1. University of Pavia Dep. of Electrical, Computer and Biomedical Engineering Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology Web site: http://lab-bioinfo.unipv.it Lab Director: Prof. Paolo Magni Contact: paolo.magni@unipv.itpaolo.magni@unipv.it

4. Clinical Data Mining Biomedical Knowledge Management Decision Support Systems Telemedicine E-learning Bioinformatics Mathematical Modelling of biological systems Synthetic Biology BMI director: Prof. Riccardo BellazziBMS director: Prof. Paolo Magni lab-bioinfo.unipv.it Dipartimento di Ingegneria Industriale e dell’Informazione labmedinfo.org

6. Bioinformatics

7. Mathematial Modelling

8. Synthetic Biology

9. Areas Synthetic Biology Mathematical modelling Bioinformatics

10. Areas Synthetic Biology Mathematical modelling Bioinformatics

11. Susanna Zucca 1 marzo 2016 Bioinformatics (1/4) Next Generation Sequencing (NGS)  Implementation and validation of data analysis pipelines for several sequencing applications;  optimization of cluster and cloud environments;  development of new algorithms and procedures for NGS data analysis. Big Data problem new spin-off of the University of Pavia, born to provide bioinformatic solutions in NGS data anaysis Ongoing collaborations

12. Susanna Zucca 1 marzo 2016 Bioinformatics (2/4) Applications: Diagnosis of neurological diseases Workflow: Stroke diagnosys

13. Susanna Zucca 1 marzo 2016 Bioinformatics (3/4) Network-based pharmacology  Network-based approaches to integrate different data and knowledge sources;  identification of combinations of hit targets to act with pharmacological therapy.

14. Susanna Zucca 1 marzo 2016 Bioinformatics (4/4) Tissue Engineering and Developmental Biology  Implementation of tools for the monitoring of stem cell pluripotency;  extraction of quantitative measures of the cell status from whole- genome expression profiles.

15. Susanna Zucca 1 marzo 2016 Areas Synthetic Biology Mathematical modelling Bioinformatics

16. Susanna Zucca 1 marzo 2016 Mathematical Modelling (1/4) Model-based drug development PRECLINICAL CLINICAL Experimental Setting 0 Data CollectionModelling - Data Analysis

17. Susanna Zucca 1 marzo 2016 Mathematical Modelling (2/4) Pharmacokinetic/pharmacodynamic (PK/PD) models What drug does to the body What body does to the drug

18. Susanna Zucca 1 marzo 2016 Mathematical Modelling (3/4) Advanced expertise in Bayesian techniques (and Markov Chain Monte Carlo algorithms) Population analysis Deconvolution methods PK/PD models Support of drug development and registration (in vitro, preclinical, clinical studies) Development of PK-PD models to quantitatively describe kinetics, mechanism of action and the effects on relevant endpoints of new compounds currently under investigation Research Areas Oncology: solid tumor, blood cancer, biomarkers, drug-drug interaction Biologicals: autoimmune diseases Study design: paediatrics, optimal design Tools Matlab, R, NONMEM, Monolix, WinBUGS, Stan, SimulX, PsN, Berkeley-Madonna

19. Susanna Zucca 1 marzo 2016 Sharing knowledge to improve drug development

20. Susanna Zucca 1 marzo 2016 Mathematical Modelling (4/4) Collaborations

21. Susanna Zucca 1 marzo 2016 Future perspectives  Move to the system pharmacology: integrating more information about pathways and mechanisms in the mathematical models  Participate in a foundation for the continuation of the DDMoRe project and the maintenance of its products  New EU projects (including IMI)  New collaborations  New peoples

22. Susanna Zucca 1 marzo 2016 Areas Synthetic Biology Mathematical modelling Bioinformatics

23. Susanna Zucca 1 marzo 2016 Synthetic Biology (1/5) De-novo synthesis of novel organisms Design of supplementary functions in existing organisms Predictable biological functions Datasheets Mathematical models System 1System 2 InputOutput Bottom-up design in biological engineering:

24. Susanna Zucca 1 marzo 2016 Synthetic Biology (2/5) Medicine (e.g. novel methods for drug production) Energy (e.g. optimized production of biofuels from waste or renewable materials) Environment (e.g. detection of toxic compounds) Information processing

25. Susanna Zucca 1 marzo 2016 Synthetic Biology (3/5) Typical workflow for the realization of novel artificial biological functions:

26. Susanna Zucca 1 marzo 2016 Basic research studies to facilitate the design of predictable functions:  Investigate predictable design via model systems with diverse synthetic circuit architectures  Design of user-friendly genetic tools and predictive mathematical models Synthetic Biology (4/5) Applied research studies:  Production of biofuels from industrial waste  Low-cost synthesis of biopolymers  Automatic control schemes for recombinant protein production

27. Susanna Zucca 1 marzo 2016 Synthetic Biology (5/5) New research fields…  Metabolic engineering  CRISPR-dCAS9 gene regulation

28. University of Pavia Dep. of Electrical, Computer and Biomedical Engineering Grazie per l’attenzione!