systems biology – challenges in experimental and theoretical sciences prof. stefan hohmann...

Systems Biology – challenges in experimental and theoretical sciences

Prof. Stefan Hohmann

Department of Cell and Molecular Biology

Göteborg University, Sweden

[email protected]

CMB - Cell and Molecular Biology - Group Stefan Hohmann

Systems Biology – an approach

Understanding the higher-order properties of systems of biomolecules (rather than individual biomolecules) by applying to biology approaches of mathematics, theoretical physics, computer sciences and engineering.

Using mathematical models may move biology from a descriptive to a predictive discipline.

Predictive capabilities to treatment of diseases and bioengineering.


Systems Biology - directions

Top-down or data-driven

Networks from large-scale data

Bottom-up or model-driven

Dynamic modelling – simulating processes over time


EC funds several projects on dynamic modelling in FP6

• 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


Quantifying signal transduction


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


Types of measurements to estimate parameters

• Rate of changes of phospho-MAPK• Certain other phospho-proteins• Rate of changes of mRNA levels of reporter genes• Levels and rate of change and transport of glycerol• Rate of change of certain protein-protein interactions• Hog1 MAPK nuclear shuttling


Types of perturbations to test mathematical models

• Genetic changes in pathways• Genetic changes in responses (osmoregulation)• Specific kinase inhibitors• Changes in experimental conditions


Sln1AspP

ATPADP

Ypd1

Ssk1AspP

Pi

high osmolarity

?

Ypd1HisP

Ssk1

Sln1HisP Sln1v1

TCSv2TCS

v3TCS

v4TCS

v5TCS

Sln1

Ssk1

Hog1Glucose

DHAP

G3P

GlycerolTranslation

Gpd1, Gpp2,….

Gpd1

Gpp2

Signalpathway

Metabolism

Fps1

Osmotic stress

Osmoticstress

Glycerolextern

Plasma membrane

cytosol nucleus

e

i

MAP kinase cascade

Phospho relaysystem

Hog1

TranscriptionGPD1, GPP2,….

Gene expressionSsk2 Ssk2P

Pi

Pbs2 Pbs2P Pbs2P2

Pi

ATP ADP ATP ADP

Pi

Hog1

Ssk1

v1MAP

v2MAP

v-1MAP

v3MAP

v-2MAP v-3

MAP

ATP ADPATP ADP

Hog1P Hog1P2

Pi

ATP ADP ATP ADP

Pi

v4MAP v5

MAP

v-4MAP v-5

MAP

Hog1P2

Hog1P2nuc

mRNAnuc mRNAcyt

Proteinsnucleus

cytosol

vts

vex vrd

vpd

Hog1nuc

Hog1

vtrans

vtrans1

vtrans2

Glucose

Gluc-6-P

Fruc-1,6-BP

GAP DHAP

Pyruvate

Ethanol

synthesis

synthesis

3 CO2

G3P

Glycerol

NADH NAD

ADP ATP

4 NAD

4 NADH

NAD

NADH

NADH

NAD

2 ADP2 ATP NADH NAD

ATP

ADP

ATP

ADP

ATP ADP

ADP ATP

Glk1

Gpp2Gpd1

Fps1

Glucose uptake

Glycerol, ex

Phosphorelay module

MAP kinasecascade module

Gene expression module

Biophysical changes

i = f (Glycerol)Waterflow over membrane = f (i, e, t)

Volume change = f (Waterflow)(see text)

I nternal osmotic pressure

External osmotic pressure

Metabolismmodule

Figure 1

vdephos

Ptp2

vtl

v15

v14

v16

v13

v12v11

v3

v10

v1

v2

v4

v5

v6

v9

v7 v8

Integration of signalling, gene expression,metabolism, transport and biophysical changes

CMB - Cell and Molecular Biology - Group Stefan HohmannEdda Klipp

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


Systems Biology of AMP-activated protein kinase

AMPKIN

AMPK is the cellular energy regulator in eukaryotes and a possible target for drugs towards diabetes type II


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 – left project) Sweden

AMPKIN


• 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

Types of measurements to estimate parameters

AMPKIN


Types of perturbations to test mathematical models

• Genetic changes in pathways• Genetic changes in metabolism• Specific kinase inhibitors• Changes in experimental conditions

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

AMPKIN


FP7 calls with deadline April 2007

• A system approach to eukaryotic unicellular organism biology.

• Modelling of T-cell activation.• Fundamental approaches to stem cell differentiation.• Developing an integrated in vitro, in vivo and systems

biology modelling approach to understanding apoptosis in the context of health and disease.

UNICELLSYS

• Eukaryotic unicellular organism biology – systems biology of the control of cell growth and proliferation

• Large collaborative project 2008-2012, five years• EC-funding 11.7 million €• Sixteen partners and more than 30 principle

investigators• Bringing together major capacity in data generation

and dynamic modelling


UNICELLSYSNo Organisation Expertise Expertise and roles in project

1 UGOT S Hohmann, T Nyström, A Blomberg, P Sunnerhagen, M Goksör

Signal transduction, ageing, stress responses, phenomics, global gene expression, single cell analyses

2 FCC M Jirstrand, H Schmidt Systems theory, software implementation

3 DTU J Nielsen, C Workman Metabolomics, genome-wide reconstruction, networks, bioinformatics

4 ETHZ U Sauer, R Aebersold, M Peter, J Stelling Metabolomics, Proteomics, signal transduction, single cell analysis, dynamic modelling, systems theory

5 UPF F Posas Signal transduction, stress responses, quantitative analyses

6 CRG L Serrano Protein design, protein complexes, modelling of transcriptional networks.

7 VUA H Westerhoff, B Bakker Metabolomics, different modelling approaches, biological theory

8 UNIMAN S Oliver, D Kell, P Mendes High-throughput phenotyping; physiology, quantitative transcriptomics, proteomics, metabolomics; modelling; database design, data standards

9 ABER R King High-throughput phenotyping; machine learning; logical modelling

10 UNIMIB L Alberghina, M Vanoni, E Martegani Cell cycle control, signal transduction, quantitative analyses

11 MPG E Klipp, S Krobitsch Dynamic modelling, signal transduction, transcriptomics, protein interaction

12 UOXF B Novak Cell cycle, dynamic modelling

13 MUW K Kuchler, G Ammerer Signal transduction, proteomics, protein interaction

14 UEDIN J Beggs, D Tollervey RNA metabolism, ribosome biogenesis, quantitative measurements

UNICELLSYSThe overall objective of UNICELLSYS is a quantitative understanding of fundamental characteristics of eukaryotic unicellular organism biology: how cell growth and proliferation are controlled and coordinated by both extracellular and intrinsic stimuli. Achieving an understanding of the principles with which systems of bio-molecules function requires integrating quantitative experimentation with simulations of dynamic mathematical models in a systems biology approach.

Growth

Development

Proliferation

Nutrients

Stress

Hormone

PKA, TOR, Snf1, Snf3/Rgt2

PHD

PKA

PKA, HOG, PKC

?

STE

STE, PKC


Conclusions

• Quantitative understanding of cell and organism physiology is a multidisciplinary endeavour

• Major challenges in data generation (quantitative, molecule numbers, time resolved, single cells....)

• Major challenges for modelling (abstraction, parameter and model identification/discrimination, model reduction, integration of different processes, molecule-module-cell-organ-organism, stochastic processes....)

• Challenges in defining appropriate research infrastructures and forms of collaboration locally and Europe-wide


Present collaborators and funding

• The QUASI EC Project (2007): F Posas, M Peter, G Ammerer, E Klipp, M Grøtli, P Sunnerhagen

• The MalariaPorin EC Project (2007): E Beitz, P Agre, S Flitsch, H Grubmüller

• The Sleeping Beauty EC Project (2008): E Lubzens, M Clark, R Reinhard, J Cerda, J Nielsen

• The Systems Biology Early Stage Training EC project (2008): R van Driel, E Klipp, R Heinrich

• The Yeast Systems Biology Network (2008) with about 20 groups in Europe (EC-funded Coordination Action) and 40 groups world-wide

• The Sweden-Japan Vinnova project (2009): H Kitano

• The AMPKIN EC Project (2009): D Carling, J Nielsen, O Wolkenhauer, Biovitrum/Arexis AB

• The Aqua(glycero)porin RTN EC Project (2010): S Flitsch, H Grubmüller, P Deen, A Engel, S Nielsen, R Neutze, J Cerda, Z Vajda, E Klipp

• The CELLCOMPUT NEST EC Project (2011): F Posas, R Solé, M , E Klipp, M Grøtli

• The UNICELLSYS EC Project (2012): 16 different partners

• Funding from the Swedish Research Council (2007)

• Ingvar grant from SSF (2010) to Karin Lindqvist

• Funding from the Swedish Research Council (2007) to Markus Tamás (position and project)

• Faculty platforms in Quantitative Biology and Chemical Biology (2009/11) with groups in in physics (D Hanstorp), chemistry (M Grøtli), computational biology (M Jirstrand, O Nerman, B Wennberg), structural biology (R Neutze) and biology (T Nyström, A Blomberg, P Sunnerhagen)


Courses and conferences


systems biology – challenges in experimental and theoretical sciences prof. stefan hohmann...

Documents

systems biology challenges

grtli swedenbarcelona

f posas spainvienna

g ammerer austriazrich

project swedencmb cell

time cmb cell

biophysical changescmb

e klipp germanycmb cell