Biocomplexity: the post-genome challenge in ecotoxicology.

Area of Science:

• Environmental toxicology
• Ecotoxicology
• Computational toxicology

Background:

• Linking molecular/subcellular pollutant interactions to ecological consequences is a major challenge in ecotoxicology.
• A knowledge gap exists in developing effective environmental/ecological impact and risk assessment procedures.
• Novel industrial processes like biotechnology and nanotechnology present new toxicological challenges.

Purpose of the Study:

• To address key challenges in environmental toxicology over the next decade.
• To explore the use of biomarkers and simulation models for risk assessment.
• To bridge the gap between molecular toxicology and ecological consequences.

Main Methods:

• Utilizing diagnostic biomarkers to assess population health and link molecular damage to higher-level effects.
• Developing predictive simulation models of toxic effects on cellular and physiological processes.
• Interdisciplinary research focusing on physico-chemical speciation, complex mixtures, and hierarchical biological organization.

Main Results:

• Biomarkers provide insights into population health and can link molecular damage to reduced performance and reproductive success.
• Computational simulation models, such as virtual cell models, facilitate predictive risk assessment.
• Research highlights the need to link pollutant impacts across all biological organization levels to ecosystem and human health.

Conclusions:

• Addressing environmental toxicology challenges requires interdisciplinary approaches and advanced tools like biomarkers and simulation models.
• Effective risk assessment necessitates linking molecular-level effects to broader ecological and human health outcomes.
• Predictive modeling is crucial for anticipating and mitigating the harmful impacts of future environmental events and industrial innovations.