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Dr. Siegfried Neumann-jm: SiSIS, Sept. 2005 Page 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
1Projektträger Jülich, Jülich, Germany 2German Cancer Research Center, Heidelberg, Germany 3MERCK KGaA, Chemical Section R+D, Darmstadt, Germany
NiSIS Symposium, Portugal, October 2005
Dr. Siegfried Neumann-jm: SiSIS, Sept. 2005 Page 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
Dr. Siegfried Neumann-jm: SiSIS, Sept. 2005 Page 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
Dr. Siegfried Neumann-jm: SiSIS, Sept. 2005 Page 4
Expert panel structuring thematic priority recommendations
coreexpertpanel (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 December 2001December 2001NovemberNovemberMayMay JulyJuly SeptemberSeptember
„„Systems of Life - Systems of Life - Systems Biology“Systems Biology“
The Design-Process
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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.
Dr. Siegfried Neumann-jm: SiSIS, Sept. 2005 Page 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 systemsbiology on a medically relevant cell type.
!
Challenges
Dr. Siegfried Neumann-jm: SiSIS, Sept. 2005 Page 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
BiologyBiology
Systems Systems EngineeringEngineering
Bioinformatics Bioinformatics MathematicsMathematics
Tools (HTS)Tools (HTS)
Systemic Systemic BehaviourBehaviour
Algorithms Algorithms Software Software DatabasesDatabases
Systems BiologySystems Biology
biological models biological models generation of quantitative generation of quantitative data, anlysis of functional data, anlysis of functional relations; tool developmentrelations; tool development
modelling (study on modelling (study on regulation, structure, regulation, structure, robustness, etc. of system) robustness, etc. of system)
establishment of databases, establishment of databases, development of development of in silicoin silico models and software models and software
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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
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Structure of the National Competence NetworkHepatoSys
Platform Cell biology
Platform Modeling
Coordinating Committee
Steering Committee
Collaborative Network
“Regeneration”
Project Management
CollaborativeNetwork
“Detox/Dediff.”
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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
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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 3modeling 3
number of partners 25
Facts on the Starting Phase
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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
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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)
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The Network onDetoxification / Dedifferentiation
• Detoxification• Cytochrome P 450 isoforms• Molecular dynamics• Kinetic experiments• Polymorphisms
• Dedifferentiation• Change of metabolic pathways during dedifferentiation
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The Network on Regeneration
• BackgroundLiver regeneration is a highly regulated process
• GoalUnderstanding the pathways involved
• MethodData-based mathematical models
• Long term goalSupport development of liver cell lines
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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
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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
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Hamburg
Mainz
Jena
Heidelberg
BirlinghovenDresden
Geographic Distribution of the Projects
Freiburg
Stuttgart
Aachen
Berlin
Magdeburg
Collaborative Projects
Platform Cell Biology
Platform Modeling
LeipzigDüsseldorfBochum
Geographic Distribution of the Projects
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Coordination of the Competence NetworkSystems 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
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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
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Websites
• Federal Ministry of Education and Research www.bmbf.de• PTJ – the Project Management Organisation Jülich www.fz-juelich.de/ptj/• Competence Network Systems Biology www.systembiologie.de• The Database for Systems Biology Researchers http://www.bcc.univie.ac.at/cgi-bin/molg/sysbiol/SysBiol.pl
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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.”
Dr. Siegfried Neumann-jm: SiSIS, Sept. 2005 Page 24
Genome Transcriptomics
Gene Regulation Expression
Proteomics
Proteins Metabolism
PhenotypeandPotential for Diseases
MetabolomicsTissues
andCells
Whole Organism
Physiomics
cit from Nicolson (2002), modified
Descriptional and analytical levels in Systems Biology
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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:Signalsstressors etc
cellGene expression
Protein profile
Metabolic profile
Time
Time
Time
Time
Time
Outputs:Biological endpointspathologydegenerationregeneration
Note: time differentialsin all interactionstages
Nicolson, J.K. at al. Nature Reviews Drug Discovery 1, 153 (2002)
Dr. Siegfried Neumann-jm: SiSIS, Sept. 2005 Page 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
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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)
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Deliverables and limitations of approaches by integrative biology to drug research and development
Omics Cell systemsComputational
biology
• Hypothesis generation + + +
• Target identification/validation (+) + (+)
• Quantitative analysis of dynamic parameters - (+) +
• Rational design of perturbanceof 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
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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)
Dr. Siegfried Neumann-jm: SiSIS, Sept. 2005 Page 30
Data-based mathematical modelling of the JAK2-STAT5 Pathway (Klingmueller, pers. commun,.)
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Mathematical prediction: Dynamical parameters of nuclear import (k3), export (k4) and delay () most sensitive to perturbation
Experimental verification of mathematical prediction
JAK2-STAT5 PathwayPredicting Steps Most Sensitive for Perturbation
(Klingmueller, pers. commun.)
Dr. Siegfried Neumann-jm: SiSIS, Sept. 2005 Page 32
Systems Biology: Selected commercial players
Company Core Technologies Approach Deliverables
Accelrys Software Tools Software for process simulation Simulation of biological and chemical process
BayerTechnologyServices
Software toolsPK-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
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Systems Biology: Selected commercial players ctd.
Company Core Technologies Approach Deliverables
Iconix HTP molecular biologyData-mining
Integration of chemistry and genomics to profile drug candidates to predict toxicity
Predictive toxicology
Ingenuity OntologyPathway databaseComputing 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 diseasesPathway 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 IDClinical trial design
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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
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The Multiple Input of Systems Biology into Molecular Medicine
Drug DiscoveryClinical
DevelopmentTherapy
Markers
Safety, Toxicity
Efficacy Response/Non response
Safety/Efficacy
Diagnosis/Prognosis
Disease Progression
Target - Identification, - Characterization, - Prioritization
Pathway Elucidation, Network Analysis
Animal ModelValidation
Targets
Mode of ActionTrial Design
Product Decision
Combination with Other Drugs
DiseaseIndications
Dr. Siegfried Neumann-jm: SiSIS, Sept. 2005 Page 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
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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 proteinsSouth 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 andtransport
• Education in SB
Systems Biology Department • Bioinformatics, structural genomics, Quantitative StructureHarvard 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 SBMichigan Life Sciences Institute(Saltiel et al.), Stanford UniversityBiosciences Initiative (Bio-X, Scott et al.),Duke’s Institute for Genome Sciences andPolicy (Willard et al.)
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Recent Highlights in SB International Crosslinking
EU-Initiatives • EU SYSBIO, SYMBIONIC• EUREKA InSysBio Project• SYSMO (AU, DE, NL, GB, NO, SP)
WTEC/USA: http://wtec.org/sysbioReports on US, EU and Japan activities
WTEC/USA: Workshop on setting up a repository for systemsbiology software, February 17-18, 2005, Washington, USA
5. International Conference on Systems BiologyOctober 9-13, 2004, Heidelberg, Germany
6. International Conference on Systems BiologyOctober 2005, Cambridge, USA, Org: Marc Kirschner, Harvardhttp://www.ICSB2005.org
Start of PanAsian electronic International Molecular Biology Laboratory (e IMBL)Seoul, July 12-13, 2005
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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
Dr. Siegfried Neumann-jm: SiSIS, Sept. 2005 Page 40
See also:First FEBS Advanced Course on
Systems Biology: From Molecules & Modeling To CellsMarch 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
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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
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• 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)