A computational cell-based model of lumen formation during angiogenesis

Sonja E. M. Boas1,2 and Roeland M. H. Merks1,2

1Dept of Life Sciences, CWI, Amsterdam; 2NCSB-NISB, Amsterdam, The Netherlands.

During embryogenesis, blood vessels can split and branch to form new vessels. This remodeling, called angiogenesis, is required for many processes throughout our entire lifespan, such as wound healing and also tumour growth. New blood vessels become hollow (lumen formation) to allow blood perfusion. Years of extensive study in the wet lab resulted in three main hypotheses to explain lumen formation: vacuolation, repulsion and cavitation. Which of these mechanisms is predominant during angiogenesis is a topic of intense debate. We present a multiscale computational model that reproduces the three lumen formation mechanisms from a single underlying mechanism describing membrane polarization and vesicle cycling. Which of the three mechanisms shows up depends of the relative orientation of the cells within the vessel.

Our model represents the branching of a growing vessel that eventually forms a lumen. The shape of the cells is modeled explicitly and polarization of the membrane is included to facilitate local lumen formation. Membrane polarization, into a basolateral and an apical region, is triggered by contact with the extracellular matrix. To model the repulsion hypothesis, apical membranes of different cells can repulse one other. Polarized membrane that is internalized can become vesicles, which preferentially fuse to one another and to the apical membrane. These vesicles can subsequently be secreted to form a lumen in consensus with the vacuolation hypothesis. Cells that lost contact with the extracellular matrix depolarize and eventually die to form an open space, resembling the cavitation hypothesis. Each of these mechanisms in the computational model can be examined to gain new insights in their function and relative importance in lumen formation during branching.

Acknowledgments: We thank the Indiana University and the Biocomplexity Institute for providing the CC3D modeling environment.

Optimal and robust regulation of gene expression

Evert Bosdriesz, Douwe Molenaar, Bas Teusink and Frank Bruggeman Unicellular organisms operate in changing environments. They are subject to strong selective pressure on growth rate maximization which requires the maximization of specific rate of catalysis in biosynthetic modules and ultimately of the entire self-replicating machinery. This goal is attained by synthesizing the biosynthetic machinery in proportions that are optimal under the given environmental conditions. This raises the question what kind of regulatory network can reliably tune gene expression levels towards their optimum. Here we present a simple feedback mechanism for the regulation of gene expression that maximizes the flux per total amount of enzyme in a pathway. We show that this mechanism is able to attain optimal expression levels over a wide range of external conditions, and that the ability to do so is extremely robust. These properties arise because, for nearly optimal enzyme concentration, small fluctuations in the enzyme concentration lead to extremely large fluctuations in the metabolite levels, making these a very reliable regulatory signal. As an example, we study the regulation of the main biosynthetic machinery of Escherichia coli in silico. Because of its simplicity and robustness, we expect that this feedback motif is more commonly used in gene expression regulation.

Title: Modelling PPI networks from protein abundance data in neurons Joachim Kutzera Protein protein interaction in cells is widely explored using analysis of data from high throughput immunoprecipitation. However, especially the search for complexes of interacting proteins in this data is still a challenge. The poster explains our new method for detecting such protein complexes. The method is a heuristic clustering algorithm based on Nearest Neighbor Network clustering and uses both the protein occurrence and the abundance data from multiple immunoprecipitation experiments. The poster gives an overview about our project and shows the application of our method on artificial and real IP data. It further explains how the cluster results can be verified based on known clusters.

Cell-based simulations of stretch guidance in network formation and sprouting René van Oers, Roeland Merks Dept of Life Sciences, CWI, Amsterdam; NCSB-NISB, Amsterdam It is known that cell migration is influenced by substrate stretch. Cells placed on stretched substrates tend to orient and extend along the stretch. Cells not only respond to externally imposed stretch, but can create stretch themselves, thereby influencing neigboring cells. This has important consequences for morphogenesis, for instance in blood vessel development. Endothelial cells in vitro form connections along stretch lines generated by their own contractile forces [1,2]. We study this phenomenon in the Cellular Potts Model (CPM), by adding cell traction and stretch-guidance. The CPM has previously been used to study blood vessel development via various possible mechanisms, including cell elongation, chemotaxis, and contact inhibition [3]. We integrated the CPM grid with a finite element (FE) mesh, so substrate stretch can be determined. Then we added a strain-term to the CPM algorithm, driving cell extensions along stretch. Traction forces were based on cell shape, according to a recent model by Lemmon [4]. With stretch guidance and cell traction implemented we first studied cell-cell interaction by comparing the behavior of single cells and pairs of neighboring cells on substrates of various stiffness. In agreement with similar experimental studies [1], we found that cell-cell interaction via stretch is strongest when the substrate is neither too soft nor too stiff. In simulations with multiple cells this interaction results in 'stretch highways' between nearby groups of cells, which in turn cause the cells to migrate and connect over these highways [2]. Apart from network formation, we found that this mechanism also produces 'sprouting' from an initial cluster of cells, resembling the sprouting behavior of endothelial cells in vitro.

Following stretch lines generated by their own traction forces, cells migrate in sprouts from an initial cluster of cells. References: [1] Reinhart-King CA, Dembo M, Hammer DA. Cell-cell mechanical communication through compliant

substrates. Biophys J 2008a;95:6044–51. [2] Califano JP, Reinhart-King CA. Exogenous and endogenous force regulation of endothelial cell

behavior. J Biomech 2010;43:79-86. [3] Merks RMH, Glazier JA. Dynamic mechanisms of blood vessel growth. Nonlinearity 2006;19:C1-

C10. [4] Lemmon CA, Romer LH. A predictive model of cell traction forces based on cell geometry.

Biophys J 2010;99:L78-80.

Tip-stalk cell differentiation enables fast formation of highly connected vascular networks Margriet Palm and Roeland Merks Angiogenesis, the formation of new blood vessels from existing vessels, is driven by the collective behavior of cells from the blood vessel wall. To better understand how the collective behavior of these cells results in the formation of vascular networks we use a cell-based computational model. In this model properties and behavior of single cells are prescribed and the model predicts the tissue that is formed by these cells. In sprouting angiogenesis, three distinct phenotypes of cells are recognized. Tip and stalk cells that form the sprout and phalanx cells that remain in the vessel trunk. Tip cells are highly motile and extend a multitude of philopodia, and lead the growing sprout. Stalk cells follow the tip cells and form a stable sprout. Experiments have shown that without either tip or stalk cells no functional vasculature can be formed. Yet, it is not well known how tip-stalk cell differentiation helps vascular network formation. Furthermore, it is unclear how the dynamic selection of tip cells influences network formation. We have added tip and stalk cell differentiation and dynamic tip cell selection to our cell-based model of angiogenesis. With this model we investigated both the importance of tip-stalk cell differentiation and the role of dynamic tip cell selection. We analyzed both the final vascular networks and the dynamics of the network formation. With the model with only tip-stalk cell differentiation and no dynamic tip cell selection we show that tip-stalk cell differentiation can influence network topology. The final toplogy depends on the proportion of tip and stalk cells. We identified an optimal tip-stalk cell proportion at which the most highly connected networks are formed. Furthermore, tip-stalk cell differentiation increases the speed of network formation in our model. When tip cells are selected dynamically, the networks are still highly connected but they become less dense. Furthermore, dynamic tip cell selection allows for branch stabilization after anastomosis. Altogether our computational model suggests that tip-stalk cell differentiation may affect the toplogy of the vascular network and the dynamics of vascular network formation.

Strategies for analyzing spectral count data in label-free tandem mass spectrometry-based

proteomics

Thang V. Pham and Connie R. Jimenez

OncoProteomics Laboratory, Department of Medical Oncology, VU University Medical Center De Boelelaan 1117, 1081 HV Amsterdam, the Netherlands

Label-free strategies for quantitative proteomics provide a versatile and economical alternative to

labeling-based proteomics strategies. We have shown for different types of biological samples obtained

via relatively simple and more complex workflows using analysis of technical and biological replicates

(cancer cell secretome, Piersma et al., Journal of Proteome Research 2010; subnuclear fractions isolated

from clinical material, Albrethsen et al., Molecular and Cellular Proteomics 2010; depleted cerebrospinal

fluid, Fratantoni et al., Proteomics Clinical Applications 2010) that spectral counting-based label-free

quantitation is a promising avenue for biomarker discovery. Analyzing spectral count data generated

from these studies is however not straightforward as commonly used techniques for genomics data

analysis are not suitable.

We employ the beta-binomial distribution for significance analysis of independent samples, which

integrates both within-sample variation and between-sample variation into a single statistical model

(Pham et al., Bioinformatics 2010). For cluster analysis we devise a novel distance measure between

samples based on the Jeffrey divergence. This measure prevents highly abundant proteins from

dominating others in contribution to the total sample difference. Finally, a new test based on the beta-

binomial distribution has been developed for significance analysis of paired samples.

We showed that the beta-binomial test performs favorably in comparison with other methods on

several datasets in terms of both true detection rate and false positive rate. In addition, it can be applied

for experiments with one or more replicates, and for multiple condition comparisons. We showed the

quality of the new distance measure for clustering. Finally, we demonstrated the usefulness of our

strategies in the identification of several potential biomarkers in the secretomes of mouse embryonic

fibroblasts, the enrichment of brain derived proteins in the depleted cerebrospinal fluid fraction, and

the identification of proteins enriched in the nuclear matrix fraction and regulation in adenoma to

carcinoma progression in colorectal cancer.

ESTABLISHING A MATHEMATICAL MODEL OF THE CALCIUM

SENSING RECEPTOR

SUSANNE ROTH

The external calcium sensing receptor (CaSR) is a G-protein coupled Receptor (GPCR)belonging to the family C of GPCRs. It is expressed on the surface of a huge variety ofcell types, involved mainly in the calcium homeostasis in the serum. The active CaSR ispresent at the plasma membrane as homodimer and it is known to have binding sites formultiple ligands besides calcium. It is able to activate the G proteins Gq/11, Gi/o, andG12/13, thus diverse signalling pathways are activated by this receptor. Calcium bindingoccurs in a cooperative manner and the receptor can exists in several different confor-mational states. At the moment, the kinetic mechanism of ligand-specific signalling aswell as the regulatory mechanisms that cause signalling biased to specific activation ofthe different G-proteins are still unknown. The aim of this research project is to estab-lish a mathematical model of the processes on the receptor level that explains how theCaSR is capable of achieving ligand-dependent signalling through cooperativity, confor-mational changes, and allostery. After the mathematical model describing all processeson the receptor level is established, kinetic parameters will be fitted to experimentaldata, allowing new insights into the regulation of the CaSR.

1

Anne Schwabe Inferring burst statistics from single molecule mRNA FISH Transcription in eukaryotes as well as prokaryotes can occur in bursts of high activity with many polymerase complexes initiating transcription in short succession, interspersed by periods of little or no transcriptional activity. Since monitoring single transcription events over time is technically challenging, we propose an alternative method of investigating these burst statistics through the use of single molecule mRNA FISH. Using multiple probe sets targeted to different regions of the same gene (5', middle, 3'), coupled to different fluorescent dyes, gives an indirect measure of the number of transcribing polymerase molecules that have progressed up to the corresponding gene region. Mathematical modeling of the joint distributions of fluorescence intensities at transcription sites collected from many fixed cells, thus allows inferences about the underlying burst statistics.

time

fix cellsΔtx

Δdx

probe set 1 probe set 2 probe set 3

gene of interest

transcription initiation events

determine fluorescence intensity of transcription sitesin all colors relative to spots (single mRNAs) in the cytoplasm

for many cells and use to model burst statistics

Pol II

the timing of recent initiation events is reflected in the positions of Pol II molecules along the gene body, and therefore in the

joint distribution of fluorescence intensities of all 3 colors

ACTIVATION OF -CATENIN SIGNALING BY THE HCMV-ENCODED CHEMOKINE RECEPTOR US28 VIA A NOVEL PATHWAY INVOLVING RHO-ROCK, 14-3-3 AND CBY PROTEINS Erik Slinger1,3*, Ellen V. Langemeijer1,4*

, Sabrina de Munnik1, Andreas Schreiber1, David Maussang1, Henry Vischer1, Sander R. Piersma2, Connie R. Jiménez2, Hessam Tabeian1, Folkert Verkaar1, Rob Leurs1, Marco Siderius1 and Martine J. Smit1 1 From the Division of Medicinal Chemistry, Leiden/Amsterdam Center for Drug Research, VU University Amsterdam, the Netherlands, 2 From OncoProteomics Laboratory, Department of Medical Oncology, VU University Medical Center - Cancer Center Amsterdam, Amsterdam, the Netherlands. 3 Immunology Institute, Mount Sinai School of Medicine, New York, NY, USA. 4 Medicinal Chemistry, Leiden/Amsterdam Center for Drug Research, Leiden University Leiden, the Netherlands. Address correspondence to M.J. Smit, de Boelelaan 1083, 1081 HV Amsterdam, The Netherlands. Fax +31 (0)20 5987610: E-mail: mj.smit@vu.nl * authors contributed equally to this manuscript

Key words:

Human cytomegalovirus, HCMV, US28, -catenin, Chibby (Cby), 14-3-3

Abstract Classical Wnt-mediated β-catenin signaling entails disruption of

the destruction complex, sequestering of CK1 and GSK-3,

thereby preventing the phosphorylation and subsequent

degradation of -catenin. Chronic activation of Wnt/-catenin

signaling is found in a variety of human malignancies including

melanoma, colorectal and hepatocellular carcinomas.

Interestingly, expression of the HCMV-encoded chemokine

receptor US28 in intestinal epithelial cells promotes intestinal

neoplasia in transgenic mice, which is associated with increased

activation of the catenin pathway. In this study we show that

this viral receptor leads to activation of -catenin and enhanced

β-catenin-dependent transcription through a distinct and novel

mechanism. Analysis of the US28 signalosome and inhibitor

studies indicates the involvement of the Rho-ROCK pathway

and a prominent role for 14-3-3 proteins and the 14-3-3 target

protein, Chibby (Cby), culminating in activation of active (non-

phosphorylated) β-catenin. The non-classical activation of the β-

catenin signal transduction pathway by this viral chemokine

receptor may provide alternative regulation of this pathway,

crucial in development of colon cancer and of importance in

HCMV-associated diseases.

Quantitative dissection of the modes of growth inhibition by weak organic acids

in yeast

Azmat Ullah, Rick Orij, Stanley Brul, and Gertien Smits

Department of Molecular Biology and Microbial Food Safety, Swammerdam Institute for

Life Sciences (SILS), University of Amsterdam, The Netherlands

Weak organic acids are naturally occurring compounds that are commercially used as

preservatives in food and beverage industries to extend the shelf life of food products by

inhibiting microbial growth. There are a number of theories that explain the antifungal

properties of these weak acids but the exact mechanism is still unknown. We set out to

quantitatively determine the contributions of the various mechanisms of antifungal

activity of these weak acids, as well as the mechanisms that yeast uses to counteract

their effects. We analyzed the effects of four weak organic acids, differing in lipophilicity,

on growth and intracellular pH (pHi) in Saccharomyces cerevisiae.

The lipophilicity of the acids correlated with the rate of acidification of the cytosol.

However, our data revealed that not initial acidification, but rather the cell’s ability to

restore pHi of cells was a determinant for growth inhibition. This in turn depended on the

nature of the organic anion. We identified long term acidification as the major cause of

growth inhibition in acetic acid stress. Restoration of pHi, and consequently growth rate,

in the presence of this weak acid required the full activity of the plasma membrane

ATPase Pma1p. Surprisingly, the proposed anion export pump Pdr12p was shown to

play an important role in the ability of yeast cells to restore pHi upon lipophilic acid

stress, probably through a charge interaction of anion and proton transport.

On the evolution of morphogenetic models: mechano-chemical interactions and an integrated view of cell differentiation, growth, pattern formation and morphogenesis Severine Urdy In the 1950s, embryology was conceptualized as four relatively independent problems: cell differentiation, growth, pattern formation and morphogenesis. The mechanisms underlying the first three traditionally have been viewed as being chemical in nature, whereas those underlying morphogenesis have usually been discussed in terms of mechanics. Often, morphogenesis and its mechanical processes have been regarded as subordinate to chemical ones. However, a growing body of evidence indicates that the biomechanics of cells and tissues affect in striking ways those phenomena often thought of as mainly under the control of cell-cell signalling. This accumulation of data has led to a revival of the mechano-transduction concept in particular, and of complexity in general, causing us now to consider whether we should retain the traditional conceptualization of development. The researchers’ semantic preferences for the terms ‘patterning’, ‘pattern formation’ or ‘morphogenesis’ can be used to describe three main ‘schools of thought’ which emerged in the late 1970s. In the ‘molecular school’, the term patterning is deeply tied to the positional information concept. In the ‘chemical school’, the term ‘pattern formation’ regularly implies reaction-diffusion models. In the ‘mechanical school’, the term ‘morphogenesis’ is more frequently used in relation to mechanical instabilities. Major differences among these three schools pertain to the concept of self-organization, and models can be classified as morphostatic or morphodynamic. Various examples illustrate the distorted picture that arises from the distinction among differentiation, growth, pattern formation and morphogenesis, based on the idea that the underlying mechanisms are respectively chemical or mechanical. Emerging quantitative approaches integrate the concepts and methods of complex sciences and emphasize the interplay between hierarchical levels of organization via mechano-chemical interactions. They draw upon recent improvements in mathematical and numerical morphogenetic models and upon considerable progress in collecting new quantitative data. This review highlights a variety of such models, which exhibit important advances, such as hybrid, stochastic and multiscale simulations.

Low stress weekends promote adaptation to stressful weeks: The design

principles of the biological response to stress.

Nilgun Yilmaz, Amsterdam, The Netherlands; Alexey Kolodkin, Luxembourg, EU; Nick

Plant, Surrey, UK; Hans Westerhoff, Manchester, UK

Robustness is a fundamental and essential property of evolvable biological systems. It

allows system to conserve its functionalities against internal/external perturbations and

uncertainties. Product inhibition, feed-forward and feed-back inhibition and stimulation,

and regulatory loops within signal transduction networks are a few of the approaches

generated by biological systems to maintain both their robustness and adaptability. In

this study, we described the interactions of the stress hormone cortisol with its two

nuclear receptors, the high affinity glucocorticoid receptor (GR) and the low affinity

pregnane X-receptor (PXR) by using a mathematical model based on realistic kinetic

parameters. We demonstrate the importance of regulatory loops within this network, in

terms of both pharmacodynamic and pharmacokinetic responses. Next, we demonstrate

the network response following cortisol challenge; both a single peak in cortisol

concentration, reminiscent of a single stress event and a repeated cortisol challenge,

reminiscent of repeat stress events with differing frequencies and time frames. As a

conclusion, we reveal that the network is robust towards low frequency perturbations,

shows adaptation at moderate stress frequencies, but shifts to an altered steady state at

high frequency stimulation. The latter might be viewed as a predisposing factor towards

stress-induced pathologies.