Citation: Santiago Quinteros, Aleksander Gundersen, Jean-Sebastien L'Heureux, J. Antonio H. Carraro, Richard Jardine. Øysand research site: Geotechnical characterisation of deltaic sandy-silty soils[J]. AIMS Geosciences, 2019, 5(4): 750-783. doi: 10.3934/geosci.2019.4.750
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The mathematical formalisation of cell motion is a fascinating topic which has attracted the attention of physicists and mathematicians over the past fifty years. In more recent times, the development of sophisticated technologies capable of capturing high-resolution videos of moving cells has renewed the interest of the physical and mathematical communities. This has promoted the formulation of several models, which rely on different mathematical approaches, to reproduce qualitative behaviours emerging from cell motion. We refer the interested reader to[1, 2, 5, 7, 8, 9, 10, 11, 14, 17, 18, 22, 31, 36, 37] and references therein.
Accumulating evidence indicates that the interaction between epithelial and mesenchymal cells plays a pivotal role in cancer development and metastasis formation [20, 39, 40]. Here we propose a model for the co-culture dynamics of epithelial-like and mesenchymal-like cells. In our model, mesenchymal-like and epithelial-like cells in motion are grouped into two distinct populations. Following a kinetics approach, the microscopic state of each cell is identified by its position and velocity (i.e., a point in the phase space), and the two populations are characterised through the related phase space densities. Macroscopic quantities (e.g., local cell densities) are then expressed in terms of microscopic averages [3, 4]. We make use of the mesoscopic formalism developed in[8, 22] to include microscopic aspects of cell motion which cannot be captured by macroscopic models. Moreover, we adapt the strategies presented in[15] to model proliferation, competition for nutrients and adhesive interactions between epithelial-like cells.
A calibration of the model based on experimental datasets is beyond our present scope. In this work, our aim, rather, is to present a sample of numerical results which display the emergence of different spatial patterns, depending on parameters and initial data. The remainder of the paper is organised as follows. The main features of the biological system and the mathematical model are introduced in Section 2. In Section 3, we discuss the main numerical results. Finally, some research perspectives are summarised in Section 4.
In this section, we introduce the key features of the biological system and the mathematical model. In Subsection 2.1, we state the biological problem and the assumptions we need in view of the mathematical formalisation. In Subsection 2.2, we describe the modelling strategies we designed to translate the biological phenomena into mathematical terms, and we present the model.
The term Epithelial-Mesenchymal Transition (EMT) refers to the temporary and reversible switching between epithelial and mesenchymal phenotypes. During EMT, non-motile epithelial cells, collectively embedded via cell-cell junctions (i.e., homotypic adhesion), convert into motile mesenchymal cells [20, 39]. This strongly reduces cell adhesion and enhances cell motility.
EMT is commonly observed in various non-pathological conditions, namely during embryonic development and tissue repair in the adult organism. However, this phenotypic reprogramming has been linked to cancer progression [39,40]. For instance, EMT may favour the seeding of secondary tumours at distant sites and the creation of metastases [20]. In particular, EMT has been implicated in the formation of the fibrous capsules observed in hepatic tumours, which seem to be mainly composed of cells that express a mesenchymal-like phenotype [25].
We consider a monolayer of epithelial-like and mesenchymal-like cells in co-culture on a regular plastic surface (i.e., a petri dish). Cellular movement is seen as the superposition of persistent spontaneous motion and chemotactic response. The former is due to the tendency of cells to randomly orient themselves, whilst the latter is guided by chemotactic cytokines. In this framework, we focus on the following biological phenomena:
(ⅰ) secretion from cells of chemotactic cytokines;
(ⅱ) diffusion and consumption of chemotactic cytokines;
(ⅲ) random motion of cells and chemotaxis;
(ⅳ) adhesive interactions between epithelial cells;
(ⅴ) cell proliferation and competition for nutrients.
To reduce biological complexity to its essence, we make the prima facie assumption that the diffusion of chemotactic cytokines is isotropic. Moreover, we model cell motion in two dimensions only, since we focus on a monolayer co-culture (i.e., we let cells grow side by side and not one on top of the other), and we assume that the status of motion of a cell is left unaltered by interactions which do not lead to homotypic adhesion. Finally, we stress that this paper does not deal with several evolutionary aspects. For instance, we consider a sample where both epithelial-like and mesenchymal-like cells are present from the beginning, and we do not let EMT occur. Moreover, we do not include the effects of cellular heterogeneity and therapeutic actions, which we explored previously in[12,15, 16, 28, 29].
We divide epithelial-like and mesenchymal-like cells in motion into two populations labeled, respectively, by the index
At any instant of time
f1=f1(t,x,v):R+×X×V→R+,f2=f2(t,x,v):R+×X×V→R+ |
and the local densities
n3=n3(t,x):R+×X→R+,n4=n4(t,x):R+×X→R+. |
The local densities of cells in motion and the local cell density can be computed as
n1,2(t,x)=∫Vf1,2(t,x,v)dv,ϱ(t,x)=3∑i=1ni(t,x), | (1) |
while the total cell density and the average local cell density are
N(t)=∫Xϱ(t,x)dx,ˉϱ(t)=N(t)|X|, | (2) |
where
The following notations and assumptions are used to model the biological phenomena of interest (vid. Table 1 for a summary of the model parameters):
Biological Phenomena | Parameters |
Secretion from cells of chemotactic cytokines | |
Consumption of chemotactic cytokines | |
Random motion vs chemotactic reorientation | |
Homotypic adhesion | |
Cell proliferation | |
Competition for nutrients |
A(ϱ;α):=αϱ(t,x)2. | (3) |
B(v,∇xn4;β,|V|):=1|V|+b(|v|,|∇xn4|;β,|V|)(v⋅∇xn4(t,x)) | (4) |
with
b(|v|,|∇xn4|;β,|V|):=|V|−1β+|v||∇xn4(t,x)|. | (5) |
The functional
∫VB(v,⋅;β,|V|)dv=1,∀β,|V|∈R+. | (6) |
In definition (4),
Ghij(v;γ,|V|):={γ|V|,if i=1,j=1,3and h=31−γ|V|,if i=1,j=1,3and h=10,otherwise,∀v∈V, | (7) |
3∑h=1∫VGhij(v;γ,|V|)dv=1,forγ∈[0,1],|V|∈R+,i=1 and j=1,3. | (8) |
In definition (7), the parameter
M(N;μ):=μN(t), | (9) |
which relies on the natural assumption that a higher total density of cells corresponds to a lower concentration of available nutrients, and thus to a higher chance of cell death.
The evolution of the functions
∂tf1(t,x,v)+v⋅∇xf1(t,x,v)=∫VB(v,∇xn4;β,|V|)f1(t,x,v∗)dv∗−f1(t,x,v)⏟ inflow & outflow due to random motion and chemotactic reorientation+∫V∫VG111(v;γ,|V|)f1(t,x,v∗)f1(t,x,v∗)dv∗dv∗⏟ inflow due to changes of velocity caused by adhesive interactions+n3(t,x)∫VG113(v;γ,|V|)f1(t,x,v∗)dv∗⏟ inflow due to changes of velocity caused by adhesive interactions−f1(t,x,v)∫V∫V(G111(v∗;γ,|V|)+G311(v∗;γ,|V|))f1(t,x,v∗)dv∗dv∗⏟ outflow due to homotypic adhesion−f1(t,x,v)n3(t,x)∫V(G113(v∗;γ,|V|)+G313(v∗;γ,|V|))dv∗⏟ outflow due to homotypic adhesion+κf1(t,x,v)−M(N;μ)f1(t,x,v)⏟ proliferation and competition, | (10) |
∂tf2(t,x,v)+v⋅∇xf2(t,x,v) =∫VB(v,∇xn4;β,|V|)f2(t,x,v∗)dv∗−f2(t,x,v)⏟ inflow & outflow due to random motion and chemotactic reorientation+κf2(t,x,v)−M(N;μ)f2(t,x,v)⏟ proliferation and competition, | (11) |
∂tn3(t,x)=∫V∫V∫VG311(v;γ,|V|)f1(t,x,v∗)f1(t,x,v∗)dv∗dv∗dv⏟ inflow due to homotypic adhesion+n3(t,x)∫V∫VG313(v;γ,|V|)f1(t,x,v∗)dv∗dv⏟ inflow due to homotypic adhesion+κn3(t,x)−M(N;μ)n3(t,x)⏟ proliferation and competition, | (12) |
∂tn4(t,x)=νϱ(t,x)+Δxn4(t,x)⏟ secretion and diffusion−A(ϱ;α)n4(t,x).⏟ consumption | (13) |
Plugging definitions (3), (4), (5), (7) and (9) into equations (10)-(13), we obtain
∂tf1+v⋅∇xf1=1|V|(1+v⋅∇xn4β+|v||∇xn4|)n1−f1⏟ random motion and chemotactic reorientation+(1−γ)|V|n1(n1+n3)−(n1+n3)f1⏟ homotypic adhesion+(κ−μN)f1,⏟ proliferation and competition | (14) |
∂tf2+v⋅∇xf2=1|V|(1+v⋅∇xn4β+|v||∇xn4|)n2−f2⏟ random motion and chemotactic reorientation+(κ−μN)f2,⏟ proliferation and competition | (15) |
∂tn3=γ(n1+n3)n1⏟ homotypic adhesion+(κ−μN)n3,⏟ proliferation and competition | (16) |
∂tn4=νϱ+Δxn4⏟ secretion and diffusion−αn4ϱ2.⏟ consumption | (17) |
In this section, we discuss a sample of numerical results which display the emergence of different spatial patterns, depending on parameters and initial data. In Subsection 3.1, we describe the simulation setup and the method we use for calculating numerical solutions. In Subsection 3.2, we focus on a sample composed of mesenchymal-like cells only, where proliferation and competition phenomena do not take place. The results we present highlight how different initial cell densities can cause the emergence of different spatial patterns, such as spots, stripes and hole structures. In Subsection 3.3, we show that, when there is little competition for nutrients, epithelial-like and mesenchymal-like cells can segregate and create honeycomb structures. Finally, the simulations discussed in Subsection 3.4 provide a possible explanation for how the interplay between epithelial-cell adhesion and mesenchymal-cell spreading paves the way for the formation of ring-like structures.
For simplicity, we follow [33] and approximate cellular velocities in polar coordinates. We also assume that all moving cells are characterised by the same modulus of the velocity, which is normalised to unity. This can be justified by noting that the main differences between the adhesive behaviours of epithelial-like and mesenchymal-like cells are already captured by the modelling strategies described in the previous section. As a result, we set
vx=cosθ,vy=sinθ,θ∈[0,2π), | (18) |
so that the phase space distributions can be computed by tracking velocity angles and space only.
To perform numerical simulations, we set
Δx=L2M,xi=iΔx,i=−M,…,0,…,M,Δy=L2M,yj=jΔy,j=−M,…,0,…,M,Δθ=2πm+1,θk=kΔθ,k=0,…,m. |
The phase space densities and the local densities are approximated as
f1,2(t,x,v)≈f1,2(hΔt,xi,yj,θk)=fh1,2(xi,yj,θk) |
and
n3,4(t,x)≈n3,4(hΔt,xi,yj)=nh3,4(xi,yj), |
where
We numerically solve the problem defined by the discretised versions of equations (14)-(17), periodic boundary conditions and suitable initial data. Simulations are performed in MATLAB with
The method for calculating numerical solutions is based on a time-splitting scheme. We begin by updating
A flux-limiting scheme (see for instance [27]) is used to treat the advective terms. First, we advect
fh+1/4i,j,k=fhi,j,k−λ[Fhi,j,k−Fhi,j−1,k], |
with
Fhi,j,k=Hhi,j,k−(1−ϕhi,j,k)[Hhi,j,k−Lhi,j,k]. |
In the above equation,
Lhi,j,k=sin(θk)fhi,j,k, |
and, according to the Richmyer two-step Lax-Wendroff method,
Hhi,j,k=sin(θk)fh+1/4i,j+1/2,k, |
with
fh+1/4i,j+1/2,k=12(fhi,j,k−fhi,j+1,k)−λ2[Lhi,j+1,k−Lhi,j,k]. |
The quantity
ϕhi,j,k=max(0,min(1,2rhi,j,k),min(rhi,j,k,2)), |
with
rhi,j,k=fhi,j,k−fhi,j−1,kfhi,j+1,k−fhi,j,k. |
An analogous procedure is used to compute
Then, we update
On the other hand,
We consider different initial conditions to mimic different biological scenarios. Moreover, we vary the values of the parameters
We focus on a sample composed of mesenchymal-like cells and chemotactic cytokines only. Cells are initially characterised by a uniform space distribution parametrised by
The results of Fig. 1 highlight how increasing values of the parameter
(ⅰ) if
(ⅱ) if
(ⅲ) if
In analogy with classical macroscopic models of chemotaxis, cells tend to aggregate in the local maxima of the chemoattractant (data not shown). However, the quadratic dependence of the consumption rate of cytokines on the local cell density [see definition (3)] seems to prevent finite time blow-up, which is observed in standard macroscopic models. This allows for the formation of bounded aggregation patterns. Additional simulations (data not shown) suggest that relevant patterns cannot emerge when
Remark 1. The total cell density
We assume that the sample is initially composed of chemotactic cytokines, mesenchymal-like cells and epithelial-like cells in motion. The distribution of cytokines is a small positive random perturbation of the zero level. Cells are uniformly distributed in space and their distributions are parametrised by
The results in Fig. 2 highlight how decreasing values of the parameter
(ⅰ) if
(ⅱ) if
(ⅲ) if
These results have been obtained with
(ⅰ) when
(ⅱ) when
(ⅲ) when
Additional simulations (data not shown) support the conclusion that relevant patterns cannot emerge for
We consider a sample where epithelial-like cells at rest and chemotactic cytokines are not present at time
f1,2(t=0,x,y,θ)=C1,2e−x2+y220,∀(x,y,θ)∈X×[0,2π),C1,2∈R+. | (19) |
We are interested in the case where the total cell density does not vary over time. Therefore, we neglect the effects of non-conservative phenomena (i.e., we set
The results presented in Fig. 3 show that, while epithelial-like cells rapidly stop moving because of adhesive interactions [vid. Fig. 3(A)], mesenchymal-like cells diffuse throughout the sample [vid. Fig. 3(B)] and follow the chemotactic path created by the diffusing cytokines [vid. Fig. 3(C)]. The resulting pattern is an expanding ring-like structure made of mesenchymal-like cells, which surrounds a cluster of epithelial-like cells kept at rest by homotypic adhesion.
The results presented here have been obtained with
Such ring-like structures of mesenchymal-like cells resemble the fibrous capsules observed in hepatic tumours, which seem to be mainly composed of cells expressing a mesenchymal-like phenotype [25]. They also share some striking similarities with patterns arising from different biological contexts, such as tumour growth [13] and evolution of bacterial populations [38], and with the outcomes of chemotaxis models that incorporate some specific effects related to the finite size of individual cells [35].
We have developed an integro-differential model for the co-culture dynamics of epithelial-like and mesenchymal-like cells. The strategy that we have used to model the consumption of the chemoattractant seems to prevent blow-up in finite time, which is usually observed in classical macroscopic models of chemotaxis. This allows for the formation of bounded aggregation patterns, whose long-time behaviour depends on the asymptotic value of the average local density of cells. In fact, higher asymptotic values of the average local cell density lead to the emergence of different spatial patterns, such as spots, stripes and honeycomb structures. Furthermore, our results support the idea that epithelial-like and mesenchymal-like cells can segregate when there is little competition for nutrients. Finally, we have shown how the interplay between epithelial-cell adhesion and mesenchymal-cell spreading can pave the way for the formation of ring-like structures, which resemble the fibrous capsules frequently observed in hepatic tumours.
Future research will be addressed to refine the current modelling strategies. For instance, a natural improvement of the model would be to define the rate of death due to competition for nutrients as a function of the local cell density. Furthermore, since cell motion implies resource reallocation (i.e., redistribution of energetic resources from proliferation-oriented tasks toward development and maintenance of motility), it might be worth considering different proliferation/death rates for moving cells and cells at rest. From the analytical point of view, it would be interesting to study the ring-like patterns discussed in Subsection 3.4. In this respect, the techniques employed in [38] may prove to be useful. Finally, spatial dynamics play a pivotal role in the evolution of many living complex systems, including biological and social systems (see for instance [32]). Therefore, another possible research direction would be to investigate if the modelling approach presented here could be used profitably to model the dynamics of other living systems.
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1. | Zhong Liu, Shaobin Zhong, Qiang Liu, Chenxi Xie, Yunzhu Dai, Chuan Peng, Xin Chen, Ruhai Zou, Thyroid nodule recognition using a joint convolutional neural network with information fusion of ultrasound images and radiofrequency data, 2021, 0938-7994, 10.1007/s00330-020-07585-z | |
2. | Wenqiang Luo, Zhiwei Chen, Qi Zhang, Baiying Lei, Zhong Chen, Yuan Fu, Peidong Guo, Changchuan Li, Teng Ma, Jiang Liu, Yue Ding, Osteoporosis Diagnostic Model Using a Multichannel Convolutional Neural Network Based on Quantitative Ultrasound Radiofrequency Signal, 2022, 48, 03015629, 1590, 10.1016/j.ultrasmedbio.2022.04.005 | |
3. | Huiying Wen, Wei Zheng, Min Li, Qing Li, Qiang Liu, Jianhua Zhou, Zhong Liu, Xin Chen, Multiparametric Quantitative US Examination of Liver Fibrosis: A Feature-Engineering and Machine-Learning Based Analysis, 2022, 26, 2168-2194, 715, 10.1109/JBHI.2021.3100319 | |
4. | Zhuhuang Zhou, Ruiyang Gao, Shuicai Wu, Qiying Ding, Guangyu Bin, Po-Hsiang Tsui, Scatterer size estimation for ultrasound tissue characterization: A survey, 2024, 225, 02632241, 114046, 10.1016/j.measurement.2023.114046 | |
5. | Qianfeng Ma, Lifeng Gong, Liyuan Ma, Tao Jiang, Application of ultrasound attenuation imaging and shear wave elastography in the diagnosis of metabolic fatty liver disease, 2024, 40, 0264-8725, 2830, 10.1080/02648725.2023.2202537 | |
6. | Andrew C. Markel, Cameron Hoerig, Allan K.N. de Alencar, Kenneth F. Swan, Alexander D. Gleed, Lili Shi, Gabriella Pridjian, Jonathan Mamou, Carolyn L. Bayer, Quantitative Ultrasound-Based Characterization of Placental Tissue Microstructure in a Rat Model of Preeclampsia, 2025, 03015629, 10.1016/j.ultrasmedbio.2025.04.017 |
Biological Phenomena | Parameters |
Secretion from cells of chemotactic cytokines | |
Consumption of chemotactic cytokines | |
Random motion vs chemotactic reorientation | |
Homotypic adhesion | |
Cell proliferation | |
Competition for nutrients |