Contributions to the Mathematical Systems Medicine of Antimicrobial Therapy and Genotype-Phenotype Inference.

The following summary of my publications describes the main ideas in the corresponding research articles and clarfifies my contribution in multi-author publications. I decided to apply for habilitation according to x2.I.1.(c) of the Habilitationsordnung (this path is usually referred as Kumulative Habilitation"). I selected 13 first- or last author publications for this habilitation that concern contributions to the mathematical systems medicine of antiviral therapy [tMH10, tMS+11, FtK+11, tMMS12, DSt12, DWSt15, Dt16, DSt16, DDKt18, DSD+19, DDKt19], as well as inference of genotype-phenotype associations [SDH+15, SSJ+18]. The selected publications represent my major contributions in this research eld since submitting my doctoral thesis in September 2009

Sie haben mich immer in der Zuruckgesogenheit meiner Lebensart fur isoliert von der welt gehalten; und dooh ist vielleioht niemand inniger damit verbunden als ich

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Absolute Protein Amounts and Relative Abundance of Volume-regulated Anion Channel (VRAC) LRRC8 Subunits in Cells and Tissues Revealed by Quantitative Immunoblotting

The volume-regulated anion channel (VRAC) plays an important role in osmotic cell volume regulation. In addition, it is involved in various physiological processes such as insulin secretion, glia-neuron communication and purinergic signaling. VRAC is formed by hetero-hexamers of members of the LRRC8 protein family, which consists of five members, LRRC8A-E. LRRC8A is an essential subunit for physiological functionality of VRAC. Its obligate heteromerization with at least one of its paralogues, LRRC8B-E, determines the biophysical properties of VRAC. Moreover, the subunit composition is of physiological relevance as it largely influences the activation mechanism and especially the substrate selectivity. However, the endogenous tissue-specific subunit composition of VRAC is unknown. We have now developed and applied a quantitative immunoblot study of the five VRAC LRRC8 subunits in various mouse cell lines and tissues, using recombinant protein for signal calibration. We found tissue-specific expression patterns of the subunits, and generally relative low expression of the essential LRRC8A subunit. Immunoprecipitation of LRRC8A also co-precipitates an excess of the other subunits, suggesting that non-LRRC8A subunits present the majority in hetero-hexamers. With this, we can estimate that in the tested cell lines, the number of VRAC channels per cell is in the order of 10,000, which is in agreement with earlier calculations from the comparison of single-channel and whole-cell currents

Implementing literature-based curriculum in primary grades

Literature-based reading instruction -- Writing centers -- Library corners

Pharmacokinetics and Pharmacodynamics of the Reverse Transcriptase Inhibitor Tenofovir and Prophylactic Efficacy against HIV-1 Infection

Antiviral pre-exposure prophylaxis (PrEP) through daily drug administration can protect healthy individuals from HIV-1 infection. While PrEP was recently approved by the FDA, the potential long-term consequences of PrEP implementation remain entirely unclear. The aim of this study is to predict the efficacy of different prophylactic strategies with the pro-drug tenofovir- disoproxil-fumarate (TDF) and to assess the sensitivity towards timing- and mode of TDF administration (daily- vs. single dose), adherence and the number of transmitted viruses. We developed a pharmacokinetic model for TDF and its active anabolite tenofovir-diphosphate (TFV-DP) and validated it with data from 4 different trials, including 4 distinct dosing regimes. Pharmacokinetics were coupled to an HIV model and viral decay following TDF mono-therapy was predicted, consistent with available data. Subsequently, a stochastic approach was used to estimate the % infections prevented by (i) daily TDF-based PrEP, (ii) one week TDF started either shortly before, or -after viral exposure and (iii) a single dose oral TDF before viral challenge (sd-PrEP). Analytical solutions were derived to assess the relation between intracellular TFV-DP concentrations and prophylactic efficacy. The predicted efficacy of TDF was limited by a slow accumulation of active compound (TFV-DP) and variable TFV-DP half-life and decreased with increasing numbers of transmitted viruses. Once daily TDF-based PrEP yielded 80% protection, if at least 40% of pills were taken. Sd-PrEP with 300 mg or 600 mg TDF could prevent 50% infections, when given at least before virus exposure. The efficacy dropped to 10%, when given 1 h before 24 h exposure. Efficacy could not be increased with increasing dosage or prolonged administration. Post-exposure prophylaxis poorly prevented infection. The use of drugs that accumulate more rapidly, or local application of tenofovir gel may overcome the need for drug administration long before virus exposure

S̲tochastic S̲imulation A̲lgorithm For Effective Spreading Dynamics On T̲ime-Evolving A̲daptive N̲etworX̲ (SSATAN-X)

Modelling and simulating of pathogen spreading has been proven crucial to inform containment strategies, as well as cost-effectiveness calculations. Pathogen spreading is often modelled as a stochastic process that is driven by pathogen exposure on time-evolving contact networks. In adaptive networks, the spreading process depends not only on the dynamics of a contact network, but vice versa, infection dynamics may alter risk behavior and thus feed back onto contact dynamics, leading to emergent complex dynamics. However, numerically exact stochastic simulation of such processes via the Gillespie algorithm is currently computationally prohibitive. On the other hand, frequently used ‘parallel updating schemes’ may be computationally fast, but can lead to incorrect simulation results. To overcome this computational bottleneck, we propose SSATAN-X. The key idea of this algorithm is to only capture contact dynamics at time-points relevant to the spreading process. We demonstrate that the statistics of the contact- and spreading process are accurate, while achieving ~100 fold speed-up over exact stochastic simulation. SSATAN-X’s performance increases further when contact dynamics are fast in relation to the spreading process, as applicable to most infectious diseases. We envision that SSATAN-X may extend the scope of analysis of pathogen spreading on adaptive networks. Moreover, it may serve to create benchmark data sets to validate novel numerical approaches for simulation, or for the data-driven analysis of the spreading dynamics on adaptive networks