Computational characterization of
biomolecular networks in
physiology and disease
Kakajan Komurov, Ph.D
Department of Systems Biology
University of Texas MD Anderson Cancer Center
Classical to Systems Biology
Gene 1
Gene 2
Function 1
Function 2
...
Gene/protein/molecule-centric research
Classical to Systems Biology
Phenotype 1
Phenotype 2
Phenotype 3
...
Classical to Systems Biology
•
Systems-level analyses
•
High throughput experiments
– high content data
•
Genomics, proteomics,
metabolomis, … - “omics”
fields
•
Extensive use of computational
tools
•
Computational systems biology
Phenotype 1
Phenotype 2
Phenotype 3
...
Computational systems biology
• Studying organizational principles
of biological systems
– Dynamic structure – function
relationship in biological networks
• Developing computational tools to
analyze/interpret large-scale data
Computational systems biology
• Studying organizational principles
of biological systems
– Dynamic structure – function
relationship in biological networks
• Developing computational tools to
analyze/interpret large-scale data
Dynamics of protein interaction
networks
Protein network
Gene expression program
Stimulus
Dynamics of protein interaction
networks
Protein network
Gene expression program
Stimulus
Remodeling of the network
Dynamic organizational principles in
protein networks
Komurov and White (2007), Komurov, Gunes, White (2009)
Dynamic organizational principles in
protein networks
Komurov and White (2007), Komurov, Gunes, White (2009)
Cancer systems biology
• Extensive data collection at
the whole-genome level
– The Cancer Genome Atlas
Project
– Expression Oncology project
– Alliance for Signaling project
• System-level understanding
of cellular processes
activated in cancer
• Computational methods to
maximize analytic power,
generate testable hypotheses
Biological complexity
•
•
•
•
~22,000 annotated human genes in RefSeq
~60,000 known protein-protein interactions in human
Millions of indirect relationships between genes
Typical genomic experiment: millions of data points
Objectives
• Analyze data within the context of a priori
information
–
–
–
–
Physical interactions
Function similarity
Sequence similarity
Co-localization
• Extract most relevant genes/subnetworks
– Genes with high data values
– Coordinately regulated genes with similar functions
– Genes with partially redundant functions
• How to score importance/relevance of a
gene/subnetwork to the given experimental
context?
NetWalk
• Principle: relevance of a gene depends on its
measured experimental value and its connections
to other relevant genes
• Random walk – based method for scoring
network interactions for their relevance to the
supplied data
• Simultaneously assesses the local network
connectivity and the data values of genes
• No data cutoffs, assesses the whole data
distribution
Deriving node relevance scores
Transition probability
Relevance score at step k
Left eigenvector of the transition
probability matrix
Deriving Edge Flux (EF) value
Node relevance score = visitation probability
Deriving Edge Flux (EF) value
Node relevance score = visitation probability
Edge Flux
Too much bias towards network
topology
Deriving Edge Flux (EF) value
Node relevance score = visitation probability
Edge Flux
Background node visitation score
Normalized Edge Flux
Low dose vs. high dose DNA damage
Statistical analyses using EF values instead of gene values
Identifying link communities instead of gene communities
Development of drug resistance in breast
cancer
• Lapatinib: drug that blocks activity of HER2
oncoprotein
• Patients with activated HER2 have good initial
response to the drug, but develop resistance in a
short time
• Our strategy: identify networks supporting the drug
resistance of breast cancer cells to lapatinib
Cell culture model of drug resistance in
breast cancer
Strategy
SKBR3
SKBR3-R
SKBR3
SKBR3-R
+Lapatinib (1uM)
Perform NetWalk analysis of gene expression data
to identify most active networks in lapatinib resistance
Over-represented networks in lapatinib resistance
Drug resistance can be reversed by
diabetes drugs
1.2
fraction
Surviving
Survival
11
0.8
0.8
0.6
0.6
Control SKBR3
GCGR inhibitor
SKBR3-R(5uM)
0.4
0.4
0.2
0.2
00
00
0.1 0.3125 0.50.625
0.15625
1
1.25
Lapatinib
concentration
Metformin
concentration(uM)
(mM)
2.52
5
Acknowledgments
• Ph.D Mentor: Michael White, Ph.D
• Current Mentor: Prahlad Ram, Ph.D
• Ram lab:
– Melissa Muller, Ph.D
– Jen-Te Tseng
– Sergio Iadevaia, Ph.D
• Ju-Seog Lee, Ph.D
• Yun-Yong Park, Ph.D
• Collaborators:
– Luay Nakhleh, Ph.D (Rice
University)
– Michael Davies, M.D Ph.D (MDA)
– Mehmet Gunes, Ph.D (UNR)