Poster: Biological Network Models

Development of a Collaboration Platform for Comprehensive Lung Biological Network Models For Toxicological Risk Assessment

Stephanie Boue, Anselmo Di Fabio, Brett Fields, William Hayes, Julia Hoeng, Jennifer Park, Manuel Peitsch, Walter Schlage and Marja Talikka


Marja Talikka, PhD, Philip Morris International

Marja Talikka: Biological Network Models

We have built a series of causal biological network models to provide a biological framework to study adverse outcome pathways initiated by exposure to environmental chemicals and drugs.

The networks consist of causal statements constructed using the Biological Expression Language (BEL) and are based on both literature knowledge and molecular datasets.

The networks capture a series of steps that help explain potential pathways between the initiating molecular event and the adverse outcome.

A web-based crowdsourcing platform provides an easy way to share the network models allowing frequent updates by the scientific community. The platform is meant to facilitate the collaborative review of ~50 networks capturing a wide range of biological processes and is part of the sbvIMPROVER Network Verification Challenge (NVC).

The goal of the NVC is to obtain input from the scientific community, entered as votes, comments, and literature references, to improve the network models and to make them as comprehensive as possible.

During the first NVC, the scientific community contributed with over 2000 votes and 800 literature evidences added to the website (

Networks continue to be improved during the second NVC (NVC2) with a long-term goal of making them the most up-to-date and comprehensive network models available to the scientific community for toxicological risk assessment and drug development.


1. Gebel S, Lichtner RB, Frushour B et al. (2013) Construction of a Computable Network Model for DNA Damage, Autophagy, Cell Death, and Senescence. Bioinformatics and Biology Insights 7:97-117
2. Hoeng J, Deehan R, Pratt D et al. (2012) A network-based approach to quantifying the impact of biologically active substances. Drug Discov Today 17:413-418
3. Hoeng J, Talikka M, Martin F et al. (2013) Case study: the role of mechanistic network models in systems toxicology. Drug discovery today
4. Park WJ, Kothapalli KS, Reardon HT et al. (2012) A novel FADS1 isoform potentiates FADS2-mediated production of eicosanoid precursor fatty acids. Journal of Lipid Research 53:1502-1512
5. Schlage WK, Westra JW, Gebel S et al. (2011) A computable cellular stress network model for non-diseased pulmonary and cardiovascular tissue. BMC systems biology 5:168
6. Westra JW, Schlage WK, Frushour BP et al. (2011) Construction of a Computable Cell Proliferation Network Focused on Non-Diseased Lung Cells. BMC systems biology 5:105
7. Westra JW, Schlage WK, Hengstermann A et al. (2013) A Modular Cell-Type Focused Inflammatory Process Network Model for Non-Diseased Pulmonary Tissue. Bioinform Biol Insights 7:167-192 8. Ansari S, Binder J, Boue S et al. (2013) On Crowd-verification of Biological Networks. Bioinformatics and biology insights 7:307