Computable causal network models for systems biology-based risk assessment
International Toxicology Summit & Expo
Marja Talikka, Walter K. Schlage, Stephan Gebel, Emilija Veljkovic, Michael Peck, Stephanie Boue, Ignacio Gonzalez-Suarez, Ulrike Kogel, Carole Mathis, Carine Poussin, Julia Hoeng and Manuel C. Peitsch
mics� data that contain measurements of thousands of molecular species in a single experiment are a powerful tool to
probe how environmental toxicants, including tobacco products, contribute to disease. However, to gain any meaningful
mechanistic insight from these data, they need to be interpreted in the context of relevant biological processes. To address the
needs of a systems biology-based risk assessment approach in a systems toxicology setting such as the �21st Century Toxicology�,
we are building biological network models that can retrieve mechanistic information from �omics� data.
The biological networks describe causal node-edge relationships using the Biological Expression Language (BEL), which
allows for the semantic representation of these relationships in a computable format. The literature model, which is built from
relationships mined from relevant scientific literature, is further enhanced with additional nodes derived from Reverse Causal
Reasoning (RCR) analysis of transcriptomic data sets. The integrated model is manually reviewed and refined, and finally,
validated using independent datasets.
To date, we have constructed a series of network models describing cell proliferation, cellular stress, DNA damage and the
cellular fates induced by stress, as well as networks describing pulmonary and cardiovascular inflammation, tissue repair and
angiogenesis. Our network back bones have strict boundaries focusing on non-diseased cells/tissues to capture the molecular
events that are activated prior to disease onset.
The analysis of �omics� data at network-level places the differentially expressed genes into the context of known biological
signaling and provides invaluable mechanistic insight into the effects of potentially harmful exposures
Marja Talikka completed her PhD in 2000 at the University of Helsinki. She pursued postdoctoral studies at the Rockefeller University focusing on proneural basic-helix-loop-helix transcription factors using the frog embryo as a model system. Her second postdoctoral research in the Swiss Institute for Cancer Research (ISREC) focused on the role of subtilisin-like proprotein convertases in Alzheimer?s disease. She joined Philip Morris International Biological Systems Research in 2011
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