Topic and research project: 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. Methods for propensity matching and machine learning are implemented in a project, in which we are developing a rule for macrolide /beat-lactam combination therapy in community-acquired pneumonia (CAP) of moderate severity.
Topic: Employing machine learning for remodeling the Anopheles gambiae immune response to Plasmodium falciparum infection
Research project: Malaria, caused by the Plasmodium parasite, affects approximately 3 billion people worldwide each year. In sub-Saharan Africa, the major vector for Plasmodium falciparum is the female Anopheles gambiae mosquito. Not only humans, but also the vector is damaged by malaria and an effective and elaborated immune response has evolved. We use systems biology approaches to find new targets that confer resistance to A. gambiae preventing its vector capability.
Besides this, machine learning methods are applied to identify patient variables highly correlating with death after hospitalization of community-acquired pneumonia (CAP) of moderate severity.
Dr. Anna Mühlig
Topic: Germ detection by UV auto-fluorescence
Research project: Benefiting from the new UV-LED generation, UV-fluorescence becomes a very powerful tool in life sciences. We are developing a new handheld device for direct germ detection in a very short timeframe based on auto-fluorescence of the germs. The project “GermDetect” belongs to the BMBF supported Advanced UV for life consortium.
Dr. Daniela Röll
Topic and research project: Host response to infectious diseases, single cell sequencing and identification of specific gene regulators
Dr. Oyelade Olanrewaju Jelili
Topic: Deciphering the protein-protein interaction network of Anopheles gambiae: A computational approach based on machine learning.
Research Project: Malaria tropica is the most severe form of malaria, and particularly in sub-Saharan African countries a life threatening central health care problem. The disease is mainly transmitted by the mosquito Anopheles gambiae. The project aims to find targets for insecticide treatment tipping particularly the infected mosquitoes. Protein-protein interactions play an important role in the prediction of protein function and the prediction of a target protein for insecticides. However, there exists only very limited information about protein-protein interactions for A. gambiae. This project aims to develop computational models to infer protein-protein interactions of Anopheles gambiae using protein-protein interaction information of Drosophila melanogaster and other model organisms.
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 nutritonal uptake in opportunistic pathogenic microorganisms in host cells, such as P. falciparum in the red blood cell, in which 13C analysis is difficult.
Topic: Constructing gene regulatory networks for Anopheles gambiae
Research project: In this project, we aim to identify gene regulatory processes that drive the immune response of Anopheles gambiae against Plasmodium falciparum. For this, we infer transcription factor target gene interactions of Anopheles from Drosophila using Support Vector Machines. We will use the developed gene regulatory network to identify key regulators of the mosquito immune system.
Topic: Identifying of target patients for fluoroquinolones therapy of community acquired pneumonia (CAP) employing decision trees
Research project: The role of fluoroquinolones therapy in community-acquired pneumonia (CAP) of moderate severity is a matter of debate. We develop a method based on decision trees and etiological and clinical parameters to support a personalized decision pro or contra fluoroquinolones for the best clinical outcome of the individual patient.
Topic: Identifying the gene regulators of mTert in prostate cancer
Research project: Understanding telomere length maintenance mechanisms is central in cancer biology as their dysregulation is one of the hallmarks for immortalization of cancer cells. Important for this well-balanced control is the transcriptional regulation of the telomerase gene. Employing Mixed Integer linear Programming based gene regulatory network models, the group identified novel regulators of the telomerase in prostate cancer. In this project, we are experimentally validating the results employing chromatin immunoprecipitation, functional and TRAP assays.
Topic: Germ detection by UV auto-fluorescence
Research project: We are establishing standard operating procedures to implement and validate an UV-LED based germ detection device.