Biological Gene and Protein Networks

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Biological Gene and Protein Networks

• Xin Zhang
• Department of Computer Science and Engineering

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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

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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)

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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.

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Sources http//www.ornl.gov/sci/techresources/Hum
an_Genome/graphics/slides/images/REGNET.jpg
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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

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Why Study GRN?

• Genes are not independent
• 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

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Modeling Gene Regulatory Networks

• Linear Model
• Bayesian Networks
• Differential Equations
• Boolean Network
• Originally introduced by Kauffman (1969)
• Boolean network is a kind of Graph
• 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)
• Gene expression is quantized to only two level
• 1 (On) and 0 (OFF)
• Every function has the result value of each node

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Boolean Network Example
Nodes (genes)
Source From Biosystems 20033443
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Boolean Network as models of gene regulatory
networks

• Cyclin E and cdk2 work together to phosphorylate
  the Rb protein and inactivate it
• Cdk2/Cyclin E is regulated by two switches
• Positive switch complex called CAK
• Negative switch P21/WAF1
• The CAK complex can be composed of two gene
  products
• Cyclin H
• Cdk7
• When cyclin H and cdk7 are present, the complex
  can activate cdk2/cyclin E.

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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.

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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
• 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.

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Learning Causal Relationship with Steady State
Data

• How to infer causal relationship?
• 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

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Microarray data

• Gene up-regulate, down-regulate

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How we Study Gene Causal Network?

• We present an algorithm for learning causal
  relationship with knowledge of topological
  ordering information
• Studying conditional dependencies and
  independencies among variables
• Learning mutual information among genes
• Incorporating topological information

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We applied the learning algorithm in Melanoma
Dataset

• melanoma -- malignant tumor occurring most
  commonly in skin

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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
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Important Information we discovered
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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

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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

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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
• Transcription factor binding location data

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Learning Causality in Motifs

• Network motifs are the simplest units of network
  architecture.
• They be used to assemble a transcriptional
  regulatory network.

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Learning GRN with feedback loops
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Learning GRN with feedback loops (Cond)
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Protein-Protein Interactions
From Towards a proteome-scale map of the human
proteinprotein interaction network Rual, Vidal
et al. Nature 437, 1173-1178 (2005)
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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.

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Reference databases

• Interactions
• MIPS
• DIP
• YPD
• Intact (EBI)
• BIND/ Blueprint
• GRID
• MINT

• Prediction server
• Predictome (Boston U)
• Plex (UTexas)
• STRING (EMBL)
• Protein complexes
• MIPS
• YPD

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How to Study PPI?

• High-throughput data
• Two-hybrid systems
• Mass Spectrometry
• Microarrays
• Genomic data
• Phylogenetic profile
• Rosetta Stone method
• Gene neighboring
• Gene clustering
• Other Data Sources

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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

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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 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.

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Phylogenetic Profile
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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
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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
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What we have done (1)

• Logic analysis on phylogenetic profile
• Plus combine phylogenetic profile data with
  Rosetta Stone method

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Our Learning Results
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What we have done (2)

• Combining more data sources to learn disease
  related protein protein interactions
• Phylogenetic profiles
• Other genome sequence data
• Gene ontology
• OMIM database provides rich sources regarding
  human genes and genetic disorders.

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Learning from multiple data sources Gene
ontology

• Gene ontology (GO) is a controlled vocabulary
  used to describe the biology of a gene product in
  any organism.
• molecular function of a gene product,
• the biological process in which the gene product
  participates, and
• the cellular component where the gene product can
  be found

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Disease related protein protein interactions
Mad Cow disease related protein protein
interactions
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Future work and Possible Project Topics

• Learning from multiple data sources
• Disease related protein-protein interactions
• Learning from different species

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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