Citation: Frédérique Clément, Béatrice Laroche, Frédérique Robin. Analysis and numerical simulation of an inverse problem for a structured cell population dynamics model[J]. Mathematical Biosciences and Engineering, 2019, 16(4): 3018-3046. doi: 10.3934/mbe.2019150
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Throughout the paper, we work over an algebraically closed field
Σk=Σk(C,L)⊆Pr |
of
Assume that
σk+1:Ck×C⟶Ck+1 |
be the morphism sending
Ek+1,L:=σk+1,∗p∗L, |
which is a locally free sheaf of rank
Bk(L):=P(Ek+1,L) |
equipped with the natural projection
H0(Bk(L),OBk(L)(1))=H0(Ck+1,Ek+1,)=H0(C,L), |
and therefore, the complete linear system
βk:Bk(L)⟶Pr=P(H0(C,L)). |
The
It is clear that there are natural inclusions
C=Σ0⊆Σ1⊆⋯⊆Σk−1⊆Σk⊆Pr. |
The preimage of
Theorem 1.1. Let
To prove the theorem, we utilize several line bundles defined on symmetric products of the curve. Let us recall the definitions here and refer the reader to [2] for further details. Let
Ck+1=C×⋯×C⏟k+1times |
be the
Ak+1,L:=Tk+1(L)(−2δk+1) |
be a line bundle on
The main ingredient in the proof of Theorem 1.1 is to study the positivity of the line bundle
Proposition 1.2. Let
In particular, if
In this section, we prove Theorem 1.1. We begin with showing Proposition 1.2.
Proof of Proposition 1.2. We proceed by induction on
Assume that
rz,k+1,L:H0(Ck+1,Ak+1,L)⟶H0(z,Ak+1,L|z) |
is surjective. We can choose a point
rz,k+1,L:H0(Ck+1,Ak+1,L)⟶H0(z,Ak+1,L|z) |
where all rows and columns are short exact sequences. By tensoring with
rz,k+1,L:H0(Ck+1,Ak+1,L)⟶H0(z,Ak+1,L|z) |
in which we use the fact that
Since
Lemma 2.1. Let
Proof. Note that
B′/A′⊗A′A′/m′q=B′/(m′qB′+A′)=B′/(m′p+A′)=0. |
By Nakayama lemma, we obtain
We keep using the notations used in the introduction. Recall that
αk,1:Bk−1(L)×C⟶Bk(L). |
To see it in details, we refer to [1,p.432,line –5]. We define the relative secant variety
Proposition 2.2. ([2,Proposition 3.15,Theorem 5.2,and Proposition 5.13]) Recall the situation described in the diagram
αk,1:Bk−1(L)×C⟶Bk(L). |
Let
1.
2.
3.
As a direct consequence of the above proposition, we have an identification
H0(Ck+1,Ak+1,L)=H0(Σk,IΣk−1|Σk(k+1)). |
We are now ready to give the proof of Theorem 1.1.
Proof of Theorem 1.1. Let
b:˜Σk:=BlΣk−1Σk⟶Σk |
be the blowup of
b:˜Σk:=BlΣk−1Σk⟶Σk |
We shall show that
Write
γ:˜Σk⟶P(V). |
On the other hand, one has an identification
ψ:Ck+1⟶P(V). |
Also note that
ψ:Ck+1⟶P(V). |
Take an arbitrary closed point
α−1(x)⊆π−1k(x″)∩β−1k(x′). |
However, the restriction of the morphism
[1] | M. E. Gurtin and R. C. MacCamy, Non-linear age-dependent population dynamics, Arch. Ration. Mech. Anal., 54(1974): 281–300. |
[2] | B. L. Keyfitz and N. Keyfitz, The McKendrick partial differential equation and its uses in epidemiology and population study, Math. Comput. Model., 26(1997): 1–9. |
[3] | L. M. Abia, O. Angulo and J. C. López-Marcos, Age-structured population models and their numerical solution, Ecol. Modell., 188(2005):112–136. |
[4] | C. Chiu, Nonlinear age-dependent models for prediction of population growth, Math. Biosci., 99(1990): 119–133. |
[5] | A. M. de Roos, Numerical methods for structured population models: the escalator boxcar train, Numer. Methods Partial. Differ. Equ., 4(1988): 173–195. |
[6] | W. Rundell, Determining the birth function for an age structured population, Math. Popul. Stud., 1(1989): 377–395. |
[7] | W. Rundell, Determining the death rate for an age-structured population from census data, SIAM J. Appl. Math., 53(1993): 1731–1746. |
[8] | M. Gyllenberg, A. Osipov, and L. Pivrinta, The inverse problem of linear age-structured population dynamics, J. Evol. Equ., 2(2002): 223–239. |
[9] | A. J. Lotka, The structure of a growing population, Hum. Biol., 3(1931): 459–493. |
[10] | A. J. Lotka, On an integral equation in population analysis, Ann. Math. Stat., 10(1939): 144–161. |
[11] | F. Clément, F. Robin and R. Yvinec, Analysis and calibration of a linear model for structured cell populations with unidirectional motion : Application to the morphogenesis of ovarian follicles, SIAM J. Appl. Math., 79(2019): 207–229. |
[12] | P. Gabriel, Measure solutions to the conservative renewal equation. ESAIM Proc. Surveys, 62(2018): 68–78. |
[13] | P. Gwiazda, T. Lorenz and A. Marciniak-Czochra, A nonlinear structured population model: Lipschitz continuity of measure-valued solutions with respect to model ingredients, J. Differ. Equ., 248(2010): 2703–2735. |
[14] | P. Gabriel, S. P. Garbett, V. Quaranta et al., The contribution of age structure to cell population responses to targeted therapeutics, J. Theor. Biol., 311(2012): 19–27. |
[15] | A. Perasso and U. Razafison, Identifiability problem for recovering the mortality rate in an agestructured population dynamics model, Inverse Probl. Sci. Eng., 24(2016): 711–728. |
[16] | B. Perthame and J. P. Zubelli, On the inverse problem for a size-structured population model. Inverse Problems, 23(2007): 1037. |
[17] | M. Doumic, B. Perthame and J. P. Zubelli, Numerical solution of an inverse problem in sizestructured population dynamics, Inverse Problems, 25(2009): 045008. |
[18] | T. Bourgeron, M. Doumic and M. Escobedo, Estimating the division rate of the growthfragmentation equation with a self-similar kernel, Inverse Problems, 30(2014): 025007. |
[19] | M. Iannelli, T. Kostova and F. Augusto Milner, A fourthorder method for numerical integration of age and sizestructured population models, Numer. Methods Partial. Differ. Equ., 25(2009): 918–930. |