Photo of Prof Francesco Falciani

Prof Francesco Falciani

Functional and Comparative Genomics

    Research

    Research Grants
    • Understanding the heterogeneity of chronic lymphocytic leukaemia through the elucidation of how genetic alterations influence protein expression at the whole genome level.
    • Development of New Mathematical Sciences Capabilities for Healthcare Technologies
    • Pathfinder – exploring the commercial market for multi-omics analysis software
    • Prioritised expression of stress-related proteins in environmental thermoadaptive responses of animals
    • To deliver the quantification of 30 Unilever chemicals used in C elegans toxicity testing
    • In search of a common BaP signature: Bioinformatics analysis of metabolomics/lipidomics and miRNA expression of AML/CLL/BNHL cells treated with BaP.
    • To evaluate the necessity of using adult fish over fish embryos for toxicity testing
    • Developing the Ecotoxicological - Predictive - Information - Connectivity Map (EPIC-map)
    • The use of systems toxicology to re-construct molecular pathways of adverse outcome: Can chemical mixture toxicity be predicted to aid environmental risk assessment and regulation?
    • Adverse Outcome Pathways for Endocrine Disruption in aquatic animals – a conceptual approach for mechanistically-based Risk assessment (EDRISK)
    • Efficient Biological Networks Discovery and Analysis.
    • Immunodynamics and Infectious disease risk in the natural environment
    • Open source pipelines for integrated metabolomics analysis by NMR and mass spectrometry
    • Kinome-wide analysis of KRASand MYC driven cancer cell models exposed to clinical kinase and bromodomain inhibitors
    • A Systems Biology Platform for Predictive Ecotoxicology in Daphnia magna
    • Solutions for present and future emerging pollutants in land and water resources management (SOLUTIONS)
    • Synergy COPD
    • Cdc7 Study
    • Modelling the interaction of the vascular/tumor niche using a systems biology approach
    • Towards predictive biology: using stress responses in a bacterial pathogen to link molecular state to phenotype

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