AG Kaderali Bioinformatics / Mathematical Modeling of Infection

Transcription

1 AG Kaderali Bioinformatics / Mathematical Modeling of Infection Prof. Dr. Lars Kaderali Hiddensee, October 27, 2015 Institut für Bioinformatik

2 Research Overview Experimental Data High-Throughput Screening 1 Gene Expression Microarrays Live Cell Imaging... Next Generation Sequencing Statistical and Bioinformatics Methods for (raw) Data Processing Forward Modeling 2 Mathematical Modeling 3 Network Bioinformatics -quantitative, dynamic understanding of infection processes - Projects on Viral Replication, Immune Response, Interaction between virus and immune system, drug effects -learn predictive models from data -data mining - several projects on understanding host-pathogen interactions e.g. from RNAi screening or sequencing data Inverse Modeling Integrate these approaches to understand Infection and Immune Response

3 SysVirDrug: Novel Antiviral Drugs! Most antiviral drugs inhibit viral enzymes - Spectrum of activity? (may not target all types) - Development of resistance - Less chance of side-effects Growing interest in developing inhibitors of essential host factors - Broad spectrum (related viruses use common replication strategy) - - Host factors unlikely to become resistant viral RNA - Side-effects?

4 RNAi to Identify Host-Pathogen Interactions Virus nuclei neg. control Host Factor Hepatitis C virus Dengue virus High-throughput, SARS-coronavirus sirna-based Screening Chikungunya virus Enterovirus (Coxsackie B3) Sophisticated, networkand pathway-based Bioinformatics Analysis Reiss, Woerz et al., Cell Host&Microbe 2011 Pönisch et al., PLoS Pathogens, 2015 Amberkar&Kaderali, BMC Alg Mol Biol, 2015

5 Model Analysis and Inhibitor Design Model Analysis Virtual screening Host Factors Experimental Validation Mathematical Model Antiviral Compounds

6 Mathematical Model to Explain Differences in Cellular Hep-C Virus Replication Permissivenes Difference in replication in high and low permissive cells is explained by the different expression level of a host factor which participates in the formation of replication vesicles Binder et al., PLoS Pathogens, 2009 Clausznitzer et al., Virus Res., 2015 Ivanisenko et al., PLOS One, 2014

7 Sensitivity Analysis Interference with RNA polymerization is significantly more potent than impeding protease activity!

8 Modeling Innate Immune Recognition m r + i m m i k M k -M (i, L) k F m i m i Cooperativity of the Rig-I RNA binding reaction underlies nonlinear features of pathway activation F 1600 bp 400 bp 200 bp 100 bp 40 bp Binder et al., J Biol Chem, 2011 ImmunoQuant

9 Outlook: Systems Biology of Infection Host Factors PK / PD Models Innate & Adaptive Immune Response Cell Populations, Tissues, Organs,... Transfected RNA Viral Life Cycle Model Ribosomes RNA processed Polyprotein Translation complexes RIG-I Pathway Cellular factor Viral polymerase, NS5B New plusstrand RNA + Plus-strand intermediate complex Polymerase Clinical Data Minus-strand intermediate complex Polymerase Minus-strand RNA Influence of additional host factors Comparison accross different viruses Optimization of Intervention Strategies (Drug Combinations) PK/PD models + Polymerase Jak/Stat Pathway

10 Possible Collaborations What we can offer High-throughput data analysis (biostatistics, bioinformatics) Data integration, data mining on infection processes Mathematical modeling of infection and immune response Computer simulations of infectious processes, mathematical modeling and model analysis to generate / test hypotheses about mechanisms What we are looking for New collaborations in particular with experimental / virological / immunological partners on elucidating host-pathogen interactions High-Throughput ( global view ) data for bioinformatics analysis Detailed dynamic data for development of kinetic mathematical models So far worked with viruses, many of the methods and tools used should be transferable to bacteria