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The goal of this project is to develop a framework for a cell that takes in consideration its internal structure, using an agent-based approach. In this framework, a cell was simulated as many sub-particles interacting to each other. This sub-particles can, in principle, represent any internal structure from the cell (organelles, etc). In the model discussed here, two types of sub-particles were used: membrane sub-particles and cytosolic elements. A kinetic and dynamic Delaunay triangulation was used in order to define the neighborhood relations between the sub-particles. However, it was soon noted that the relations defined by the Delaunay triangulation were not suitable to define the interactions between membrane sub-particles. The cell membrane is a lipid bilayer, and does not present any long range interactions between their sub-particles. This means that the membrane particles should not be able to interact in a long range. Instead, their interactions should be confined to the two-dimensional surface supposedly formed by the membrane. A method to select, from the original three-dimensional triangulations, connections restricted to the two-dimensional surface formed by the cell membrane was then developed. The algorithm uses as starting point the three-dimensional Delaunay triangulation involving both internal and membrane sub-particles. From this triangulation, only the subset of connections between membrane sub-particles was considered. Since the cell is full of internal particles, the collection of the membrane particles' connections will resemble the surface to be obtained, even though it will still have many connections that do not belong to the restricted triangulation on the surface. This "thick surface" was called a quasi-surface. The following step was to refine the quasi-surface, cutting out some of the connections so that the ones left made a proper surface triangulation with the membrane points. For that, the quasi-surface was separated in clusters. Clusters are defined as areas on the quasi-surface that are not yet properly triangulated on a two-dimensional surface. Each of the clusters was then re-triangulated independently, using re-triangulation methods also developed during this work. The interactions between cytosolic elements was given by a Lennard-Jones potential, as well as the interactions between cytosolic elements and membrane particles. Between only membrane particles, the interactions were given by an elastic interaction. For each particle, the equation of motion was written. The algorithm chosen to solve the equations of motion was the Verlet algorithm. Since the cytosol can be approximated as a gel, it is reasonable to suppose that the sub-cellular particles are moving in an overdamped environment. Therefore, an overdamped approximation was used for all interactions. Additionally, an adaptive algorithm was used in order to define the size of the time step used in each interaction. After the method to re-triangulate the membrane points was implemented, the time needed to re-triangulate a single cluster was studied, followed by an analysis on how the time needed to re-triangulate each point in a cluster varied with the cluster size. The frequency of appearance for each cluster size was also compared, as this information is necessary to guarantee that the total time needed by to re-triangulate a cell is convergent. At last, the total time spent re-triangulating a surface was plotted, as well as a scaling for the total re-triangulation time with the variation. Even though there is still a lot to be done, the work presented here is an important step on the way to the main goal of this project: to create an agent-based framework that not only allows the simulation of any sub-cellular structure of interest but also provides meaningful interaction relations to particles belonging to the cell membrane.

The central goal of this investigation is to describe the dynamic reaction of a multicellular tumour spheroid to treatment with radiotherapy. A focus will be on the triggered dynamic cell cycle reaction in the spheroid and how it can be employed within fractionated radiation schedules.
An agent-based model for cancer cells is employed which features inherent cell cycle progression and reactions to environmental conditions. Cells are represented spatially by a weighted, dynamic and kinetic Voronoi/Delaunay model which also provides for the identification of cells in contact within the multicellular aggregate. Force-based interaction between cells will lead to rearrangement in response to proliferation and can induce cell quiescence via a mechanism of pressure-induced contact inhibition. The evolution of glucose and oxygen concentration inside the tumour spheroid is tracked in a diffusion solver in correspondence to in vitro or in vivo boundary conditions and a corresponding local nutrient uptake by single cells.
Radiation effects are implemented based on the measured single cell survival in the linear-quadratic model. The survival probability will be affected by the radiosensitivity of the current cycle phase and the local oxygen concentration. Quiescent cells will reduce the effective dose they receive as a consequence of their increased radioresistance. The radiation model includes a fast response to fatal DNA damage through cell apoptosis and a slow response via cell loss due to misrepair during the radiation-induced G2-block.
A simplified model for drug delivery in chemotherapy is implemented.
The model can describe the growth dynamics of spheroids in accordance to experimental data, including total number of cells, histological structure and cell cycle distribution. Investigations of possible mechanisms for growth saturation reveal a critical dependence of tumour growth on the shedding rate of cells from the surface.
In response to a dose of irradiation, a synchronisation of the cell cycle progression within the tumour is observed. This will lead to cyclic changes in the overall radiation sensitivity of the tumour which are quantified using an enhancement measure in comparison to the expected radiosensitivity of he tumour. A transient strong peak in radiosensitivity enhancement is observed after administration of irradiation. Mechanisms which influence the peak timing and development are systematically investigated, revealing quiescence and reactivation of cells to be a central mechanism for the enhancement.
Direct redistribution of cells due to different survival in cell cycle phases, re-activation of quiescent cells in response to radiation-induced cell death and blocking of DNA damaged cells at the G2/M checkpoint are identified as the main mechanisms which contribute to a synchronisation and determine the radiosensitivity increase. A typical time scale for the development of radiosensitivity and the relaxation of tumours to a steady-state after irradiation is identified, which is related to the typical total cell cycle time.
A range of clinical radiotherapy schedules is tested for their performance within the simulation and a systematic comparison with alternative delivery schedules is performed, in order to identify schedules which can most effectively employ the described transient enhancement effects. In response to high-dose schedules, a dissolution of the tumour spheroid into smaller aggregates can be observed which is a result of the loss of integrity in the spheroid that is associated with high cell death via apoptosis. Fractionated irradiation of spheroids with constant dose per time unit but different inter-fraction times clearly reveals optimal time-intervals for radiation, which are directly related to the enhancement response of the tumour.
In order to test the use of triggered enhancement effects in tumours, combinations of trigger- and effector doses are examined for their performance in specific treatment regimens. Furthermore, the automatic identification and triggering in response to high enhancement periods in the tumour is analysed.
While triggered schedules and automatic schedules both yield a higher treatment efficiency in comparison to conventional schedules, treatment optimisation is a revealed to be a global problem, which cannot be sufficiently solved using local optimisation only.
The spatio-temporal dynamics of hypoxia in the tumour are studied in response to irradiation. Microscopic, diffusion-induced reoxygenation dynamics are demonstrated to be on a typical time-scale which is in the order of fractionation intervals. Neoadjuvant chemotherapy with hydroxyurea can yield a drastic improvement of radiosensitivity via cell cycle synchronisation and specific toxicity against radioresistant S-phase cells.
The model makes clear predictions of radiation schedules which are especially effective as a result of triggered cell cycle-based radiosensitivity enhancement. Division of radiation into trigger and effector doses is highly effective and especially suited to be combined with adjuvant chemotherapy in order to limit regrowth of cells.

Different numerical approaches and algorithms arising in the context of modelling of cellular tissue evolution are discussed in this thesis. Being suited in particular to off-lattice agent-based models, the numerical tool of three-dimensional weighted kinetic and dynamic Delaunay triangulations is introduced and discussed for its applicability to adjacency detection. As there exists no implementation of a code that incorporates all necessary features for tissue modelling, algorithms for incremental insertion or deletion of points in Delaunay triangulations and the restoration of the Delaunay property for triangulations of moving point sets are introduced. In addition, the numerical solution of reaction-diffusion equations and their connection to agent-based cell tissue simulations is discussed. In order to demonstrate the applicability of the numerical algorithms, biological problems are studied for different model systems: For multicellular tumour spheroids, the weighted Delaunay triangulation provides a great advantage for adjacency detection, but due to the large cell numbers the model used for the cell-cell interaction has to be simplified to allow for a numerical solution. The agent-based model reproduces macroscopic experimental signatures, but some parameters cannot be fixed with the data available. A much simpler, but in key properties analogous, continuum model based on reaction-diffusion equations is likewise capable of reproducing the experimental data. Both modelling approaches make differing predictions on non-quantified experimental signatures. In the case of the epidermis, a smaller system is considered which enables a more complete treatment of the equations of motion. In particular, a control mechanism of cell proliferation is analysed. Simple assumptions suffice to explain the flow equilibrium observed in the epidermis. In addition, the effect of adhesion on the survival chances of cancerous cells is studied. For some regions in parameter space, stochastic effects may completely alter the outcome. The findings stress the need of establishing a defined experimental model to fix the unknown model parameters and to rule out further models.