Multiscale analysis and simulation of a signalling process with surface diffusion

We present and analyze a model for cell signaling processes in biological tissues. The model includes diffusion and nonlinear reactions on the cell surfaces and both inter- and intracellular signaling. Using techniques from the theory of two-scale convergence as well the unfolding method, we show convergence of the solutions to the model to solutions of a two-scale macroscopic problem. We also present a two-scale bulk-surface finite element method for the approximation of the macroscopic model. We report on some benchmarking results as well as numerical simulations in a biologically relevant regime that illustrate the influence of cell-scale heterogeneities on macroscopic concentrations

Backward difference time discretization of parabolic differential equations on evolving surfaces

A linear parabolic differential equation on a moving surface is discretized in space by evolving surface finite elements and in time by backward difference formulas (BDF). Using results from Dahlquist's G-stability theory and Nevanlinna & Odeh's multiplier technique together with properties of the spatial semi-discretization, stability of the full discretization is proven for the BDF methods up to order 5 and optimal-order convergence is shown. Numerical experiments illustrate the behaviour of the fully discrete method

Reaction-diffusion systems on evolving domains with applications to the theory of biological pattern formation

In this thesis we investigate a model for biological pattern formation during growth development. The pattern formation phenomenon is described by a reaction-diffusion system on a time-dependent domain. We prove the global existence of solutions to reaction-diffusion systems on time-dependent domains.We extend global existence results for a class of reaction-diffusion systems on fixed domains to the same systems posed on spatially linear isotropically evolving domains. We demonstrate that the analysis is applicable to many systems that commonly arise in the theory of pattern formation. Our results give a mathematical justification to the widespread use of computer simulations of reaction-diffusion systems on evolving domains. We propose a finite element method to approximate the solutions to reaction-diffusion systems on time-dependent domains. We prove optimal convergence rates for the error in the method and we derive a computable error estimator that provides an upper bound for the error in the semidiscrete (space) scheme. We have implemented the method in the C programming language and we verify our theoretical results with benchmark computations. The method is a robust tool for the study of biological pattern formation, as it is applicable to domains with irregular geometries and nonuniform evolution. This versatility is illustrated with extensive computer simulations of reaction-diffusion systems on evolving domains. We observe varied pattern transitions induced by domain evolution, such as stripe to spot transitions, spotsplitting, spot-merging and spot-annihilation. We also illustrate the striking effects of spatially nonuniform domain evolution on the position, orientation and symmetry of patterns generated by reaction-diffusion systems. To improve the efficiency of the method, we have implemented a space-time adaptive algorithm where spatial adaptivity is driven by an error estimator and temporal adaptivity is driven by an error indicator.We illustrate with numerical simulations the dramatic improvements in accuracy and efficiency that are achieved via adaptivity. To demonstrate the applicability and generality of our methodology, we examine the process of parr mark pattern formation during the early development of the Amago trout. By assuming the existence of chemical concentrations residing on the surface of the Amago fish which react and diffuse during surface evolution, we model the pattern formation process with reactiondiffusion systems posed on evolving surfaces. An important generalisation of our study is the experimentally driven modelling of the fish’s developing body surface. Our results add weight to the feasibility of reaction-diffusion system models of fish skin patterning, by illustrating that a reaction-diffusion system posed on an evolving surface generates transient patterns consistent with those experimentally observed on the developing Amago trout. Furthermore, we conclude that the surface evolution profile, the surface geometry and the curvature are key factors which play a pivotal role in pattern formation via reaction-diffusion systems

Stability analysis and simulations of coupled bulk-surface reaction–diffusion systems

In this article, we formulate new models for coupled systems of bulk-surface reaction–diffusion equations on stationary volumes. The bulk reaction–diffusion equations are coupled to the surface reaction–diffusion equations through linear Robin-type boundary conditions. We then state and prove the necessary conditions for diffusion-driven instability for the coupled system. Owing to the nature of the coupling between bulk and surface dynamics, we are able to decouple the stability analysis of the bulk and surface dynamics. Under a suitable choice of model parameter values, the bulk reaction–diffusion system can induce patterning on the surface independent of whether the surface reaction–diffusion system produces or not, patterning. On the other hand, the surface reaction–diffusion system cannot generate patterns everywhere in the bulk in the absence of patterning from the bulk reaction–diffusion system. For this case, patterns can be induced only in regions close to the surface membrane. Various numerical experiments are presented to support our theoretical findings. Our most revealing numerical result is that, Robin-type boundary conditions seem to introduce a boundary layer coupling the bulk and surface dynamics

Modelling and simulation of cell migration in heterogeneous media

Cell motility is a complex process comprising, among others, cytoskeletal remodelling and growth, active force generation, chemical transport and reaction of multiple substances inside and outside the cell as well as on the cell membrane, anisotropic material behavior of the cell and of the extracellular matrix (ECM), and proteolytic cleavage of the ECM. We are working on an evolving finite element based computational framework that is able to incorporate models and methods for all the key features of cell motility. The approach exploits a mesoscopic point of view and aims at a comprehensive 3D model allowing for the simulation of cell motility at reasonable computational costs. In the presentation we will first sketch the overall framework, addressing all the respective models and methods, and will then focus on some essential subunits

Bayesian parameter identification for Turing systems on stationary and evolving domains

In this study, we apply the Bayesian paradigm for parameter identification to a well-studied semi-linear reaction-difusion system with activatordepleted reaction kinetics, posed on stationary as well as evolving domains. We provide a mathematically rigorous framework to study the inverse problem of finding the parameters of a reaction-diffusion system given a final spatial pattern. On the stationary domain the parameters are finite dimensional, but on the evolving domain we consider the problem of identifying the evolution of the domain, i.e. a time dependent function. While others have considered these inverse problems using optimisation techniques, the Bayesian approach provides a rigorous mathematical framework for incorporating the prior knowledge on uncertainty in the observation and in the parameters themselves, resulting in an approximation of the full probability distribution for the parameters, given the data. Furthermore, using previously established results, we can prove wellposedness results for the inverse problem, using the well-posedness of the forward problem. Although the numerical approximation of the full probability is computationally expensive, parallelised algorithms make the problem solvable using high-performance computing

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