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Systems biology:
the new scientific paradigm
Lilia Alberghina
Dept. Biotechnology and Biosciences
University of Milano-Bicocca, Milan, Italy
Meeting FIRB 2003
Bisceglie 17-19 Luglio 2005
Why systems biology?
• Much of twentieth-century biology has been an attempt to reduce
biological phenomena to the behaviour of molecules. Despite the
enormous success of this approach, a discrete biological function can
only rarely be attributed to an individual molecule.
• In biological systems large numbers of functional diverse, and
frequently multifunctional, sets of elements interact selectively and
non linearly to produce coherent behaviour.
• To describe biological functions, we need a vocabulary that contains
concepts such as amplification, adaptation, robustness, insulation,
error correction and coincidence detection.
(Hartwell et al., 1999; Kitano, 2002)
1
Why systems biology?
Complex cellular processes, such as cell cycle, need to be analysed
in a way to determine the logical and informational circuits that
yield their behaviour.
To do so the identification, characterization and identification of the
logical and informational modules that operate in a cell is
required. They include feed back loops, switches, thresholds,
timers, amplifiers etc.
The identification and characterization of interacting molecules and
biochemical activities of each module will allow to test by
modeling and simulation how a particular biological phenomenon
is generated.
(Nurse, Nature, 2002)
2
Systems biology
Systems biology has two distinct branches:
Knowledge discovery and data mining, which
extracts the hidden patterns from huge quantities
of experimental data, forming hypothesis as result
and simulation-based analysis, providing
predictions to be tested by in vitro and in vivo
studies.
(Kitano, 2002)
3
Bioinformatics and Systems biology
Bioinformatics aims to extract information from biological data:
•
•
•
prediction of 3D structure from sequence data
clustering of mRNA expression data
molecular evolution analysis
Systems biology aims to define and understand control circuits and
executive steps of the many complex processes that govern cell
functions.
It needs to integrate and structure “omics” data into regulatory circuits.
It aims to understand the new properties that arise from interactions of
molecules into networks.
Both disciplines utilize biological data and computer methods,
but Systems biology go further
to biological control mechanisms
that are non-linear and therefore often counter intuitive
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Systems biology: the strategy
A cell can be dissected into “modules”, subsystems of interacting
molecules (proteins, DNA, RNA and small molecules) that perform a
given task in a way largely independent from the context.
For instance:
• glycolysis and other metabolic pathways
• signal transduction pathways
• cell cycle
• apoptosis
5
not so well known
quite well known in
molecular terms
The iterative steps of modular systems biology
• Global functional analysis
1
• Determination of the major functional
modules involved in the process under study and
their regulatory connections.
• Determination of a basic modular blueprint of
the process and validation of its dynamics
• Development of a molecular
model of a given module
• Validation of its plausibility
• Refinement of the molecular model
• Control analysis (robustness, bifurcation,
connectivity, etc)
• Predictions
3
5
• 4M Strategy to identify major components
of a given module
2
• Extensive environmental (i.e. C and N
source modulation) and genetic (i.e. gene
dosage, deletion, point mutation)
perturbations of the function of the module
• Refinement of spatio-temporal analysis
(localization, determination of kinetic
constants, etc.)
• Post-genomic analysis
4
• Experimental verification of predictions
6
• Repeat Steps 4 and 6 till
necessary
• Repeat for all the other modules in 1 so to
have the molecular model of the
entire process
6
7
• Achievement of a complete
molecular model of a given module
8
9
5
The iterative steps of modular systems biology
• Global functional analysis
1
• Determination of the major functional
modules involved in the process under study and
their regulatory connections.
• Determination of a basic modular blueprint of
the process and validation of its dynamics
• Development of a molecular
model of a given module
• Validation of its plausibility
• Refinement of the molecular model
• Control analysis (robustness, bifurcation,
connectivity, etc)
• Predictions
3
5
The 4M Strategy is:
MINING of literature data,
• 4M
Strategy to identify
components
MANIPULATION
of major
the module
ofstructure
a given module
and function,
2
MEASUREMENT of regulatory
components,
• Extensive
environmental
(i.e. C and N
MODELING
and simulation.
source modulation) and genetic (i.e. gene
dosage, deletion, point mutation)
perturbations of the function of the module
• Refinement of spatio-temporal analysis
(localization, determination of kinetic
constants, etc.)
• Post-genomic analysis
4
• Experimental verification of predictions
6
• Repeat Steps 4 and 6 till
necessary
• Repeat for all the other modules in 1 so to
have the molecular model of the
entire process
7
7
• Achievement of a complete
molecular model of a given module
8
9
5
A case study: the cell cycle
• In the budding yeast Saccharomyces cerevisiae cell cycle execution
and control require the coordinated, time-dependent activity of at
least 15 % of its 6000 genes
• The coupling of cell growth to cell division is a universal but poorly
understood feature of the cell cycle
• The main regulatory event(s) takes place at START, when cells must
reach a critical cell size (Ps) to enter into S phase.
G1
8
S G2 M
Ps
Circuits proposed to control Start in budding yeast
Rupes I., TIG (2002)
9
Jorgensen P. et al., Science (2002)
3
Tyson & Novak’s model of budding yeast cell cycle
Chen et al., Mol Biol Cell (2004)
• It accounts only for a few tens of molecular components against the
hundreds involved
• It does not offer satisfactory molecular explanations for the setting of
the critical cell size Ps
10
4
From several rounds of experimental investigation
Two growth regulated thresholds
involving Cln3/Far1 and Clb5,6/Sic1
control entrance into S phase and
set the critical cell size
Alberghina et al., J. Cell. Biol., 2004
Rossi et al., manuscript in preparation
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4
Work in progress in collaboration with Dr. Edda Klipp,
MPI for Molecular Genetics, Berlin
Key players in G1/S transition
• Compounds
• Interactions
Modeling of biochemical pathways
• Basic elements of biochemical pathways
• Control and Response in biochemical networks
Data and Data Integration
• Experimental Information
• Determination of Kinetics
Model of the G1/S transition
• Equations
• Time Courses Experiment versus Simulation
• Hypotheses
12
5
Model of the
network controlling the G1 to S transition
13
5
A few preliminary results
Modeling glucose versus ethanol growth - 2
3,5
Sic1
3
Clb5
2,5
Clb5/Sic1
2
1,5
1
0,5
0
0
20
40
60
80
100
120
Concentration (M)
Protein levels (Relative Units)
GLUCOSE
4
0.02
0.015
0.01
0.005
0
140
0
20
40
60
80
100 120 140
Time (minutes)
Time (minutes)
0.02
4
3,5
3
Sic1
2,5
Clb5
Clb5/Sic1
2
1,5
1
0,5
0
0
50
100
150
200
Time (minutes)
14
250
300
350
Concentration (M)
Protein levels (Relative Units)
ETHANOL
0.015
0.01
0.005
0
0
50
100
150
200
250
300
350
Time (minutes)
5
The Foresight
• Systems biology addresses the analysis of entire biological systems.
• This entails the investigation of all components and networks
contributing to a systems. With dynamic computer modelling the
properties of a cell, and ultimately the organism, can be simulated
and altered.
• The general strategy of systems biology employs iterative cycles of
experimentations and predictions, which have been successfully
applied in exact sciences, such as physics and chemistry.
• Should they prove successful in systems biology, then biology will be
transformed from an essentially descriptive discipline into an exact
science.
Concept paper of Systems X, Switzerland, 2004
15
What is happening in Europe?
It all begins in
2004?
International
Systems Biology
Conference 2004
in Heidelberg
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Leading Countries
– leading
position of
USA
generally
agreed
– ranking
reflects
bibliometri
c
performan
ce except
Japan: low
publication
activities
140
Expert votes (N=131)
120
100
80
60
40
20
0
A
US
G
an
m
r
e
y
n
pa
Ja
UK
el
a
r
Is
N
ds
an
l
r
e
et h
ed
Sw
en
Source: EUSYSBIO survey
by Fraunhofer ISI 2004
Introduction
17
Actors
Themes
OrganiTraining
zation
Frame-
Con-
work
clusions
Leading Institutions
– most
leading
institutes
outside
Europe
– reflects
advanced
stage of
USA, JP
and IL
Institute for Systems Biology (US)
MIT (US)
Weizmann Institute (IL)
UCSD Systems Biology (US)
Caltech (US)
Kitano Inst. (JP)
Keio University (JP)
Harvard (US)
Free University Amsterdam (NL)
Stanford (US)
0
5
10
15
20
25
30
35
Number of top 3 votes (N=137)
Source: EUSYSBIO survey
by Fraunhofer ISI 2004
Introduction
18
Actors
Themes
OrganiTraining
zation
Frame-
Con-
work
clusions
40
45
What is happening now in Europe?
• National programs
– German Hepatocon: Systeme des Lebens (>20 M€)
– Finland: System Biology and Bioinformatics (10.5 M€)
– UK:
• BBSRC Integrative Biology 5 years program (>50 M€)
• Three 9 M€ Centres in Manchester, Newcastle, London
(yeast, aging, mammalian cells)
• Three more to come
– The Netherlands:
• BCA; Amsterdam research school on Systems Biology
funding not yet settled
• SBNL; set of organism focused programs (L. lactis, S.
cerevisiae, E. coli, Silicon cell, ...), funding not yet settled
• CMSB: Centre for Medical Systems Biology (Leiden,
VUAmsterdam, Rotterdam): topdown
– …..
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Publication forum
Defining Systems Biology
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•
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•
•
•
•
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•
•
•
•
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Snoep, Jacky L.
Goryanin, Igor
Fell, David
Eduardo Sontag
Kummer, Ursula
Ehrenberg, Måns
Westerhoff, Hans V.
Kholodenko, B.N. & Bruggeman, F.
Wanner, B., Finney, A., Hucka, M.
Sauer, U.
Gilles, D.
Reuss, M.
Heinrich, R.
Hohmann, S.
Lauffenburger, D.
Novak, B.
Alberghina, Lilia
Eils, R.
Kitano, H.
The Grand
Challenge:
The interim
report
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The
Grand
Challenge:
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Transnational initiatives
•
•
•
•
German BMBF: SysMo
Preparing for microbial System Biology program
Wants to go transnational
Finnish, Dutch partners are goning to be
involved
• 2005/6: call for proposals
• ERANET application (FP6 support to organize
this)
23
ESF forward look: Eurocore
and more?
• Various professorships in
Systems Biology
– Rostock
– Manchester
– Harvard
– Bielefeld
• European Master’s
programs:
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See www.systembiology.net
The next appointment
International Systems
6 International Conference on
Biology
Systems Biology
Conference
October 19 – 22, 2005 Boston, MA
2005
in Boston
th
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The team @ in Milan and Berlin
Lilia Alberghina
Marco Vanoni
Edda Klipp
Matteo Barberis
Paola Coccetti
Andrea Mastriani
Lorenzo Querin
Riccardo L. Rossi
Vittoria Zinzalla
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MAX PLANCK
INSTITUT FÜR
MOLEkULARE
GENETIK
Dip. di Biotecnologie e Bioscienze
ESF forward look: Eurocore
and more?
• Study to see if System Biology could be
the ESF theme
• This would then make ESF catalyze
common research programs between
national science foundations
• Part of ESF ambitions to form a European
Research Council
• Aims for reporting Spring 2005
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