Research article Special Issues

Nonlocal multiscale modelling of tumour-oncolytic viruses interactions within a heterogeneous fibrous/non-fibrous extracellular matrix


  • Received: 17 December 2021 Revised: 20 February 2022 Accepted: 07 March 2022 Published: 14 April 2022
  • In this study we investigate computationally tumour-oncolytic virus (OV) interactions that take place within a heterogeneous extracellular matrix (ECM). The ECM is viewed as a mixture of two constitutive phases, namely a fibre phase and a non-fibre phase. The multiscale mathematical model presented here focuses on the nonlocal cell-cell and cell-ECM interactions, and how these interactions might be impacted by the infection of cancer cells with the OV. At macroscale we track the kinetics of cancer cells, virus particles and the ECM. At microscale we track (i) the degradation of ECM by matrix degrading enzymes (MDEs) produced by cancer cells, which further influences the movement of tumour boundary; (ii) the re-arrangement of the microfibres that influences the re-arrangement of macrofibres (i.e., fibres at macroscale). With the help of this new multiscale model, we investigate two questions: (i) whether the infected cancer cell fluxes are the result of local or non-local advection in response to ECM density; and (ii) what is the effect of ECM fibres on the the spatial spread of oncolytic viruses and the outcome of oncolytic virotherapy.

    Citation: Abdulhamed Alsisi, Raluca Eftimie, Dumitru Trucu. Nonlocal multiscale modelling of tumour-oncolytic viruses interactions within a heterogeneous fibrous/non-fibrous extracellular matrix[J]. Mathematical Biosciences and Engineering, 2022, 19(6): 6157-6185. doi: 10.3934/mbe.2022288

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  • In this study we investigate computationally tumour-oncolytic virus (OV) interactions that take place within a heterogeneous extracellular matrix (ECM). The ECM is viewed as a mixture of two constitutive phases, namely a fibre phase and a non-fibre phase. The multiscale mathematical model presented here focuses on the nonlocal cell-cell and cell-ECM interactions, and how these interactions might be impacted by the infection of cancer cells with the OV. At macroscale we track the kinetics of cancer cells, virus particles and the ECM. At microscale we track (i) the degradation of ECM by matrix degrading enzymes (MDEs) produced by cancer cells, which further influences the movement of tumour boundary; (ii) the re-arrangement of the microfibres that influences the re-arrangement of macrofibres (i.e., fibres at macroscale). With the help of this new multiscale model, we investigate two questions: (i) whether the infected cancer cell fluxes are the result of local or non-local advection in response to ECM density; and (ii) what is the effect of ECM fibres on the the spatial spread of oncolytic viruses and the outcome of oncolytic virotherapy.



    Over the last two decades, many researches used LKF method to get stability results for time-delay systems [1,2]. The LKF method has two important technical steps to reduce the conservatism of the stability conditions. The one is how to construct an appropriate LKF, and the other is how to estimate the derivative of the given LKF. For the first one, several types of LKF are introduced, such as integral delay partitioning method based on LKF [3], the simple LKF [4,5], delay partitioning based LKF [6], polynomial-type LKF [7], the augmented LKF [8,9,10]. The augmented LKF provides more freedom than the simple LKF in the stability criteria because of introducing several extra matrices. The delay partitioning based LKF method can obtain less conservative results due to introduce several extra matrices and state vectors. For the second step, several integral inequalities have been widely used, such as Jensen inequality [11,12,13,14], Wirtinger inequality [15,16], free-matrix-based integral inequality [17], Bessel-Legendre inequalities [18] and the further improvement of Jensen inequality [19,20,21,22,23,24,25]. The further improvement of Jensen inequality [22] is less conservative than other inequalities. However, The interaction between the delay partitioning method and the further improvement of Jensen inequality [23] was not considered fully, which may increase conservatism. Thus, there exists room for further improvement.

    This paper further researches the stability of distributed time-delay systems and aims to obtain upper bounds of time-delay. A new LKF is introduced via the delay partitioning method. Then, a less conservative stability criterion is obtained by using the further improvement of Jensen inequality [22]. Finally, an example is provided to show the advantage of our stability criterion. The contributions of our paper are as follows:

    The integral inequality in [23] is more general than previous integral inequality. For r=0,1,2,3, the integral inequality in [23] includes those in [12,15,21,22] as special cases, respectively.

    An augmented LKF which contains general multiple integral terms is introduced to reduce the conservatism via a generalized delay partitioning approach. For example, the tt1mhx(s)ds, tt1mhtu1x(s)dsdu1, , tt1mhtu1tuN1x(s)dsduN1du1 are added as state vectors in the LKF, which may reduce the conservatism.

    In this paper, a new LKF is introduced based on the delay interval [0,h] is divided into m segments equally. From the LKF, we can conclude that the relationship among x(s), x(s1mh) and x(sm1mh) is considered fully, which may yield less conservative results.

    Notation: Throughout this paper, Rm denotes m-dimensional Euclidean space, A denotes the transpose of the A matrix, 0 denotes a zero matrix with appropriate dimensions.

    Consider the following time-delay system:

    ˙x(t)=Ax(t)+B1x(th)+B2tthx(s)ds, (2.1)
    x(t)=Φ(t),t[h,0], (2.2)

    where x(t)Rn is the state vector, A,B1,B2Rn×n are constant matrices. h>0 is a constant time-delay and Φ(t) is initial condition.

    Lemma2.1. [23] For any matrix R>0 and a differentiable function x(s),s[a,b], the following inequality holds:

    ba˙xT(s)R˙x(s)dsrn=0ρnbaΦn(a,b)TRΦn(a,b), (2.3)

    where

    ρn=(nk=0cn,kn+k+1)1,
    cn,k={1,k=n,n0,n1t=kf(n,t)ct,k,k=0,1,n1,n1,
    Φl(a,b)={x(b)x(a),l=0,lk=0cl,kx(b)cl,0x(a)lk=1cl,kk!(ba)kφk(a,b)x(t),l1,
    f(l,t)=tj=0ct,jl+j+1/tj=0ct,jt+j+1,
    φk(a,b)x(t)={bax(s)ds,k=1,babs1bsk1x(sk)dskds2dss1,k>1.

    Remark2.1. The integral inequality in Lemma 2.1 is more general than previous integral inequality. For r=0,1,2,3, the integral inequality (2.3) includes those in [12,15,21,22] as special cases, respectively.

    Theorem3.1. For given integers m>0,N>0, scalar h>0, system (2.1) is asymptotically stable, if there exist matrices P>0, Q>0, Ri>0,i=1,2,,m, such that

    Ψ=ξT1Pξ2+ξT2Pξ1+ξT3Qξ3ξT4Qξ4+mi=1(hm)2ATdRiAdmi=1rn=0ρnωn(timh,ti1mh)Ri×ωn(timh,ti1mh)<0, (3.1)

    where

    ξ1=[eT1ˉET0ˉET1ˉET2ˉETN]T,
    ξ2=[ATdET0ET1ET2ETN]T,
    ξ3=[eT1eT2eTm]T,
    ξ4=[eT2eT3eTm+1]T,
    ˉE0=hm[eT2eT3eTm+1]T,
    ˉEi=hm[eTim+2eTim+3eTim+m+1]T,i=1,2,,N,
    Ei=hm[eT1eTim+2eT2eTim+3eTmeTm(i+1)+1]T,i=0,1,2,,N,
    Ad=Ae1+B1em+1+B2mi=0em+1+i,
    ωn(timh,ti1mh)={eiei+i,n=0,nk=0cn,keicn,0ei+1nk=1cn,kk!e(k1)m+k+1,n1,
    ei=[0n×(i1)nIn×n0n×(Nm+1i)]T,i=1,2,,Nm+1.

    Proof. Let an integer m>0, [0,h] can be decomposed into m segments equally, i.e., [0,h]=mi=1[i1mh,imh]. The system (2.1) is transformed into

    ˙x(t)=Ax(t)+B1x(th)+B2mi=1ti1mhtimhx(s)ds. (3.2)

    Then, a new LKF is introduced as follows:

    V(xt)=ηT(t)Pη(t)+tthmγT(s)Qγ(s)ds+mi=1hmi1mhimhtt+v˙xT(s)Ri˙x(s)dsdv, (3.3)

    where

    η(t)=[xT(t)γT1(t)γT2(t)γTN(t)]T,
    γ1(t)=[tt1mhx(s)dst1mht2mhx(s)dstm1mhthx(s)ds],γ2(t)=mh[tt1mhtu1x(s)dsdu1t1mht2mht1mhu1x(s)dsdu1tm1mhthtm1mhu1x(s)dsdu1],,
    γN(t)=(mh)N1×[tt1mhtu1tuN1x(s)dsduN1du1t1mht2mht1mhu1t1mhuN1x(s)dsduN1du1tm1mhthtm1mhu1tm1mhuN1x(s)dsduN1du1],
    γ(s)=[x(s)x(s1mh)x(sm1mh)].

    The derivative of V(xt) is given by

    ˙V(xt)=2ηT(t)P˙η(t)+γT(t)Qγ(t)γT(thm)Qx(thm)+mi=1(hm)2˙xT(t)Ri˙x(t)mi=1hmti1mhtimh˙xT(s)Ri˙x(s)ds.

    Then, one can obtain

    ˙V(xt)=ϕT(t){ξT1Pξ2+ξT2Pξ1+ξT3Qξ3ξT4Qξ4+mi=1(hm)2ATdRiAd}ϕ(t)mi=1hmti1mhtimh˙xT(s)Ri˙x(s)ds, (3.4)
    ϕ(t)=[xT(t)γT0(t)γT1(t)γTN(t)]T,
    γ0(t)=[xT(t1mh)xT(t2mh)xT(th)]T.

    By Lemma 2.1, one can obtain

    hmti1mhtimh˙xT(s)Ri˙x(s)dsrl=0ρlωl(timh,ti1mh)Ri×ωl(timh,ti1mh). (3.5)

    Thus, we have ˙V(xt)ϕT(t)Ψϕ(t) by (3.4) and (3.5). We complete the proof.

    Remark3.1. An augmented LKF which contains general multiple integral terms is introduced to reduce the conservatism via a generalized delay partitioning approach. For example, the tt1mhx(s)ds, tt1mhtu1x(s)dsdu1, , tt1mhtu1tuN1x(s)dsduN1du1 are added as state vectors in the LKF, which may reduce the conservatism.

    Remark3.2. For r=0,1,2,3, the integral inequality (3.5) includes those in [12,15,21,22] as special cases, respectively. This may yield less conservative results. It is worth noting that the number of variables in our result is less than that in [23].

    Remark3.3. Let B2=0, the system (2.1) can reduces to system (1) with N=1 in [23]. For m=1, the LKF in this paper can reduces to LKF in [23]. So the LKF in our paper is more general than that in [23].

    This section gives a numerical example to test merits of our criterion.

    Example 4.1. Consider system (2.1) with m=2,N=3 and

    A=[011001],B1=[0.00.10.10.2],B2=[0000].

    Table 1 lists upper bounds of h by our methods and other methods in [15,20,21,22,23]. Table 1 shows that our method is more effective than those in [15,20,21,22,23]. It is worth noting that the number of variables in our result is less than that in [23]. Furthermore, let h=1.141 and x(0)=[0.2,0.2]T, the state responses of system (1) are given in Figure 1. Figure 1 shows the system (2.1) is stable.

    Table 1.  hmax for different methods.
    Methods hmax NoDv
    [15] 0.126 16
    [20] 0.577 75
    [21] 0.675 45
    [22] 0.728 45
    [23] 0.752 84
    Theorem 3.1 1.141 71
    Theoretical maximal value 1.463

     | Show Table
    DownLoad: CSV
    Figure 1.  The state trajectories of the system (2.1) of Example 4.1.

    In this paper, a new LKF is introduced via the delay partitioning method. Then, a less conservative stability criterion is obtained by using the further improvement of Jensen inequality. Finally, an example is provided to show the advantage of our stability criterion.

    This work was supported by Basic Research Program of Guizhou Province (Qian Ke He JiChu[2021]YiBan 005); New Academic Talents and Innovation Program of Guizhou Province (Qian Ke He Pingtai Rencai[2017]5727-19); Project of Youth Science and Technology Talents of Guizhou Province (Qian Jiao He KY Zi[2020]095).

    The authors declare that there are no conflicts of interest.



    [1] T. Rozario, D. W. DeSimone, The extracellular matrix in development and morphogenesis: a dynamic view, Dev. Biol., 341 (2010), 126–140. https://doi.org/10.1016/j.ydbio.2009.10.026 doi: 10.1016/j.ydbio.2009.10.026
    [2] B. Yue, Biology of the extracellular matrix: an overview, J. Galucoma, 23 (2015), S20–S23. https://doi.org/10.1097/IJG.0000000000000108 doi: 10.1097/IJG.0000000000000108
    [3] V. Gkretsi, T. Stylianopoulos, Cell adhesion and matrix stiffness: coordinating cancer cell invasion and metastasis, Front. Oncol., 8 (2018), 145. https://doi.org/10.3389/fonc.2018.00145 doi: 10.3389/fonc.2018.00145
    [4] C. Fountzilas, S. Patel, D. Mahalingam, Review: oncolytic virotherapy, updates and future directions, Oncotarget, 8 (2017), 102617–102639.
    [5] H. L. Kaufman, F. J. Kohlhapp, A. Zloza, Oncolytic viruses: a new class of immunotherapy drugs, Nat. Rev. Drug Discov., 14 (2015), 642–662. https://doi.org/10.1038/nrd4663 doi: 10.1038/nrd4663
    [6] J. Pol, G. Kroemer, L. Galluzzi, First oncolytic virus approved for melanoma immunotherapy, Oncoimmunology, 5 (2016), e1115641. https://doi.org/10.1080/2162402X.2015.1115641 doi: 10.1080/2162402X.2015.1115641
    [7] S. J. Russell, K. W. Peng, J. C. Bell, Oncolytic virotherapy, Nat. Biotechnol., 30 (2012), 658–670. https://doi.org/10.1038/nbt.2287 doi: 10.1038/nbt.2287
    [8] J. Wojton, B. Kaur, Impact of tumor microenvironment on oncolytic viral therapy, Cytokine Growth Factor Rev., 21 (2010), 127–134. https://doi.org/10.1016/j.cytogfr.2010.02.014 doi: 10.1016/j.cytogfr.2010.02.014
    [9] N. J. Armstrong, K. J. Painter, J. A. Sherratt, A continuum approach to modelling cell-cell adhesion, J. Theor. Biol., 243 (2006), 98–113. https://doi.org/10.1016/j.jtbi.2006.05.030 doi: 10.1016/j.jtbi.2006.05.030
    [10] A. Gerisch, M. A. Chaplain, Mathematical modelling of cancer cell invasion of tissue: local and non-local models and the effect of adhesion, J. Theor. Biol., 250 (2008), 684–704. https://doi.org/10.1016/j.jtbi.2007.10.026 doi: 10.1016/j.jtbi.2007.10.026
    [11] P. Domschke, D. Trucu, A. Gerisch, M. A. J. Chaplain, Mathematical modelling of cancer invasion: implications of cell adhesion variability for tumour infiltrative growth patterns, J. Theor. Biol., 361 (2014), 41–60. https://doi.org/10.1016/j.jtbi.2014.07.010 doi: 10.1016/j.jtbi.2014.07.010
    [12] J. J. Crivelli, J. Földes, P. S. Kim, J. R. Wares, A mathematical model for cell cycle-specific cancer virotherapy, J. Biol. Dyn., 6 (2012), 104–120. https://doi.org/10.1080/17513758.2011.613486 doi: 10.1080/17513758.2011.613486
    [13] R. Eftimie, J. Dushoff, B. W. Bridle, J. L. Bramson, D. J. Earn, Multi-stability and multi-instability phenomena in a mathematical model of tumor-immune-virus interactions, Bull. Math. Biol., 73 (2011), 2932–2961. https://doi.org/10.1007/s11538-011-9653-5 doi: 10.1007/s11538-011-9653-5
    [14] R. Eftimie, C. K. MacNamara, J. Dushoff, J. L. Bramson, D. J. Earn, Bifurcations and chaotic dynamics in a tumour-immune-virus system, Math. Model. Nat. Phenom., 11 (2016), 65–85. https://doi.org/10.1051/mmnp/201611505 doi: 10.1051/mmnp/201611505
    [15] J. L. Gevertz, J. R. Wares, Developing a minimally structured mathematical model of cancer treatment with oncolytic viruses and dendritic cell injections, Comput. Math. Methods Med., 2018 (2018), 1–14. https://doi.org/10.1155/2018/8760371 doi: 10.1155/2018/8760371
    [16] J. P. W. Heidbuechel, D. Abate-Daga, C. E. Engeland, H. Enderling, Mathematical modeling of oncolytic virotherapy, in Oncolytic Viruses, Humana, New York, (2020), 307–320. https://doi.org/10.1007/978-1-4939-9794-7_21
    [17] M. A. Nowak, R. M. May, Virus Dynamics: Mathematical Principles of Immunology and Virology, Oxford University Press, Oxford, 2000.
    [18] D. Wodarz, Computational modeling approaches to the dynamics of oncolytic viruses, Wiley Interdiscip. Rev. Syst. Biol. Med., 8 (2016), 242–252. https://doi.org/10.1002/wsbm.1332 doi: 10.1002/wsbm.1332
    [19] D. R. Berg, C. P. Offord, I. Kemler, M. K. Ennis, L. Chang, G. Paulik, et al., In vitro and in silico multidimensional modeling of oncolytic tumor virotherapy dynamics, PLOS Comput. Biol., 15 (2019), e1006773. https://doi.org/10.1371/journal.pcbi.1006773 doi: 10.1371/journal.pcbi.1006773
    [20] K. Jacobsen, S. S. Pilyugin, Analysis of a mathematical model for tumor therapy with a fusogenic oncolytic virus, Math. Biosci., 270 (2015), 169–182. https://doi.org/10.1016/j.mbs.2015.02.009 doi: 10.1016/j.mbs.2015.02.009
    [21] J. Malinzi, P. Sibanda, H. Mambili-Mamboundou, Analysis of virotherapy in solid tumor invasion, Math. Biosci., 263 (2015), 102–110. https://doi.org/10.1016/j.mbs.2015.01.015 doi: 10.1016/j.mbs.2015.01.015
    [22] Y. Tao, M. Winkler, Global classical solutions to a doubly haptotactic cross-diffusion system modeling oncolytic virotherapy, J. Differ. Equation, 268 (2020), 4973–4997. https://doi.org/10.1016/j.jde.2019.10.046 doi: 10.1016/j.jde.2019.10.046
    [23] D. Wodarz, A. Hofacre, J. W. Lau, Z. Sun, H. Fan, N. L. Komarova, Complex spatial dynamics of oncolytic viruses in vitro: mathematical and experimental approaches, PLoS Comput. Biol., 8 (2012), e1002547. https://doi.org/10.1371/journal.pcbi.1002547 doi: 10.1371/journal.pcbi.1002547
    [24] A. Alsisi, R. Eftimie, D. Trucu, Non-local multiscale approaches for tumour-oncolytic viruses interactions, Math. Appl. Sci. Eng., 1 (2020), 249–273. https://doi.org/10.5206/mase/10773 doi: 10.5206/mase/10773
    [25] A. Alsisi, R. Eftimie, D. Trucu, Non-local multiscale approach for the impact of go or grow hypothesis on tumour-viruses interactions, Math. Biosci. Eng., 18 (2021), 5252–5284. https://doi.org/10.3934/mbe.2021267 doi: 10.3934/mbe.2021267
    [26] T. Alzahrani, R. Eftimie, D. Trucu, Multiscale modelling of cancer response to oncolytic viral therapy, Math. Biosci., 310 (2019), 76–95. https://doi.org/10.1016/j.mbs.2018.12.018 doi: 10.1016/j.mbs.2018.12.018
    [27] T. Alzahrani, R. Eftimie, D. Trucu, Multiscale moving boundary modelling of cancer interactions with a fusogenic oncolytic virus: the impact of syncytia dynamics, Math. Biosci., 323 (2020), 108296. https://doi.org/10.1016/j.mbs.2019.108296 doi: 10.1016/j.mbs.2019.108296
    [28] L. R. Paiva, C. Binny, S. C. Ferreira, M. L. Martins, A Multiscale mathematical model for oncolytic virotherapy, Cancer Res., 69 (2009), 1205–1211. https://doi.org/10.1158/0008-5472.CAN-08-2173 doi: 10.1158/0008-5472.CAN-08-2173
    [29] L. R. Paiva, H. S. Silva, S. C. Ferreira, M. L. Martins, Multiscale model for the effects of adaptive immunity suppression on the viral therapy of cancer, Phys. Biol., 10 (2013), 025005. https://doi.org/10.1088/1478-3975/10/2/025005 doi: 10.1088/1478-3975/10/2/025005
    [30] D. Trucu, P. Lin, M. A. J. Chaplain, Y. Wang, A multiscale moving boundary model arising in cancer invasion, Multiscale Model. Simul., 11 (2013), 309–335. https://doi.org/10.1137/110839011 doi: 10.1137/110839011
    [31] R. Shuttleworth, D. Trucu, Multiscale modelling of fibres dynamics and cell adhesion within moving boundary cancer invasion, Bull. Math. Biol., 81 (2019), 2176–2219. https://doi.org/10.1007/s11538-019-00598-w doi: 10.1007/s11538-019-00598-w
    [32] N. Bhagavathula, A. W. Hanosh, K. C. Nerusu, H. Appelman, S. Chakrabarty, J. Varani, Regulation of E-cadherin and β-catenin by Ca2+ in colon carcinoma is dependent on calcium-sensing receptor expression and function, Int. J. Cancer, 121 (2007), 1455–1462. https://doi.org/10.1002/ijc.22858 doi: 10.1002/ijc.22858
    [33] U. Cavallaro, G. Christofori, Cell adhesion in tumor invasion and metastasis: loss of the glue is not enough, Biochim. Biophys. Acta Rev. Cancer, 1552 (2001), 39–45. https://doi.org/10.1016/S0304-419X(01)00038-5 doi: 10.1016/S0304-419X(01)00038-5
    [34] J. D. Humphries, A. Byron, M. J. Humphries, Integrin ligands at a glance, J. Cell Sci., 119 (2006), 3901–3903. https://doi.org/10.1242/jcs.03098 doi: 10.1242/jcs.03098
    [35] K. S. Ko, P. D. Arora, V. Bhide, A. Chen, C. A. McCulloch, Cell-cell adhesion in human fibroblasts requires calcium signaling, J. Cell Sci., 114 (2001), 1155–1167. https://doi.org/10.1242/jcs.114.6.1155 doi: 10.1242/jcs.114.6.1155
    [36] B. P. L. Wijnhoven, W. N. M. Dinjens, M. Pignatelli, E-cadherin-catenin cell-cell adhesion complex and human cancer, Br. J. Surg., 87 (2000), 992–1005. https://doi.org/10.1046/j.1365-2168.2000.01513.x doi: 10.1046/j.1365-2168.2000.01513.x
    [37] M. Chaplain, G. Lolas, Mathematical modelling of cancer invasion of tissue: dynamic heterogeneity, Networks Heterog. Media, 1 (2006), 399–439. https://doi.org/10.3934/nhm.2006.1.399 doi: 10.3934/nhm.2006.1.399
    [38] Z. Gu, F. Liu, E. A. Tonkova, S. Y. Lee, D. J. Tschumperlin, M. B. Brenner, Soft matrix is a natural stimulator for cellular invasiveness, Mol. Biol. Cell, 25 (2014), 457–469. https://doi.org/10.1091/mbc.e13-05-0260 doi: 10.1091/mbc.e13-05-0260
    [39] A. M. Hofer, S. Curci, M. A. Doble, E. M. Brown, D. I. Soybel, Intercellular communication mediated by the extracellular calcium-sensing receptor, Nat. Cell Biol., 2 (2000), 392–398. https://doi.org/10.1038/35017020 doi: 10.1038/35017020
    [40] D. Hanahan, R. A. Weinberg, Hallmarks of cancer: the next generation, Cell, 144 (2011), 646–674. https://doi.org/10.1016/j.cell.2011.02.013 doi: 10.1016/j.cell.2011.02.013
    [41] R. A. Weinberg, The Biology of Cancer, Garland Science, New York, 2006.
    [42] D. Trucu, P. Domschke, A. Gerisch. M. Chaplain, Multiscale computational modelling and analysis of cancer invasion, in Mathematical Models and Methods for Living Systems (eds. L. Preziosi, M. A. J. Chaplain and A. Pugliese), Springer, Cham, (2016), 275–321. https://doi.org/10.1007/978-3-319-42679-2_5
    [43] F. Sabeh, R. Shimizu-Hirota, S. J. Weiss, Protease-dependent versus -independent cancer cell invasion programs: three-dimensional amoeboid movement revisited, J. Cell Biol., 185 (2009), 11–19. https://doi.org/10.1083/jcb.200807195 doi: 10.1083/jcb.200807195
    [44] K. Wolf, S. Alexander, V. Schacht, L. M. Coussens, U. H. von Andrian, J. van Rheenen, et al., Collagen-based cell migration models in vitro and in vivo, Semin. Cell Dev. Biol., 20 (2009), 931–941. https://doi.org/10.1016/j.semcdb.2009.08.005 doi: 10.1016/j.semcdb.2009.08.005
    [45] K. Wolf, Y. I. Wu, Y. Liu, J. Geiger, E. Tam, C. Overall, et al., Multi-step pericellular proteolysis controls the transition from individual to collective cancer cell invasion, Nat. Cell Biol., 9 (2007), 893–904. https://doi.org/10.1038/ncb1616 doi: 10.1038/ncb1616
    [46] B. I. Camara, H. Mokrani, E. Afenya, Mathematical modeling of glioma therapy using oncolytic viruses, Math. Biosci. Eng., 10 (2013), 565–578. https://doi.org/10.3934/mbe.2013.10.565 doi: 10.3934/mbe.2013.10.565
    [47] K. J. Painter, N. J. Armstrong, J. A. Sherratt, The impact of adhesion on cellular invasion processes in cancer and development, J. Theor. Biol., 264 (2010), 1057–1067. https://doi.org/10.1016/j.jtbi.2010.03.033 doi: 10.1016/j.jtbi.2010.03.033
    [48] R. Shuttleworth, D. Trucu, Multiscale dynamics of a heterotypic cancer cell population within a fibrous extracellular matrix, J. Theor. Biol., 486 (2020), 110040. https://doi.org/10.1016/j.jtbi.2019.110040 doi: 10.1016/j.jtbi.2019.110040
    [49] L. Peng, D. Trucu, P. Lin, A. Thompson, M. A. Chaplain, A multiscale mathematical model of tumour invasive growth, Bull. Math. Biol., 79 (2017), 389–429. https://doi.org/10.1007/s11538-016-0237-2 doi: 10.1007/s11538-016-0237-2
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