Marvin Martens, originally trained as a biomedical scientist / developmental biologist at Maastricht University (NL) and Universite Pierre et Marie Curie (FR), transitioned into bioinformatics during his master projects. This has led him to the Department of Bioinformatics (BiGCaT) at Maastricht University, where he pursued his PhD. Throughout his doctoral research, Marvin's research predominantly revolved around Adverse Outcome Pathways (AOPs) and data integration. His contributions extended to several impactful toxicology projects within the European Union, including EU-ToxRisk, OpenRiskNet, NanoSolveIT, RiskGONE, CIAO, and his current engagement in the Dutch project VHP4Safety. During his PhD, his work spanned AOP development, data and service integration, database FAIRification (most notably the AOP-Wiki), semantic web applications, and in-depth transcriptomic data analysis. His PhD thesis has recently been accepted and will soon be defended. Marvin's passion and expertise are anchored in the optimisation of AOPs, which he envisions as central hubs for the seamless integration of toxicological data, knowledge, and tools.
OpenTox 2023 Virtual Conference Session 2
Adverse Outcome Pathways (AOPs) have emerged as a powerful framework for understanding the linkages between molecular events and their consequences in ecotoxicology and human health risk assessment. In recent years, the integration of computational methods, collectively known as "in silico" approaches, has revolutionized our ability to accelerate AOP development, make predictions, and enhance risk assessment processes. This conference session, featuring esteemed invited speakers, will delve into a handful of invaluable contributions of in silico approaches to the world of AOPs and their critical role in advancing risk assessment.
In silico approaches encapsulate a wide range of computational techniques and modeling tools, and offer a variety of functionalities to enhance risk assessment. For example, they can improve risk assessment by enabling the rapid generation of hypotheses for AOPs. By leveraging existing data and knowledge, researchers can formulate AOP hypotheses, streamlining testing strategies. Additionally, in silico methods involve predictive modeling, using sophisticated computational models to predict adverse outcomes. This predictive power reduces the need for extensive experimental studies, making risk assessment more efficient. Furthermore, in silico approaches seamlessly integrate diverse data sources, including omics data, chemical properties, and toxicological information, leading to a better understanding of AOPs. These methods are invaluable for exposure and dose-response assessment, predicting how stressors affect AOPs, a fundamental aspect of risk assessment. Lastly, in silico tools contribute to hazard identification and prioritization, aiding regulatory agencies and stakeholders in the effective allocation of resources for improved risk management.
During this session, participants will discuss and gain insights into the synergy between in silico approaches and AOPs, understanding how these techniques can revolutionize risk assessment and contribute to safer environmental and human health protection.