BioDIVE

BioDIVE is a free and versatile platform for applications that enable analysis of large datasets, including gene expression and high-content screening data, to extract relevant biological insights.

Get new insights from your data with the BioDIVE applications!

CBN score is a network-based approach for quantifying biological impact.

CBN Score allows users to upload differential gene expression data, process that data through the causal network models, and then download interpretable and hierarchical results that quantify the effects of exposure.

The algorithms used are based on the Network Perturbation Amplitude (NPA) and Biological Impact Factor (BIF) methodology which identifies and maps patterns in network perturbations.

GladiaTOX is an all-in-one solution for the analysis of high-content screening (HCS) data.

GladiaTOX enables the standardization of HCS data and dependable, reproducible analysis procedures that facilitate rapid-decision making processes.

Written in the R programming language, GladiaTOX enables robust and efficient storage, processing, and reporting of HCS data, in line with the landmark 21^st Century Toxicology program. An evolution of the ToxCast PipeLine (TCPL), which was created for the US Environmental Protection Agency, GladiaTOX has been developed by Philip Morris International (PMI) in collaboration with Filer Consulting (Durham, North Carolina, USA), lead author of the original TCPL.

GladiaTOX has been released in GitHub in 2019. It is now available via the BioDIVE application.

You can find an example input file here.

Network perturbation amplitude (NPA)

• Thomson, T.M. et al, 2013. Quantitative assessment of biological impact using transcriptomic data and mechanistic network models. Toxicology and applied pharmacology 272, 863–78.
• Martin, F. et al, 2014. Quantification of biological network perturbations for mechanistic insight and diagnostics using two-layer causal models. BMC bioinformatics 15, 238.
• Hoeng, J. et al., 2012. A network-based approach to quantifying the impact of biologically active substances. Drug discovery today 17, 413–8.
• Hoeng, J. et al., 2014. Case study: the role of mechanistic network models in systems toxicology. Drug discovery today19, 183–92.

Causal Bionet (CBN)

• Boue, S. et al., 2015. Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems. Database: the journal of biological databases and curation, 65–93.

Causal network models

• De Leon, H. et al., 2014. A vascular biology network model focused on inflammatory processes to investigate atherogenesis and plaque instability. Journal of translational medicine 12, 185.
• Gebel, S. et al., 2013. Construction of a computable network model for DNA damage, autophagy, cell death, and senescence. Bioinformatics and biology insights 7, 97–117.
• Park, J.S. eet al, 2013. Construction of a Computable Network Model of Tissue Repair and Angiogenesis in the Lung. Clinical Toxicology S12.
• Schlage, W.K. et al, 2011. A computable cellular stress network model for non-diseased pulmonary and cardiovascular tissue. BMC systems biology 5, 168.
• Westra, J.W. et al, 2011. Construction of a computable cell proliferation network focused on non-diseased lung cells. BMC systems biology 5, 105.
• Westra, J.W. et al, 2013. A modular cell-type focused inflammatory process network model for non-diseased pulmonary tissue. Bioinformatics and biology insights 7, 167–92.

GladiaTOX

• Belcastro, V. et al., 2019. GladiaTOX: GLobal Assessment of Dose-IndicAtor in TOXicology. Bioinformatics 35, 4190–4192.

Applications of GladiaTOX

• Marescotti, D. et al., 2019. Systems toxicology assessment of a representative e-liquid formulation using human primary bronchial epithelial cells. Toxicology Reports 7, 67–80.

High content screening

• Marescotti, D. et al., 2016. High Content Screening Analysis to Evaluate the Toxicological Effects of Harmful and Potentially Harmful Constituents (HPHC). Journal of visualized experiments: JoVE.