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Symposium "Multiscale Modeling of Morphogenesis" on occasion of PhD Defense Sonja Boas

 

On 22 December 2015, 10:00-13:00 we will organize a minisymposium "Multiscale Modeling of Morphogenesis". The symposium will be held December 22, at the Academiegebouw Leiden zaal 1. See below for the program.

The symposium is on the occasion of the PhD defense of Sonja Boas later that day, 16:15 in the 'Senaatskamer' of the Academiegebouw Leiden.

You are all very welcome to both events.

With best wishes,
Sonja Boas and Roeland Merks


Program:

10.00-10.40 Sonja Boas, CWI and Leiden University: Tip cell overtaking occurs as a side effect of sprouting in computational models of angiogenesis
10.40-11.20 Joost Beltman, Leiden University: Modeling searching and killing of target cells by T cells
11.20-11.40 Break
11.40-12.20 Kirsten ten Tusscher, Utrecht University: Modeling lateral root development; Bootstrapping and taming new meristems
12.20-13.00 Liesbet Geris, Université de Liège: Computational bone tissue engineering: in vitro, in vivo, in silico
15:15 “Lekenpraatje” Sonja Boas
16:15 PhD Defense Sonja Boas

Abstracts:
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Tip cell overtaking occurs as a side effect of sprouting in computational models of angiogenesis

Sonja E. M. Boas1,2 and Roeland M.H. Merks1,2
1. Life Sciences, Centrum Wiskunde & Informatica (CWI), The Netherlands;
2. Mathematical Institute, Leiden University, The Netherlands


During angiogenesis, the formation of new blood vessels from existing ones, endothelial cells differentiate into tip and stalk cells, after which one tip cell leads the sprout. More recently, this picture has changed. It has become clear that endothelial cells compete for the tip position during angiogenesis: a phenomenon named tip cell overtaking. The biological function of tip cell overtaking is not yet known. From experimental observations, it is unclear to what extent tip cell overtaking is a side effect of sprouting or to what extent it is regulated through a VEGF-Dll4-Notch signaling network and thus might have a biological function. To address this question, we studied tip cell overtaking in computational models of angiogenic sprouting in absence and in presence of VEGF-Dll4-Notch signaling.

We looked for tip cell overtaking in two existing Cellular Potts models of angiogenesis. In these simulation models angiogenic sprouting-like behavior emerges from a small set of plausible cell behaviors. In the first model, cells aggregate through contact-inhibited chemotaxis. In the second model the endothelial cells assume an elongated shape and aggregate through (non-inhibited) chemotaxis. In both these sprouting models the endothelial cells spontaneously migrate forwards and backwards within sprouts, suggesting that tip cell overtaking might occur as a side effect of sprouting. In accordance with other experimental observations, in our simulations the cells' tendency to occupy the tip position can be regulated when two cell lines with different levels of Vegfr2 expression are contributing to sprouting (mosaic sprouting assay), where cell behavior is regulated by a simple VEGF-Dll4-Notch signaling network.

Our modeling results suggest that tip cell overtaking can occur spontaneously due to the stochastic motion of cells during sprouting. Thus, tip cell overtaking and sprouting dynamics may be interdependent and should be studied and interpreted in combination. VEGF-Dll4-Notch can regulate the ability of cells to occupy the tip cell position in our simulations. We propose that the function of VEGF-Dll4-Notch signaling might not be to regulate which cell ends up at the tip, but to assure that the cell that randomly ends up at the tip position acquires the tip cell phenotype.

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Modeling searching and killing of target cells by T cells
Joost Beltman - Leiden Academic Center for Drug Research, Leiden University

T cells can detect and kill virus-infected cells and tumor cells, and play a critical role in immune protection. Little is known on how these killer cells migrate to infected cells, and on how many killers are required to control a viral infection or tumor. Here, we analyze the searching and killing by T cells in order to obtain a better understanding of these processes. First, we describe and analyze the migration of T cells within HSV-1 infected epidermis in vivo and demonstrate that activated T cells display a subtle, distance-dependent chemotaxis towards clusters of infected cells. Through a combination of long-term imaging and modeling, we subsequently show that this behavior is crucial for efficient target localization and T cell accumulation at effector sites. Second, we use a modeling approach to investigate how the killing efficiency varies with killer cell and target cell numbers. We find that the relative saturation in killer and target cell densities depends on whether a T cell can kill multiple target cells at the same time, and whether a target cell can be killed by many T cells together. Such data can be well described by a double-saturation function with two different saturation constants, and we subsequently apply this function to experimental data.

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Modeling lateral root development; Bootstrapping and taming new meristems
Kirsten ten Tusscher, Bioinformatics and Theoretical Biology, Utrecht University

In plants, new organs such as lateral roots are continuously formed
throughout the life of a plant. Since plant cells are tethered to
one another via cell walls, formation of a new organ can not occur
through the migration of stem cells from elsewhere. Thus, formation
of a new lateral root requires the de-novo formation of a meristem
with a stem cell niche.

Using a multi-scale modeling approach I will demonstrate how feedbacks
between the major plant hormone auxin, the PLETHORA transcription factors
and auxin transporting and biosynthesizing genes allow for the boostrapping
of a new meristem. Furthermore, I will show that as a side effect of their
requirement to generate a new meristem from scratch, these positive feedbacks
generate a meristem that keeps growing in size. Finally I will show how an
antagonism that arises only secondarily, controlled by the meristem itself,
can allow for stabilisation of meristem size and how this is consistent
with experimental data.
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Liesbet Geris, Biomechanics Research Unit, University of Liège, Belgium; Prometheus, division of Skeletal Tissue Engineering , University of Leuven, Belgium; Biomechanics Section, University of Leuven, Belgium
Computational bone tissue engineering: in vitro, in vivo, in silico

The creation of man-made living implants is the holy grail of tissue engineering (TE). As basic science advances, one of the major challenges in TE is the translation of the increasing biological knowledge on complex cell and tissue behavior into a predictive and robust engineering process. Mastering this complexity is an essential step towards clinical applications of TE. Computational modeling allows to study the biological complexity in a more integrative and quantitative way. Specifically, computational tools can help in quantifying and optimizing the TE product and process but also in assessing the influence of the in vivo environment on the behavior of the TE product after implantation.  
In this talk, I will use the example of bone tissue engineering to demonstrate how computational modeling can contribute in all aspects of the TE product development cycle: cells, carriers, culture conditions and clinics.  Depending on the specific question that needs to be answered the optimal model systems can vary from single scale to multiscale.  Furthermore, depending on the available information, model systems can be purely data-driven or more hypothesis-driven in nature. 
The first example that will be discussed is that of cell culture.  Gene regulatory models can help to investigate the stability of specific cell states and their basins of attraction. Furthermore, specific medium compositions can be designed to push cells into a certain state and to keep them there.  Both a literature-based and a data-based approach have been developed to capture the processes of chondrogenic differentiation in the growth plate. The second example is that of biomaterial design. In order to optimize bioceramics-based biomaterials for bone tissue engineering, we have developed models simulating the degradation of the biomaterials upon in vivo implantation, as well as the influence the degradation products have on the local biology. Extensive screening experiments have guided the model formation.  In turn, the model is used to predict the bone formation capacity of bioceramics-based biomaterials in combination with a specific cell source. For the culture of tissue engineering constructs composed of cells and carriers, bioreactors are used. In order to follow-up the biological events occurring inside the bioreactor, computational models are of great help. We have developed a model capable of simulating neotissue growth in perfusion bioreactors, including the influence of scaffold geometry, fluid flow, oxygen and lactate on the speed of growth. A last example will briefly touch upon the possibilities of computational models in assessing the in vivo effect of specific treatment strategies for bone regeneration. We have developed a model of in vivo bone regeneration with a thorough description of the role of angiogenesis and we are currently testing the effect of a variety of patient properties (defect size, type of trauma, congenital problems) on the regeneration outcome.