Xenobiotic metabolism and causal biological networks

Xenobiotic metabolism is activated in response to exposure to chemical substances foreign to the body (i.e. xenobiotics, such as toxic compounds, drugs…). Very briefly, enzymes involved in this process convert xenobiotics into hydrophilic derivatives that can be eliminated through excretion into the aqueous compartments of the tissues1,2. Previously developed causal biological network models scripted in the Biological Expression Language (BEL) which focused on pulmonary and vascular biology have been shared with and reviewed by the scientific community in past Network Verification Challenges (NVC1 and NVC2)3-6. Since the liver is the main site of xenobiotic metabolism in mammals, we have developed a new suite of network models that represent biotransformation and chemical elimination involved in Phase I, Phase II, and Phase III of the xenobiotic metabolism in the liver (see figure below).


Based on such causal network models7, Philip Morris International has developed computational methods that allow to understand the mechanisms behind and predict the effect of exposure based on transcriptomics datasets. This methodology enables to translate the gene expression fold-changes into differential values for each network node, and to summarize this at the network level to provide a quantitative assessment of the degree of perturbation of the network model, the Network Perturbation Amplitude (NPA)8,9. Combining multiple relevant network models, the overall biological impact of a perturbing agent, the Biological Impact Factor (BIF)9, can be calculated by aggregating individual NPA scores (see figure below).

The NVC3 Challenge

The 3rd sbv IMPROVER Network Verification Challenge (“NVC3”) aims to verify and enhance three (one for each metabolic phase) unpublished causal biological network models that include the signaling pathways leading to the activation of enzymes involved in the three phases of liver xenobiotic metabolism.

The NVC3 is expected to increase the networks' value and promote their use in research applications, including:

  • Toxicology: to predict adverse outcomes and gain mechanistic understanding in response to exposures;
  • Pharmacology: to establish drug mode of action and predict adverse drug effect;
  • Personalized medicine: patient diagnosis and prediction of treatment outcome;
  • Basic research: to build biological hypotheses from high-dimensional data sets such as those derived from microarray profiling studies.

The biological networks are available on a dedicated challenge website and more details about the challange are availble on this flyer.


How does the challenge work?

Compete. Collaborate. Contribute.

Review and refine the networks to create a better set of open source, freely available biological network models for the scientific community. Challenge your peers and see in real time how you rank in the leaderboard. Join now and be part of this exciting challenge.

1. Online Crowd Verification
The scientific community participates in an online verification process to review, challenge and enhance network models describing xenobiotic metabolism in liver (June 2017 – 30th April 2018).

2. Challenge awards
A gift card of 150 USD when the participant reaches 3,000 points. Once the online verification process is finished, the best performing participants will be rewarded with a travel grant.

3. Network dissemination and continuous improvement
Based on the outcomes of the online verification, the biological network models will be finalized and published on this platform, and be available for continuous use and refinement.


  1. Nakata, K., Y. Tanaka, T. Nakano, T. Adachi, H. Tanaka, T. Kaminuma and T. Ishikawa (2006). “Nuclear receptor-mediated transcriptional regulation in Phase I, II, and III xenobiotic metabolizing systems.” Drug Metab Pharmacokinet
  2. Xu, C., C. Y. Li and A. N. Kong (2005). “Induction of phase I, II and III drug metabolism/ transport by xenobiotics.” Arch Pharm Res
  3. sbv IMPROVER project team et al.,(2013) “On Crowd-verification of Biological Networks.” Bioinform Biol Insights.
  4. sbv IMPROVER Project Team et al.,(2015). “Reputation-based collaborative network biology”. Pac Symp Biocomput
  5. sbv IMPROVER project team et al., (2015). “Enhancement of COPD biological networks using a web-based collaboration interface” F1000Res.
  6. sbv IMPROVER project team and challenge best performers et al., (2016). “Community-Reviewed Biological Network Models for Toxicology and Drug Discovery Applications.” Gene Regul Syst Bio.
  7. Boue, S., Talikka, M., Westra, J. W., Hayes, W., Di Fabio, A., Park, J., Schlage, W. K., Sewer, A., Fields, R. B., Ansari, S., Martin, F., Veljkovic, E., Kenney, R. D., Peitsch, M. C. & Hoeng, J. (2015). “Causal Biological Network (CBN) database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems Database” Oxford Academic
  8. Martin, F., Sewer, A., Talikka, M., Xiang, Y., Hoeng, J. & Peitsch, M. C. (2014). “Quantification of biological network perturbations for mechanistic insight and diagnostics using two-layer causal models” BMC Bioinformatics
  9. Hoeng, J. et al. (2012) “A network-based approach to quantifying the impact of biologically active substances” 

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