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Roman Li

Roman started his scientific training as a developmental biologist, initially working on neurogenesis in chick embryos in Dundee, Scotland during his PhD. After that, he moved to Geneva to work on blood development in zebrafish embryos. 

In the last three years, he transitioned to a more applied field of toxicology. His current work focuses on developing systems toxicology approaches to assess chemical toxicity in a collaborative project between Eawag (Zurich) and Philip Morris International (Neuchatel). This project is due to finish at the end of this year, so Roman would be happy to discuss potential job opportunities.

OpenTox Euro 2019 talk: Generation of prospective adverse outcome pathways from computable biological networks

Transcriptomic data are increasingly being used as part of toxicological assessment of chemicals. In order to facilitate the interpretation of these large datasets, we have developed a novel systems toxicology method. Our method relies on the use of a computable causal biological network. 

To create such a network, observations published in scientific literature are coded into computable statements. The resulting network consists of nodes connected by edges. Nodes within the network represent biological entities such as mRNAs, proteins, protein activities, biological processes, and pathologies. Nodes are connected by directed and signed edges (i.e. upregulation or inhibition). This approach results in the network having a cause-and-effect topology.

The process of network curation shares a lot of similarities with the creation of adverse outcome pathways (AOPs). Both are based on literature. Both can describe molecular events at diverse levels of biological organization, from receptor activation to adverse outcomes. Additionally, both have directionality between the nodes, placing molecular events upstream and downstream of each other. Therefore, the network may be thought of as a network of potential AOPs that share key events. Leveraging this we extracted all possible paths that lead to adverse outcomes in the network. This resulted in linear progressions that may be further scrutinized to be considered an AOP.

In this talk I will describe how we created the network describing molecular events that lead to cardiotoxicity in zebrafish. I will highlight the utility of the network in interpreting transcriptomic datasets. Finally, I will demonstrate the process of extracting potential AOPs from the network.