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Amsterdam-Zuidoost, Netherlands

Staiger C.,Life science Group | Staiger C.,Netherlands Cancer Institute | Cadot S.,Netherlands Cancer Institute | Kooter R.,Delft Bioinformatics Laboratory | And 6 more authors.
PLoS ONE | Year: 2012

Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single genes classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single genes classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single genes classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single genes sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single genes classifiers for predicting outcome in breast cancer. © 2012 Staiger et al. Source


Beisser D.,University of Wurzburg | Brunkhorst S.,University of Wurzburg | Dandekar T.,University of Wurzburg | Klau G.W.,Life science Group | And 3 more authors.
Bioinformatics | Year: 2012

Motivation: High-throughput molecular data provide a wealth of information in ththat can be integrated into network analysis. Several approaches exist that identify functional modulese context of integrated biological networks. The objective of this study is 2-fold: first, to assess the accuracy and variability of identified modules and second, to develop an algorithm for deriving highly robust and accurate solutions.Results: In a comparative simulation study accuracy and robustness of the proposed and established methodologies are validated, considering various sources of variation in the data. To assess this variation, we propose a jackknife resampling procedure resulting in an ensemble of optimal modules. A consensus approach summarizes the ensemble into one final module containing maximally robust nodes and edges. The resulting consensus module identifies and visualizes robust and variable regions by assigning support values to nodes and edges. Finally, the proposed approach is exemplified on two large gene expression studies: diffuse large B-cell lymphoma and acute lymphoblastic leukemia. © The Author 2012. Published by Oxford University Press. All rights reserved. Source


van Hoek M.J.A.,Centrum Wiskunde and Informatica | van Hoek M.J.A.,Netherlands Institute for Systems Biology | Merks R.M.H.,Centrum Wiskunde and Informatica | Merks R.M.H.,Netherlands Institute for Systems Biology | Merks R.M.H.,Leiden University
BMC Systems Biology | Year: 2012

Background: Low-yield metabolism is a puzzling phenomenon in many unicellular and multicellular organisms. In abundance of glucose, many cells use a highly wasteful fermentation pathway despite the availability of a high-yield pathway, producing many ATP molecules per glucose, e.g., oxidative phosphorylation. Some of these organisms, including the lactic acid bacterium Lactococcus lactis, downregulate their high-yield pathway in favor of the low-yield pathway. Other organisms, including Escherichia coli do not reduce the flux through the high-yield pathway, employing the low-yield pathway in parallel with a fully active high-yield pathway. For what reasons do some species use the high-yield and low-yield pathways concurrently and what makes others downregulate the high-yield pathway? A classic rationale for metabolic fermentation is overflow metabolism. Because the throughput of metabolic pathways is limited, influx of glucose exceeding the pathway's throughput capacity is thought to be redirected into an alternative, low-yield pathway. This overflow metabolism rationale suggests that cells would only use fermentation once the high-yield pathway runs at maximum rate, but it cannot explain why cells would decrease the flux through the high-yield pathway.Results: Using flux balance analysis with molecular crowding (FBAwMC), a recent extension to flux balance analysis (FBA) that assumes that the total flux through the metabolic network is limited, we investigate the differences between Saccharomyces cerevisiae and L. lactis that downregulate the high-yield pathway at increasing glucose concentrations, and E. coli, which keeps the high-yield pathway functioning at maximal rate. FBAwMC correctly predicts the metabolic switching mode in these three organisms, suggesting that metabolic network architecture is responsible for differences in metabolic switching mode. Based on our analysis, we expect gradual, "overflow-like" switching behavior in organisms that have an additional energy-yielding pathway that does not consume NADH (e.g., acetate production in E. coli). Flux decrease through the high-yield pathway is expected in organisms in which the high-yield and low-yield pathways compete for NADH. In support of this analysis, a simplified model of metabolic switching suggests that the extra energy generated during acetate production produces an additional optimal growth mode that smoothens the metabolic switch in E. coli.Conclusions: Maintaining redox balance is key to explaining why some microbes decrease the flux through the high-yield pathway, while other microbes use "overflow-like" low-yield metabolism. © 2012 van Hoek and Merks; licensee BioMed Central Ltd. Source


Szabo A.,Life science Group | Szabo A.,Netherlands Institute for Systems Biology | Merks R.M.H.,Life science Group | Merks R.M.H.,Netherlands Institute for Systems Biology | Merks R.M.H.,Leiden University
Frontiers in Oncology | Year: 2013

Despite a growing wealth of available molecular data, the growth of tumors, invasion of tumors into healthy tissue, and response of tumors to therapies are still poorly understood. Although genetic mutations are in general the first step in the development of a cancer, for the mutated cell to persist in a tissue, it must compete against the other, healthy or diseased cells, for example by becoming more motile, adhesive, or multiplying faster. Thus, the cellular phenotype determines the success of a cancer cell in competition with its neighbors, irrespective of the genetic mutations or physiological alterations that gave rise to the altered phenotype. What phenotypes can make a cell "successful" in an environment of healthy and cancerous cells, and how? A widely used tool for getting more insight into that question is cell-based modeling. Cell-based models constitute a class of computational, agent-based models that mimic biophysical and molecular interactions between cells. One of the most widely used cell-based modeling formalisms is the cellular Potts model (CPM), a lattice-based, multi particle cell-based modeling approach. The CPM has become a popular and accessible method for modeling mechanisms of multicellular processes including cell sorting, gastrulation, or angiogenesis. The CPM accounts for biophysical cellular properties, including cell proliferation, cell motility, and cell adhesion, which play a key role in cancer. Multiscale models are constructed by extending the agents with intracellular processes including metabolism, growth, and signaling. Here we review the use of the CPM for modeling tumor growth, tumor invasion, and tumor progression. We argue that the accessibility and flexibility of the CPM, and its accurate, yet coarse-grained and computationally efficient representation of cell and tissue biophysics, make the CPM the method of choice for modeling cellular processes in tumor development. © 2013 Szabó and Merks. Source


Boas S.E.M.,Life science Group | Boas S.E.M.,Netherlands Institute for Systems Biology | Merks R.M.H.,Life science Group | Merks R.M.H.,Netherlands Institute for Systems Biology | Merks R.M.H.,Leiden University
Journal of the Royal Society Interface | Year: 2014

A key step in blood vessel development (angiogenesis) is lumen formation: the hollowing of vessels for blood perfusion. Two alternative lumen formation mechanisms are suggested to function in different types of blood vessels. The vacuolation mechanism is suggested for lumen formation in small vessels by coalescence of intracellular vacuoles, a view that was extended to extracellular lumen formation by exocytosis of vacuoles. The cell-cell repulsion mechanismis suggested to initiate extracellular lumen formation in large vessels by active repulsion of adjacent cells, and active cell shape changes extend the lumen. We used an agent-based computer model, based on the cellular Potts model, to compare and study both mechanisms separately and combined. An extensive sensitivity analysis shows that each of the mechanisms on its own can produce lumens in a narrow region of parameter space. However, combining both mechanisms makes lumen formation much more robust to the values of the parameters, suggesting that the mechanisms may work synergistically and operate in parallel, rather than in different vessel types. © 2014 The Authors. Source

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