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Biological Gene and Protein
Networks
Xin Zhang
Department of Computer Science and Engineering
Biological Networks
 Gene regulatory network: two genes are connected
if the expression of one gene modulates expression
of another one by either activation or inhibition
 Protein interaction network: proteins that are
connected in physical interactions or metabolic and
signaling pathways of the cell;
 Metabolic network: metabolic products and
substrates that participate in one reaction;
Background Knowledge
 Cell reproduction, metabolism, and responses to
the environment are all controlled by proteins;
 Each gene is responsible for constructing a single
protein;
 Some genes manufacture proteins which control
the rate at which other genes manufacture proteins
(either promoting or suppressing);
 Hence some genes regulate other genes (via the
proteins they create) ;
What is Gene Regulatory
Network?
 Gene regulatory networks (GRNs) are the on-off
switches of a cell operating at the gene level.
 Two genes are connected if the expression of one
gene modulates expression of another one by either
activation or inhibition
 An example.
Sources: http://www.ornl.gov/sci/techresources/Human_Genome/graphics/slides/images/REGNET.jpg
Simplified Representation of GRN
 A gene regulatory network can be represented
by a directed graph;
 Node represents a gene;
 Directed edge stands for the
modulation (regulation) of one
node by another:
 e.g. arrow from gene X to gene
Y means gene X affects
expression of gene Y
Why Study GRN?
 Genes are not independent;

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They regulate each other and act collectively;
This collective behavior can be observed using
microarray;
 Some genes control the response of the cell to
changes in the environment by regulating other
genes;
 Potential discovery of triggering mechanism
and treatments for disease;
Modeling Gene Regulatory Networks
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Linear Model;
Bayesian Networks;
Differential Equations;
Boolean Network
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Originally introduced by Kauffman (1969)
Boolean network is a kind of Graph
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Gene expression is quantized to only two level:
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G(V, F) – V is a set of nodes ( genes ) as x1 , x2, …, xn
F is a list of Boolean functions f(x1 , x2, …, xn)
1 (On) and 0 (OFF);
Every function has the result value of each node;
Boolean Network Example
Nodes (genes)
Iteration
1
2
3
4
5
6
x1
0
1
X1
1
1
0
0
0
0
x2
0
1
X2
1
1
1
0
0
0
x3
0
1
X3
0
1
1
1
0
0
f1 ( x1 , x2 , x3 )
110
111
011
 x2  x3
f 3 ( x1 , x2 , x3 )
 x2
000
trajectory 1
f 2 ( x1 , x2 , x3 )
 x1
001
100
010
101
trajectory 2
Start!
Source From Biosystems 20033443
Boolean Network as models of
gene regulatory networks
cdk7
•Cyclin E and cdk2 work together to
phosphorylate the Rb protein and
inactivate it
cdk2
CAK
Rb
Cycin H
p21/WAF1
•Cdk2/Cyclin E is regulated by two
switches:
Cyclin E
DNA synthesis
•Positive switch complex called
CAK;
•Negative switch P21/WAF1;
•The CAK complex can be composed of
two gene products:
•Cyclin H;
cdk7
•Cdk7
cdk2
cyclin H
cyclin E
p21/WAF1
Rb
•When cyclin H and cdk7 are present,
the complex can activate cdk2/cyclin E.
Learning Causal Relationships
 High-throughput genetic technologies
empowers to study how genes interact with
each other;
 Learning gene causal relationship is
important:

Turning on a gene can be achieved directly or through
other genes, which have causal relationship with it.
Causality vs. Correlation
Example: rain and falling_barometer
 Observed that they are either both true or both false,
so they are related. Then write
rain = falling_barometer
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Neither rain causes falling_barometer nor vice-versa.
Thus if one wanted rain to be true, one could not achieve it
by somehow forcing falling_barometer to be true. This
would have been possible if falling_barometer caused rain.
 We say that the relationship between rain and
falling_barometer is correlation, but not cause.
Learning Causal Relationship with
Steady State Data
 How to infer causal relationship?

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In wet-labs, knocking down the possible subsets of a
gene;
Use time series gene expression data;
 Problem?

Human tissues gene expression data is only available in
the steady state observation;
 (IC) algorithm by Pearl et al to infer
causal information but not in biological
domain;
Microarray data
Genes
Samples
 Gene up-regulate, down-regulate;
How we Study Gene Causal
Network?
 We present an algorithm for learning causal
relationship with knowledge of topological
ordering information;
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Studying conditional dependencies and
independencies among variables;
Learning mutual information among genes;
Incorporating topological information;
We applied the learning
algorithm in Melanoma Dataset
 melanoma -- malignant tumor occurring
most commonly in skin;
Knowledge we have
 The 10 genes involved in this study chosen from 587 genes
from the melanoma data;
 Previous studies show that WNT5A has been identified as a
gene of interest involved in melanoma;
 Controlling the influence of WNT5A in the regulation can
reduce the chance of melanoma metastasizing;
Partial biological prior knowledge:
MMP3 is expected to be the end of the pathway
Important Information we
discovered
WNT5A
Pirin causatively influences WNT5A –
“In order to maintain the level of
WNT5A we need to directly control
WNT5A or through pirin”.
Causal connection between WNT5A and
MART-1
“WNT5A directly causes MART-1”
Future Work and Possible
Project Topic
 Build a GUI simulation system for studying
gene causal networks;
 Learning from multiple data sources;
 Learning causality in Motifs;
 Learning GRN with feedback loops;
Build a GUI Simulation System


We have done the simulation study and real data
application;
Need to develop a GUI interface for
systematically studying causal network;
Learning from multiple data
sources
 We have gene expression data and
topological ordering information;
 Incorporating some other data sources as
prior knowledge for the learning;
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Transcription factor binding location data;
…
Learning Causality in Motifs
•Network motifs are the simplest units of network
architecture.
Learning GRN with feedback
loops
Learning GRN with feedback loops (Con’d)
From:
Towards a proteome-scale map of the human protein–protein interaction network
Rual, Vidal et al. Nature 437, 1173-1178 (2005)
Protein-Protein Interactions
Why Study Protein-Protein Interactions
 Most proteins perform functions by interacting with
other proteins;
 Broader view of how they work cooperatively in a
cell;
 Studies indicate that many diseases are related to
subtle molecular events such as protein interactions;
 Beneficial for the process of drug design.
Reference databases
 Interactions
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MIPS
DIP
YPD
Intact (EBI)
BIND/ Blueprint
GRID
MINT
 Prediction server
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Predictome (Boston U)
Plex (UTexas)
STRING (EMBL)
 Protein complexes
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MIPS
YPD
How to Study PPI?
 High-throughput data
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Two-hybrid systems
Mass Spectrometry
Microarrays
 Genomic data
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Phylogenetic profile
Rosetta Stone method
Gene neighboring
Gene clustering
 Other Data Sources
Using phylogenetic profiles
to predict protein function
 Basic Idea:
Sequence alignment is a good way to infer protein function,
when two proteins do the exact same thing in two different
organisms.
 But can we decide if two proteins function in the
same pathway?
 Assume that if the two proteins function together
they must evolve in a correlated fashion:

every organism that has a homolog of one of the proteins
must also have a homolog of the other protein
Phylogenetic Profile
 The phylogenetic profile of a protein is a string
consisting of 0s and 1s, which represent the absence
or presence of the protein in the corresponding
sequenced genome;
Protein P1:
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0
0
1
0
1
1
0
0
For a given protein, BLAST against N sequenced
genomes.
If protein has a homolog in the organism n, set coordinate
n to 1. Otherwise set it to 0.
Phylogenetic Profile
Species
Proteins
Pellegrini M, Marcotte EM, Thompson MJ, Eisenberg D, Yeates TO, Assigning protein functions by comparative genome analysis: protein phylogenetic profiles.
Proc Natl Acad Sci U S A. 96(8):4285-8,. 1999
Rosetta Stone Method Identifies
Protein Fusions
Monomeric proteins that are found fused in another
organism are likely to be functionally related and
physically interacting.
Marcotte EM, Pellegrini M, Ng HL, Rice DW, Yeates TO, Eisenberg D, Detecting protein
function and protein-protein interactions from genome sequences. Science 285(5428):751-3,
1999
What we have done (1)
 Logic analysis on phylogenetic profile;
 Plus combine phylogenetic profile data with
Rosetta Stone method;
Our Learning Results
What we have done (2)
 Combining more data sources to learn
disease related protein protein interactions:
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Phylogenetic profiles
Other genome sequence data
Gene ontology
OMIM database: provides rich sources regarding
human genes and genetic disorders.
Learning from multiple data
sources – Gene ontology
 Gene ontology (GO) is a controlled vocabulary used to
describe the biology of a gene product
inthe
any
organism.
Selecting
terms
allows you to view an

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Ontology database which displays a list of
molecular function of a gene product,
proteins associated with these particular
the biological process in which the gene
product participates,
and(Ontology
words/concepts
or their children
tutorial
available
.
the cellular component where the gene
product
canalso)
be found
Disease related protein protein
interactions
Mad Cow disease
related protein
protein interactions
Future work and Possible
Project Topics
 Learning from multiple data sources;
 Disease related protein-protein interactions;
 Learning from different species;
References
 Pearl, J. Causality : Models, Reasoning, and Inference. 2000
 Akutsu, T., et al. Identification of Genetic Networks from A Small
Number of Gene Expression Patterns under the Boolean Network
Models.
 Lee, et al, Transcriptional Regulatory Networks in Saccharomyces
cerevisiae Science 298: 799-804 (2002).
 Pellegrini, et al. Assigning protein functions by comparative genome
analysis: Protein phylogenetic profiles. (1999) PNAS 96, 4285-4288.
 Marcotte, et al. Localizing proteins in the cell from their phylogenetic
profiles. (2000) PNAS 97, 12115-12120
 David Eisenberg, Edward M. Marcotte, Ioannis Xenarios & Todd O.
Yeates(2000) Nature 405, 823-826