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  • 1. Systems of Life - Systems Biology Network Activities on Systems Biology A. Hepato Sys B. International Initiatives Presentation by Gisela Miczka1 , Roland Eils2 and Siegfried Neumann3 1 Projektträger Jülich, Jülich, Germany  2 German Cancer Research Center, Heidelberg, Germany  3 MERCK KGaA, Chemical Section R+D, Darmstadt, Germany NiSIS Symposium, Portugal, October 2005
  • 2. Outline A. Hepato Sys – The German Initiative on Systems Biology of Human Hepatocytes • The Design of the Programme • Goals, Structure and Projects • Coordination and Project Management, Websites B. International Initiatives in System Biology • Systems Biology for Drug Research • International Crosslinks • Commercial Suppliers
  • 3. 2001: How to establish a BMBF funded national research network on Systems Biology Start of the „design-process“: Discussion forum with a multidisciplinary team of 9 leading scientists to develop a funding strategy. The key criteria are • medium to long term research programme • synergy with existing BMBF funded research programmes in Genomics and Proteomics • considers the international status of the art • reckognizes international standards and contributes to them
  • 4. Expert panel structuring thematic priority recommendations core expert panel (9) documentation informations elicit thematic topic funding- strategies WS 1 WS 2 WS 3 WS 4 data-screening, conferences, interviews external expert panel (>70) March 2001March 2001 DecemberDecember 20012001 NovemberNovemberMayMay JulyJuly SeptemberSeptember „„Systems of Life -Systems of Life - Systems Biology“Systems Biology“ The Design-Process
  • 5. Goal of the Systems Biology Initiative on Hepatocytes (HepatoSys) The long-term goal of this systems biology approach is to understand the dynamic processes in a human cell and to build up mechanism-based mathematical models of these processes in order to predict the behaviour of the system under defined conditions.
  • 6. • high complexity of mammalian cells • human diffentiated cells are not easy to handle and not easy to cultivate while keeping differentiation and metabolic properties simular to in vivo living cells • the mathematical tools for modelling of cellular dynamics and systems analysis basically are not developed for complex systems Aim to overcome the obstacles in order to do systems biology on a medically relevant cell type. ! Challenges
  • 7. The Approach • Set up an interdisciplinary competence network linking bioscience with computer science, mathematics and engineering sciences • Start with studies on defined biological functions • Establish standardized cells, methods, and tools
  • 8. BiologyBiology SystemsSystems EngineeringEngineering BioinformaticsBioinformatics MathematicsMathematics Tools (HTS)Tools (HTS) SystemicSystemic BehaviourBehaviour AlgorithmsAlgorithms SoftwareSoftware DatabasesDatabases Systems BiologySystems Biology biological modelsbiological models generation of quantitativegeneration of quantitative data, anlysis of functionaldata, anlysis of functional relations; tool developmentrelations; tool development modelling (study onmodelling (study on regulation, structure,regulation, structure, robustness, etc. of system)robustness, etc. of system) establishment of databases,establishment of databases, development ofdevelopment of in silicoin silico models and softwaremodels and software
  • 9. Why Hepatocytes? Attractivity • central functions in metabolism (for lipids, carbohydrates, amino acids …) • central role in the uptake and conversion of drugs (transport, metabolic conversions, detoxification ...) • regeneration ability i. e. high impact on problems in pharmacology and pathophysiology
  • 10. Structure of the National Competence Network HepatoSys Platform Cell biology Platform Modeling Coordinating Committee Steering Committee Collaborative Network “Regeneration” Project Management Collaborative Network “Detox/Dediff.”
  • 11. Members of the Steering Committee Prof. Dr. Dieter Oesterhelt, MPI for Biochemistry Munich (chairman) Dr. Roland Eils, DKFZ Heidelberg Prof. Dr. Joseph Heijnen, Technical University of Delft, NL Prof. Dr. Karl Kuchler, Institute for Medical Biochemistry, University of Wien, AU Prof. Dr. Siegfried Neumann, Merck KGaA Darmstadt, Senior Consultant R+D Prof. Dr. Hans V. Westerhoff, Molecular Cell Physiology & Mathematical Biochemistry, BioCentrum Amsterdam, NL
  • 12.  call for project proposals December 2001  number of proposals 40  start of the research work January 2004 under this programme  first funding period 15 Mio. € /3 years collaborative projects 2 platform projects: cell biology 3 modeling 3 number of partners 25 Facts on the Starting Phase
  • 13. The Project Committee on HepatoSys • Dr. Jens Timmer, University Freiburg (chairman) • Prof. Dr. –Ing. Matthias Reuss, University Stuttgart • Prof. Dr.-Ing. Ernst-Dieter Gilles, MPI for Komplex Technical Systems, Magdeburg • Prof. Dr. Augustinus Bader, Biomedizinisch- Biotechnologisches Zentrum, Leipzig
  • 14. Main Objectives of HepatoSys Network on detoxification and dedifferentiation in hepatocytes (Speaker: Prof. Reuss, Univ. Stuttgart-Hohenheim) Network on regeneration of hepatocytes (Speaker: Dr. Jens Timmer, Univ. Freiburg) Platform Cell biology: Development of new cells, of optimized culture conditions, of high throughput technology and supply of cells for the projects in the national network (Speaker: Prof. Bader, Univ. Leipzig) Platform Modeling: Development of bioinformatics and mathematical tools for data management, data handling etc. and service for the projects of the national network (Speaker: Prof. Gilles, MPI Magdeburg)
  • 15. The Network on Detoxification / Dedifferentiation • Detoxification • Cytochrome P 450 isoforms • Molecular dynamics • Kinetic experiments • Polymorphisms • Dedifferentiation • Change of metabolic pathways during dedifferentiation
  • 16. The Network on Regeneration • Background Liver regeneration is a highly regulated process • Goal Understanding the pathways involved • Method Data-based mathematical models • Long term goal Support development of liver cell lines
  • 17. The Cell Biology Platform • Distributing Standardized Cell Material • Primary hepatocytes (man, mouse, rat) • Isolation protocol, culturing, starving & stimulation following SOPs • Developing Human Cell Lines Based on • Conditionally immortalized cells • Somatic stem cells • Bioreactors with controlled microenvironment
  • 18. The Modeling Platform • Work out concepts on central data management • Develops algorithms and software for modeling • Supply project partners of the biology networks with project-specific tools in systems theory • Develop integrated systems biology research on their own concepts
  • 19. Hamburg Mainz Jena Heidelberg Birlinghoven Dresden Geographic Distribution of the Projects Freiburg Stuttgart Aachen Berlin Magdeburg Collaborative Projects Platform Cell Biology Platform Modeling LeipzigDüsseldorf Bochum Geographic Distribution of the Projects
  • 20. Coordination of the Competence Network Systems Biologe • Secretarial office for the BMBF Funding Initiative „Systems for Life – Systems Biology“ at University of Freiburg (Dr. Timmer‘s office) • Flyer, Brochures, Articles, Poster ... • Webpages, Internet Representation ... • Public Relation with Journalists and Media • Conference Visits and Reports • Scientific Coordination of Interdisciplinary Research Groups
  • 21. Project Management for the Competence Network Systems Biology • Workshop – Partnering, Kick-Off Workshops, Annual Status Workshops (last one on April 28 to 29, 2005, next in November 2005) • Conference Organization by DECHEMA e.V.– Conference Office for the 5th International Conference on Systems Biology, October 9 –13, 2004 in Heidelberg • Coordination of due diligance, contracting and implemen- tation for a Central Data Management for the funding Initiative Systems Biology • Organizing the Scientific Report Systems for PTJ, BMBF, and Steering Committee
  • 22. Websites • Federal Ministry of Education and Research • PTJ – the Project Management Organisation Jülich • Competence Network Systems Biology • The Database for Systems Biology Researchers
  • 23. Systems Biology – The Concepts Systems biology integrates the molecular parts list into quantitative models of biological functions Kitano, H. Science 295, 1662 (2002): “To understand biology at the system level, we must examine the structure and dynamics of cellular and organismal function, rather than the characteristics of isolated parts of a cell or organism.”
  • 24. Genome Transcriptomics Gene Regulation Expression Proteomics Proteins Metabolism Phenotype and Potential for Diseases Metabolomics Tissues and Cells Whole Organism Physiomics cit from Nicolson (2002), modified Descriptional and analytical levels in Systems Biology
  • 25. It is all dynamics in biological systems Measurements by the -omics technologies do not necessarily reflect real-world or endpoint observations Real world 'omics world Inputs: Signals stressors etc cell Gene expression Protein profile Metabolic profile Time Time Time Time Time Outputs: Biological endpoints pathology degeneration regeneration Note: time differentials in all interaction stages Nicolson, J.K. at al. Nature Reviews Drug Discovery 1, 153 (2002)
  • 26. Current topics in systems biology Problems encountered when we try to understand life processes by simulation and modeling • Complexity • n Dimensionality • Holistic versus reductionistic working modes • Change, dynamics • Pleiotropy and redundancy in biology • Deterministic versus stochastic mathematics • Bioinformatics ≠ System Engineering • Need to end in understanding physiology and disease processes
  • 27. Complexity and emergent properties in biology 1. Complex inputs that stimulate multiple pathways 2. Integrated networks respond to the inputs by multiple outputs 3. Interactions between multiple cell types in multi cellular organisms (like man) 4. Multiple contexts and environments for each cell type or combination of cell types To understand the effects of a target or a drug, data must be derived from cell responses in multiple environment. Butcher et al. Nature Biotechnol. 22, 1253 (2004)
  • 28. Deliverables and limitations of approaches by integrative biology to drug research and development Omics Cell systems Computational biology • Hypothesis generation + + + • Target identification/validation (+) + (+) • Quantitative analysis of dynamic parameters - (+) + • Rational design of perturbance of a system - (+) + • Systems connectivities - + + • Disease model properties - + - • Disease indication / trial design - +/- (+) • Data quantity • Data quality • Need for functional annotation work Limitations: • Availability of all types • Limited modeling of systemic effects • Missing experimental data sets • Availability of suitable cell material • Very slow throughput • Computational limitations
  • 29. Examples of computational models relevant to human disease biology Approach System Comments Disease physiology Heart Diabetes Asthma Quantitative models of the heart from genes to physiology Approaches for modeling diabetes Math. models of chronic asthma for prediction of therapy response Integrative cell models Cancer Cardio- myocytes Network models containing 1000 genes/proteins, 3000 components predicted effect of specific gene knock downs, Cancer pharmacogenetics-polymorphisms, pathways and beyond Linking modules (int. Metabolism, electrophysiology and mechanics) for computational modul of cardiomyocytes Pathway models Multiple EGFR/MAPK NF-KB Wnt Pathway Emergent properties of signaling in network models Computational models of EGFR signaling and network model Signal processing of NF-KB signaling pathway Experimental and theoretical analysis of the Wnt Pathway, roles of APC and axin. (cit. Butcher, E. C. et al., Nature Biotechnol. 22, 1253 (2004), modified)
  • 30. Data-based mathematical modelling of the JAK2-STAT5 Pathway (Klingmueller, pers. commun,.)
  • 31. Mathematical prediction: Dynamical parameters of nuclear import (k3), export (k4) and delay (τ) most sensitive to perturbation Experimental verification of mathematical prediction JAK2-STAT5 Pathway Predicting Steps Most Sensitive for Perturbation (Klingmueller, pers. commun.)
  • 32. Systems Biology: Selected commercial players Company Core Technologies Approach Deliverables Accelrys Software Tools Software for process simulation Simulation of biological and chemical process Bayer Technology Services Software tools PK-MAP™ PK-SIM™ Prediction, interpretation and extrapolation of pharmacokinetics / pharmacodynamics High quality estimates of ADME and PK BG Medicine Bioselective Targets ™ Biosystems Markers ™ Application of SB for target discovery, biomarkers and predictive toxicology Targets, biomarker identification Predictive toxicology Entelos Math. models (diff. equations) for simulation and analysis Dynamic models for disease processes on molecular, cellular and physiological levels (Physio Labs) Target ID, Evaln. Leads, Biomarkers, Clinical trial design Gene GO META core analysis Network analysis of HAT expression data Gene profile analysis in breast cancer
  • 33. Systems Biology: Selected commercial players ctd. Company Core Technologies Approach Deliverables Iconix HTP molecular biology Data-mining Integration of chemistry and genomics to profile drug candidates to predict toxicity Predictive toxicology Ingenuity Ontology Pathway database Computing on DB Identification of altered pathways from diff. expression date Target ID based on pathway analysis Icoria Inc. (former Paradigm Genetics) Biochemical Profiling Platform Metabolic Profiling Biomarkers for DD and diagnosis Physiomics plc In silico simulations Computer models for human diseases Pathway simulation, multiple cell systems In silico tests for interpretation of PK and PD Surromed HTP molecular biology Data-mining Profile immune cell populations, proteins and small molecules for biomarkers. Fingerprint pathways involved in disease and therapeutic response Biomarker ID Clinical trial design
  • 34. Systems Biology at Work in Drug Discovery of Big Companies Drug Company Research Activity Specialist Partner • Eli Lily / Lilly Systems Biology in Singapore Explore network pathways, use dynamic models to simulate cellular responses to drugs, 140 Mio. $ over 5 years commitment • Novartis Focus on pathway studies Cellzome AG • Novo Nordisk AS SB approach to the combinatorial nature of signal transduction • Johnson + Johnson's Pharmaceutical R+D Using PhysioLabs mathematical models for analysis of dynamic relationships within human biological networks (Diabetes II, hematology , clin. development, phase IV clinical trials) Entelos • Organon Using PhysioLabs on Rheumatoid Arthritis drug targets Entelos • Astra Zeneca SB in predictive toxicology Beyond Genomics • Glaxo Smith Kline SB in metabolic disease pathways, drug mechanism of action, identify new biomarkers Beyond Genomics Lit. zit.: Littlehales, C.: Bio News Dec. 20047January 2005, p. 9, modified
  • 35. The Multiple Input of Systems Biology into Molecular Medicine Drug Discovery Clinical Development Therapy Markers Safety, Toxicity Efficacy Response/ Non response Safety/ Efficacy Diagnosis/ Prognosis Disease Progression Target - Identification, - Characterization, - Prioritization Pathway Elucidation, Network Analysis Animal Model Validation Targets Mode of Action Trial Design Product Decision Combination with Other Drugs Disease Indications
  • 36. Research centers on systems biology in the USA (1) Institute for Systems Biology Integration of the different levels of biological information, (Hood et al.; Seattle) modeling of integral systems - microorganism models and yeast - immune system, cancer, hematopoeitic development The Molecular Science Institute Development of prediction biology (Brenner, Brent; Berkeley) - genomic, evolutionary studies on E. coli - protein/protein interactions - computational biology, instrumentation Dept. on Bioengineering Systematic analysis of genetic circuits (Palsson, UCSD) - coordinated activities of multiple gene products in metabolism and cell motility - in silico metabolic routing in E. coli Caltech Modeling of nonlinear systems in E. coli (Simon, Doyle, Kitano, et al.) - Simulation systems for gene regulation and metabolism - Modeling and simulation of the cell cycle Biomolecular Systems Initiative (BSI) Studies on cellular networks (within cells and between cells) at Pacific Northwest Natl. Laboratory - in microbiological systems by (Wiley et al.) - quantitative and integrative cell biology
  • 37. Research centers on systems biology in the USA (2) Alliance for Cellular Analysis of G protein coupled or related signal Signaling (AfCS) transduction in mammalian cells (Gilman, Univ. Texas - identification of all involved proteins South Western) - analysis of kinetics of information fluxes - modeling cellular signaling MIT Computational and Systems • Quantitative biology of cellular functions by Biology Initiative (CSBI) experimentation, modeling and simulation in mammalian (Sorger, Tidor, Lauffenburger) cells and tissues - regulation of proliferation, adhesion, migration and transport • Education in SB Systems Biology Department • Bioinformatics, structural genomics, Quantitative Structure Harvard Medical School Activity Relationships in multicomponent complexes (Kirschner, Mitchison, Harvard) - Synthetic biological systems - Molecular understanding of physiological centre • Education in SB Princeton Integrative Genomics • Interdisciplinary research programmes on quantitative biology (Botstein et al.), University of • Education in SB Michigan Life Sciences Institute (Saltiel et al.), Stanford University Biosciences Initiative (Bio-X, Scott et al.), Duke’s Institute for Genome Sciences and Policy (Willard et al.)
  • 38. Recent Highlights in SB International Crosslinking  EU-Initiatives • EU SYSBIO, SYMBIONIC • EUREKA InSysBio Project • SYSMO (AU, DE, NL, GB, NO, SP)  WTEC/USA: Reports on US, EU and Japan activities  WTEC/USA: Workshop on setting up a repository for systems biology software, February 17-18, 2005, Washington, USA  5. International Conference on Systems Biology October 9-13, 2004, Heidelberg, Germany  6. International Conference on Systems Biology October 2005, Cambridge, USA, Org: Marc Kirschner, Harvard  Start of PanAsian electronic International Molecular Biology Laboratory (e IMBL) Seoul, July 12-13, 2005
  • 39. This is a website of SYSMO: SYSMO is a transnational funding program for the Systems Biology of MicroOrganisms, of The German BMBF, the Dutch NWO-ALW, and the Austrian bm:bmk. Additional countries have been invited to join soon. At present SYSMO is already active in supporting the training of scientists and students in Systems Biology. Its first activity is the strong support(in terms of travel fellowships) of the FEBS advanced course (see below). A second, much larger activity is a transnational research program for Systems Biology of Microorganisms. Countries are now asked to express their interest in participating in and supporting this program. On SYSMO
  • 40. See also: First FEBS Advanced Course on Systems Biology: From Molecules & Modeling To Cells March 12- 18, 2005, Gosau, Austria, EU Organized by: Roland Eils (Heidelberg), Karl Kuchler (Vienna), Anneke Koster (Amsterdam), and Hans V. Westerhoff (Amsterdam) Program and all information Flyer (pdf) Registration Pre-registration
  • 41. Systems Biology – How to implement into pharmaceutical research and development? (1) • Interdisciplinary approach needed, develop common conceptual understanding of biologists, mathematicians and bioinformatics experts • Define cellular models and experiments with reproducable properties - sampling - culture conditions - validated analytical technologies - exp. schedules • Iterative approaches needed between model builders and biological experimentators • Provide sufficient IT hardware resources and software tools
  • 42. • Drug researchers should join accademic initiatives for strategic cooperative projects • Drug R+D should form precompetitive R+D platforms for developing SB tools and informatics standards - Speak a common research language - Share IT resources - Train researchers on an integrative approach • Drug R+D should contribute views on strategic research priorities to academic research directors and share strategic concepts with national and cross-border research planning panels on precompetitive level • The potential of systems biology for drug discovery and development needs a major success story in industry (Ideker, 2004) Systems Biology – How to implement into pharmaceutical research and development? (2)