Use of systems biology to decipher host–pathogen interaction networks and predict biomarkers

AbstractIn systems biology, researchers aim to understand complex biological systems as a whole, which is often achieved by mathematical modelling and the analyses of high-throughput data. In this review, we give an overview of medical applications of systems biology approaches with special focus on host–pathogen interactions. After introducing general ideas of systems biology, we focus on (1) the detection of putative biomarkers for improved diagnosis and support of therapeutic decisions, (2) network modelling for the identification of regulatory interactions between cellular molecules to reveal putative drug targets and (3) module discovery for the detection of phenotype-specific modules in molecular interaction networks. Biomarker detection applies supervised machine learning methods utilizing high-throughput data (e.g. single nucleotide polymorphism (SNP) detection, RNA-seq, proteomics) and clinical data. We demonstrate structural analysis of molecular networks, especially by identification of disease modules as a novel strategy, and discuss possible applications to host–pathogen interactions. Pioneering work was done to predict molecular host–pathogen interactions networks based on dual RNA-seq data. However, currently this network modelling is restricted to a small number of genes. With increasing number and quality of databases and data repositories, the prediction of large-scale networks will also be feasible that can used for multidimensional diagnosis and decision support for prevention and therapy of diseases. Finally, we outline further perspective issues such as support of personalized medicine with high-throughput data and generation of multiscale host–pathogen interaction models

Overcoming the model‐data‐fit problem in porous media : a quantitative method to compare invasion‐percolation models to high‐resolution data

Invasion percolation (IP) models offer a computationally inexpensive way to simulate multiphase flow in porous media, but only very few studies have compared their results to actual laboratory experimental image data. One reason might be the difficulty in quantitative assessment: IP models do not have a notion of experimental time but only have an integer counter for simulation steps that imply a time order. Previous experiments‐to‐model comparison studies have either used perceptual similarity or spatial moments as measures of comparison. In this work, we present an objective and quantitative comparison method that overcomes the limitations of the traditional approaches. First, we perform a volume‐based time matching between real‐time experiments and IP model results. Then, we evaluate the quality of fit using a diffused version of the so‐called Jaccard coefficient, which is known from image recognition. We demonstrate our method's applicability on a laboratory‐scale experimental video of gas injection in homogeneous, saturated sand, comparing it to a Macro‐IP model's simulation results. We consider random realizations of the initial entry pressure field to capture the sand's inherent pore‐scale heterogeneity. We find that our proposed method is reliable and intuitive in identifying realistic model realizations. The “strictness”of the method can be adjusted to relevant scales of interest via the blurring (diffusion) radius of the compared images. Beyond the application presented here, our comparison method can be used to compare any high‐resolution space‐time model output to experimental data given as raster images, thus providing valuable insights for model development in many research areas.German Research Foundation (DFG)Projekt DEA

Comparison of the sensitivity of the UKCAT and A levels to sociodemographic characteristics: a national study

Background: The UK Clinical Aptitude Test (UKCAT) was introduced to facilitate widening participation in medical and dental education in the UK by providing universities with a continuous variable to aid selection; one that might be less sensitive to the sociodemographic background of candidates compared to traditional measures of educational attainment. Initial research suggested that males, candidates from more advantaged socioeconomic backgrounds and those who attended independent or grammar schools performed better on the test. The introduction of the A* grade at A level permits more detailed analysis of the relationship between UKCAT scores, secondary educational attainment and sociodemographic variables. Thus, our aim was to further assess whether the UKCAT is likely to add incremental value over A level (predicted or actual) attainment in the selection process. Methods: Data relating to UKCAT and A level performance from 8,180 candidates applying to medicine in 2009 who had complete information relating to six key sociodemographic variables were analysed. A series of regression analyses were conducted in order to evaluate the ability of sociodemographic status to predict performance on two outcome measures: A level ‘best of three’ tariff score; and the UKCAT scores. Results: In this sample A level attainment was independently and positively predicted by four sociodemographic variables (independent/grammar schooling, White ethnicity, age and professional social class background). These variables also independently and positively predicted UKCAT scores. There was a suggestion that UKCAT scores were less sensitive to educational background compared to A level attainment. In contrast to A level attainment, UKCAT score was independently and positively predicted by having English as a first language and male sex. Conclusions: Our findings are consistent with a previous report; most of the sociodemographic factors that predict A level attainment also predict UKCAT performance. However, compared to A levels, males and those speaking English as a first language perform better on UKCAT. Our findings suggest that UKCAT scores may be more influenced by sex and less sensitive to school type compared to A levels. These factors must be considered by institutions utilising the UKCAT as a component of the medical and dental school selection process

P-West High Intensity Secondary Beam Area Design Report

This report gives the initial design parameters of a 1000 GeV High Intensity Superconducting Secondary Beam Laboratory to be situated in the Proton Area downstream of the existing Proton West experimental station. The area will provide Fermilab with a major capability for experimentation with pion and antiproton beams of intensities and of energies available at no other laboratory and with an electron beam with excellent spot size, intensity, and purity at energies far above that available at electron machines. Detailed beam design, area layouts, and cost estimates are presented, along with the design considerations

Dynamics of amino acid metabolism of primary human liver cells in 3D bioreactors

The kinetics of 18 amino acids, ammonia (NH3) and urea (UREA) in 18 liver cell bioreactor runs were analyzed and simulated by a two-compartment model consisting of a system of 42 differential equations. The model parameters, most of them representing enzymatic activities, were identified and their values discussed with respect to the different liver cell bioreactor performance levels. The nitrogen balance based model was used as a tool to quantify the variability of runs and to describe different kinetic patterns of the amino acid metabolism, in particular with respect to glutamate (GLU) and aspartate (ASP)

Regulatory network modelling of iron acquisition by a fungal pathogen in contact with epithelial cells

Peer reviewedPublisher PD

Dynamic Assessment of Narrative Competence

In Developmental Education, language plays an essential role as a tool for communication (and thinking). Learning to produce coherent messages (“narratives”) with both cultural and personal value in the context of meaningful socio-cultural practices is considered as an important goal of Developmental Education. Narratives are essential for human action as they function as a tool for giving meaning to reality. Therefore, close observation and assessment of children’s narratives is essential in the context of Developmental Education. Over the past years we have developed a Dynamic Assessment (DA) instrument for assessing children’s narrative competence. This instrument combines two common approaches to DA, namely standardised interventionist DA and interactionist DA. With the help of this instrument, teachers are able to gain insight into children’s actual narrative competence as well as their developmental potential and their receptivity to certain forms of assistance to reach this potential. Our experience up to now shows that it is possible to assess children’s narrative competence in a valid and reliable manner

Integrative modeling of transcriptional regulation in response to antirheumatic therapy

<p>Abstract</p> <p>Background</p> <p>The investigation of gene regulatory networks is an important issue in molecular systems biology and significant progress has been made by combining different types of biological data. The purpose of this study was to characterize the transcriptional program induced by etanercept therapy in patients with rheumatoid arthritis (RA). Etanercept is known to reduce disease symptoms and progression in RA, but the underlying molecular mechanisms have not been fully elucidated.</p> <p>Results</p> <p>Using a DNA microarray dataset providing genome-wide expression profiles of 19 RA patients within the first week of therapy we identified significant transcriptional changes in 83 genes. Most of these genes are known to control the human body's immune response. A novel algorithm called TILAR was then applied to construct a linear network model of the genes' regulatory interactions. The inference method derives a model from the data based on the Least Angle Regression while incorporating DNA-binding site information. As a result we obtained a scale-free network that exhibits a self-regulating and highly parallel architecture, and reflects the pleiotropic immunological role of the therapeutic target TNF-alpha. Moreover, we could show that our integrative modeling strategy performs much better than algorithms using gene expression data alone.</p> <p>Conclusion</p> <p>We present TILAR, a method to deduce gene regulatory interactions from gene expression data by integrating information on transcription factor binding sites. The inferred network uncovers gene regulatory effects in response to etanercept and thus provides useful hypotheses about the drug's mechanisms of action.</p

Integrative inference of gene-regulatory networks in Escherichia coli using information theoretic concepts and sequence analysis

<p>Abstract</p> <p>Background</p> <p>Although <it>Escherichia coli </it>is one of the best studied model organisms, a comprehensive understanding of its gene regulation is not yet achieved. There exist many approaches to reconstruct regulatory interaction networks from gene expression experiments. Mutual information based approaches are most useful for large-scale network inference.</p> <p>Results</p> <p>We used a three-step approach in which we combined gene regulatory network inference based on directed information (DTI) and sequence analysis. DTI values were calculated on a set of gene expression profiles from 19 time course experiments extracted from the Many Microbes Microarray Database. Focusing on influences between pairs of genes in which one partner encodes a transcription factor (TF) we derived a network which contains 878 TF - gene interactions of which 166 are known according to RegulonDB. Afterward, we selected a subset of 109 interactions that could be confirmed by the presence of a phylogenetically conserved binding site of the respective regulator. By this second step, the fraction of known interactions increased from 19% to 60%. In the last step, we checked the 44 of the 109 interactions not yet included in RegulonDB for functional relationships between the regulator and the target and, thus, obtained ten TF - target gene interactions. Five of them concern the regulator LexA and have already been reported in the literature. The remaining five influences describe regulations by Fis (with two novel targets), PhdR, PhoP, and KdgR. For the validation of our approach, one of them, the regulation of lipoate synthase (LipA) by the pyruvate-sensing pyruvate dehydrogenate repressor (PdhR), was experimentally checked and confirmed.</p> <p>Conclusions</p> <p>We predicted a set of five novel TF - target gene interactions in <it>E. coli</it>. One of them, the regulation of <it>lipA </it>by the transcriptional regulator PdhR was validated experimentally. Furthermore, we developed DTInfer, a new R-package for the inference of gene-regulatory networks from microarrays using directed information.</p