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Individual-based and continuum models of phenotypically heterogeneous growing cell populations

  • Existing comparative studies between individual-based models of growing cell populations and their continuum counterparts have mainly been focused on homogeneous populations, in which all cells have the same phenotypic characteristics. However, significant intercellular phenotypic variability is commonly observed in cellular systems. In light of these considerations, we develop here an individual-based model for the growth of phenotypically heterogeneous cell populations. In this model, the phenotypic state of each cell is described by a structuring variable that captures intercellular variability in cell proliferation and migration rates. The model tracks the spatial evolutionary dynamics of single cells, which undergo pressure-dependent proliferation, heritable phenotypic changes and directional movement in response to pressure differentials. We formally show that the continuum limit of this model comprises a non-local partial differential equation for the cell population density function, which generalises earlier models of growing cell populations. We report on the results of numerical simulations of the individual-based model which illustrate how proliferation-migration tradeoffs shaping the evolutionary dynamics of single cells can lead to the formation, at the population level, of travelling waves whereby highly-mobile cells locally dominate at the invasive front, while more-proliferative cells are found at the rear. Moreover, we demonstrate that there is an excellent quantitative agreement between these results and the results of numerical simulations and formal travelling-wave analysis of the continuum model, when sufficiently large cell numbers are considered. We also provide numerical evidence of scenarios in which the predictions of the two models may differ due to demographic stochasticity, which cannot be captured by the continuum model. This indicates the importance of integrating individual-based and continuum approaches when modelling the growth of phenotypically heterogeneous cell populations.

    Citation: Fiona R Macfarlane, Xinran Ruan, Tommaso Lorenzi. Individual-based and continuum models of phenotypically heterogeneous growing cell populations[J]. AIMS Bioengineering, 2022, 9(1): 68-92. doi: 10.3934/bioeng.2022007

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  • Existing comparative studies between individual-based models of growing cell populations and their continuum counterparts have mainly been focused on homogeneous populations, in which all cells have the same phenotypic characteristics. However, significant intercellular phenotypic variability is commonly observed in cellular systems. In light of these considerations, we develop here an individual-based model for the growth of phenotypically heterogeneous cell populations. In this model, the phenotypic state of each cell is described by a structuring variable that captures intercellular variability in cell proliferation and migration rates. The model tracks the spatial evolutionary dynamics of single cells, which undergo pressure-dependent proliferation, heritable phenotypic changes and directional movement in response to pressure differentials. We formally show that the continuum limit of this model comprises a non-local partial differential equation for the cell population density function, which generalises earlier models of growing cell populations. We report on the results of numerical simulations of the individual-based model which illustrate how proliferation-migration tradeoffs shaping the evolutionary dynamics of single cells can lead to the formation, at the population level, of travelling waves whereby highly-mobile cells locally dominate at the invasive front, while more-proliferative cells are found at the rear. Moreover, we demonstrate that there is an excellent quantitative agreement between these results and the results of numerical simulations and formal travelling-wave analysis of the continuum model, when sufficiently large cell numbers are considered. We also provide numerical evidence of scenarios in which the predictions of the two models may differ due to demographic stochasticity, which cannot be captured by the continuum model. This indicates the importance of integrating individual-based and continuum approaches when modelling the growth of phenotypically heterogeneous cell populations.



    Suppose the quasi-periodic linear system

    dxdt=A(ω1t,ω2t,,ωrt)x (1.1)

    in which tR, xCr, A(ω1t,ω2t,,ωrt) is quasi-periodic(q-p) time dependent r×r matrix and the basic frequencies ω1,,ωr are rational independent.

    The system (1.1) is said to be reducible, if there exists a so called quasi-periodic Lyapunov-Perron (L-P) transformation x=P(ω1t,,ωrt)y, so that the transformed system is a linear system with constant coefficients. We call the transformation x=P(ω1t,,ωrt)y is quasi-periodic L-P transformation, if P(t) is non singular and P, P1 and ˙P are quasi-periodic and are bounded in tR.

    Many researchers have discussed the reducibility problems for quasi-periodic linear systems. For r=1, i.e., the periodic case, the well known Floquet theorem states that there always exists a periodic change of variables x=P(ω1t)y so that the system ˙x=A(ω1t)x is reducible to a constant coefficient system ˙y=By,˙φ=ω, where B is a constant matrix. For r>1, i.e., quasi-periodic case, there is an example in [1] which shows that the system (1.1) is not always reducible. Earlier for q-p case, Coppel [2] proved that a linear differential equation with bounded coefficient matrix is pseudo-autonomous if and only if it is almost reducible and Johnson and Sell [3] showed that if (1.1) satisfies the full spectrum assumption, then there is a quasi-periodic linear change of variables x=P(ω1t,,ωrt)y that transforms (1.1) to a constant coefficient system ˙y=By, where B is a constant matrix. Their results failed for the pure imaginary spectrum [4].

    The first reducibility result by KAM method was given by Dinaburg and Sinai [5] who proved that the linear Schrödinger equation d2xdt2+q(ω1t,ω2t,,ωrt)x=λx is reducible for 'most' large enough λ in measure sense, where ω is fixed satisfying the Diophantine condition: |k,ω|>α1|k|τ, 0kZr, where α,τ are positive constants. See also Rüssmann [6] for a refined result.

    In 1992 Jorba and Simó [7] considered the following linear differential system

    dxdt=(A+λˉQ+λ2Q(ω1t,,ωrt))x,xRd, (1.2)

    in which A,ˉQ are constant diagonal matrices, and Q is an analytic q-p matrix having r basic frequencies, and with a small parameter λ. Using the KAM method, They proved that there exists a positive measure Cantor subset E(0,λ0), λ01 such that for any λE, the system (1.2) is reducible, provided that the following non-degeneracy conditions

    |αi(λ)αj(λ)|>δ>0,|ddλ(αi(λ)αj(λ))|>χ>0,1i<jd (1.3)

    where αi(λ), 1im, are the eigenvalues of ˉA=A+λˉQ. In 1999, Xu [8] improved the result for the weaker non-degeneracy conditions.

    Eliasson [9] considered the following linear Shrödinger equation

    d2xdt2+(λ+Q(ωt)x=0.

    For almost all λ(a,b), the full measure reducibility result is proved in a Lebesgue measure sense provided that Q is small. On the other hand, Krikorian [10] generalized the work for linear systems with coefficients in so(3). Then, Eliasson [11] discussed the full measure reducibility result for the following parameter dependent systems

    dxdt=(A(λ)+Q(ω1t,,ωrt,λ))x, (1.4)

    in which tR, xCd, a constant matrix A of dimension d×d, the parameter λ(a,b), and an analytic mapping Q:Tr×(a,b)gl(m,C), a Diophantine vector (ω1,,ωr) and for sufficiently small |Q|.

    He and You [12] proved the positive measure reducibility result for the following quasi-periodic skew-product systems: dxdt=(A(λ)+Q(φ,λ))x,˙φ=ω, close to constant. The result is proved by using KAM method, under weaker non-resonant conditions and non-degeneracy conditions.

    All the above mentioned results only discuss the reducibility of linear systems with the Diophantine condition

    |k,ω|α1|k|τ,0kZd, (1.5)

    where α>1 and τ>d1.

    In our problem, we are going to focussed on the Brjuno-Rüssmann condition (see [13,14]) which is slightly weaker than the Diophantine condition (1.5), if the frequencies ω=(ω1,,ωd) satisfy

    |k,ω|α1Δ(|k|),0kZd, (1.6)

    where α>1 and some Rüssmann approximation function Δ. These are continuous, increasing and unbounded functions Δ:[0,+)[1,+) such that Δ(0)=1 and

    +1lnΔ(t)t2dt<.

    Remark: If we have Δ(t)=tτ, then the Brjuno-Rüssmann conditions (1.6) becomes the Diophantine conditions (1.5).

    Furthermore, in this article we will generalize the result of He and You [12] for quasi-periodic linear systems using Brjuno-Rüssmann non-resonant condition which is slightly weaker than the Diophantine condition.

    This article is organized as: at the end of Section 1, the statement of the main result is given and in Section 2 proof of the main result is given.

    To state our main result, we now give some definitions and results.

    Definition 1.1. ([15,16])

    A vector ωRd is Brjuno if the following condition is satisfied

    n=12nln(1Ωn)<,Ωn=minνZd,0<|ν|2n|ω,ν|.

    The set of Brjuno vectors is of full Lebesgue measure. In particular, it contains all Diophantine vectors. Conversely, there are vectors that are Brjuno and are not Diophantine.

    This article aims to discuss the positive measure reducibility for q-p linear systems like (1.4) proposed by He and You [12]. The existed positive measure reducibility is discussed by using the Diophantine conditions, but we will discuss the positive measure reducibility using the Brjuno-Rüssmann condition.

    Equivalently, for the system (1.4), we suppose the following skew-product system

    dxdt=(A(λ)+Q(φ,λ))x,˙φ=ω, (1.7)

    where xCr, the parameter λΛ=(a,b), A is a r×r constant matrix, and Q(φ,λ) is an analytic mapping from Tr×(a,b) to gl(m,C), (ω1,ω2,,ωn) is a Brjuno vector and |Q| is sufficiently small.

    In our discussion, we will use the following equivalent formulation of reducibility:

    Consider

    dZdt=b(t)Z (1.8)

    be an analytic q-p linear system. For the skew-product system, it can be rewritten as:

    dZdt=B(φ)Z,˙φ=ω, (1.9)

    where b,B are in the Lie algebra g=g(m,C) and their solutions have values in the Lie group G=GL(m,C). For a complex neighbourhood Wh(Tr) if B is an analytic on Wh(Tr), then we represent BCωh(Tr,g). It is said that the analytic g-valued functions B1,B2Cωh(Tr,g) are conjugated, if a L-P transformation G-valued function PCωh(Tr,G), s.t. for the solutions Z1,Z2 corresponding to B1,B2, we have the following relation

    Z2=P(φ)Z1

    and the conjugate relation can be denoted by:

    B1B2(modP).

    It is easy to prove that B1B2(modP) can equivalently be written in the form of following equality

    B2=DωPP1+PB1P1, (1.10)

    where Dω=φ˙φ denotes the derivative in the direction of frequency vector ω. B1 is known to be reducible if it conjugates to a constant B2.

    In our article, we shall prove that, for any λΛ=(a,b), where λ is the parameter and Λ is a positive measure set, then a L-P transformation P(φ), such that the system A+Q(φ) is transformed into a constant system A.

    For the positive measure reducibility, we will use the non-degeneracy conditions (or the transverse conditions as in Eliasson and Krikorian terminology). Without loss of generality, let's suppose a block-diagonal matrix A(λ)=diag(A1(λ),,As(λ)) with

    dist(σ(Ai),σ(Aj))>ϱ>0,forij,

    where σ(Ai) represents the eigenvalues set for Ai. Let (see in [12] for definition)

    Jij(k,λ)=ik,ωIlilj+(IliAj(λ)ATi(λ)Ilj),
    J(k,λ)=ik,ωIn2+(InA(λ)AT(λ)In),
    dij(k,λ)=det[ik,ωIlilj+(IliAj(λ)ATi(λ)Ilj)].

    For the skew-product system (1.7), by using Lemmas 1.1 and 1.2 in [12], we set for k,ωR

    gij(k,λ)={αuσ(Ai),βvσ(Aj)(ik,ω(αu(λ)βv(λ)),ij;αu,αvσ(Ai),uv(ik,ω(αu(λ)αv(λ)),i=j.

    Remark: It is easily seen that if ACω(Λ,g) and the division of σ(A) is sufficiently separated, then all gij are analytic functions of λ, 1i,js.

    For the proof of this remark (see in [17]).

    Thus, we assume the following:

    Non-degeneracy Conditions: There exist an integer d0 and ς0 such that

    max0ld|lλlgij(k,λ)|>ς,for all1i,js (1.11)

    uniformly hold λΛ and k,ωR.

    Remark: The condition (1.11) will assure that the small denominator condition always holds for the "most" parameter λ. Here, we take only those k in which |k,ω|2δ0, because for the large enough |k,ω|, we always see that the matrix ik,ωIlilj(IliAj(λ)ATi(λ)Ilj) is automatically non-singular and the small denominator condition is satisfied. It can easily be seen that the condition (1.11) is weaker than the non-degeneracy condition (1.3) used by Jorba and Simó.

    Moreover, the property that gij(k,λ) depends analytically on λ can be preserved under small perturbations, and at each iterative step, we will preserve the non-degeneracy conditions.

    To state the main result, consider Q as an analytic g-valued function that can be defined on a complex neighbourhood of Tr×Λ:

    Wh(Tr×Λ)={(ϑ,λ)Cr×Λ|dist(ϑ,Tr)<h},

    where λΛ=(a,b). Defined the norm of Q as:

    ||Q||h=max0ldsup(ϑ,λ)Wh(Tr×Λ)|lQλl|

    similarly

    ||A||=max0ldsupλΛ|lA(λ)λl|

    where |||| denotes the matrix norm.

    Theorem 1.1. Consider the skew-product system (1.7) in which ω is a fixed Brjuno vector and it satisfies the Brjuno-Rüssmann condition (1.6) and A(λ) satisfies the non-degeneracy condition (1.11), and there exists K>0 such that ||A||K. Then there exist ε>0,h>0, such that if ||Q(,)||h=ε1<ε, the measure of the set of parameter λs for which the system (1.7) is non-reducible is no larger than CL(10ε1)c, with some positive constants C,c, and L denotes the length of the parameter interval Λ.

    Theorem 1.1 will be proven by KAM iteration. At each iterative step, we have a L-P transformation close to identity as

    P(φ)=I+Z(φ), (2.1)

    where Z(φ)Cωh(Tr,g), P(φ)Cωh(Tr,G) and by using the L-P transformation (2.1), the quasi-periodic system dxdt=(A+Q)x is changed into

    dxdt=(DωPP1+P(A+Q)P1)x.

    Since Z is very small and in the expansion form P1 can be written as:

    P1=IZ+Z2+O(||Z||3).

    So, we have

    DωPP1+P(A+Q)P1=DωZ(IZ+Z2+O(||Z||3))+(I+Z)(A+Q)(IZ+Z2+O(||Z||3))=A+DωZ+[Z,A]+QDωZZ+[Z,Q]+AZ2ZAZ+O(||Z||3). (2.2)

    In general, we have to find a small Z in which the transformed system is still of the form dxdt=(A++Q+)x, where A+ is block-diagonal as A and Q+ is much smaller than Q.

    To do this, we have to calculate Z solving the following linearized equation

    DωZ[A,Z]=Q (2.3)

    where [A,Z]=AZZA and to prove

    Q+=DωZZ+[Z,Q]+AZ2ZAZ+O(||Z||3)

    is more smaller.

    In this subsection, we will solve the linearized equation, for this we need the following:

    Definition: Let u=(u1,,um)Tm. Its norm is denoted by ||u|| and is defined as:

    ||u||=max1im|ui|.

    Definition: For a m×m matrix S=(sij), its operator norm is denoted by ||S|| and is equivalent to m×max|sij|.

    Notation: Let FCωh(Tr×Λ,g) and its Fourier series is F=kZrFkeik,φ, then the kth Fourier coefficients of F denoted by Fk, given by Fk=Treik,φF(φ)dφ.

    Remark 2.1. For FCωh(Tr×Λ,g), we have

    |Fk||F|he|k|h.

    Note. For kZd, we denote |k|=dn=1|kn|. Similarly, for a function f, its modulus is denoted by |f|.

    Throughout the discussion, to simplify notations, the letters c,C denote different positive constants.

    By substituting the Fourier series expansions of Z,Q into the Eq (2.3), and then by equating the corresponding Fourier coefficients on both sides, we obtain

    ik,ωZk(AZkZkA)=Qk. (2.4)

    suppose that the eigenvalues of the linear operator ik,ωId+[A,] in the left part are

    ik,ω(αiαj),1i,jn,αi,αjσ(A).

    The eigenvalues will be αiαj for k=0. As the considered matrix A=diag(A1,,As) is a block-diagonal with different blocks Ai,Aj and each block have different eigenvalues, .i.e. αuβv if αuAi,βvAj for ij, from conclusions as seen from other researchers [12,17,18,19,20], we see that the matrix IliAjATiIlj is non-singular if ij.

    In block-diagonal form, let Qk can be written as (Qkij), where Qkij is a matrix of order li×lj, 1i,js and li,lj are the orders of matrices Ai,Aj respectively.

    Now, for k=0, we solve the equation (2.4). Suppose

    Qd0=(Q011,,Q0ss)

    and

    Q0=Q0Qd0.

    For k=0, the equation (2.4) can be written as

    AZ0Z0A=Q0 (2.5)

    Equation (2.5) can not be solved completely because the eigenvalues involved the multiplicity. However, the following equation

    AZ0Z0A=Q0

    has a solution Z0=(Z0ij) with Z0ii=0 and

    AiZ0ijZ0ijAj=Q0ij,forij

    has the unique solution Z0ij.

    Moreover, we have the estimate [12]

    ||J1ij(0,λ)||maxij||[IljAi(λ)ATj(λ)Ili]1||cnKliljϱliljC(ϱ,n)Klilj, (2.6)

    and

    max0lr||lλlJ1ij(0,λ)||=max1lr||lλl(adJijdetJij)||C(ϱ,n,r)K(lilj)2

    as dist(σ(Ai(λ)),σ(Aj(λ)))>ϱ>0, for ij. Moreover, we get

    max0lr||lλlZ0(λ)||Cmax0lr||lλl(J1ij(0,λ)||||lλlQ0(λ)||C(ϱ,n,r)Kn4max0lr||lλlQ0(λ)||. (2.7)

    Now, we solve the Eq (2.4) for k0. From Lemma 3.2 as seen in [12], the solution of (2.4) is equivalent to the solution of the following vector equation

    J(k,λ)Zk(λ)=Qk(λ) (2.8)

    By using corollaries [12], Eq (2.8) is solvable the matrix J(k,λ) is invertible. Suppose P=I+Zk is a L-P transformation. Then by using the L-P transformation, the new system becomes

    dxdt=(A++Q+)x

    where

    A+=A+Qd0Q+=DωZZ1+[Z,Q]+AZ2ZAZ+O(||Z||3) (2.9)

    Since A and Qd0 are block-diagonal matrices, therefore A+ is also a block-diagonal. Next, we will show that in a smaller domain Q+ is much smaller and the non-degeneracy condition is satisfied by A+.

    Estimation of Q+.

    First of all, we estimate Zk. Actually, to control the solution of Zk, we need the following small denominator condition, .i.e. if there exist N>0 such that i,j

    |gij(k,λ)|N1Δ(|k|),1i,js. (2.10)

    where Δ is an approximation function as defined above.

    In order to estimate Zk, we need to estimate the operator J1ij(k,λ) for k0.

    Lemma 2.1. For k0 and the small denominator conditions (2.10) are satisfied by all parameters λ, then we have

    ||J1ij(k,λ)||cKliljN(Δ(|k|))lilj,ij, (2.11)
    ||J1(k,λ)||cKn2αnNn2(Δ(|k|))n2, (2.12)
    max0lr||lλlJ1(k,λ)||cKn4α2rnN2rn2(Δ(|k|))2rn2. (2.13)

    where c denotes constant.

    Proof. Since Jij is a non-singular matrix, so its inverse is defined as J1ij=adJij/detJij. By the small denominator conditions (2.10), we have

    |J(k,λ)|=|[ik,ωIn2+(InA(λ)AT(λ)In)]|(N1)n2(α1Δ(|k|))n

    and

    |Jij(k,λ)|=|[ik,ωIlilj+(IliAj(λ)ATi(λ)Ilj)]|(N1)lilj(α1Δ(|k|))li

    using the definition of the norm ||Jij|| and the small denominator condition (2.10), the estimate (2.11)can be found easily. Also as detJ=1i,jsdetJij, similarly we can calculate the estimations (2.12) and (2.13).

    For k0, from Eq (2.8), we have

    Zk(λ)=J1(k,λ)Qk(λ) (2.14)

    as Zk,Qk are the transpose of Zk and Qk respectively, therefore it is easy to prove ||Zk||=||Zk||,||Qk||=||Qk|| (see in [12] for the proof).

    In our article, we represent F(λ) a λ-dependent matrix as:

    |F(λ)|=max0lr||lF(λ)λl||.

    Since QCωh(Tr×Λ,g), then by the Remark 2.1, we have

    |Qk||Q|he|k|h.

    As a result, for k0 and for any 0<ˉh<h, we have

    |Zk(λ)||J1(k,λ)||Qk(λ)|CKn4α2rnN2rn2(Δ(|k|))2rn2|Q|he|k|h

    or

    |Zk(λ)|CKn4α2rnN2rn2(Δ(|k|))2rn2|Q|he|k|(hˉh)e|k|ˉh. (2.15)

    In particular, take an approximation function Δ(t)=etδ,δ<1, which satisfy the Brjuno-Rüssmann condition (1.6), since the function etδ2rn2et(hˉh) has the maximal value at t=(2rn2δhˉh)1δ1, one has

    |Zk(λ)|CKn4α2rnN2rn2|Q|he[2rn2(2rn2δhˉh)δδ1(2rn2δhˉh)1δ1(hˉh)]e|k|ˉhC(n,r,δ,α)Kn4N2rn2[|Q|h(hˉh)δ2δ1δ1|Q|h(hˉh)δδ1]e|k|ˉh. (2.16)

    Consider

    Z(t,λ)=kZrZk(λ)eik,t

    choose h:0<h<ˉh s.t. if ˉhh=hh<1. So, using the Lemma 4 in [7], we obtain

    |Z|hkZr|Zk|e|k|hCKn4|Q0|+CKn4N2rn2[|Q|h(hˉh)δ2δ1δ1|Q|h(hˉh)δδ1]kZr{0}e(ˉhh)|k|CKn4N2rn2[|Q|h(hˉh)δ2δ1δ1|Q|h(hˉh)δδ1](2ˉhh)me(ˉhh)m2C(n,r,δ,α,m)Kn4N2rn2[1(hh)δ2δ1δ1+m1(hh)δδ1+m]|Q|h. (2.17)

    Let s=δ2δ1δ1+m, and s=δδ1+m, we get

    |Z|hCKn4N2rn2[1(hh)s1(hh)s]|Q|h. (2.18)

    similarly, we can find

    |DωZ|hCKn4N2rn2[1(hh)s+11(hh)s+1]|Q|h
    |DωZZ|hCKn4N2rn2[1(hh)2s+11(hh)2s+1]|Q|2h
    |AZ2|h=|ZAZ|hCKn4N2rn2[1(hh)2s1(hh)2s]|Q|2h
    |[Z,Q]|h2|Z|h|Q|hCKn4N2rn2[1(hh)s1(hh)s]|Q|2h

    Hence, from Eq (2.9), we get

    |Q+|hCK2n4+1N2r+1n2[1(hh)2s+11(hh)2s+1]|Q|2h. (2.19)

    Verification of the non-degeneracy conditions for A+.

    Since

    A+=A+Qd0=diag(A1+Q011,,As+Q0ss).

    Let

    D+ij(k,λ)=det[ik,ωIlilj+(Ili(Aj(λ)+Q0jj(λ))(ATi(λ)+QT0ii(λ))Ij)].

    The new determinant D+ij is analytic with respect to λ as well.

    The above determinant can be rewritten as

    D+ij(k,λ)=Dij(k,λ)+Yij(k,λ).

    where Dij(k,λ)=det[ik,ωIlilj+(IliAj(λ)ATi(λ))Ij)] and Yij(k,λ) is a summary of 2lilj1 determinants denoted by yt(k,λ)(1t2lilj1). Furthermore, there exist at least one column in each determinant yt such that the entries in this column are either 0 or of the form cd, where c and d are entries of Q0jj and Q0ii respectively.

    As |Qd0|h|Q|h<ε, we get

    |lλlD+ij(k,λ)|C|A|ε,for1lr.

    similarly,

    |lλl(g+ij(k,λ)gij(k,λ))|C|A|ε,for1lr. (2.20)

    So, we have

    |lλlg+ij(k,λ)|ςC|A|εςCKε=ς,for1lr. (2.21)

    The proof is obvious. Note that, here we only need to choose such ks so that |(k,λ)| is not large enough, .i.e., |(k,λ)|CK, where |A|K, because for large enough |(k,λ)|, the matrix J(k,λ) becomes automatically non-singular. So, when |(k,λ)| has large values, then J+(k,λ) becomes naturally non-singular and no need to preserve non-degenerate property.

    Alternatively, we know from the perturbation theory of matrices that the continuous change of eigenvalues depends on the entries, and by Ostrowski theorem (see [21]), the distance between eigenvalues of any two blocks can be estimated as

    minijdist(σ(A+i),σ(A+j))=ϱ+>ϱcε1n.

    Now, we summarize the above discussions in the following conclusion.

    Conclusion 1.

    Consider Λ subset of (a, b) be some parameter segment, a one parameter family of constant elements ACω(Λ,g), and QCωh(Tr×Λ,g) be the perturbation. Suppose that there exist K,ε,N>0 s.t.

    |A|K,|Q|h<ε,

    for all λΛ, the non-degeneracy conditions (1.11) and the small denominator conditions (2.10) hold.

    Then, h>0 and a map ZCωh(Tr×Λ,g), and

    A+Cω(Λ,g)
    Q+Cωh(Tr×Λ,g),

    such that

    1) A+=A+Qd0,A++Q+A+Q

    2) We have the estimation (2.19), .i.e. |Q+|hCK2n4+1N2r+1n2[1(hh)2s+11(hh)2s+1]|Q|2h.

    3) We have preserved the non-degeneracy conditions.i.e., max0lr|lλlg+ij(k,λ)|ς.

    4) ϱ+>ϱcε1n,K+<K+ε.

    In this subsection, we will prove that the perturbation Q goes to zero very quickly provided that the small divisor conditions hold.

    First of all, consider the following two iterative sequences:

    hm=(12+12m)h1, (2.22)
    Nm=((65)m+1ηhm1hm)γ=(h1)γ2mγ((65)m+1η)γ (2.23)

    where γr is a constant, and η will be considered as in the following lemma

    Lemma 2.2. There exist positive constants η<1,b, s.t., if ε1 is sufficiently small, then m1

    εmηbe(65)m,
    Km2m1K1.

    Proof. Suppose that if we do this up to mth step, we have

    |Qm|hmεmηbe(65)m

    and

    KmKm1+εm12m1K1.

    By induction, we need to prove that

    |Qm+1|hm+1ηbe(65)m+1 (2.24)

    and

    Km+12mK1. (2.25)

    Indeed Eq (2.25) is satisfied as

    Km+1Km+εmKm+ηbe(65)mKm+12Km22m1K1=2mK1.

    And from Eq (2.19), we have

    εm+1CK2n4+1mN2r+1n2m[1(hmhm+1)2s+11(hmhm+1)2s+1]ε2m.

    To prove Eq (2.24), we need

    CK2n4+1mN2r+1n2m[1(hmhm+1)2s+11(hmhm+1)2s+1]η2be(65)2mηbe(65)m+1.

    Then by using Eqs (2.22) and (2.25), we have

    CK2n4+11h(2s+1)12m(2n4+1)+(m+1)(2s+1)N2r+1n2mη2be(4/5)(65)m1. (2.26)

    Let Rm(η)=N2r+1n2mηb1, if we choose

    b>2r+1n2γ+1, (2.27)

    then by Eq (2.23) we see that for smaller value of η, the value of Rm also goes smaller. Now, firstly we set η=η0<1. As the sequence

    2m(2n4+1)+(m+1)(2s+1)+mrRm(η0)e(4/5)(65)m,

    is bounded from above, let's denote its maximum by ˉβ. In order to satisfy Eq (2.26), it is enough to choose η s.t.

    CK2n4+11h(2s+1)1ˉβη1.

    Thus, define

    ηmin{CK(2n4+1)1h2s+11ˉβ1,η0},

    and so we obtained the Eq (2.26). If we choose η=(10ε1)1/b, then it is enough to take

    ε1min{CKb(2n4+1)1hb(2s+1)110βb,ηbe65}. (2.28)

    Hence, the proof of lemma is finished.

    From Eq (2.18), it can be seen that the sequence |Zm|hm converges to 0 with super-exponential velocity, then by the transformation Pm=I+Zm, we have PmI, and so the composition of transformations PmPm1P1 will also be convergent. On the other hand, from conclusion 1, we have

    ςmςm1CKmεm,

    so

    ςmςC1im1Kiεiς2, (2.29)

    for small enough ε1. Thus, the preservation of the non-degeneracy conditions is proved. By the way, for small enough ε1, we also have the estimate

    ϱmϱC1im1ε1niϱ2. (2.30)

    In this subsection, we will show that the set of parameters satisfying the small denominator conditions is of the large Lebesgue measure. In the end, we estimate the measure of the removed parameter set. At the mth step, for i,j,1i,js, we denote the removed set as:

    Rmkij={λ:|gmij(k,λ)|N1mΔ(|k|)}

    and consider

    Rmk=1i,jsRmkij,
    Rm=0kZrRmk.

    To calculate the estimate for the measure of Rmkij, the following lemma is needed:

    Lemma 2.3. Consider g(x) is a CM function on the closure ˉI, where IR1 is an interval of length L. Let Ih={x:|g(x)|h,h>0}. If for some constant r>0,|g(M)(x)|r for xI, then |Ih|cLh1/M, where |Ih| denotes the Lebesgue measure of Ih and constant c=2(2+3++M+r1).

    For the proof, see [22].

    Then, let L denotes the length of the parameter interval Λ, and using above Lemma 2.3, we obtain

    mes(Rmkij)cL(N1mΔ(|k|))1/r

    where c=2(2+3++r+2/ς), as gmij(k,λ)Cm(Λ) and using the non-degeneracy conditions and Eq (2.30). Thus,

    mes(Rm)Cn2LN1rm0kZr(1Δ(|k|))1/r.

    For Δ(|k|)=e|k|δ,δ<1, we have

    mes(Rm)Cn2LN1rm0kZre|k|δ/rC(n,r,δ,ς)LN1rm.

    By Eq (2.23), Nm>2mγηγ, we have

    N1rmηγr12mγr.

    Therefore, for η=(10ε1)1b and γr, one has

    mes(m=1Rm)CLηγrm=12mγrCLηγrC(n,r,δ,ς,γ,ϱ)L(10ε1)c,where,c=γbr.

    Hence, the proof of the main result is completed.

    In this article, we discussed the positive measure reducibility for quasi-periodic linear systems and proved that the system (1.7) is reduced to a constant coefficient system. The result was proved for a Brjuno vector ω and small parameter λ by using the KAM method, Brjuno-Rüssmann condition and non-degeneracy condition.

    The authors extend their appreciation to the Yibin University, Yibin, China.

    The authors declare no conflicts of interest in this paper.


    Acknowledgments



    T.L. gratefully acknowledges support from the MIUR grant “Dipartimenti di Eccellenza 2018-2022” (Project no. E11G18000350001). F.R.M. gratefully acknowledges support from the RSE Saltire Early Career Fellowship ‘Multiscale mathematical modelling of spatial eco-evolutionary cancer dynamics’ (Fellowship No. 1879).

    Conflict of interest



    The authors declare no competing interests.

    [1] Chaplain MAJ, Giverso C, Lorenzi T, et al. (2019) Derivation and application of effective interface conditions for continuum mechanical models of cell invasion through thin membranes. SIAM J Appl Math 79: 2011-2031. https://doi.org/10.1137/19M124263X
    [2] Ranft J, Basan M, Elgeti J, et al. (2010) Fluidization of tissues by cell division and apoptosis. Proc Nat Acad Sci USA 107: 20863-20868. https://doi.org/10.1073/pnas.1011086107
    [3] Giverso C, Ciarletta P (2016) On the morphological stability of multicellular tumour spheroids growing in porous media. Eur Phys J E 39: 92. https://doi.org/10.1140/epje/i2016-16092-7
    [4] Bubba F, Perthame B, Pouchol C (2020) Hele–shaw limit for a system of two reaction-(cross-) diffusion equations for living tissues equations for living tissues. Arch Ration Mech Anal 236: 735-766. https://doi.org/10.1007/s00205-019-01479-1
    [5] David N, Ruan X (2022) An asymptotic preserving scheme for a tumor growth model of porous medium type. ESAIM Math Model Numer Anal 56: 121-150. https://doi.org/10.1051/m2an/2021080
    [6] Lorenzi T, Lorz A, Perthame B (2017) On interfaces between cell populations with different mobilities. Kinet Relat Mod 10: 299-311. https://doi.org/10.3934/krm.2017012
    [7] Bresch D, Colin T, Grenier E, et al. (2010) Computational modeling of solid tumor growth: the avascular stage. SIAM J Sci Comput 32: 2321-2344. https://doi.org/10.1137/070708895
    [8] Ciarletta P, Foret L, Ben Amar M (2011) The radial growth phase of malignant melanoma: multi-phase modelling, numerical simulations and linear stability analysis. J R Soc Interface 8: 345-368. https://doi.org/10.1098/rsif.2010.0285
    [9] Gallinato O, Colin T, Saut O, et al. (2017) Tumor growth model of ductal carcinoma: from in situ phase to stroma invasion. J Theor Biol 429: 253-266. https://doi.org/10.1016/j.jtbi.2017.06.022
    [10] Perthame B (2014) Some mathematical aspects of tumor growth and therapy. ICM 2014-International Congress of Mathematicians.
    [11] Lowengrub JS, Frieboes HB, Jin F, et al. (2009) Nonlinear modelling of cancer: bridging the gap between cells and tumours. Nonlinearity 23: R1. https://doi.org/10.1088/0951-7715/23/1/R01
    [12] Kuznetsov M, Clairambault J, Volpert V (2021) Improving cancer treatments via dynamical biophysical models. Phys Life Rev 39: 1-48. https://doi.org/10.1016/j.plrev.2021.10.001
    [13] Lorenzi T (2022) Cancer modelling as fertile ground for new mathematical challenges. Phys Life Rev 40: 3-5. https://doi.org/10.1016/j.plrev.2022.01.003
    [14] Anderson ARA (2007) A hybrid multiscale model of solid tumour growth and invasion: evolution and the microenvironment. Single-cell-based models in biology and medicine. Switzerland: Birkhäuser Basel 3-28.
    [15] Van Liedekerke P, Palm MM, Jagiella N, et al. (2015) Simulating tissue mechanics with agent-based models: concepts, perspectives and some novel results. Comput Part Mech 2: 401-444. https://doi.org/10.1007/s40571-015-0082-3
    [16] Inoue M (1991) Derivation of a porous medium equation from many Markovian particles and the propagation of chaos. Hiroshima Math J 21: 85-110. https://doi.org/10.32917/hmj/1206128924
    [17] Oelschläger K (1989) On the derivation of reaction-diffusion equations as limit dynamics of systems of moderately interacting stochastic processes. Probab Theory Relat Fields 82: 565-586. https://doi.org/10.1007/BF00341284
    [18] Penington CJ, Hughes BD, Landman KA (2011) Building macroscale models from microscale probabilistic models: a general probabilistic approach for nonlinear diffusion and multispecies phenomena. Phys Rev E 84: 041120. https://doi.org/10.1103/PhysRevE.84.041120
    [19] Chaplain MAJ, Lorenzi T, Macfarlane FR (2020) Bridging the gap between individual-based and continuum models of growing cell populations. J Math Biol 80: 343-371. https://doi.org/10.1007/s00285-019-01391-y
    [20] Lorenzi T, Murray PJ, Ptashnyk M (2020) From individual-based mechanical models of multicellular systems to free-boundary problems. Interface Free Bound 22: 205-244. https://doi.org/10.4171/IFB/439
    [21] Baker RE, Parker A, Simpson MJ (2019) A free boundary model of epithelial dynamics. J Theor Biol 481: 61-74. https://doi.org/10.1016/j.jtbi.2018.12.025
    [22] Oelschläger K (1990) Large systems of interacting particles and the porous medium equation. J Differ Equ 88: 294-346. https://doi.org/10.1016/0022-0396(90)90101-T
    [23] Murray PJ, Edwards CM, Tindall MJ, et al. (2009) From a discrete to a continuum model of cell dynamics in one dimension. Phys Rev E 80: 031912. https://doi.org/10.1103/PhysRevE.80.031912
    [24] Murray PJ, Edwards CM, Tindall MJ, et al. (2012) Classifying general nonlinear force laws in cell-based models via the continuum limit. Phys Rev E 85: 021921. https://doi.org/10.1103/PhysRevE.85.021921
    [25] Dyson L, Maini PK, Baker RE (2012) Macroscopic limits of individual-based models for motile cell populations with volume exclusion. Phys Rev E 86: 031903. https://doi.org/10.1103/PhysRevE.86.031903
    [26] Johnston ST, Baker RE, McElwain DL, et al. (2017) Co-operation, competition and crowding: a discrete framework linking Allee kinetics, nonlinear diffusion, shocks and sharp-fronted travelling waves. Sci Rep 7: 1-19. https://doi.org/10.1038/srep42134
    [27] Johnston ST, Simpson MJ, Baker RE (2012) Mean-field descriptions of collective migration with strong adhesion. Phys Rev E 85: 051922. https://doi.org/10.1103/PhysRevE.85.051922
    [28] Johnston ST, Simpson MJ, Baker RE (2015) Modelling the movement of interacting cell populations: a moment dynamics approach. J Theor Biol 370: 81-92. https://doi.org/10.1016/j.jtbi.2015.01.025
    [29] Deroulers C, Aubert M, Badoual M, et al. (2009) Modeling tumor cell migration: from microscopic to macroscopic models. Phys Rev E 79: 031917. https://doi.org/10.1103/PhysRevE.79.031917
    [30] Drasdo D (2005) Coarse graining in simulated cell populations. Adv Complex Syst 8: 319-363. https://doi.org/10.1142/S0219525905000440
    [31] Simpson MJ, Merrifield A, Landman KA, et al. (2007) Simulating invasion with cellular automata: Connecting cell-scale and population-scale properties. Phys Rev E 76: 021918. https://doi.org/10.1103/PhysRevE.76.021918
    [32] Ambrosi D, Preziosi L (2002) On the closure of mass balance models for tumor growth. Math Mod Meth Appl Sci 12: 737-754. https://doi.org/10.1142/S0218202502001878
    [33] Byrne HM, Chaplain MAJ (1997) Free boundary value problems associated with the growth and development of multicellular spheroids. Eur J Appl Math 8: 639-658. https://doi.org/10.1017/S0956792597003264
    [34] Byrne HM, Drasdo D (2009) Individual-based and continuum models of growing cell populations: a comparison. J Math Biol 58: 657. https://doi.org/10.1007/s00285-008-0212-0
    [35] Greenspan HP (1976) On the growth and stability of cell cultures and solid tumors. J Theor Biol 56: 229-242. https://doi.org/10.1016/S0022-5193(76)80054-9
    [36] Bru A, Albertos S, Subiza JL, et al. (2003) The universal dynamics of tumor growth. Biophys J 85: 2948-2961. https://doi.org/10.1016/S0006-3495(03)74715-8
    [37] Byrne HM, Preziosi L (2003) Modelling solid tumour growth using the theory of mixtures. Math Med Biol 20: 341-366. https://doi.org/10.1093/imammb/20.4.341
    [38] Drasdo D, Hoehme S (2012) Modeling the impact of granular embedding media, and pulling versus pushing cells on growing cell clones. New J Phys 14: 055025. https://doi.org/10.1088/1367-2630/14/5/055025
    [39] Brock A, Chang H, Huang S (2009) Non-genetic heterogeneity—A mutation-independent driving force for the somatic evolution of tumours. Nat Rev Genet 10: 336-342. https://doi.org/10.1038/nrg2556
    [40] Chisholm RH, Lorenzi T, Clairambault J (2016) Cell population heterogeneity and evolution towards drug resistance in cancer: biological and mathematical assessment, theoretical treatment optimisation. Biochim Biophys Acta Gen Subj 1860: N2627-2645. https://doi.org/10.1016/j.bbagen.2016.06.009
    [41] Huang S (2013) Genetic and non-genetic instability in tumor progression: Link between the fitness landscape and the epigenetic landscape of cancer cells. Cancer Metastasis Rev 32: 423-448. https://doi.org/10.1007/s10555-013-9435-7
    [42] Chisholm RH, Lorenzi T, Desvillettes L (2016) Evolutionary dynamics of phenotype-structured populations: From individual-level mechanisms to population-level consequences. Z Angew Math Phys 67: 1-34. https://doi.org/10.1007/s00033-016-0690-7
    [43] Hughes BD (1995) Random walks and random environments: random walks. UK: Oxford University Press.
    [44] Perthame B, Quiros F, Vazquez JL (2014) The Hele–Shaw asymptotics for mechanical models of tumor growth. Arch Ration Mech Anal 212: 93-127. https://doi.org/10.1007/s00205-013-0704-y
    [45] Basan M, Risler T, Joanny JF, et al. (2009) Homeostatic competition drives tumor growth and metastasis nucleation. HFSP J 3: 265-272. https://doi.org/10.2976/1.3086732
    [46] Novikov NM, Zolotaryova SY, Gautreau AM (2021) Mutational drivers of cancer cell migration and invasion. Br J Cancer 124: 102-114. https://doi.org/10.1038/s41416-020-01149-0
    [47] Alfonso JCL, Talkenberger K, Seifert M, et al. (2017) The biology and mathematical modelling of glioma invasion: a review. J R Soc Interface 14: 20170490. https://doi.org/10.1098/rsif.2017.0490
    [48] Giese A, Bjerkvig R, Berens ME, et al. (2003) Cost of migration: invasion of malignant gliomas and implications for treatment. J Clin Oncol 21: 1624-1636. https://doi.org/10.1200/JCO.2003.05.063
    [49] Yan M, Yang X, Shen R, et al. (2018) miR-146b promotes cell proliferation and increases chemosensitivity, but attenuates cell migration and invasion via FBXL10 in ovarian cancer. Cell Death Dis 9: 1-17. https://doi.org/10.1038/s41419-018-1093-9
    [50] Wang SD, Rath P, Lal B, et al. (2012) EphB2 receptor controls proliferation/migration dichotomy of glioblastoma by interacting with focal adhesion kinase. Oncogene 31: 5132-5143. https://doi.org/10.1038/onc.2012.16
    [51] Godlewski J, Bronisz A, Nowicki MO, et al. (2010) microRNA-451: A conditional switch controlling glioma cell proliferation and migration. Cell Cycle 9: 2814-2820. https://doi.org/10.4161/cc.9.14.12248
    [52] Aktipis CA, Boddy AM, Gatenby RA, et al. (2013) Life history trade-offs in cancer evolution. Nat Rev Cancer 13: 883. https://doi.org/10.1038/nrc3606
    [53] Gallaher JA, Brown JS, Anderson ARA (2019) The impact of proliferation-migration tradeoffs on phenotypic evolution in cancer. Sci Rep 9: 1-10. https://doi.org/10.1038/s41598-019-39636-x
    [54] Gerlee P, Anderson ARA (2009) Evolution of cell motility in an individual-based model of tumour growth. J Theor Biol 259: 67-83. https://doi.org/10.1016/j.jtbi.2009.03.005
    [55] Gerlee Pand Nelander S.The impact of phenotypic switching on glioblastoma growth and invasion. PLoS Comput Biol (2012) 8: e1002556. https://doi.org/10.1371/journal.pcbi.1002556
    [56] Giese A, Loo MA, Tran N, et al. (1996) Dichotomy of astrocytoma migration and proliferation. Int J Cancer 67: 275-282.
    [57] Hatzikirou H, Basanta D, Simon M, et al. (2012) ‘Go or Grow’: the key to the emergence of invasion in tumour progression?. Math Med Biol 29: 49-65. https://doi.org/10.1093/imammb/dqq011
    [58] Orlando PA, Gatenby RA, Brown JS (2013) Tumor evolution in space: The effects of competition colonization tradeoffs on tumor invasion dynamics. Front Oncol 3: 45. https://doi.org/10.3389/fonc.2013.00045
    [59] Pham K, Chauviere A, Hatzikirou H, et al. (2012) Density-dependent quiescence in glioma invasion: Instability in a simple reaction–diffusion model for the migration/proliferation dichotomy. J Biol Dyn 6: 54-71. https://doi.org/10.1080/17513758.2011.590610
    [60] Tang M, Vauchelet N, Cheddadi I, et al. (2013) Composite waves for a cell population system modeling tumor growth and invasion. Chinese Ann Math Ser B 34: 295-318. https://doi.org/10.1007/s11401-013-0761-4
    [61] Ardaseva A, Anderson ARA, Gatenby RA, et al. (2020) Comparative study between discrete and continuum models for the evolution of competing phenotype-structured cell populations in dynamical environments. Phys Rev E 102: 042404. https://doi.org/10.1103/PhysRevE.102.042404
    [62] Bubba F, Lorenzi T, Macfarlane FR (2020) From a discrete model of chemotaxis with volume-filling to a generalized Patlak–Keller–Segel model. Proc R Soc A 476: 20190871. https://doi.org/10.1098/rspa.2019.0871
    [63] Macfarlane FR, Chaplain MAJ, Lorenzi T (2020) A hybrid discrete-continuum approach to model Turing pattern formation. Math Biosci Eng 17: 7442-7479. https://doi.org/10.3934/mbe.2020381
    [64] Stace REA, Stiehl T, Chaplain MAJ, et al. (2020) Discrete and continuum phenotype-structured models for the evolution of cancer cell populations under chemotherapy. Math Mod Nat Phen 15: 14. https://doi.org/10.1051/mmnp/2019027
    [65] Smith JT, Tomfohr JK, Wells MC, et al. (2004) Measurement of cell migration on surface-bound fibronectin gradients. Langmuir 20: 8279-8286. https://doi.org/10.1021/la0489763
    [66] Lorenzi T, Perthame B, Ruan X (2021) Invasion fronts and adaptive dynamics in a model for the growth of cell populations with heterogeneous mobility. Eur J Appl Math 2021: 1-18. https://doi.org/10.1017/S0956792521000218
    [67] Lorenzi T, Painter KJ (2022) Trade-offs between chemotaxis and proliferation shape the phenotypic structuring of invading waves. Int J Non Linear Mech 139: 103885. https://doi.org/10.1016/j.ijnonlinmec.2021.103885
    [68] Corcoran A, Del Maestro RF (2003) Testing the “go or grow” hypothesis in human medulloblastoma cell lines in two and three dimensions. Neurosurgery 53: 174-185. https://doi.org/10.1227/01.NEU.0000072442.26349.14
    [69] Hoek KS, Eichhoff OM, Schlegel NC, et al. (2008) In vivo switching of human melanoma cells between proliferative and invasive states. Cancer Res 68: 650-656. https://doi.org/10.1158/0008-5472.CAN-07-2491
    [70] Stepien TL, Rutter EM, Kuang Y (2018) Traveling waves of a go-or-grow model of glioma growth. SIAM J Appl Math 78: 1778-1801. https://doi.org/10.1137/17M1146257
    [71] Vittadello ST, McCue SW, Gunasingh G, et al. (2020) Examining go-or-grow using fluorescent cell-cycle indicators and cell-cycle-inhibiting drugs. Biophys J 118: 1243-1247. https://doi.org/10.1016/j.bpj.2020.01.036
    [72] Zhigun A, Surulescu C, Hunt A (2018) A strongly degenerate diffusion-haptotaxis model of tumour invasion under the go-or-grow dichotomy hypothesis. Math Methods Appl Sci 41: 2403-2428. https://doi.org/10.1002/mma.4749
    [73] Dhruv HD, McDonough Winslow WS, Armstrong B, et al. (2013) Reciprocal activation of transcription factors underlies the dichotomy between proliferation and invasion of glioma cells. PLoS One 8: e72134. https://doi.org/10.1371/journal.pone.0072134
    [74] Xie Q, Mittal S, Berens ME (2014) Targeting adaptive glioblastoma: an overview of proliferation and invasion. Neuro-Oncol 16: 1575-1584. https://doi.org/10.1093/neuonc/nou147
    [75] Simpson MJ, Baker RE, Buenzli PR, et al. (2022) Reliable and efficient parameter estimation using approximate continuum limit descriptions of stochastic models.. https://doi.org/10.1101/2022.02.02.478913
    [76] Nardini JT, Baker RE, Simpson MJ, et al. (2021) Learning differential equation models from stochastic agent-based model simulations. J Roy Soc Interface 18: 20200987. https://doi.org/10.1098/rsif.2020.0987
    [77] Simpson MJ, Sharp JA, Baker RE (2014) Distinguishing between mean-field, moment dynamics and stochastic descriptions of birth–death–movement processes. Phys A Stat Mech Appl 395: 236-246. https://doi.org/10.1016/j.physa.2013.10.026
    [78] Johnston ST, Simpson MJ, Crampin EJ (2020) Predicting population extinction in lattice-based birth–death–movement models. Proc Roy Soc A 476: 20200089. https://doi.org/10.1098/rspa.2020.0089
    [79] Powathil GG, Swat M, Chaplain MAJ (2015) Systems oncology: towards patient-specific treatment regimes informed by multiscale mathematical modelling. Sem Cancer Biol 30: 13-20. https://doi.org/10.1016/j.semcancer.2014.02.003
    [80] Rejniak KA, Anderson ARA (2011) Hybrid models of tumor growth. Wiley Interdiscip Rev Syst Biol Med 3: 115-125. https://doi.org/10.1002/wsbm.102
    [81] Jafari Nivlouei S, Soltani M, Carvalho J, et al. (2021) Multiscale modeling of tumor growth and angiogenesis: Evaluation of tumor-targeted therapy. PLoS Comp Biol 17: e1009081. https://doi.org/10.1371/journal.pcbi.1009081
    [82] Jafari Nivlouei S, Soltani M, Shirani E, et al. (2022) A multiscale cell-based model of tumor growth for chemotherapy assessment and tumor-targeted therapy through a 3D computational approach. Cell Prolif 2022: e13187. https://doi.org/10.1111/cpr.13187
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