To ensure the ontology and data warehouse developed in WP2 and WP3 meets the safety-by-design and community needs, WP4 will develop computational infrastructure capable to analyse and extract knowledge out of diverse types of ENM-related theoretical descriptors, experimental data and associated metadata, including provenance of experimental data and experimental conditions and protocols. The modelling tools will be used to assess potential risks of ENMs, prioritize ENMs for experimental testing, and contribute to the development of ‘safe-by-design’ ENMs, as the potential toxicity and environmental impact of ENMs will be predicted during the design phase. By extending the set of features beyond the classical chemical structure descriptors, and by incorporating in vitro highthroughput and high-content data as biological descriptors, we will take into account the supramolecular pattern of ENMs and will support mechanism-of-action based modelling approaches. This WP will also develop functionalities for the automatic generation of reports on nQSAR models and predictions that can be used for regulatory purposes, thus complementing the risk assessment of engineered ENMs and reducing experimental and animal testing, which are very demanding in terms of time, cost and experimental facilities. The development of optimal experimental design and inter-laboratory testing facilities will support and advance the synergy between experimental and computational scientists in order to generate in a focused and efficient way reliable, consistent and rich-ininformation experimental data.
Description of work
WP4 will pursue a linked data and iterative approach that will exploit and integrate all data and metadata captured in the database warehouse of the project, provide feedback for optimal experimental design and guidelines for the generation of additional high-quality, focused, reliable and consistent experimental data and information. A bundle of modelling and analysis tools will be developed and implemented throughout the project, compliant to the OpenTox Application Programming Interface (API) and particularly tailored to the needs of ENM predictive toxicology, including new theoretical descriptors, methods for identifying key structural, physicochemical and biological features that are responsible for ENM bioactivity, modelling algorithms for correlating ENM properties with their biological and environmental impacts, optimal experimental design procedures and inter-laboratory testing facilities. The flexible computational infrastructure will be implemented based on interoperable, standards-compliant and modular web services maximising cross-talk and interaction between different and diverse sources of data.
Description of Tasks
Analysis & Modelling
WORK PACKAGE LEADER
National Technical University of Athens (NTUA)
To ensure the ontology and data warehouse developed in WP2 and WP3 meets the safety-by-design and community needs, WP4 will develop computational infrastructure capable to analyse and extract knowledge out of diverse types of ENM-related theoretical descriptors, experimental data and associated metadata, including provenance of experimental data and experimental conditions and protocols. The modelling tools will be used to assess potential risks of ENMs, prioritize ENMs for experimental testing, and contribute to the development of ‘safe-by-design’ ENMs, as the potential toxicity and environmental impact of ENMs will be predicted during the design phase. By extending the set of features beyond the classical chemical structure descriptors, and by incorporating in vitro highthroughput and high-content data as biological descriptors, we will take into account the supramolecular pattern of ENMs and will support mechanism-of-action based modelling approaches. This WP will also develop functionalities for the automatic generation of reports on nQSAR models and predictions that can be used for regulatory purposes, thus complementing the risk assessment of engineered ENMs and reducing experimental and animal testing, which are very demanding in terms of time, cost and experimental facilities. The development of optimal experimental design and inter-laboratory testing facilities will support and advance the synergy between experimental and computational scientists in order to generate in a focused and efficient way reliable, consistent and rich-ininformation experimental data.
WP4 will pursue a linked data and iterative approach that will exploit and integrate all data and metadata captured in the database warehouse of the project, provide feedback for optimal experimental design and guidelines for the generation of additional high-quality, focused, reliable and consistent experimental data and information. A bundle of modelling and analysis tools will be developed and implemented throughout the project, compliant to the OpenTox Application Programming Interface (API) and particularly tailored to the needs of ENM predictive toxicology, including new theoretical descriptors, methods for identifying key structural, physicochemical and biological features that are responsible for ENM bioactivity, modelling algorithms for correlating ENM properties with their biological and environmental impacts, optimal experimental design procedures and inter-laboratory testing facilities. The flexible computational infrastructure will be implemented based on interoperable, standards-compliant and modular web services maximising cross-talk and interaction between different and diverse sources of data.