Download Why a need for Systems Biology

Systems Biology 1
24 / 9 2007
Bodil Nordlander – trained as a molecular biologist
Outline
1. What is Systems Biology?
2. Why a need for Systems Biology (motivation)?
3. How is Systems Biology conducted?
4. Drivers for Technology
5. Networks versus pathways
6. Examples of systems; signal transduction pathways
metabolic pathways etc
What is Systems biology?
How is systems biology different from ”classical” reductionist biology?
-In classical biology the study of isolated parts, such as the function of
a protein, transcriptional control of a gene etc, of systems have been
performed and from these functions hypothesis of different models/hypothesis
have been set-up. It is these hypothesis that are the foundation for our understanding of different processes in the cell.
-In systems biology, from complex biological phenomenon we try to identify a
hypothesis (this is due to a combined effort between experimentalists and modelers)
which can be used to perform studies of the biological process. The model is used
to test different hypothesis.
What is Systems biology?
Central Dogma
• The central dogma of information flow in biology: Information flows from DNA to
RNA to protein. With other words: the amino acid sequence making up a protein, its
structure and function, is determined by the DNA transcription.
• “This states that once ‘information’ has passed into protein it cannot get out
again. In more detail, the transfer of information from nucleic acid to nucleic acid,
or from nucleic acid to protein may be possible, but transfer from protein to
protein, or from protein to nucleic acid is impossible. Information means here the
precise determination of sequence, either of bases in the nucleic acid or of amino
acid residues in the protein.”
Francis Crick, On Protein Synthesis, in Symp. Soc. Exp. Biol. XII, 138-167 (1958)
DNA
TRANSCRIPTION
REPLICATION
www.brc.dcs.gla.ac.uk, David Gilbert, Systems Biology (1) Introduction
TRANSLATION
RNA
PROTEIN
What is Systems Biology?
The information about how a system works does not lie in the genome but rather in
how proteins work together in the context of the organ / tissue / cell etc.
http://www.zum.de/Faecher/Materialien/beck/bilder/transsri5.jpg
ocw.mit.edu/.../0/chp_subtilisinbp.jpg
http://www.biochem.northwestern.edu/mayo/Lab%20GIF%20Images/Signaling.gif
What is Systems Biology?
1. Understanding how biomolecules (proteins, metabolites, RNA....) function together
(i.e. in a system), rather than in isolation. System-level understanding!
2. Airplane analogy (Hiroaki Kitano)
3. To get a system-level understanding you need to know: the system structure
(protein-protein interactions, biochemical pathways etc), System dynamics (how does
a system behave over time?) Few systems with this understanding!
4. What is a model? An abstract representation of the process which also can explain
properties / features of the process. (E.klipp, Systems biology in practice)
What is a Systems Biologist?
Mathematical modelers
Experimentalists
Systems Biologists
Common goal: Is to understand complex systems by combining mathematical modeling
and experimental studies. Systems biology offer the chance to predict the outcome of complex
processes. How do cells work ? How are cellular processes regulated? How do cells react to
environmental pertubations? Etc etc etc etc etc
http://pubs.acs.org/cen/coverstory/8120/8120biology.html
What is Systems Biology?
Quantitative versus Qualitative??
Qualitative analysis: It tries to answer the questions why and how, it catagorises data
into patterns. In biology, qualitative research has provided a huge amount of information
which is the basis for today´s and future research. It has been the basis for the
reductionist era of molecular biology.
Quantitative analysis: It tries to answer the questions what, where and when, relies on
the analysis on numerical data which can be quantified, time-series data. In Systems
Biology, the temporal and spatial dynamics of each molecular spicies are of interest!
(ref: http://en.wikipedia.org)
Parameters: Quantities which have a value e.g. Km of an enzyme. These values are
normally set in a model whereas variables change.
What is Systems Biology?
What is a biological system?:
1. Consists of components that interact such in order to form a functional unit.
2. Defined at different hierarchical levels with different extent of detail (enzyme,
glycolysis, cellular, tissue, organ, whole organism, ecosystems).
www4.liber.se/kemionline/gymkeb/bilder/12_a.jpg
http://biologi.uio.no/plfys/haa/gif/form142.gif
http://www.acuhealthzone.com/images/anatomy_of_human_body.gif
System biology, Definitions and perspectives, Topics in current Genetics 2005
Why a need for Systems Biology (motivation)?
Nucleotide sequence
Nucleotide structure
Gene expression
Protein sequence
Protein function
Protein-protein interactions (pathways)
Cell
Cell to cell signalling
Tissues
Organs
Physiology
Organism
Why a need for Systems Biology (motivation)?
1. Testing if the biological hypothesis is accurate – is it likely that the experimental
data explains the model?
2. Testing quantitative predictions of behaviors. This allows us to minimize the
number of experiments and do the critical ones which can give us most
information.
3. A model provides the opportunity to address critical scientific questions.
4. Cellular regulation depends on time and space, which a model can address.
Input to system
A
B
Our model
C
D
E
F
G
Output
H
I
Function
Example of a model which links together different biological processes taking into
account time and space, e.g. the compartments cytosol and nucleus are included.
Figure 1
Phosphorelay module
high osmolarity
Osmotic stress
v2TCS ?
v1TCS
Sln1HisP
Sln1
Sln1AspP
ADP ATP
Sln1
v3TCS
Ypd1
Plasma
membrane
Signal
pathway
Ypd1HisP
v4TCS
Ssk1AspP
Ssk1
v5TCS
MAP
kinase Hog1
cascade
Pi
Internal
osmotic
pressure
Phospho
relay
Ssk1 system
e
i
Glucose
v10
Ssk1
Ssk2
Pi
MAP kinase
cascade
module

v1MAP
ATP ADP
ADP ATP
v2MAP
ATP ADP
Pbs2
NAD
2 ADP
2 ATP
NADH
Gpp2
Glycerol
Transcription
GPD1, GPP2,….
Translation
Fps1
Gpd1, Gpp2,….
Glycerol
v4
G3P
Glycerol
extern
Osmotic
stress
v3MAP
v5
GAP
v6
v9
4 NAD
v7 v8
3 CO2
v12 Gpp2
G3P
NADH NAD
ATP ADP
Pyruvate
4 NADH
DHAP
Gpd1
v11
synthesis
NADH
NAD
NAD
v14
NADH
Ethanol
v16
ADP
v15
ATP
ATP ADP
Pbs2P
v-2MAP
Pi
Pbs2P2
v-3MAP
Hog1P2
vtrans
Pi

v4MAP
ATP ADP
Hog1
v5MAP
Hog1P
Hog1P2
v-5MAP
Pi
nucleus
Hog1P2nuc
vts
Gene
expression
module
Hog1
vtrans1
cytosol
ATP ADP
v-4MAP
Pi
v13 Fps1
ADP
Metabolism
Gene expression

Glycerol, ex
ATP
v3
Fruc-1,6-BP
DHAP
Hog1
nucleus
Ssk2P
v-1MAP
ADP
Gluc-6-P
Gpd1
cytosol
ATP
Glk1 v2
synthesis
Glucose
Metabolism
module
Glucose
uptake
v1
External
osmotic
pressure
vtrans2
Ptp2
Hog1nuc
vdephos
mRNAnuc
vex
vpd
vtl
Proteins
mRNAcyt
vrd
Biophysical changes
i = f(Glycerol)
Waterflow over membrane = f(i, e, t)
Volume change = f(Waterflow)
(see text)
Why a need for Systems Biology (motivation)?
5. If you have a model you can analyse which parts of the system which contribute
most to the desired properties of the model.
6. Signaling networks can interact in multivarious ways which complexity requires a
model.
Why a need for Systems Biology (motivation)?
7. Investigate the principles underlying biological robustness. It is an essential
property of biological systems (Kitano H, Science v.292, 2002). ”The persistent
of a system´s characteristic behaviour under perturbation or conditions of
uncertainty” (System modeling in cellular biology, zoltan Szallasi et al, 2006).
What design elements are thought to be required to avoid harmful disturbances:
1) redundancy (back- up systems) 2) Feedback control 3) Structure complex
systems into modules which have semi-autonomous functions etc etc.
A robust system is for instance believed to adapt to environmental stresses, it
has slow degradation of a system´s function after damage and parameter
insensitivity to specific kinetic parameters.
To take into account principles of robustness might provide some
guidelines for how we model and analyse model complexity.
THE EQUILIBRIA OF LIFE
WATER AVAILABILITY
NUTRIENTS
TEMPERATURE
RADIATION
SURVIVAL
OPTIMISATION OF GROWTH
CHEMICALS
COMPETITION
From Marcus Krantz
WASTE
Why a need for Systems Biology (motivation)?
8. To understand general ”design principles” shaped by evolution; some people
believe that there exist functional modules as a critical level of biological
organisation (ref. Hartwell L.H. Nature 1999, vol 402, 2 Dec). A module ” a discrete entity whose
function is separable from those of other modules”, e.g. a ribosome which
synthesizes proteins is spatially isolating its function, signalling pathways etc.
What are ”design principles” : e.g. positive or negative feedback-loops, amplifiers,
parallel circuits (common terms to engineers)? Are they found in nature?
Negative feedback: reduces output
Positive feedback: increases output, or
Bipolar feedback: Either increase or decrease output.
Hypothetical module
CYTOSOL
PLASMA MEMBRANE
A signalling pathway provides the means for the
cell to sense aspects of its surroundings and/or
condition. It usually consists of:
A sensor or receptor able to respond to the
environment.
One or more cytoplasmic signal transducers,
perhaps acting on cytoplasmic targets.
A shuttling component able to carry the
signal into the nucleus, activating
NUCLEAR MEMBRANE
NUCLEUS
one or more transcription factors.
Mechanism of feedback control.
GENE EXPRESSION
From Marcus Krantz
Kinases and phosphates are common, using
(de)phosphorylation as the signal.
How is Systems Biology conducted?
It is also a coordinated study of:
Integrated study of:
1. Investigating cellular components
and their interactions.
2. Experimentation
3. Computational methods.
1. Experimental data
2. Data processing
3. Modeling
Input to system
A
Our model
B
C
D
E
F
G
H
I
Output or funcion
E.Klipp, Systems Biology in Practice
How is Systems Biology conducted? How did we do?
A signalling pathway
In yeast – HOG pathway
1. The biological knowledge was gathered from literature and own observations.
2. The structure of the pathway was decided and converted into equations (static).
3. Static model
dynamic model. The model structure was analysed and
parameters optimised. Quantitative experimental data was used to compare
with simulations.
4. The model was tested by simulations and new experiments –validation! etc etc.
4. Drivers for Technology
Experimental techniques steadily improves in the direction of Systems Biology
- Large Scale studies (-Omics) which produces an enormous amount of data at
different levels of cellular organization. This data can be integrated into mathematical
models and to fill gaps of unknown players. These methods constantely improves and
new arise.
- Improved conventional methods; better quantification methods, single-cell
analysis methods (e.g. microscopy with microfluidic systems), quantitative
measurements of gene expression, protein levels etc.
-Increased awareness of studying the favourite system quantitatively instead of
qualitatively leading to improved techniques and an increased usage of certain
methods. This awareness might lead to better planned experiments if using a
mathematical model. Experimental planning!
-To include engineers in biology will lead to improved or new highly sophisticated
techniques. And more statistical analysis!!!
4. Drivers for Technology
Omics -
Focuses on large scale and holistic data/information to understand life in encapsulated omes
- Genomics (the study of genes, regulatory and non-coding sequences )
- Transcriptomics (RNA and gene expression)
- Proteomics (Systematic study of protein expression)
- Interactomics (studying the interactome, which is the interaction among proteins)
-Metabolomics (the study of small-molecule metabolite profiles in cells)
- Phenomics (describes the state of an organism as it changes with time)
- and so on......
5. Networks versus pathways
Pathways or Networks (common terms in systems biology)?
-Pathways: a more defined system which you analyse and study.
Interactions are shown by arrows and in most cases the nature of
this interaction is known.
-Networks: a complex connectivity. You link many proteins together
with arrows to get the general topology. We probably know some biochemical
steps but we do not understand the whole network.
Network
http://www1.qiagen.com/literature/qiagennews/weeklyarticle/05_06/e8/images/GeneNetwork.gif
Pathway
http://www.bio.davidson.edu/COURSES/GENOMICS/2002/James/pathway.jpg
6. Examples of systems
JAK-STAT signaling pathway
Biology
-Hormone (Epo)
Core model
-Receptor binding Epo
-Binding leads to transphosphorylation
of JAK2 and phosphorylation of the
cytoplasmic receptor domains.
-Phosphotyrosine residues 343 and 401
recruit monomeric STAT-5 (x1), which gets
phosphorylated (x2), it then dimerises (x3),
and migrates to the nucleus (x4).
In nucleus: Stimulated transcription of target
genes.
What happens then?
I. Swameye, PNAS, Feb.4, 2003
6. Examples of systems
Data - Simulations
A + B : experimental data
C + D : testing two hypothesis
Time-series measurements
6. Examples of systems
The High Osmolarity Glycerol (HOG) pathway in yeast
Figure 1
Phosphorelay module
high osmolarity
v2TCS ?
v1TCS
Sln1HisP
Sln1
Sln1AspP
Osmotic stress
ADP ATP
Sln1
v3TCS
Ypd1
Plasma
membrane
Signal
pathway
Ypd1HisP
v4TCS
Ssk1AspP
Ssk1
v5TCS
MAP
kinase Hog1
cascade
Pi
Internal
osmotic
pressure
Phospho
relay
Ssk1 system
e
External
osmotic
pressure
i
Glucose
v10
Ssk1
Ssk2
Pi
MAP kinase
cascade
module

v1MAP
ATP ADP
ADP ATP
v2MAP
ATP ADP
Pbs2
NAD
2 ADP
2 ATP
NADH
Gpp2
Glycerol
Transcription
GPD1, GPP2,….
Translation
Fps1
Gpd1, Gpp2,….
Glycerol
v4
G3P
Glycerol
extern
Osmotic
stress
v3MAP
v5
GAP
v6
v9
4 NAD
v7 v8
3 CO2
Gpd1
v11
v12 Gpp2
G3P
NADH NAD
ATP ADP
Pyruvate
4 NADH
DHAP
synthesis
NADH
NAD
NAD
v14
NADH
Ethanol
v16
ADP
v15
ATP
ATP ADP
Pbs2P
v-2MAP
Pi
Pbs2P2
v-3MAP
Hog1P2
vtrans
Pi

v4MAP
ATP ADP
Hog1
v5MAP
Hog1P
Hog1P2
v-5MAP
Pi
nucleus
Hog1P2nuc
vts
Gene
expression
module
Hog1
vtrans1
cytosol
ATP ADP
v-4MAP
Pi
v13 Fps1
ADP
Metabolism
Gene expression

Glycerol, ex
ATP
v3
Fruc-1,6-BP
DHAP
Hog1
nucleus
Ssk2P
v-1MAP
ADP
Gluc-6-P
Gpd1
cytosol
ATP
Glk1 v2
synthesis
Glucose
Metabolism
module
Glucose
uptake
v1
vtrans2
Ptp2
Hog1nuc
vdephos
mRNAnuc
vex
Edda Klipp et al, Nature Biotechnology 2005, number 8,
vpd
vtl
Proteins
mRNAcyt
vrd
Biophysical changes
i = f(Glycerol)
Waterflow over membrane = f(i, e, t)
Volume change = f(Waterflow)
(see text)
6. Examples of systems
Concentration, relative
1.
Experimental data
A
0.8
Gpd1
0.6
mRNA
0.4
Hog1P2
0.2
Ssk1
0
Glycerol, relative
0
30
60
90
Time / min
1.
Glycin
Glycex
Gpd1
0.6
mRNA
0.4
Hog1P2
0.2
0
120
C
B
0.8
0
Glycerol, relative
Concentration, relative
Simulations
1.5
D
30
60
90
Time / min
120
Glycin
1
0.5
Glycex
0
0
Time / min
Pressure /MPa
Volume, relative
1.
E
0.9
0.8
3.
2.5
2.
1.5
1.
0.5
30
60
90
Time / min
120
i
F
e
t
0.7
0
30
60
90
Time / min
120
Edda Klipp et al, Nature Biotechnology 2005, number 8,
0
30
60
90
Time / min
120
Future perspectives
Short term goal
- Get answers to questions like: what happens, why does it happen and how
is specificity achieved?
- To discover new principles and mechanisms for biological function
- Biotechnology: to get predictive cells
-To create a detailed model of cell regulation, focused on signal-transduction
cascades. This could lead to system-level insights into mechanisms which
could be the basis for drug discovery.
-To understand cells and eventually tissues and organs
In pharmaceutical industry: to get predictive medicines (to avoid side-effects,
to individualise medicines)
Long term goal
Summary – What did we learn?
- Systems biology is an approach where mathematical modeling and quantitative
experimental data are combined to get a system-level understanding of your
biological system.
- Systems biology offers the chance to predict the outcome of complex processes and
it decreases the number of experiments (experimental planninig).
- To take into account principles of robustness might provide some guidelines for how
we model and analyse model complexity.
-To conduct systems biology often involves: 1) set up pathway structure based on
previous knowledge (static) 2) Simulating experimental data to determine parameters
3) Predictions to test model.
-Qualitative data and quantitative data are of different types.
-It drives technology forward!!!! This might be the bottle-neck today, but when we have
better technologies / methods systems biology could move faster towards a promising
future.
Long term applications: To get better and predictive medicines.
References:
Articles
Hartwell et al. Nature,V 402,1999, From molecular to modular cell biology
Peter K Sorger, Current opinion in Cell Biology 2005, A reductionist´s systems biology
Hiroaki Kitano, Science, V 295, 2002 Systems Biology: A Brief Overview
Hiroaki Kitano, Nature, V420 2002, Computational Systems biology
Books
E.Klipp et al, System Biology in Practice, Wiley-vch verlag 2005, ISBN-13 978-3-527-31078-4
L.Alberghina, H.V Westerhoff (Eds.), Systems Biology, Topics in Current Genetics, Springer-verlag 2005,
ISBN-13 978-3-540-22968-1
Zoltan Szallasi, Jörg Stelling, Vipul Periwal (Eds), System Modeling in Cellular Biology, A Bradford book 2006,
ISBN 0-262-19548-8
Resources on the net:
http://en.wikipedia.org/wiki/Main_Page
www.brc.dcs.gla.ac.uk, David Gilbert, Systems Biology (1) Introduction