Dieter Beule

Evolutionary search for low autocorrelated binary sequences

The search for low autocorrelated binary sequences is a classical example of a discrete frustrated optimization problem. We demonstrate the efficiency of a class of evolutionary algorithms to tackle the problem. A suitable mutation operator using a preselection scheme is constructed, and the optimal parameters of the strategy are determined.

Combining frequency and positional information to predict transcription factor binding sites.

MOTIVATION Even though a number of genome projects have been finished on the sequence level, still only a small proportion of DNA regulatory elements have been identified. Growing amounts of gene expression data provide the possibility of finding coregulated genes by clustering methods. By analysis of the promoter regions of those genes, rather weak signals of transcription factor binding sites may be detected. RESULTS We introduce the new algorithm ITB, an Integrated Tool for Box finding, which combines frequency and positional information to predict transcription factor binding sites in upstream regions of coregulated genes. Motifs are extracted by exhaustive analysis of regular expression-like patterns and by estimating probabilities of positional clusters of motifs. ITB detects consensus sequences of experimentally verified transcription factor binding sites of the yeast Saccharomyces cerevisiae. Moreover, a number of new binding site candidates with significant scores are predicted. Besides applying ITB on yeast upstream regions, the program is run on human promoter sequences. AVAILABILITY ITB is available upon request.

Extracting information from cDNA arrays

High-density DNA arrays allow measurements of gene expression levels (messenger RNA abundance) for thousands of genes simultaneously. We analyze arrays with spotted cDNA used in monitoring of expression profiles. A dilution series of a mouse liver probe is deployed to quantify the reproducibility of expression measurements. Saturation effects limit the accessible signal range at high intensities. Additive noise and outshining from neighboring spots dominate at low intensities. For repeated measurements on the same filter and filter-to-filter comparisons correlation coefficients of 0.98 are found. Next we consider the clustering of gene expression time series from stimulated human fibroblasts which aims at finding co-regulated genes. We analyze how preprocessing, the distance measure, and the clustering algorithm affect the resulting clusters. Finally we discuss algorithms for the identification of transcription factor binding sites from clusters of co-regulated genes. (c) 2001 American Institute of Physics.

Tumour ischaemia by interferon-γ resembles physiological blood vessel regression

The relative contribution of the effector molecules produced by T cells to tumour rejection is unclear, but interferon-γ (IFNγ) is critical in most of the analysed models. Although IFNγ can impede tumour growth by acting directly on cancer cells, it must also act on the tumour stroma for effective rejection of large, established tumours. However, which stroma cells respond to IFNγ and by which mechanism IFNγ contributes to tumour rejection through stromal targeting have remained unknown. Here we use a model of IFNγ induction and an IFNγ–GFP fusion protein in large, vascularized tumours growing in mice that express the IFNγ receptor exclusively in defined cell types. Responsiveness to IFNγ by myeloid cells and other haematopoietic cells, including T cells or fibroblasts, was not sufficient for IFNγ-induced tumour regression, whereas responsiveness of endothelial cells to IFNγ was necessary and sufficient. Intravital microscopy revealed IFNγ-induced regression of the tumour vasculature, resulting in arrest of blood flow and subsequent collapse of tumours, similar to non-haemorrhagic necrosis in ischaemia and unlike haemorrhagic necrosis induced by tumour necrosis factor. The early events of IFNγ-induced tumour ischaemia resemble non-apoptotic blood vessel regression during development, wound healing or IFNγ-mediated, pregnancy-induced remodelling of uterine arteries. A better mechanistic understanding of how solid tumours are rejected may aid the design of more effective protocols for adoptive T-cell therapy.

Global analysis of host tissue gene expression in the invasive front of colorectal liver metastases

Host cell reactions are a crucial determinant for tumor invasion. We analyzed on a genomewide scale gene expression differences between microdissected tissues taken from unaffected liver tissue of a human colorectal tumor (LS174) growing in the livers of nude mice and tissue from the host part of the invasive front. Due to the low degree of interspecies cross‐hybridization of 15% as determined on Affymetrix microarrays, our xenograft model allowed for the distinction of genes of murine versus human origin even if the respective tissues could not be isolated separately. Using the gene ontology (GO) classification, we were able to determine patterns of up‐ and downregulated genes in the liver part of the invasive front. We observed a pronounced overrepresentation, e.g., of the GO terms “extracellular matrix,” “cell communication,” “response to biotic stimulus,” “structural molecule activity” and “cell growth,” indicating a very pronounced host cell response to tumor invasion. On the single gene level, hepatic stellate cell (HSC) activation markers were overrepresented in the liver part of the invasion front. Immunohistochemistry and qPCR confirmed an activation of HSC as well as an increased number of HSC in the invasive front as compared to the noninvaded liver tissue. In summary, our data demonstrate the feasibility of an interspecies differential gene expression approach on a genomewide scale.

Maternal immune activation results in complex microglial transcriptome signature in the adult offspring that is reversed by minocycline treatment

Maternal immune activation (MIA) during pregnancy has been linked to an increased risk of developing psychiatric pathologies in later life. This link may be bridged by a defective microglial phenotype in the offspring induced by MIA, as microglia have key roles in the development and maintenance of neuronal signaling in the central nervous system. The beneficial effects of the immunomodulatory treatment with minocycline on schizophrenic patients are consistent with this hypothesis. Using the MIA mouse model, we found an altered microglial transcriptome and phagocytic function in the adult offspring accompanied by behavioral abnormalities. The changes in microglial phagocytosis on a functional and transcriptional level were similar to those observed in a mouse model of Alzheimer’s disease hinting to a related microglial phenotype in neurodegenerative and psychiatric disorders. Minocycline treatment of adult MIA offspring reverted completely the transcriptional, functional and behavioral deficits, highlighting the potential benefits of therapeutic targeting of microglia in psychiatric disorders.

Adiabatic equation of state and ionization equilibrium of dense plasma

We consider the adiabatic equation of state for partially ionized non-ideal plasma. Plasma isentropes are calculated with the Pade technique in the chemical picture. The interplay of ionization/dissociation equilibrium and non-ideality is investigated.

SIMULATION OF IONIZATION-FRONT PROPAGATION IN DENSE PLASMA BY MARKOFF AUTOMATA

Front propagation in partially ionized dense plasma is described by reaction-diffusion equations. We introduce the new method of Markoff automata for the stochastic simulation of reaction and diffusion in dense plasma which is capable to perform simulations fast and accurate. The method is applied to the investigation of ionization fronts of different shape. A comparison with analytic results for profile and velocity of plane fronts confirms the stochastic simulation data. Furthermore, we present data for an expanding ionization spot.

Biomarker Discovery and Redundancy Reduction towards Classification using a Multi-factorial MALDI-TOF MS T2DM Mouse Model Dataset

BackgroundDiabetes like many diseases and biological processes is not mono-causal. On the one hand multi-factorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics.ResultsWe present a comprehensive work-flow tailored for analyzing complex data including data from multi-factorial studies. The developed approach aims at revealing effects caused by a distinct combination of experimental factors, in our case genotype and diet. Applying the developed work-flow to the analysis of an established polygenic mouse model for diet-induced type 2 diabetes, we found peptides with significant fold changes exclusively for the combination of a particular strain and diet. Exploitation of redundancy enables the visualization of peptide correlation and provides a natural way of feature selection for classification and prediction. Classification based on the features selected using our approach performs similar to classifications based on more complex feature selection methods.ConclusionsThe combination of ANOVA and redundancy exploitation allows for identification of biomarker candidates in multi-dimensional MALDI-TOF MS profiling studies with complex experimental design. With respect to feature selection our method provides a fast and intuitive alternative to global optimization strategies with comparable performance. The method is implemented in R and the scripts are available by contacting the corresponding author.

Stochastic kinetics of electron transitions in nonideal plasmas

Abstract The time evolution of the population of excited atomic and ionic levels for an ensemble of reacting particles in dense plasmas is studied on the basis of a master equation. Many-body effects taken into account via energy-level shifts that are determined by the interaction parts of the chemical potentials of free and bound particles. These chemical potentials are calculated from an approximation for free partially ionized plasmas accounting for exchange and correlation as well as for short-range repulsion due to bound shell electrons. Using semi-empirical cross sections the method is applied to investigate the recombination of dense plasma containing carbon and hydrogen. The master equation can be combined with a temperature equation to account for different thermodynamic constraints. We compare the isentropic, isothermal, and isoenergetic recombination of dense hydrogen plasma.