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Bioinformatics III
“Systems biology”
“Cellular networks”
“Computational cell biology”
Course will teach mathematical methods that are applied
from protein complexes to interaction networks
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Bioinformatics III
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Organization of biological cells
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Organization of biological cells
(Top) since the 1950ies, a paradigm became established that information flows
from DNA over RNA to protein synthesis which then gives rise to particular
phenotypes.
(Middle) The upcome of structural biology - the first crystal structure of the
protein Myoglobin was determined in 1960 - emphasized the importance of the
three-dimensional structures of proteins determining their function.
(Bottom) Today, we have realized the central role played by molecular
interactions that influence all other elements.
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Organization of transcriptional regulatory networks
(left) The 'basic unit' comprises the transcription factor, its target gene with DNA
recognition site and the regulatory interaction between them.
(middle) Units are often organized into network 'motifs' which comprise specific
patterns of inter-regulation that are over-represented in networks. Examples of
motifs include single input multiple output (SIM), multiple input multiple output
(MIM), and feed-forward loop (FFL) motifs.
(right) Network motifs can be interconnected to form semi-independent 'modules'
many of which have been identified by integrating regulatory interaction data with
gene expression data, and imposing evolutionary conservation. The next level
consists of the entire network (not shown).
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Major metabolic pathways
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Content (ca.)
Protein interaction networks:
- different topologies (random networks, scale-free networks)
- intro of protein complexes: exp. data
- computational analysis (mathematical graphs)
- graphical layout (force minimization)
- quality check (Bayesian analysis)
- modularity
- network flow
Transcriptional regulatory networks, motifs
Signal transduction networks
Metabolic networks: metabolic flux analysis, extreme pathways, elementary modes
FFT protein-protein docking, fitting into EM maps, tomography
integration of interactome and regulome (Lichtenberg),
integration of protein networks with metabolic pathways
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Appetizer 1
Cell cycle proteins that are part
of complexes or other physical
interactions are shown within
the circle.
For the dynamic proteins, the
time of peak expression is
shown by the node color;
static proteins are represented
as white nodes.
Outside the circle, the dynamic
proteins without interactions
are positioned and colored
according to their peak time.
Lichtenberg et al. Science 307, 724 (2005)
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Appetizer 2
a, Schematics and
summary of properties for
the endogenous and
exogenous sub-networks.
b, Graphs of the static
and condition-specific
networks. Transcription
factors and target genes
are shown as nodes in
the upper and lower
sections of each graph
respectively, and
regulatory interactions are
drawn as edges; they are
coloured by the number
of conditions in which
they are active. Different
conditions use distinct
sections of the network.
c, Standard statistics (global topological measures and local network motifs) describing network structures. These vary between
endogenous and exogenous conditions; those that are high compared with other conditions are shaded. (Note, the graph for the
static state displays only sections that are active in at least one condition, but the table provides statistics for the entire network
including inactive regions.)
Luscombe, Babu, … Teichmann, Gerstein, Nature 431, 308 (2004)
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Mathematical techniques covered
Mathematical graphs
– classification of protein-protein interaction networks,
– algorithms on graphs
– regulatory networks
Bayesian networks
– protein interaction networks
Boolean networks
– transcriptional networks
Fourier transformation
– protein/protein-docking, pattern matching
Linear and convex algebra
– metabolic networks
Ordinary and stochastic differential equations
– kinetic modelling of signal transduction pathways
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Literature
lecture slides are available already; final version 3 days before lecture
suggested reading: links will be put up on course website
http://gepard.bioinformatik.uni-saarland.de/teaching...
Text books
€55,-
€37,- (Amazon)
€54,-
All 3 books are available in the computer science library!
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assignments
10 weekly assignments planned
Homework assignments are handed out in the Thursday lectures and are
available on the course website on the same day.
Homework will include many programming assignments. You can program in
any popular programming language. We recommend powerful script languages
such as Phython or Perl that allow to solve problems efficiently.
Solutions need to be returned until Thursday of the following week 14.00
to Peter Walter in room 0.11 Geb. C 7 1, ground floor, or handed in prior (!)
to the lecture starting at 14.15. 2 students may submit one joint solution.
Also possible: submit solution by e-mail as 1 printable PDF-file to
[email protected].
Tutorial: participation is recommended but not mandatory. Date: Wed 14-16 ?
Homeworks submitted on Thursdays will be discussed on the following Wednesday.
Each student needs to present his solution to one of the assignments on the
blackboard once in the tutorial session.
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tests
4 tests are planned on:
Nov. 11, Dec. 9, Jan. 13, Feb. 10
Each will last 45 minutes.
Tests will cover the material from the lectures since the start of the lecture or since
the last test.
You need to pass 3 out of 4 tests.
The grade for the tests will be computed from the best 3 tests.
If you miss one of the tests for a medical reason, please provide a medical certificate.
You will be given a chance for an oral exam on the same subject.
If you have passed only 2 tests, you may choose one of the failed or missing tests for
an oral re-exam.
If you have only passed 1 test, you have failed the class.
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Schein = successful written exam
The successful participation in the lecture course („Schein“) will be certified upon
successful completion of the written exam that will take place on Feb. 20, 2009, 10 – 12 am.
Participation at the exam is open to those students who have
- received  50% of credit points for the assignments and
- presented once during the tutorials and
- have passed 3 tests.
The final mark of the „Schein“ will be the average of the 3 best test results (50%)
and of your result in the final exam (50%).
The final exam will take 120 min and only cover the material of the assignments.
In case of illness please send E-mail to:
[email protected] and provide a medical certificate.
A „second and final chance“ exam will be offered in April 2009.
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tutors
Peter Walter, Sikander Hayat – assignments
Geb. C 7 1, room 0.11
[email protected]
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Systems biology
Biological research in the 1900s followed a reductionist approach:
detect unusual phenotype  isolate/purify 1 protein/gene, determine its
function
However, it is increasingly clear that discrete biological function can only rarely
be attributed to an individual molecule.
 new task of understanding the structure and dynamics of the complex
intercellular web of interactions that contribute to the structure and function of
a living cell.
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Systems biology
Development of high-throughput data-collection techniques,
e.g. microarrays, protein chips, yeast two-hybrid screens
allow to simultaneously interrogate all cell components at any given time.
 there exists various types of interaction webs/networks
- protein-protein interaction network
- metabolic network
- signalling network
- transcription/regulatory network ...
These networks are not independent but form „network of networks“.
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DOE initiative: Genomes to Life
a coordinated effort
slides borrowed
from talk of
Marvin Frazier
Life Sciences Division
U.S. Dept of Energy
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Facility I
Production and Characterization of Proteins
Estimating Microbial Genome Capability
• Computational Analysis
– Genome analysis of genes, proteins, and operons
– Metabolic pathways analysis from reference data
– Protein machines estimate from PM reference data
• Knowledge Captured
– Initial annotation of genome
– Initial perceptions of pathways and processes
– Recognized machines, function, and homology
– Novel proteins/machines (including
prioritization)
– Production conditions and experience
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Facility II
Whole Proteome Analysis
Modeling Proteome Expression, Regulation, and Pathways
•
Analysis and Modeling
– Mass spectrometry expression analysis
– Metabolic and regulatory pathway/ network
analysis and modeling
•
Knowledge Captured
– Expression data and conditions
– Novel pathways and processes
– Functional inferences about novel
proteins/machines
– Genome super annotation: regulation, function,
and processes (deep knowledge about cellular
subsystems)
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Facility III
Characterization and Imaging of Molecular Machines
Exploring Molecular Machine Geometry and Dynamics
•
Computational Analysis, Modeling and Simulation
– Image analysis/cryoelectron microscopy
– Protein interaction analysis/mass spec
– Machine geometry and docking modeling
– Machine biophysical dynamic simulation
•
Knowledge Captured
– Machine composition, organization, geometry,
assembly and disassembly
– Component docking and dynamic simulations
of machines
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Facility IV
Analysis and Modeling of Cellular Systems
Simulating Cell and Community Dynamics
•
•
Analysis, Modeling and Simulation
– Couple knowledge of pathways, networks, and
machines to generate an understanding of
cellular and multi-cellular systems
– Metabolism, regulation, and machine
simulation
– Cell and multicell modeling and flux
visualization
Knowledge Captured
– Cell and community measurement data sets
– Protein machine assembly time-course data sets
– Dynamic models and simulations of cell processes
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Computing and Information
Infrastructure Capabilities
“Genomes To Life” Computing Roadmap
Protein machine
Interactions

Molecule-based
cell simulation
Molecular machine
classical simulation
Cell, pathway, and
network
simulation
Community metabolic
regulatory, signaling simulations
Constrained
rigid
docking
Constraint-Based
Flexible Docking
Current
U.S.
Computing
Genome-scale
protein threading

Comparative
Genomics
Biological Complexity
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Are biological networks special?
Statistical physics:
tries to finding universal scaling laws of systems,
e.g. how does the dynamics of a glass change
when you lower the temperature?
Phase-transition „critical slowing down“.
„Relaxtion times in spin-glasses or glasses are observed to grow
to such an extent at low temperatures that these systems do not
reach thermal equilibrium on experimentally accessible timescales. Properties of such systems are then often found to
depend on their history of preparation; such systems are said to
age.
Similar observations are made in coarsening dynamics at first
order phase transitions. Some properties of spin-glasses and
glasses must therefore be studied via dynamical approaches
which allow taking possible history dependence explicitly into
account.“
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Albert-Laszlo Barabasi
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Power laws
A power law relationship between two scalar quantities x and y is any such that the
k
relationship can be written as
y  ax
where a (the constant of proportionality) and k (the exponent of the power law) are
constants.
Power laws can be seen as a straight line on a log-log graph since, taking logs on
both sides, the above equation is equal to
log  y   log( ax k )
which has the same form as the equation for a line
 k log x   log a 
y  mx  c
Power laws are observed in many fields, including physics, biology, geography,
sociology, economics, and war and terrorism. They are among the most frequent
scaling laws that describe the scaling invariance found in many natural phenomena.
www.wikipedia.org
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Degree
The most elementary characteristic of a node is its
degree (or connectivity), k. It is defined as the
number of links between this node and other nodes.
a In the undirected network, node A has k = 5.
b In networks in which each link has a selected
direction there is an incoming degree, kin, which
denotes the number of links that point to a node,
and an outgoing degree, kout, which denotes the
number of links that start from it.
E.g., node A in b has kin = 4 and kout = 1.
An undirected network with N nodes and L links is
characterized by an average degree <k> = 2L/N
(where <> denotes the average).
Barabasi & Oltvai, Nature Reviews Genetics 5, 101 (2004)
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Degree distribution
The degree distribution, P(k), gives the
probability that a selected node has exactly k
links.
P(k) is obtained by counting the number of nodes
N(k) with k = 1,2... links and dividing by the total
number of nodes N.
The degree distribution allows us to distinguish
between different classes of networks.
Barabasi & Oltvai, Nature Reviews Genetics 5, 101 (2004)
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Clustering coefficient
In many networks, if node A is connected to B, and B is
connected to C, then it is highly probable that A also has
a direct link to C. This phenomenon can be quantified
using the clustering coefficient
Cl 
2nl
k k  1
where nI is the number of links connecting the kI
neighbours of node I to each other.
In other words, CI gives the number of 'triangles' that go
through node I, whereas kI (kI -1)/2 is the total number of
triangles that could pass through node I, should all of
node I's neighbours be connected to each other.
Barabasi & Oltvai, Nature Reviews Genetics 5, 101 (2004)
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Clustering coefficient
a Only 1 pair of node A's 5 neighbours are linked together (B
and C), which gives nA = 1 and CA = 2/20.
By contrast, none of node F's neighbours link to each other,
giving CF = 0. The average clustering coefficient, <C >,
characterizes the overall tendency of nodes to form clusters.
An important measure of the network's structure is the function
C(k), which is defined as the average clustering coefficient of all
nodes with k links. For many real networks C(k)  k-1, which is
an indication of a network's hierarchical character.
The average degree <k>, average path length <ℓ> and average
clustering coefficient <C> depend on the number of nodes and
links (N and L) in the network.
By contrast, the P(k) and C(k ) functions are independent of the
network's size and they therefore capture a network's generic
features, which allows them to be used to classify various
networks.
Barabasi & Oltvai, Nature Reviews Genetics 5, 101 (2004)
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Random networks
Aa
The Erdös–Rényi (ER) model of a random network starts with N
nodes and connects each pair of nodes with probability p, which
creates a graph with approximately pN (N-1)/2 randomly placed
links.
Ab
The node degrees follow a Poisson distribution, where most
nodes have approximately the same number of links (close to
the average degree <k>).
The tail (high k region) of the degree distribution P(k )
decreases exponentially, which indicates that nodes that
significantly deviate from the average are extremely rare.
Ac
The clustering coefficient is independent of a node's degree, so
C(k) appears as a horizontal line if plotted as a function of k.
The mean path length is proportional to the logarithm of the
network size, log N, which indicates that it is characterized by
the small-world property.
Barabasi & Oltvai, Nature Rev Gen 5, 101 (2004)
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Scale-free networks
Scale-free networks are characterized by a power-law degree
distribution; the probability that a node has k links follows
P(k) ~ k- -, where  is the degree exponent.
The probability that a node is highly connected is statistically
more significant than in a random graph, the network's properties
often being determined by a relatively small number of highly
connected nodes („hubs“, see blue nodes in Ba).
In the Barabási–Albert model of a scale-free network, at each
time point a node with M links is added to the network, it
connects to an already existing node I with probability I =
kI/JkJ, where kI is the degree of node I and J is the index
denoting the sum over network nodes. The network that is
generated by this growth process has a power-law degree
distribution with  = 3.
Barabasi & Oltvai, Nature Reviews Genetics 5, 101 (2004)
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Scale-free networks
(Bb) Power-law distributions are seen as a straight
line on a log–log plot.
(Bc) The network that is created by the Barabási–
Albert model does not have an inherent modularity,
so C(k) is independent of k.
Scale-free networks with degree exponents 2<  <3,
a range that is observed in most biological and nonbiological networks, are ultra-small, with the average
path length following ℓ ~ log log N, which is
significantly shorter than log N that characterizes
random small-world networks.
Barabasi & Oltvai, Nature Reviews Genetics 5, 101 (2004)
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Importance of the degree exponent 
The value of  in P(k)  k - determines many properties of the
system. The smaller the value of  , the more important the role
of the hubs is in the network.
In general, the unusual properties of scale-free networks are
valid only for  < 3.
For 2>  >3 there is a hierarchy of hubs, with the most
connected hub being in contact with a small fraction of all
nodes.
For  = 2 a hub-and-spoke network emerges, with the largest
hub being in contact with a large fraction of all nodes.
Here, the dispersion of the P(k) distribution, defined as 2 =
<k2> - <k>2, increases with the number of nodes (that is, 
diverges), resulting in a series of unexpected features, such as
a high degree of robustness against accidental node failures.
For  >3, the hubs are not relevant, most unusual features are
absent, and in many respects the scale-free network behaves
like a random one.
Barabasi & Oltvai, Nature Reviews Genetics 5, 101 (2004)
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Shortest path and mean path length
The distance in networks is measured by the path length,
which tells us how many links we need to pass through to
travel between two nodes.
As there are many alternative paths between two nodes,
the shortest path — the path with the smallest number of
links between the selected nodes — has a special role.
In directed networks, the distance ℓAB from node A to node
B is often different from the distance ℓBA from B to A. E.g. in
b , ℓBA = 1, whereas ℓAB = 3.
Often there is no direct path between two nodes. As shown
in b, although there is a path from C to A, there is no path
from A to C. The mean path length, <ℓ>, represents the
average over the shortest paths between all pairs of nodes
and offers a measure of a network's overall navigability.
Barabasi & Oltvai, Nature Reviews Genetics 5, 101 (2004)
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First breakthrough: scale-free metabolic networks
(d) The degree distribution, P(k), of the metabolic network illustrates its scale-free topology.
(e) The scaling of the clustering coefficient C(k) with the degree k illustrates the hierarchical
architecture of metabolism (The data shown in d and e represent an average over 43
organisms).
(f) The flux distribution in the central metabolism of Escherichia coli follows a power law,
which indicates that most reactions have small metabolic flux, whereas a few reactions, with
high fluxes, carry most of the metabolic activity.
Barabasi & Oltvai, Nature Reviews Genetics 5, 101 (2004)
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Second breakthrough: Yeast protein interaction network:
first example of a scale-free network
A map of protein–protein interactions in
Saccharomyces cerevisiae, which is
based on early yeast two-hybrid
measurements, illustrates that a few
highly connected nodes (which are also
known as hubs) hold the network
together.
The largest cluster, which contains
78% of all proteins, is shown. The colour
of a node indicates the phenotypic effect
of removing the corresponding protein
(red = lethal, green = non-lethal, orange
= slow growth, yellow = unknown).
Barabasi & Oltvai, Nature Rev Gen 5, 101 (2004)
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Summary
Many cellular networks show properties of scale-free networks
- protein-protein interaction networks
- metabolic networks
- genetic regulatory networks (where nodes are individual genes and links are
derived from expression correlation e.g. by microarray data)
- protein domain networks
However, not all cellular networks are scale-free.
E.g. the transcription regulatory networks of S. cerevisae and E.coli are examples
of mixed scale-free and exponential characteristics.
It is a topic of ongoing debate whether the analysis of subnetworks (available data
is sparse) allows conclusions on the underlying topology of the entire network.
Next lecture:
- mathematical properties of networks
- origin of scale-free topology
- topological robustness
Barabasi & Oltvai, Nature Rev Gen 5, 101 (2004)
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