systems of life - systems biology

Dr. Siegfried Neumann-jm: SiSIS, Sept. 2005

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


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


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


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


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.


• 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


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


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


Structure of the National Competence NetworkHepatoSys

Platform Cell biology

Platform Modeling

Coordinating Committee

Steering Committee

Collaborative Network

“Regeneration”

Project Management

CollaborativeNetwork

“Detox/Dediff.”


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


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


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


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)


The Network onDetoxification / Dedifferentiation

• Detoxification• Cytochrome P 450 isoforms• Molecular dynamics• Kinetic experiments• Polymorphisms

• Dedifferentiation• Change of metabolic pathways during dedifferentiation


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


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


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


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


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


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


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

http://www.bmbf.de/

http://www.bmbf.de/

http://www.fz-juelich.de/ptj/

http://www.fz-juelich.de/ptj/

http://www.systembiologie.de/

http://www.systembiologie.de/


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.”


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


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)


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


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)


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


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)


Data-based mathematical modelling of the JAK2-STAT5 Pathway (Klingmueller, pers. commun,.)


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.)


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


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


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


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


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


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.)


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


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

http://www.bmbf.de/

http://www.nwo.nl/nwohome.nsf/pages/ACPP_4WMJGH_Eng

http://www.bmbwk.gv.at/forschung/index.xml





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

http://www.febssysbio.net/








http://www.febssysbio.net/introduction.html



http://www.febssysbio.net/febs-sysbio2005-finalflyer1.pdf




http://www.febssysbio.net/registration.html

http://www.febssysbio.net/prereg.html




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


• 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)

systems of life - systems biology

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