Emergence of spatial patterns in a mathematical model for the co-culture dynamics of epithelial-like and mesenchymal-like cells

Accumulating evidence indicates that the interaction between epithelial and mesenchymal cells plays a pivotal role in cancer development and metastasis formation. Here we propose an integro-differential model for the co-culture dynamics of epithelial-like and mesenchymal-like cells. Our model takes into account the effects of chemotaxis, adhesive interactions between epithelial-like cells, proliferation and competition for nutrients. We present a sample of numerical results which display the emergence of spots, stripes and honeycomb patterns, depending on parameters and initial data. These simulations also suggest that epithelial-like and mesenchymal-like cells can segregate when there is little competition for nutrients. Furthermore, our computational results provide a possible explanation for how the concerted action between epithelial-cell adhesion and mesenchymal-cell spreading can precipitate the formation of ring-like structures, which resemble the fibrous capsules frequently observed in hepatic tumours.PostprintPeer reviewe

Populational adaptive evolution, chemotherapeutic resistance and multiple anti-cancer therapies

Resistance to chemotherapies, particularly to anticancer treatments, is an increasing medical concern. Among the many mechanisms at work in cancers, one of the most important is the selection of tumor cells expressing resistance genes or phenotypes. Motivated by the theory of mutation-selection in adaptive evolution, we propose a model based on a continuous variable that represents the expression level of a resistance gene (or genes, yielding a phenotype) influencing in healthy and tumor cells birth/death rates, effects of chemotherapies (both cytotoxic and cytostatic) and mutations. We extend previous work by demonstrating how qualitatively different actions of chemotherapeutic and cytostatic treatments may induce different levels of resistance. The mathematical interest of our study is in the formalism of constrained Hamilton-Jacobi equations in the framework of viscosity solutions. We derive the long-term temporal dynamics of the fittest traits in the regime of small mutations. In the context of adaptive cancer management, we also analyse whether an optimal drug level is better than the maximal tolerated dose

On interfaces between cell populations with different mobilities

Partial differential equations describing the dynamics of cell population densities from a fluid mechanical perspective can model the growth of avascular tumours. In this framework, we consider a system of equations that describes the interaction between a population of dividing cells and a population of non-dividing cells. The two cell populations are characterised by different mobilities. We present the results of numerical simulations displaying two-dimensional spherical waves with sharp interfaces between dividing and non-dividing cells. Furthermore, we numerically observe how different ratios between the mobilities change the morphology of the interfaces, and lead to the emergence of finger-like patterns of invasion above a threshold. Motivated by these simulations, we study the existence of one-dimensional travelling wave solutions.PostprintPeer reviewe

Tracking the evolution of cancer cell populations through the mathematical lens of phenotype-structured equations

This work was supported in part by the French National Research Agency through the “ANR blanche” project Kibord [ANR-13-BS01-0004].Background: A thorough understanding of the ecological and evolutionary mechanisms that drive the phenotypic evolution of neoplastic cells is a timely and key challenge for the cancer research community. In this respect, mathematical modelling can complement experimental cancer research by offering alternative means of understanding the results of in vitro and in vivo experiments, and by allowing for a quick and easy exploration of a variety of biological scenarios through in silico studies. Results: To elucidate the roles of phenotypic plasticity and selection pressures in tumour relapse, we present here a phenotype-structured model of evolutionary dynamics in a cancer cell population which is exposed to the action of a cytotoxic drug. The analytical tractability of our model allows us to investigate how the phenotype distribution, the level of phenotypic heterogeneity, and the size of the cell population are shaped by the strength of natural selection, the rate of random epimutations, the intensity of the competition for limited resources between cells, and the drug dose in use. Conclusions: Our analytical results clarify the conditions for the successful adaptation of cancer cells faced with environmental changes. Furthermore, the results of our analyses demonstrate that the same cell population exposed to different concentrations of the same cytotoxic drug can take different evolutionary trajectories, which culminate in the selection of phenotypic variants characterised by different levels of drug tolerance. This suggests that the response of cancer cells to cytotoxic agents is more complex than a simple binary outcome, i.e., extinction of sensitive cells and selection of highly resistant cells. Also, our mathematical results formalise the idea that the use of cytotoxic agents at high doses can act as a double-edged sword by promoting the outgrowth of drug resistant cellular clones. Overall, our theoretical work offers a formal basis for the development of anti-cancer therapeutic protocols that go beyond the ‘maximum-tolerated-dose paradigm’, as they may be more effective than traditional protocols at keeping the size of cancer cell populations under control while avoiding the expansion of drug tolerant clones.Publisher PDFPeer reviewe

Structured Equations for Complex Living Systems - Modeling, Asymptotics and Numerics

Complex living systems differ from those systems whose evolution is well described by the laws of Classical Physics. In fact, they are endowed with self-organizing abilities that result from the interactions among their constituent individuals, which behave according to specific functions, strategies or traits. These functions/strategies/traits can evolve over time, as a result of adaptation to the surrounding environment, and are usually heterogeneously distributed over the individuals, so that the global features expressed by the system as a whole cannot be reduced to the superposition of the single functions/strategies/traits. Quoting Aristotle, we can say that, within these systems, "the whole is more than the sum of its parts". As a result, when we study the dynamics of complex living systems, there are new concepts that come into play, such as adaptation, herding and learning, which do not belong to the traditional vocabulary of physical sciences and make the dynamics of these systems hardly to be forecast. Moving from the above considerations, the subject of my PhD was the development and the study of structured equations for population dynamics (partial differential equations and integro-differential equations) applied to modelling the evolution of complex living systems. In particular, I designed models for multicellular systems, living species and socio-economic systems with the aim of inspecting mechanisms underlying the emergence of collective behaviors and self-organization. In the framework of structured equations, individuals belonging to a given system are divided into different populations and heterogeneously distributed characteristics are modelled by suitable independent variables, the so-called structuring variables. For each population, a function describing the distribution of the individuals over the structuring variables is introduced, which evolves through a partial differential equation, or an integro-differential equation, whose parameter functions are defined according to the phenomena under study. I decided to use such mathematical framework since it makes possible to effectively model the afore mentioned complexity aspects of living systems and provides an efficient way to reduce complexity in view of the mathematical formalization. With particular reference to multicellular systems, I focused on the design and the study of mathematical models describing the evolutionary dynamics of cancer cell populations under the selective pressures exerted by therapeutic agents and the immune system. Proliferation, mutation and competition phenomena are included in these models, which rely on the idea that the process leading to the emergence of resistance to anti-cancer therapies and immune action can be considered, at least in principles, as a Darwinian micro-evolution. It is worth noting that most of these models stem from direct collaborations with biologists and clinicians. Besides local and global existence results for the mathematical problems linked to the models, my PhD thesis presents results related to concentration phenomena arising in phenotype-structured equations and opinion-structured equations (i.e., the weak convergence of the solutions to sums of Dirac masses), and with the derivation of macroscopic models from space-velocity structured equations. From the applicative standpoint such concentration phenomena provide a possible mathematical formalization of the selection principle in evolutionary biology and the emergence of opinions; macroscopic models, instead, offer an overall view of the systems at hand. Numerical simulations are performed with the aim of illustrating, and extending, analytical results and verifying the consistency of the model with empirical dat

The role of spatial variations of abiotic factors in mediating intratumour phenotypic heterogeneity

We present here a space- and phenotype-structured model of selection dynamics between cancer cells within a solid tumour. In the framework of this model, we combine formal analyses with numerical simulations to investigate in silico the role played by the spatial distribution of abiotic components of the tumour microenvironment in mediating phenotypic selection of cancer cells. Numerical simulations are performed both on the 3D geometry of an in silico multicellular tumour spheroid and on the 3D geometry of an in vivo human hepatic tumour, which was imaged using computerised tomography. The results obtained show that inhomogeneities in the spatial distribution of oxygen, currently observed in solid tumours, can promote the creation of distinct local niches and lead to the selection of different phenotypic variants within the same tumour. This process fosters the emergence of stable phenotypic heterogeneity and supports the presence of hypoxic cells resistant to cytotoxic therapy prior to treatment. Our theoretical results demonstrate the importance of integrating spatial data with ecological principles when evaluating the therapeutic response of solid tumours to cytotoxic therapy

Dissecting the dynamics of epigenetic changes in phenotype-structured populations exposed to fluctuating environments

International audienceAn enduring puzzle in evolutionary biology is to understand how individuals and populations adapt to fluctuating environments. Here we present an integro-differential model of adaptive dynamics in a phenotype-structured population whose fitness landscape evolves in time due to periodic environmental oscillations. The analytical tractability of our model allows for a systematic investigation of the relative contributions of heritable variations in gene expression, environmental changes and natural selection as drivers of phenotypic adaptation. We show that environmental fluctuations can induce the population to enter an unstable and fluctuation-driven epigenetic state. We demonstrate that this can trigger the emergence of oscillations in the size of the population, and we establish a full characterisation of such oscillations. Moreover, the results of our analyses provide a formal basis for the claim that higher rates of epimutations can bring about higher levels of intrapopulation heterogeneity, whilst intense selection pressures can deplete variation in the phenotypic pool of asexual populations. Finally, our work illustrates how the dynamics of the population size is led by a strong synergism between the rate of phenotypic variation and the frequency of environmental oscillations, and identifies possible ecological conditions that promote the maximisation of the population size in fluctuating environments

Cathodic protection modelling of a propeller shaft

Current and potential distributions on a stainless steel propeller shaft protected by galvanic anodes were investigated by means of Finite Element Method (FEM) modelling. The effect of seawater flow and shaft rotation was evaluated. The results of simulations are compared with experimental measurements performed on steady shaft in natural seawater. Modest polarization can be noticed in all operating conditions, not sufficient for preventing biofilm action on localized corrosion initiation. Only in stagnant conditions, without any water renewal, the consumption of oxygen leads to an appreciable potential decreasing to match the limits of normal protection indicated in the European standards