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