Mitarbeiter | Staff
Dr. Marcus Oswald
Topic: Specialist for propensity score matching, modelling and solving Mixed-Integer-Programs which arise in bioinformatic problems of the group, especially on networks and machine learning, statistics. System administration of the group.
Research project: Developing an individualised treatment approach for antibiotics therapy for patients hospitalised with moderate community-acquired pneumonia (CAP)
The role of macrolides, β-lactams and fluoroquinolones in CAP is controversially discussed. We aim to identify patient characteristics with which patients can be identified benefitting from a specific antibiotic therapy to implement this later into the clinical routine. We apply machine learning/artificial intelligence concepts to data available on admission of hospitalised patients from the observational, prospective, multinational CAPNETZ study to investigate patient variables of all relevant aspects for CAP treatment and management based on appr. 12,000 patients. In a pilot study, we identified a treatment rule for macrolide/beta-lactam combination therapy in respect to a beta-lactam mono therapy].
Dr. Vasily S. Romanov
Topic and research project: Data mining is a powerful contemporary approach in solving various questions. Analysis of large datasets by machine learning, in particular, helps to find hidden patterns of data and to build statistical models, which are used for describing new data and predicting outcomes of corresponding processes. Such methods are utilized in several of our projects, which deal with different types of data. Applying machine learning algorithms in analysis of Excitation Emission Matrices (EEMs) of fluorescence spectra allows us to distinguish even small variations in light intensity. This approach is successfully used on our experimental dataset of the “GermDetect” project, which aims at detection by UV autofluorescence of very small amounts of biological contaminations. Besides, data mining methods are applied to datasets of patients with community-acquired pneumonia (CAP) in order to find the best antibiotic combination for the treatment, which depends on various patient factors and medical parameters.
Topic: Predicting nutritional uptakes of Bacillus subtilis and Plasmodium falciparum by integrating gene expression profiles into constrained based metabolic models
Research project: Uptake of nutrition is essential for every organism. Hence, these pathways may suit as therapeutic targets tipping invading pathogens into the host. To implement the method, we are developing flux balance analysis (FBA) based models using the stoichiometric knowledge of the metabolic reactions of a cell, 13C metabolic flux data and transcriptomic data from the model organism B. subtilis. We aim to predict nutritional uptakes only basing on the transcription profiles. This approach can then also be used in more complex settings, such as for investigating the nutritional uptake in opportunistic pathogenic microorganisms in host cells, such as P. falciparum in the red blood cell, in which 13C analysis is difficult.
Alicia Hiemisch M.Sc. (in maternal leave)
Topic: Identifying host factors as drug targets for SARS-CoV-2 from RNAi knockdown screens
Research project: A novel coronavirus was recently discovered and termed SARS-CoV-2. Patients with COVID19 can worsen in a short time-lapse and die of multiple organ failure. Until now, there is no established therapy for COVID–19. A promising alternative can be to repurpose clinically approved drugs. The goal of the project is to use omics data from a genome-wide (druggable) RNAi knockdown screen, proteomics profiles of SARS-CoV-2 infected cells and other data to identify host-dependency factors that are needed for virus entry into the cell, replication, and spread.
Topic: Development of an analysis pipiline for identifying virus induced circular RNA in the host cell
Research project: Using data of our collaboration partners from the Virology department of Frankfurt University, we aim to identify circular RNA being involed in the patho-mechanisms of virus induced reprogramming of host cells.
Mark Kriegbaum B.Sc.
Topic: Cellular network analysis using imaging data of siRNA induced knockdown screens
Research project: Using imiging data of collaboration partners from Heidelberg University, we aim to identify activating and inhibiting signaling interactions between directly connected proteins in celluar networks. This is enabled by machine learning based phenotype comparisons.