( x, ϱ(t) ) + ϕ(x, t) ) 2 max x ϕ(x, t) = 0 ( = ϕ(t, x(t)) n(x, t) weakly in measures, supp ( n(t) ) {ϕ(t) = 0} ).

16 Asymptotic method Proof 1. ϱ ε (t) is BV (and converges after extraction) and its limit ϱ(t) is non-decreasing 2. Because n ε (x, t) = e ϕ ε(x,t)/ε we have t ϕ ε(x, t) = R ( x, ϱ ε (t) ) + ϕ ε (x, t) 2 ε ϕ ε and ϕ ε is Lipschitz continous in x (difficulty in t) (gives the H.-J. equation in viscosity sense) 3. ϱ m n ε (x, t)dx ϱ M (gives the constraint)

17 Asymptotic method Conclusion : n ε L ϕ ε C

18 Asymptotic method Theorem (G. Barles, BP) Uniqueness With reasonable assumptions there exist a unique lipschitz continuous solution ( ϱ, ϕ) to the constraint H.-J. equation t ϕ(x, t) = b(x) ϱ(t)d(x) + ϕ 2, max x ϕ(x, t) = 0 ( = ϕ(t, x(t)) ) Open question Extend uniqueness to ϕ(x, t) = b(x) t 1 + ϱ(t) ϱ(t)d(x) + ϕ 2.

19 Asymptotic method Proof of uniqueness : the difficulty The L contraction property is lost! Define M(t) := max x [ϕ 1(x, t) ϕ 2 (x, t)] d dt M(t) R(x M(t), ϱ 1 (t)) R(x M (t), ϱ 2 (t)) C ϱ 1 (t) ϱ 2 (t) But the constraint cannot be used here to control ϱ 1 (t) ϱ 2 (t) by M(t).

20 Asymptotic method Proof of uniqueness : idea. Work on Define ψ(t) := ϕ(x, t) + d(x) R(x, ϱ) = b(x) d(x)ϱ t 0 ϱ(s)ds. t ψ(x, t) = b(x) + ψ d(x) t 0 ϱ(s)ds 2, max x ϕ(x, t) = 0 ( = ϕ(t, x(t)) ) M(t) := max x [ψ 1(x, t) ψ 2 (x, t)] d dt M(t) p M d(x M ) t 0 ϱ 1(s)ds 2 p M d(x M ) t 0 ϱ 2(s)ds 2

21 Asymptotic method t d dt M(t) p M d(x M ) ϱ 1(s)ds 2 p M d(x M ) ϱ 2(s)ds Use that solutions are Lipschitz and the specific form of R t d dt M(t) C ϱ 1(s)ds 0 But we may choose ϕ(t, x 1 ) = 0 and get t 0 ϱ 2(s)ds M(t) ψ 1 (t, x 1 ) ψ 2 (t, x 1 ) d(x 1 ) [ t ϱ 1(s)ds 0 The opposite inequality holds true similarly and thus d dt M(t) CM(t). t t 0 ϱ 2(s)ds ]

22 Asymptotic method Numerical tests : b(x) =.5 + x Direct simulation (1500 points) H.-J. solution (200 points)

23 Asymptotic method Numerical tests : b(x) =.5 + x(2 x) Direct simulation (1500 points) H.-J. solution (200 points)

24 Asymptotic method Numerical tests : min(.45 + x. 2, x) Direct simulation (1500 points) H.-J. solution (200 points)

25 Survival threshold Next ingredient is the notion of survival threshold. n(t, x) t ε n(t, x) = n(t, x) R ( x, [n(t)] ) ε nn(t, x) ε Motivated by Population really vanishes ; some traits are not represented The notion of individual is somehow included in the parameter n A because n(t, x) really vanishes at a level related to n compatibility with Monte-Carlo simulations

26 Survival threshold Next ingredient is the notion of survival threshold. n(t, x) t ε n(t, x) = n(t, x) R ( x, [n(t)] ) ε nn(t, x) ε Motivated by Population really vanishes ; some traits are not represented The notion of individual is somehow included in the parameter n A similar notion represents demographic stochasticity compatibility with Monte-Carlo simulations

27 Survival threshold n(t, x) n(t, x) ε n(t, x) = R ( x, [n(t)] ) t ε and choose a threshold of the form n n(t, x) ε n = e ϕ st/ε, ϕ st < 0 (constants). The formal constrained H.-J. equation is a free boundary problem ϕ t = ϕ 2 + R in Ω(t) := {(x, t), s.t. ϕ > ϕ st }, ϕ = in Ω c, ϕ ϕ st in Ω. Open questions : prove it rigorously ; other scales

28 n(t, x) ε n(t, x) = t n(t, x) R ( x, [n(t)] ) ε n n(t, x) ε n = e ϕ st/ε, ϕ st < 0 (constants), Formal derivation n ε = e ϕ ε/ε, then ϕ ε t ε ϕ ε ϕ ε 2 = R ( x, [n(t)] ) n n(t, x) ϕ ε t ε ϕ ε ϕ ε 2 = R ( x, [n(t)] ) e ϕst ϕε 2ε when ϕ st < ϕ ε then e ϕ st ϕ ε 2ε when ϕ st > ϕ ε then e ϕ st ϕ ε 2ε 0 (disappears) i.e. ϕ ε

29 Survival threshold Theorem Fix R(x) 0 then the free boundary problem ϕ ε ε 0 ϕ(t, x) ϕ t = ϕ 2 + R(x) in Ω(t) := {(x, t), s.t. ϕ > ϕ st }, ϕ = in Ω c, ϕ ϕ st in Ω. characterized by one of the equivalent statements it is the minimal solution the Dirichlet boundary condition should be satisfied ϕ = ϕ st on Ω(t). ϕ = is a truncation to of the global solution in R d.

30 Survival threshold When R(x) changes sign. The previous truncation formula is wrong The additional Dirichlet boundary condition is not enough, one should maybe impose also a state constraint boundary condition in Ω(t) The semi-relaxed limits can be compared to two relatively close functions of optimal control type (given by an iterative cleaning ). This implies at least that In opposition to the case R 0, the solution is changed drastically (see numerics) The limit does not depend on the specific power n

31 Survival threshold n(t, x) t ε n(t, x) = n(t, x) R(x) ε nn(t, x) ε Related to another asymptotic (other scales) : Bernouilli problem (see Lorz, Markowich, BP) n(x) + n(x) = R(x) 0, x Ω n(x) = 0 x Ω, n ν = n x Ω.

32 Numerical results Effect of the survival threshold

33 Numerical results trait x

34 Model with two nutrients : no survival threshold ϱ 1 (t) = ψ 1 (x)n(x, t)dx, ϱ 2 (t) = See Champagnat and Jabin ψ 2 (x)n(x, t)dx Direct simulation (1500 points) H.-J. solution (200 points) density n phase ϕ

35 Numerical results And the dynamics looks like

36 Open questions Uniqueness for a general R(x, ϱ) Case of multiple nutrients (See Champagnat and Jabin) R := R(x, ϱ 1, ϱ 2,..., ϱ I ), ϱ i = Survival threshold (R(x, ϱ), other scales) Explain branching ψ i (x)n(x, t)dx