Blood Vessel Tortuosity Selects against Evolution of Agressive Tumor Cells in Confined Tissue Environments: a Modeling Approach

Cancer is a disease of cellular regulation, often initiated by genetic mutation within cells, and leading to a heterogeneous cell population within tissues. In the competition for nutrients and growth space within the tumors the phenotype of each cell determines its success. Selection in this process is imposed by both the microenvironment (neighboring cells, extracellular matrix, and diffusing substances), and the whole of the organism through for example the blood supply. In this view, the development of tumor cells is in close interaction with their increasingly changing environment: the more cells can change, the more their environment will change. Furthermore, instabilities are also introduced on the organism level: blood supply can be blocked by increased tissue pressure or the tortuosity of the tumor-neovascular vessels. This coupling between cell, microenvironment, and organism results in behavior that is hard to predict. Here we introduce a cell-based computational model to study the effect of blood flow obstruction on the micro-evolution of cells within a cancerous tissue. We demonstrate that stages of tumor development emerge naturally, without the need for sequential mutation of specific genes. Secondly, we show that instabilities in blood supply can impact the overall development of tumors and lead to the extinction of the dominant aggressive phenotype, showing a clear distinction between the fitness at the cell level and survival of the population. This provides new insights into potential side effects of recent tumor vasculature renormalization approaches

Vascular networks due to dynamically arrested crystalline ordering of elongated cells

Recent experimental and theoretical studies suggest that crystallization and glass-like solidification are useful analogies for understanding cell ordering in confluent biological tissues. It remains unexplored how cellular ordering contributes to pattern formation during morphogenesis. With a computational model we show that a system of elongated, cohering biological cells can get dynamically arrested in a network pattern. Our model provides a new explanation for the formation of cellular networks in culture systems that exclude intercellular interaction via chemotaxis or mechanical traction.Comment: 11 pages, 4 figures. Published as: Palm and Merks (2013) Physical Review E 87, 012725. The present version includes a correction in the calculation of the nematic order parameter. Erratum submitted to PRE on Jun 5th 2013. The correction does not affect the conclusion

Particle-based simulation of ellipse-shaped particle aggregation as a model for vascular network formation

Computational modelling is helpful for elucidating the cellular mechanisms driving biological morphogenesis. Previous simulation studies of blood vessel growth based on the Cellular Potts model (CPM) proposed that elongated, adhesive or mutually attractive endothelial cells suffice for the formation of blood vessel sprouts and vascular networks. Because each mathematical representation of a model introduces potential artifacts, it is important that model results are reproduced using alternative modelling paradigms. Here, we present a lattice-free, particle-based simulation of the cell elongation model of vasculogenesis. The new, particle-based simulations confirm the results obtained from the previous Cellular Potts simulations. Furthermore, our current findings suggest that the emergence of order is possible with the application of a high enough attractive force or, alternatively, a longer attraction radius. The methodology will be applicable to a range of problems in morphogenesis and noisy particle aggregation in which cell shape is a key determining factor.Comment: 9 pages, 11 figures, 2 supplementary videos (on Youtube), submitted to Computational Particle Mechanics, special issue: Jos\'e-Manuel Garcia Aznar (Ed.) Particle-based simulations on cell and biomolecular mechanic

Aim of the breeding research in LIB-SP3 and the methods to be used

‘Low input’ pig production systems are usually characterised by smaller herd size, more space per animal, lower capital investment, often outdoor management, provision of bedding, greater labour requirement and focus on animal welfare. Examples of ‘low input’ pig production systems are “Iberico” in Spain, “Neuland” pigs in Germany, “Scharrelvarkens” in The Netherlands, “Natura Farm” in Switzerland, “Label Rouge” in France and “Freedom Food” in United Kingdom. Organic pig production systems have similar characteristics to those described for ‘low input systems’ above. However, organic farming standards prescribe e.g. low stocking densities, access to outdoor runs, restrict the level of ‘bought in, non-organic’ feeds etc, which usually results in higher management and feed costs and more limited dietary composition choices than in other ‘low input’ systems

Elucidating different aspects of speed of information processing: comparison of behavioral response latency and P300 latency in a modified Hick reaction time task

The aim of the present work was to get a more detailed understanding of the functional significance of the P300 latency. P300 latency is often used as measure of stimulus evaluation time. However, the interpretation of P300 latency as stimulus evaluation time was challenged by findings of a P300 latency sensitivity to response-related manipulations. In two studies with samples from two different countries, not only RT, but also P300 latency were used as measures of speed of information processing examining the Hick paradigm. P300 latency has been used as speed of information processing measure before, but to my knowledge never in the Hick task. The advantage of using the Hick paradigm is that the influence of response selection on P300 latency can be systematically investigated while keeping stimulus evaluation constant and minimal. Furthermore, a comparison of P300 latency and RT revealed some more information about the functionality of P300 latency. By contrasting both speed of information processing measures as predictors of intelligence, it was also investigated if RT and P300 latency explain common and/or unique parts of variance in intelligence. The present investigation replicated once more the increase of RT in dependence of the amount of bit of information that needs to be processed. Furthermore, in accordance with the mental speed approach of intelligence, participants with higher intelligence were performing faster in the Hick task than participants with lower intelligence levels. Moreover, this inverse relation between RT and intelligence was enhanced across complexity. In addition, the present work also revealed some new insights about the functional significance of P300 latency. These insights are the following: 1. A clear P300 component was elicited under all four bit conditions, including the 0 bit condition. This indicates that even in simple reaction time tasks some cognitive processing is activated. P300 is often associated with a context updating of the current mental representation in the working memory. Since each stimulus under the 0 bit condition is exactly the same as the previous one, present data suggests that P300 might have other or additional functions than context updating. One alternative function could be a monitoring role, which is determining the stimulus-response association. 2. P300 latency did increase across bit conditions. This indicates that P300 latency is not only sensitive to manipulations that focus on stimulus evaluation, but also to manipulations focusing on response selection. This finding is not compatible with the idea of P300 latency as an index of stimulus evaluation time. 3. RT and P300 latency are often expected to capture the time of similar underlying processes. Indeed, P300 latency is, similar as RT, increasing across bit conditions. However, P300 latency and RT were not related. This suggests that P300 latency and RT are not reflecting the same aspects of speed of information processing. P300 latency might be proportional to stimulus evaluation time in task that focus on stimulus evaluation. But, as the current results show, it is probably determined by completely different processes than RT. Further research is needed to get a more complex pictures of the determinants of the P300 component. 4. The relation between P300 latency and intelligence is still not clear. Present data does not confirm the suggestion of Houlihan et al. (1998) that the relation of RT and intelligence might be partly mediated by response-related processes. However, there might be other factors like subjective task difficulty and complexity, or the subject’s strategy that play a significant role in individual differences in both, P300 latency and intelligence. Further research is needed to get a more complex pictures of these factors

Cell-Based Modeling

A cell-based model is a simulation model that predicts collective behavior of cell-clusters from the behavior and interactions of individual cells. The inputs to a cell-based model are cell behaviors as observed in experiments or deriving from single cell models, including the cellular responses to cues from the micro-environment. The cell behaviors are encoded in a set of biologically plausible rules that the simulated cells will follow. The outputs of a cell-based model are the patterns and behaviors that follow indirectly from the cell behaviors and the cellular interactions. Cell-based models resemble agent-based models, but typically contain more biophysically-detailed descriptions of the individual cells

A cell-based model of extracellular-matrix-guided endothelial cell migration during angiogenesis.

Angiogenesis, the formation of new blood vessels sprouting from existing ones, occurs in several situations like wound healing, tissue remodeling, and near growing tumors. Under hypoxic conditions, tumor cells secrete growth factors, including VEGF. VEGF activates endothelial cells (ECs) in nearby vessels, leading to the migration of ECs out of the vessel and the formation of growing sprouts. A key process in angiogenesis is cellular self-organization, and previous modeling studies have identified mechanisms for producing networks and sprouts. Most theoretical studies of cellular self-organization during angiogenesis have ignored the interactions of ECs with the extra-cellular matrix (ECM), the jelly or hard materials that cells live in. Apart from providing structural support to cells, the ECM may play a key role in the coordination of cellular motility during angiogenesis. For example, by modifying the ECM, ECs can affect the motility of other ECs, long after they have left. Here, we present an explorative study of the cellular self-organization resulting from such ECM-coordinated cell migration. We show that a set of biologically-motivated, cell behavioral rules, including chemotaxis, haptotaxis, haptokinesis, and ECM-guided proliferation suffice for forming sprouts and branching vascular trees