[talks] Max Homilius will present his Pre FPO, "Prioritizing animal models and drug targets using functional networks", on Monday, December 11th, 2017 at 12 pm in LSI 260.

[Nicki Gotsis]

Max Homilius will present his Pre FPO, "Prioritizing animal models and drug targets using functional networks", on Monday, December 11th, 2017 at 12 pm in LSI 260. 

The members of his committee are as follows: 
Advisor: Olga Troyanskaya 
Readers: Mona Singh, Tilo Grosser (UPenn) 
Non-readers: Barbara Engelhardt, Coleen Murphy (Mol Bio) 

Abstract and title follows below. 

Title: Prioritizing animal models and drug targets using functional networks 

Abstract: Living cells maintain a complex network of biological interactions, including metabolic reactions, physical protein-protein interactions and the regulation of gene expression. Representing these interactions as complex networks can reveal molecular mechanisms underlying human disease and adverse events due to drug treatment. In my thesis I make use of functional interaction networks, which effectively predict gene function from noisy, incomplete, and heterogeneous genomics data, to develop computational approaches for human disease research and to gain insight in the mode of action of drugs. 

First, I will speak about the inference of tissue- and process-specific functional interaction networks tailored to study the biology non-steroidal anti-inflammatory drugs (NSAIDs). Next, I will present a computational approach for prioritizing animal models for human diseases. Apart from approaches reliant on shared homologous genes, we lack tools to seamlessly search across organisms to identify the model phenotype equivalent to a human disease. By jointly using genome-scale functional interaction networks in both human and model organism, I create and match genome-wide representations of human diseases and model phenotypes, and report novel genes which are prime candidates for experimental follow-up. My third project investigates the cellular mode-of-action of drugs, which is critical in assessing both beneficial and adverse effects of treatment. Transcriptional profiling is a powerful approach towards this goal and has been used extensively to identify genes and pathways that respond to drug treatment. However, the concentration required to elicit measurable changes in gene-expression is often significantly higher than a drug’s therapeutic dose. Therefore, to gain clinically relevant insights, I developed a network-based method that can detect critical genes and pathways even in the absence of large-scale expression differences in response to drug treatment. 

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