Experimental design for efficient identification of gene regulatory networks using sparse Bayesian models

<p>Abstract</p> <p>Background</p> <p>Identifying large gene regulatory networks is an important task, while the acquisition of data through perturbation experiments (<it>e.g</it>., gene switches, RNAi, heterozygotes) is expensive. It is thus desirable to use an identification method that effectively incorporates available prior knowledge – such as sparse connectivity – and that allows to design experiments such that maximal information is gained from each one.</p> <p>Results</p> <p>Our main contributions are twofold: a method for consistent inference of network structure is provided, incorporating prior knowledge about sparse connectivity. The algorithm is time efficient and robust to violations of model assumptions. Moreover, we show how to use it for optimal experimental design, reducing the number of required experiments substantially. We employ sparse linear models, and show how to perform full Bayesian inference for these. We not only estimate a single maximum likelihood network, but compute a posterior distribution over networks, using a novel variant of the expectation propagation method. The representation of uncertainty enables us to do effective experimental design in a standard statistical setting: experiments are selected such that the experiments are maximally informative.</p> <p>Conclusion</p> <p>Few methods have addressed the design issue so far. Compared to the most well-known one, our method is more transparent, and is shown to perform qualitatively superior. In the former, hard and unrealistic constraints have to be placed on the network structure for mere computational tractability, while such are not required in our method. We demonstrate reconstruction and optimal experimental design capabilities on tasks generated from realistic non-linear network simulators.</p> <p>The methods described in the paper are available as a Matlab package at</p> <p><url>http://www.kyb.tuebingen.mpg.de/sparselinearmodel</url>.</p

Auswirkungen von Privatisierungen auf Gewerkschaften:Die Privatisierung der europäischen Eisenbahnen am Beispiel der Deutschen Bahn im Kontext von Liberalisierung, Europäisierung und Globalisierung

Sind europäische Gewerkschaften in den letzten Jahrzehnten von einer Vielzahl von gesellschaftlichen Umstrukturierungen betroffen, so zwingen sie im Bereich öffentlicher Dienstleistungen insbesondere Privatisierungen und zunehmender Wettbewerb zu strategischen Anpassungen. Vor dem Hintergrund europäischer Privatisierungs- und Liberalisierungsprozesse macht insbesondere das Beispiel der Deutschen Bahn deutlich, welche Chancen und Risiken für Gewerkschaften und ArbeitnehmerInnen mit Privatisierungen verbunden sind. <br/

Cost-of-illness in psoriasis: Comparing inpatient and outpatient therapy

Treatment modalities of chronic plaque psoriasis have dramatically changed over the past ten years with a still continuing shift from inpatient to outpatient treatment. This development is mainly caused by outpatient availability of highly efficient and relatively well-tolerated systemic treatments, in particular BioLogicals. In addition, inpatient treatment is time- and cost-intense, conflicting with the actual burst of health expenses and with patient preferences. Nevertheless, inpatient treatment with dithranol and UV light still is a major mainstay of psoriasis treatment in Germany. The current study aims at comparing the total costs of inpatient treatment and outpatient follow-up to mere outpatient therapy with different modalities (topical treatment, phototherapy, classic systemic therapy or BioLogicals) over a period of 12 months. To this end, a retrospective cost-of-illness study was conducted on 120 patients treated at the University Medical Centre Mannheim between 2005 and 2006. Inpatient therapy caused significantly higher direct medical, indirect and total annual costs than outpatient treatment (13,042 € versus 2,984 €). Its strong influence on cost levels was confirmed by regression analysis, with total costs rising by 104.3% in case of inpatient treatment. Patients receiving BioLogicals produced the overall highest costs, whereas outpatient treatment with classic systemic antipsoriatic medications was less cost-intense than other alternatives

Experimental Design for Efficient Identification of Gene Regulatory Networks using Sparse Bayesian Models

Background: Identifying large gene regulatory networks is an important task, while the acquisition of data through perturbation experiments (e.g., gene switches, RNAi, heterozygotes) is expensive. It is thus desirable to use an identification method that effectively incorporates available prior knowledge -- such as sparse connectivity -- and that allows to design experiments such that maximal information is gained from each one. Results: Our main contributions are twofold: a method for consistent inference of network structure is provided, incorporating prior knowledge about sparse connectivity. The algorithm is time efficient and robust to violations of model assumptions. Moreover, we show how to use it for optimal experimental design, reducing the number of required experiments substantially. We employ sparse linear models, and show how to perform full Bayesian inference for these. We not only estimate a single maximum likelihood network, but compute a posterior distribution over networks, using a novel variant of the expectation propagation method. The representation of uncertainty enables us to do effective experimental design in a standard statistical setting: experiments are selected such that the experiments are maximally informative. Conclusions: Few methods have addressed the design issue so far. Compared to the most well-known one, our method is more transparent, and is shown to perform qualitatively superior. In the former, hard and unrealistic constraints have to be placed on the network structure for mere computational tractability, while such are not required in our method. We demonstrate reconstruction and optimal experimental design capabilities on tasks generated from realistic non-linear network simulators

Bayesian Inference and Optimal Design in the Sparse Linear Model

The sparse linear model has seen many successful applications in Statistics, Machine Learning, and Computational Biology, such as identification of gene regulatory networks from micro-array expression data. Prior work has either approximated Bayesian inference by expensive Markov chain Monte Carlo, or replaced it by point estimation. We show how to obtain a good approximation to Bayesian analysis efficiently, using the Expectation Propagation method. We also address the problems of optimal design and hyperparameter estimation. We demonstrate our framework on a gene network identification task

A simulation study on spatial and time resolution for a cost-effective positron emission particle tracking system

This work is the second part of a simulation study investigating the processing of densely packed and moving granular assemblies by positron emission particle tracking (PEPT). Since medical PET scanners commonly used for PEPT are very expensive, a PET-like detector system based on cost-effective organic plastic scintillator bars is being developed and tested for its capabilities. In this context, the spatial resolution of a resting positron source, a source moving on a freely designed model path, and a particle motion given by a DEM (Discrete Element Method) simulation is studied using Monte Carlo simulations and the software toolkit Geant4. This not only extended the simulation and reconstruction to a moving source but also significantly improved the spatial resolution compared to previous work by adding oversampling and iteration to the reconstruction algorithm. Furthermore, in the case of a source following a trajectory developed from DEM simulations, a very good resolution of about 1 mm in all three directions and an average three-dimensional deviation between simulated and reconstructed events of 2.3 mm could be determined. Thus, the resolution for a realistic particle motion within the generic grate system (which is the test rig for further experimental studies) is well below the smallest particle size. The simulation of the dependence of the reconstruction accuracy on tracer particle location revealed a nearly constant efficiency within the entire detector system, which demonstrates that boundary effects can be neglected.Comment: Published in Particuology 88 (2024) 312-322. This manuscript version is made available under the CC-BY-NC-ND 4.0 licens

Development of the front-end electronics for a cost-effective PET-like detector system

Most detector systems used for positron emission particle tracking (PEPT) are very expensive due to the use of inorganic plastic scintillators combined with a high number of readout electronic channels. This work aims to reduce the overall cost of a PEPT-capable detector system by using large and cost-effective plastic scintillators and developing custom 2 x 2 silicon photomultiplier (SiPM) arrays, preamplifiers, and discriminators. The use of long (20 mm x 20 mm x 1000 mm) plastic scintillator bars read out with photodetectors only at their respective ends allows an overall smaller number of photodetectors and associated readout electronics, which in turn reduces the overall cost of the system. In addition, the development of a custom SiPM array and preamplifier allows a free selection of interconnection and readout, as most commercial producers only offer specific types of interconnections and therefore lack other connections such as serial or hybrid. Thus, several common circuit types for SiPMs and preamplifiers were tested and compared in this work, and it was found that a serial connection implemented in a hybrid interconnection for the SiPMs and an inverting preamplifier based on a high-frequency operational amplifier provided the best results for the proposed detector system. Measured with a Na-22 source, the combination of SiPM array and preamplifier led to a rise time of 3.7 ns and a signal amplitude of 175 mV.Comment: Published in Nuclear Instruments and Methods in Physics Research A 1057 (2023) 168767. This manuscript version is made available under the CC-BY-NC-ND 4.0 licens

North Atlantic forcing of tropical Indian Ocean climate

Author Posting. © The Author(s), 2014. This is the author's version of the work. It is posted here by permission of Nature Publishing Group for personal use, not for redistribution. The definitive version was published in Nature 509 (2014): 76-80, doi:10.1038/nature13196.The response of the tropical climate in the Indian Ocean realm to abrupt climate change events in the North Atlantic Ocean is contentious. Repositioning of the intertropical convergence zone is thought to have been responsible for changes in tropical hydroclimate during North Atlantic cold spells1–5, but the dearth of high-resolution records outside the monsoon realm in the Indian Ocean precludes a full understanding of this remote relationship and its underlying mechanisms. Here we show that slowdowns of the Atlantic meridional overturning circulation during Heinrich stadials and the Younger Dryas stadial affected the tropical Indian Ocean hydroclimate through changes to the Hadley circulation including a southward shift in the rising branch (the intertropical convergence zone) and an overall weakening over the southern Indian Ocean. Our results are based on new, high-resolution sea surface temperature and seawater oxygen isotope records of well dated sedimentary archives from the tropical eastern Indian Ocean for the past 45,000 years, combined with climate model simulations of Atlantic circulation slowdown under Marine Isotope Stages 2 and 3 boundary conditions. Similar conditions in the east and west of the basin rule out a zonal dipole structure as the dominant forcing of the tropical Indian Ocean hydroclimate of millennial-scale events. Results from our simulations and proxy data suggest dry conditions in the northern Indian Ocean realm and wet and warm conditions in the southern realm during North Atlantic cold spells.This study was funded by the German Bundesministerium für Bildung und Forschung (grant 03G0189A) and the Deutsche Forschungsgemeinschaft (DFG grants HE3412/15-1 and STE1044/4-1, and the DFG Research Centre/Cluster of Excellence ‘The Ocean in the Earth System’). D.W.O. is funded by the US NSF, R.D.P.-H. is supported by Chilean FONDAP 15110009/ICM Nucleus NC120066.2014-10-3

Association between TAS2R38 gene polymorphisms and colorectal cancer risk

Molecular sensing in the lingual mucosa and in the gastro-intestinal tract play a role in the detection of ingested harmful drugs and toxins. Therefore, genetic polymorphisms affecting the capability of initiating these responses may be critical for the subsequent efficiency of avoiding and/or eliminating possible threats to the organism. By using a tagging approach in the region of Taste Receptor 2R38 (TAS2R38) gene, we investigated all the common genetic variation of this gene region in relation to colorectal cancer risk with a case-control study in a German population (709 controls and 602 cases) and in a Czech population (623 controls and 601 cases). We found that there were no significant associations between individual SNPs of the TAS2R38 gene and colorectal cancer in the Czech or in the German population, nor in the joint analysis. However, when we analyzed the diplotypes and the phenotypes we found that the non-taster group had an increased risk of colorectal cancer in comparison to the taster group. This association was borderline significant in the Czech population, (OR = 1.28, 95% CI 0.99-1.67; P(value) = 0.058) and statistically significant in the German population (OR = 1.36, 95% CI 1.06-1.75; P(value) = 0.016) and in the joint analysis (OR = 1.34, 95% CI 1.12-1.61; P(value) = 0.001). In conclusion, we found a suggestive association between the human bitter tasting phenotype and the risk of CRC in two different populations of Caucasian origin

TRACK-CF prospective cohort study: Understanding early cystic fibrosis lung disease.

BACKGROUND Lung disease as major cause for morbidity in patients with cystic fibrosis (CF) starts early in life. Its large phenotypic heterogeneity is partially explained by the genotype but other contributing factors are not well delineated. The close relationship between mucus, inflammation and infection, drives morpho-functional alterations already early in pediatric CF disease, The TRACK-CF cohort has been established to gain insight to disease onset and progression, assessed by lung function testing and imaging to capture morpho-functional changes and to associate these with risk and protective factors, which contribute to the variation of the CF lung disease progression. METHODS AND DESIGN TRACK-CF is a prospective, longitudinal, observational cohort study following patients with CF from newborn screening or clinical diagnosis throughout childhood. The study protocol includes monthly telephone interviews, quarterly visits with microbiological sampling and multiple-breath washout and as well as a yearly chest magnetic resonance imaging. A parallel biobank has been set up to enable the translation from the deeply phenotyped cohort to the validation of relevant biomarkers. The main goal is to determine influencing factors by the combined analysis of clinical information and biomaterials. Primary endpoints are the lung clearance index by multiple breath washout and semi-quantitative magnetic resonance imaging scores. The frequency of pulmonary exacerbations, infection with pro-inflammatory pathogens and anthropometric data are defined as secondary endpoints. DISCUSSION This extensive cohort includes children after diagnosis with comprehensive monitoring throughout childhood. The unique composition and the use of validated, sensitive methods with the attached biobank bears the potential to decisively advance the understanding of early CF lung disease. ETHICS AND TRIAL REGISTRATION The study protocol was approved by the Ethics Committees of the University of Heidelberg (approval S-211/2011) and each participating site and is registered at clinicaltrials.gov (NCT02270476)