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

Correlation of cognitive status, MRI- and SPECT-imaging in CADASIL patients

Although there is evidence for correlations between disability and magnetic resonance imaging (MRI) total lesion volume in autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), the significance of structural MRI abnormalities for cognitive dysfunction remains controversial. We performed detailed neuropsychological testing, high resolution MRI, and Tc-99m-ethyl cysteinate-dimer SPECT in three CADASIL patients. MR-images were rated independently by two investigators for the presence of white matter lesions, lacunar infarcts, microbleeds, and ventricular enlargement. Cortical atrophy was quantified by the use of automatic morphometric assessment of the cortical thickness. In addition, laboratory and patients' history data were collected in order to assess the individual vascular risk factor profile. The differences in cognitive performance between the three patients are neither explained by structural-, or functional neuroimaging, nor by the patient-specific vascular risk factor profiles. The neuroradiologically least affected patient met criteria for dementia, whereas the most severely affected patient was in the best clinical and cognitive state. Conventional structural and functional neuroimaging is important for the diagnosis of CADASIL, but it is no sufficient surrogate marker for the associated cognitive decline. Detailed neuropsychological assessment seems to be more useful, particularly with respect to the implementation of reliable outcome parameters in possible therapeutic trials

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

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

Peer reviewedPublisher PD

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)

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

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

Hub-Centered Gene Network Reconstruction Using Automatic Relevance Determination

Network inference deals with the reconstruction of biological networks from experimental data. A variety of different reverse engineering techniques are available; they differ in the underlying assumptions and mathematical models used. One common problem for all approaches stems from the complexity of the task, due to the combinatorial explosion of different network topologies for increasing network size. To handle this problem, constraints are frequently used, for example on the node degree, number of edges, or constraints on regulation functions between network components. We propose to exploit topological considerations in the inference of gene regulatory networks. Such systems are often controlled by a small number of hub genes, while most other genes have only limited influence on the network's dynamic. We model gene regulation using a Bayesian network with discrete, Boolean nodes. A hierarchical prior is employed to identify hub genes. The first layer of the prior is used to regularize weights on edges emanating from one specific node. A second prior on hyperparameters controls the magnitude of the former regularization for different nodes. The net effect is that central nodes tend to form in reconstructed networks. Network reconstruction is then performed by maximization of or sampling from the posterior distribution. We evaluate our approach on simulated and real experimental data, indicating that we can reconstruct main regulatory interactions from the data. We furthermore compare our approach to other state-of-the art methods, showing superior performance in identifying hubs. Using a large publicly available dataset of over 800 cell cycle regulated genes, we are able to identify several main hub genes. Our method may thus provide a valuable tool to identify interesting candidate genes for further study. Furthermore, the approach presented may stimulate further developments in regularization methods for network reconstruction from data