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VI. International Course in Yeast Systems Biology
University of Gothenburg and
Chalmers University of TechnologyA FEBS practical course and an ERASysAPP summer school
My background
Stefan Hohmann, professor in molecular microbial physiology
Biologist – studies and PhD in Germany
First post-doc 1987-90 in Darmstadt, Germany
Second post-doc and project leader 1990-95 in Leuven, Belgium
Visiting professor in Bloemfontein, South Africa, 91 and 93
Since 1996 in Göteborg
Mechanisms of signal transduction, collaboration with modellers,
building up SysBio in Gothenburg
Hohmann lab
Understanding at the molecular and systems level cellular control mechanisms by employing S. cerevisiae as a model system
Advancing system-level understanding by combining experimentation and mathematical/computational reconstruction
Hohmann lab
Signal transduction and adaptation processes, osmostress, HOG and other MAPK pathways
Nutrient-controlled regulatory responses, Snf1 pathway
Structure and function of aquaporins and aquaglyceroporins
Welcome - Introduction
Getting to know each other
Athanasios Litsios, Anna Zhukova, Carole Linster, Ceyhun
Bereketoğlu, Daniel Ganser, David Ruckerbauer, Elena
Nikonova, Ioannidis Konstantinos, Klement Stojanovski,
Lewis Tomalin, Martin Kavšček, Paul Jung, Rakesh Koppram,
Sebastian Thieme, Ulrike Münzner, Valeriia Dotsenko,
Jennifer Raaf, Brenda Bley, Johannes Becker, Lan Nguyen,
Mingji Li, Angelica Rodriguez, Mark van Logtestijn, Petri
Lahtvee
About this course
Sixth time after 2005, 2007, 2008, 2009, 2011
FEBS support 2007, 2009, 2011, 2013
Previously 16 days, this time eight days
Other courses: Metabolic engineering and systems
biology by Jens Nielsen (August 2013)
Modules studied in the course
Introductory lectures (Marija Cvijovic, Gunnar
Cedersund, Stefan Hohmann)
Principles in signalling - experimental and
modelling (Alejandro Colman-Lerner, Jörg
Schaber)
Invited lectures
Student presentations
Programme
Programme
Programme
Tuesday June 4Athanasios LitsiosAnna ZhukovaCarole LinsterCeyhun BereketoğluDaniel GanserDavid RuckerbauerElena NikonovaIoannidis Konstantinos
Friday June 7Klement StojanovskiLewis TomalinMartin KavščekPaul JungRakesh KoppramSebastian ThiemeUlrike MunznerValeriia Dotsenko
Saturday June 8Jennifer Raaf,Blenda BleyJohannes BeckerLan NguyenMingji LiAngelica RodriguezMark van LogtestijnPetri LahtveeMax 15min, 10 slides plus title and
acknowledgements, max 3 questions
Course practicalities June 6 is a public holiday, Swedish National Day
Present weather forecast for entire period: mostly friendly, around 18-22C
Access to buildings and rooms restricted, cards needed
Computers and wireless access
Lunches
Mon, Tis, Wed, Fri, Mon at Lyktan, 4 choices incl salad bar, drink, coffee/tea, you need
to be with me or you pay yourself
Tor, Sat, Sun at Lundberg cafeteria
Dinners at Lundberg cafeteria, catering service plus drinks (also beer and wine);
please express complains and wishes
Coffees at Lundberg foyé ca 1000 and 1500 every day
Social activities
Bowling at Hardrock Café 2000-2130 first day; we leave TOGETHER after dinner and
get there and back by tram; two free drinks
Midsummer activity in Lerum last day 1600-2200, bus transfer, swim weather
permitting, dinner and hopefully dance around the midsummer pole
Contact persons
Stefan Hohmann 0733 547 297
Maria Enge 0733 241 608
Göteborg
Göteborg University – Chalmers University of Technology
Biology – Medicine – Mathematics – Computer Sciences
Physics – Chemistry
Gothenburg Centre for Systems Biology with groups from Chalmers,
Fraunhofer-Chalmers, University of Gothenburg (Science
and Medical Faculty)
Where we are
On the biomedical campus of the University of Gothenburg
Mainly medical institutions but also some departments from the
Science Faculty
The University of Gothenburg is spread out around the city
The campus of Chalmers University of Technology is about 1km
away
We are approaching summer vacation, teaching period has
ended
About this course - FEBS
Federation of the European Biochemical Societies
www.febs.org
36 constituent societies and 7 associated societies
Almost 40,000 members
The European interest organisation for researchers in
biochemistry and molecular biology
Publishes FEBS Journal, FEBS Letters, FEBS OpenBio, Molecular
Oncology
Organises/sponsors FEBS Congresses, Courses, Workshops
Various types of fellowship themes
Chairman of the Course Committee: Jaak Järv, will visit last day of
course
EC-funded project 2008-2013 (ended)
11.7 million €, 5 years, 17 partner organisations
The overall objective of UNICELLSYS is a quantitative understanding of
how cell growth and proliferation are controlled and coordinated by
extracellular and intrinsic stimuli.
Many system-level principles are conserved from yeast to human. The
understanding of quantitative system properties gained in UNICELLSYS
will have significant biomedical importance.
www.unicellsys.eu
About this course - UNICELLSYS
ERASysAPP summer school
www.erasysapp.eu
A network of 16 funding organisations from 13 countries
Develop the field of applied systems biology, in the first
place towards bio-industries
Initiative collaborative projects in the field
Training and education
Modelling in systems biology, Barcelona, June 9-14, 2013
Systems biology
Aims at the determination and investigation of properties and
phenotypes that emerge through the interaction of bio-entities
(molecules, genes, cells, organs, organisms).
Those properties/phenotypes can not be predicted from those
conferred by the individual components.
Hence moves far beyond traditional, molecule-oriented
biochemistry and molecular cell biology.
Systems Biology is an intrinsically multi-disciplinary approach,
combining experimentation/data collection with mathematical
modelling and simulation.
Performing Systems Biology therefore requires scientists
educated in more than one discipline.
Promises of systems biology
Understanding biology – moving from a descriptive science to
explanation and mechanisms; applying principle of physics,
chemistry and engineering
Understanding human physiology – the genetic and molecular
networks disturbed in disease
Finding new drug targets, develop new drugs and treatments –
network drugs, drug combinations
Personalised and predictive medicine based on personal
genome and personal data
Predictive bioengineering and synthetic biology
Systems Biology mainstreams
Data-driven or top-down: building and studying networks on the basis of large-
scale data (transcriptomics, metabolomics, proteomics and combination
thereof), extension of bioinformatics. Driving discovery.
Module-driven or bottom-up: dynamic modelling of well-described networks
and pathways; requires time course quantitative data. Hypothesis-driven.
In both cases: employing experimental data and computational models.
Collaboration between experimentalists and theoreticians.
The Chalmers course focuses on networks.
This course focuses on dynamic processes and hypothesis-driven research.
Cellular processes
Understanding the dynamic
properties of cellular
processes: feedback,
bistability, robustness,
noise, threshold
Signal transduction,
metabolism, transcription,
secretion....
”Defined” cellular modules
Hartwell LH, Hopfield JJ, Leibler S, Murray AW (1999)
From molecular to modular cell biology.
Nature 402:C47-52
Cellular modules
Functional units consisting of a certain number of components with a
defined input and output that confer a certain cellular
property/phenotype.
Potentially defined by large scale analyses.
Often defined by genetic, molecular, biochemical analyses.
Time component needed to describe dynamics.
Describing dynamic operation
Reconstruction using the language of mathematics.
Simulation of pathway/module function in the computer.
Testing and improving by comparing simulation with experimental
data.
Predicting the outcome of experiments.
Failing the model to generate new knowledge.
Using the model to study and predict system properties.
Data needed Quantitative time course data (”How long does this process take in
the cell?”)
Values (”How many molecules of protein X are in the nucleus and
how many in the cytosol at time Y?”)
Properties (e.g. in vivo Km and Vmax of enzymes)
Often not commonly available data or not stored in databases
Not high-throughput in many cases
Requires new approaches/technologies for data generation
Defined system perturbations needed: conditions, genetics, drugs
Single cell data
Cell to cell variability: noise and stochastic behaviour
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 respond
but with 100% amplitude?
Noise and stochastic behaviour of cells in a population
There are several examples where stochastic variation in a population has or may have
practical consequences:
Bacterial antibiotic resistance – a fraction of the cells in a population are resistant with
slow growth as trade-off.
Competence for DNA uptake in bacteria – a subpopulation may acquire new properties
Stress tolerance in microbial populations – a fraction of slow growing cells may have
increased basal stress tolerance.
Lactose and galactose utilisation in bacteria - some cells in the population
spontaneously switch to express those genes.
Sporulation competence in Bacillus subtilis – a fraction of cells undergoes sporulation
to allow survival of the population.
Cell-fate decision, such as photoreceptor expression in the Drosophila eye, yellow-blue
sensitive distribution is 70:30.
Cancer development – some cells in a population transform but others not.
Drug susceptibility of mammalian cells, such as cancer cells.
Yeast systems biology
A genetic model organism
Best characterised eukaryotic cell
Large number of experimental tools
Large number of resources and strain collections
Simple and reproducible cultivation
Large research community
Always at the forefront to develop a new research field
Developing the experimental and computational tools for systems
biology
EC-funded project 2008-2013
11.7 million €, 5 years, 17 partner organisations
The overall objective of UNICELLSYS is a quantitative understanding of
how cell growth and proliferation are controlled and coordinated by
extracellular and intrinsic stimuli.
Many system-level principles are conserved from yeast to human. The
understanding of quantitative system properties gained in UNICELLSYS
will have significant biomedical importance.
www.unicellsys.eu
About this course - UNICELLSYS
UNICELLSYS partnership
No Organisation Names Expertise and roles in project
1 UGOT S Hohmann, T Nyström, A Blomberg, P Sunnerhagen, M Goksör
Signal transduction, stress responses, phenomics, global gene expression, single cell analyses
2 FCC M Jirstrand Systems theory, software implementation
3 DTU C Workman Bioinformatics
4 ETH U Sauer, R Aebersold, M Peter, J Stelling
Metabolomics, Proteomics, signal transduction, single cell analysis, dynamic modelling, systems theory
5 UPF F Posas, E de Nadal Signal transduction, stress responses, quantitative analyses
6 CRG L Serrano Protein design, protein complexes, modelling of transcriptional networks.
7 VUA H Westerhoff, B Teusink, J Snoep Metabolomics, different modelling approaches, biological theory
8 UNIMAN P Mendes Physiology, 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 S Krobitsch 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, M Tyers RNA metabolism, ribosome biogenesis, quantitative measurements
15 CHALMERS J Nielsen Metabolomics, genome-wide reconstruction, networks,
16 UCAM-BIOC S Oliver High-throughput phenotyping; physiology, quantitative transcriptomics, proteomics, metabolomics.
17 HUBER E Klipp Dynamic modelling, signal transduction
LEVELS OF ORGANISATION
Unicellular biology can be described at five levels of organisation:
(1) cell population
(2) single cell
(3) “whole-cell” molecular networks
(4) large systems of biomolecules
(5) defined functional modules.
UNICELLSYS addressed and integrated these levels, delivering computational simulations based on predictive mathematical models that enable the investigator to observe the response to different stimuli of a cell population with the possibility to zoom down into further levels of increasing detail.
population
cell
network
system
module
SCOPE
Growth
Development
Proliferation
Nutrients
Stress
Hormone
PKA, TOR, Snf1, Snf3/Rgt2
PHD
PKA
PKA, HOG, PKC
?
STE
STE, PKC
UNICELLSYS employed
baker’s yeast to study the
control of proliferation
(increase in cell number)
and cell growth (increase
in volume and mass) in
response to external and
intrinsic stimuli:
(1) nutrient availability
(2) stress
(3) hormone
The basic consequence of
these stimuli is a decision
by the cell on whether to
perform growth,
proliferation or
development.
Gothenburg Center for Systems Biology