Measurements for dynamic modelling

1. Measurements for dynamic modelling Stefan Hohmann Cell and Molecular Biology Göteborg (Gothenburg) University, Sweden hohmann@gmm.gu.se stefan.hohmann@gu.se (from June 1) Field of research: Experimental biology, signal transduction, cell regulation. Employing budding yeast as experimental model. Collaborations for measurements and modelling. CMB - Cell and Molecular Biology - Group Stefan Hohmann

2. Systems Biology Top-down or data-driven Networks from large-scale data Bottom-up or model-driven Dynamic modelling – simulating processes over time CMB - Cell and Molecular Biology - Group Stefan Hohmann

3. Data relevant for dynamic modelling • Physico-chemical properties of system components • Concentrations (molecules per cell) of components • Rates of changes of these concentrations • Rates of changes of interaction of the components • Velocity of movements or diffusion rates CMB - Cell and Molecular Biology - Group Stefan Hohmann

4. EC funds several projects on dynamic modelling • QUASI – yeast MAPK signalling • AMPKIN – AMP-activated protein kinase signalling • COSBICS – JAK-STAT and MAPK signalling • RIBOSYS – yeast RNA metabolism • YSBN – Coordinating yeast systems biology CMB - Cell and Molecular Biology - Group Stefan Hohmann

5. Quantifying signal transduction CMB - Cell and Molecular Biology - Group Stefan Hohmann

6. QUASI consortium • Gothenburg (biology: S Hohmann, P Sunnerhagen; chemistry: M Grøtli) Sweden • Barcelona (biology: F Posas) Spain • Vienna (biology: G Ammerer) Austria • Zürich (biology: M Peter) Switzerland • Berlin (theoretical physics: E Klipp) Germany

7. Types of measurements • Rate of changes of phospho-MAPK • Certain other phospho-proteins • Rate of changes of mRNA of reporter genes • Levels and rate of change and transport of glycerol • Rate of change of certain protein-protein interactions • Population profiling using reporter-XFP and FACS • Hog1 MAPK nuclear shuttling

8. Types of perturbations • Genetic changes in pathways • Genetic changes in responses (osmoregulation) • Specific kinase inhibitors • Changes in experimental conditions

9. Integration of signalling, gene expression, metabolism, transport and biophysical changes Edda Klipp CMB - Cell and Molecular Biology - Group Stefan Hohmann

10. Questions addressed by QUASI • Feedback control mechanisms in pheromone and high-osmolarity signalling MAPK pathways • Control of cell cycle by MAPK pathways • Control of a eukaryotic osmolyte system • Regulation of gene expression by Hog1 MAPK • Integration of converging branches of signalling pathway (HOG branches) • Pathway crosstalk CMB - Cell and Molecular Biology - Group Stefan Hohmann

11. Issues raised by QUASI • Linking different processes: signalling, gene expression, cell cycle, metabolism • Monitoring intermediates of signalling pathways (phospho-proteins) • Genetic perturbation – knock-out versus specific inhibitor • Cell-to-cell variations to interpret response profiles CMB - Cell and Molecular Biology - Group Stefan Hohmann

12. AMPKIN Systems Biology of AMP-activated protein kinase AMPK is the cellular energy regulator in eukaryotes and a possible target for drugs towards diabetes type II CMB - Cell and Molecular Biology - Group Stefan Hohmann

13. AMPKIN AMPKIN consortium • Gothenburg (biology: S Hohmann; physics: M Goksör) Sweden • Lyngby (bio-engineering: J Nielsen) Denmark • Rostock (computer science: O Wolkenhauer) Germany • London (biology: D Carling) UK • Arexis/Biovitrum (drug company – leaving project) Sweden

14. AMPKIN Types of measurements • Glycolytic flux and rates of changes of metabolite levels • Rates of changes of phospho-AMPK • Rates of changes of phosphorylated forms of certain target proteins • Activity of target enzymes • Absolute levels and rates of changes for many pathway components • Rates of changes of mRNA levels for reporter genes • Population proflies using reporter-XFP and FACS • Nuclear shuttling of Mig1

15. AMPKIN Types of perturbations • Genetic changes in pathways • Genetic changes in metabolism • Specific kinase inhibitors • Changes in experimental conditions

16. AMPKIN Questions addressed by AMPKIN • Comparative modelling of yeast and mammalian pathways • Integration of metabolism and signalling • Mechanisms controlling pathway activity • Signalling via kinases or phosphatases • Contributions of parallel pathways CMB - Cell and Molecular Biology - Group Stefan Hohmann

17. AMPKIN Issues raised by AMPKIN • Defining treatments for activating/deactivating the pathway • Sample preparation • Genetic perturbation – knock-out versus specific inhibitor • Cell-to-cell variations to interpret response profiles CMB - Cell and Molecular Biology - Group Stefan Hohmann

18. Eighteen partners, coordinator J Nielsen DTU Lyngby, Denmark • Standards for data documentation • Standards for model documentation • Dissemination • Training CMB - Cell and Molecular Biology - Group Stefan Hohmann

19. Quantitative data for dynamic modelling • Not commonly generated in high-throughput • Dedicated to very specific questions or modelling • Connected data sets – various measurements from same sample/culture/experiment • Reporting schemes/guidelines/standards and repositories CMB - Cell and Molecular Biology - Group Stefan Hohmann

20. Protein properties • Only for few metabolic enzymes • Generated in vitro with purified protein • Rarely known in vivo CMB - Cell and Molecular Biology - Group Stefan Hohmann

21. Numbers and concentrations • Readily possible for many metabolites • Possible for RNAs • Possible for proteins – but yeast data set needs to be re-checked very carefully • Protein modifications – specific antibodies or MS CMB - Cell and Molecular Biology - Group Stefan Hohmann

22. Single cell analyses • Cell-to-cell variation: interpretation of profiles obtained from cell extracts • Monitoring events in real time CMB - Cell and Molecular Biology - Group Stefan Hohmann

23. 100 80 60 percent response fraction of cells 40 20 0 0 10 20 30 40 50 60 time (min) Single cell measurements wild type percent response mutant time (min) Do all cells in the population show a graded response? Do different fractions of cells show an all/nothing response at different times? Do all mutant cells respond to max 50%? Do only 50% of the mutant cells respondbut with 100% amplitude? CMB - Cell and Molecular Biology - Group Stefan Hohmann

24. Population profiling • Using promoter-XFP reporter systems • FACS analysis CMB - Cell and Molecular Biology - Group Stefan Hohmann

25. Monitoring signalling in real time • Suitable experimental setup, e.g. microfluidics • Monitoring protein movement associated with signalling (e.g. nuclear-cytosolic shuttling) • Monitoring transient protein-protein interactions using FRET CMB - Cell and Molecular Biology - Group Stefan Hohmann

26. EC call for ”system approach to eukaryotic unicellular organism biology” • Integration of different cellular modules (cell cycle, signalling, metabolism) • Integration population – cell – network - module • Large dynamic models • Operating procedures • Measurement tools (esp proteomics, single cells) • Modelling tools (esp linking different models) • Understanding biology: how external and internal signals control cell grwoth and proliferation CMB - Cell and Molecular Biology - Group Stefan Hohmann

27. Some conclusions • Measurements for dynamic modelling are commonly small-scale and highly dedicated • Data collection is model-driven • Data should be reported in conjunction with the model • Measurements are often technically challenging • Quantitative measurements are being developed • Life cell imaging and single cell analyses methods are important CMB - Cell and Molecular Biology - Group Stefan Hohmann