Pathways, Networks and Systems Biology
OR
“what do I do with my gene list?”
BMI 705
Kun Huang
Department of Biomedical Informatics
Ohio State University
Gene Enrichment Analysis
• Gene Ontology / Pathways / Networks
• Databases and Resources
Gene Regulation (cis-)Networks
Challenges in system biology
• New computation and modeling methods
• Kinetics vs. dynamics
Scale-Free Network and Network Motifs
Where do I get the gene list?
• Comparative study
e.g., microarray experiments between two
types of samples or two disease states (can
also be from RT-PCA, proteomics, …)
• Clustering / classification of genes
e.g., co-expressed genes
• Homologue analysis
e.g., genes from BLAST
• Other sources
What do I do with the gene list?
• Find commonality among the gene
Common biological functions
Common molecular processes
Common cellular components
Common pathways
Interact with common genes
Common sequences / molecular structures
Regulated by common Transcription Factors
Involved in the same disease
…
• Generate new hypothesis based on the
commonality
How do I find commonality from my gene
list?
• Using a priori knowledge (e.g., gene
ontology, pathway, annotation, etc.)
• Fisher’s exact test (chi-square based)
How significant is
the intersection?
• Other statistical method
• Good news – most of the time you can use
software to do it
What softwares are available?
• Many
• DAVID (http://david.abcc.ncifcrf.gov/)
• Cytoscape
• GOTerm
• BiNGO
• GSEA
• GenMapp (Free)
• Pathway Architect (Commercial)
• Pathway Studio (Commercial)
• Ingenuity Pathway Analysis (Commercial)
• Manually curated
• On-demand computation
Genes
Functions, pathways and networks
Pathway – What’s out there?
240
Ingenuity Pathway Analysis (IPA)
Gene Enrichment Analysis
• Gene Ontology / Pathways / Networks
• Databases and Resources
Gene Regulation (cis-)Networks
Challenges in system biology
• New computation and modeling methods
• Kinetics vs. dynamics
Scale-Free Network and Network Motifs
Transcription in higher eukaryotes
Gene Expression
distal
promoter
region
1.
Chromatin structure
2.
Initiation of transcription
3.
Processing of transcripts
4.
Transport to cytoplasm
5.
mRNA translation
6.
mRNA stability
7.
Protein activity stability
proximal promoter region TFBS: Transcription Factor Binding
Sites
Adapted from Wesserman & Sandelin, 2004, Nature Rev. Genetics
Characterization of transcriptional
regulation
 Annotating regulatory regions (TSS and
Promoter)
 Identifying cis-regulatory modules
 Deciphering logic of regulatory networks
Transcriptional regulatory module
• cis-regulatory elements are
sequence-specific regions
transcription factors bind
CGGTTAAG
GCTAACGC
AGGCTA
• TFs combinatorially
associate with each
other to form modules
and regulate their
target genes
Gene regulatory network
Identify Cis-Regulatory Element
•TFs bind to cis-acting regulator elements (CAREs).
•CAREs are DNA motifs of length 5 – 20 (e.g., 5’
CGGnnnnnnnnnnnCCG 3’, the binding site for yeast TF,
Gal4).
•Most CAREs are in the 5’ vicinity of the gene (promoter),
but some have been identified downstream.
•Algorithms focus on identify common motifs.
• Words count.
• Probabilistic methods (weight matrix, combined with
EM search).
• Phylogenetic footprinting.
•Other features: CpG island.
Example – from JASPAR Database
• AGL3
A
C
G
T
[ 0 3 79 40 66 48 65 11 65 0 ]
[94 75 4 3 1 2 5 2 3 3 ]
[ 1 0 3 4 1 0 5 3 28 88 ]
[ 2 19 11 50 29 47 22 81 1 6 ]
Example Workflow
Gene list
Extract promoter sequences
Multiple sequence
alignment
Ab Initio Motifs Discovery
Programs
(Weeder and MEME)
Manual selection
i candidate Motifs
Screen against TRANSFAC
n final known and novel Motifs
Question : How do you extract upstream sequences for genes?
ChIPMotifs (from Dr. Victor Jin)
k Top Level Sequences
Ab Initio Motifs Discovery Programs
(Weeder and MEME)
i candidate Motifs
Bootstrap re-sampling approach to determine
optimal cutoff of Motifs and screen against nonenrichment sequences
m final statistical significant
candidate Motifs
k>i>m>n
Screen against TRANSFAC
n final known and novel Motifs
Question : How do you extract upstream sequences for genes?
Gene Enrichment Analysis
• Gene Ontology / Pathways / Networks
• Databases and Resources
Gene Regulation (cis-)Networks
Challenges in system biology
• New computation and modeling methods
• Kinetics vs. dynamics
Scale-Free Network and Network Motifs
System Sciences
Understanding!
Theory
Analysis
Modeling
• Synthesis/prediction
• Simulation
• Hypothesis generation
Prediction!
System Biology
Biology
Informatics
Domain knowledge
Data management
• Hypothesis testing
Experimental work
• Genetic manipulation
• Quantitative measurement
• Validation
• Database
Computational infrastructure
• Modeling tools
• High performance computing
Visualization
“A key element of the GTL program is an
integrated computing and technology
infrastructure, which is essential for timely and
affordable progress in research and in the
development of biotechnological solutions. In
fact, the new era of biology is as much about
computing as it is about biology. Because of
this synergism, GTL is a partnership between
our two offices within DOE’s Office of Science—
the Offices of Biological and Environmental
Research and Advanced Scientific Computing
Research.
Only with sophisticated computational power and
information management can we apply new
technologies and the wealth of emerging data to
a comprehensive analysis of the intricacies and
interactions that underlie biology. Genome
sequences furnish the blueprints,
technologies can produce the data, and
computing can relate enormous data sets to
models linking genome sequence to
biological processes and function.”
Taniguchi et al. Nature Reviews Molecular Cell Biology 7, 85–96 (February 2006) | doi:10.1038/nrm1837
Challenges in system biology
• Large data
• Kinetics vs. dynamics
• Multiple (temporal) scale
• New computation and modeling methods
• New mathematics or new physics laws
Oscillation
A
B
Maeda et al., Science, 304(5672):875-878, 2004
Simple Two Nodes Pattern
Chang et al., Multistable
and multistep dynamics in
neutrophil differentiation,
BMC Cell Biology 2006,
7:11
Bistable dynamics in a two-gene system with cross-regulation. A. Gene regulatory circuit diagram.
Blunt arrows indicate mutual inhibition of genes X and Y. Dashed arrows indicate a basal synthesis (affected
by the inhibition) and an independent first-order degradation of the factors. B. Two-dimensional XY phase
plane representing the typical dynamics of the circuit. Every point (X, Y) represents a momentary state
defined by the values of the pair X, Y. Red arrows are gradient vectors indicating the direction and extent
that the system will move to within a unit time at each of the (X, Y) positions. Collectively, the vector field
gives rise to a "potential landscape", visualized by the colored contour lines (numerical approximation). In
this "epigenetic landscape", the stable states (attractors) are in the lowest points in the valleys: a (X>>Y)
and b (Y>>X) (gray dots). C. Schematic representation of the epigenetic landscape as a section through a
and b in which every red dot represents a cell. Experimentally, this bistability is manifested as a bimodal
distribution in flow cytometry histograms in which the stable states a and b appear as peaks at the
respective level of marker expression (e.g., Y).
Marlovits et.al., Biophysical Chemistry, Vol:72, p.169-184
Pomerening et.al., Cell, Vol:122(4), p.565-578
New system biology
Kinetics vs. Dynamics
Compartmentalization (Spatial and Temporal)
Hybrid Systems and System Abstraction
• Hierarchical/multiscale description
• Discrete Event System
• New System Theory
Graph Theory and Network Theory / New
Mathematics and New Physics
Gene Enrichment Analysis
• Gene Ontology / Pathways / Networks
• Databases and Resources
Gene Regulation (cis-)Networks
Challenges in system biology
• New computation and modeling methods
• Kinetics vs. dynamics
Scale-Free Network and Network Motifs
A Tale of Two Groups
A.-L. Barabasi at University of Notre Dame
Ten Most Cited Publications:
Albert-László Barabási and Réka Albert, Emergence of scaling in random networks , Science 286, 509512 (1999). [ PDF ] [ cond-mat/9910332 ]
Réka Albert and Albert-László Barabási, Statistical mechanics of complex networks
Review of Modern Physics 74, 47-97 (2002). [ PDF ] [cond-mat/0106096 ]
H. Jeong, B. Tombor, R. Albert, Z.N. Oltvai, and A.-L. Barabási, The large-scale organization of
metabolic networks, Nature 407, 651-654 (2000). [ PDF ] [ cond-mat/0010278 ]
R. Albert, H. Jeong, and A.-L. Barabási, Error and attack tolerance in complex networks
Nature 406 , 378 (2000). [ PDF ] [ cond-mat/0008064 ]
R. Albert, H. Jeong, and A.-L. Barabási, Diameter of the World Wide Web
Nature 401, 130-131 (1999). [ PDF ] [ cond-mat/9907038 ]
H. Jeong, S. Mason, A.-L. Barabási and Zoltan N. Oltvai, Lethality and centrality in protein networks
Nature 411, 41-42 (2001). [ PDF ] [ Supplementary Materials 1, 2 ]
E. Ravasz, A. L. Somera, D. A. Mongru, Z. N. Oltvai, and A.-L. Barabási, Hierarchical organization of
modularity in metabolic networks, Science 297, 1551-1555 (2002). [ PDF ] [ cond-mat/0209244 ] [
Supplementary Material ]
A.-L. Barabási, R. Albert, and H. Jeong, Mean-field theory for scale-free random networks
Physica A 272, 173-187 (1999). [ PDF ] [ cond-mat/9907068 ]
Réka Albert and Albert-László Barabási, Topology of evolving networks: Local events and universality
Physical Review Letters 85, 5234 (2000). [ PDF ] [ cond-mat/0005085 ]
Albert-László Barabási and Zoltán N. Oltvai, Network Biology: Understanding the cells's functional
organization, Nature Reviews Genetics 5, 101-113 (2004). [ PDF ]
A Tale of Two Groups
Uri Alon at Weissman Institute
Selected Publications:
R Milo, S Itzkovitz, N Kashtan, R Levitt, S Shen-Orr, I Ayzenshtat, M Sheffer & U Alon, Superfamilies of designed and
evolved networks, Science, 303:1538-42 (2004). Pdf.
R Milo, S Shen-Orr, S Itzkovitz, N Kashtan, D Chklovskii & U Alon, Network Motifs: Simple Building Blocks of Complex
Networks, Science, 298:824-827 (2002). Pdf.
S Shen-Orr, R Milo, S Mangan & U Alon, Network motifs in the transcriptional regulation network of Escherichia coli.
Nature Genetics, 31:64-68 (2002). Pdf.
S. Mangan, S. Itzkovitz, A. Zaslaver and U. Alon, The Incoherent Feed-forward Loop Accelerates the Response-time of
the gal System of Escherichia coli. JMB, Vol 356 pp 1073-81 (2006). Pdf.
S Mangan & U Alon, Structure and function of the feed-forward loop network motif. PNAS, 100:11980-11985 (2003). Pdf.
S. Mangan, A. Zaslaver and U. Alon, The Coherent Feedforward Loop Serves as a Sign-sensitive Delay Element in
Transcription Networks. JMB, Vol 334/2 pp 197-204 (2003). Pdf.
Guy Shinar, Erez Dekel, Tsvi Tlusty & Uri Alon, Rules for biological regulation based on error minimization, PNSA.
103(11), 3999-4004 (2006). Pdf.
Alon Zaslaver, Avi E Mayo, Revital Rosenberg, Pnina Bashkin, Hila Sberro, Miri Tsalyuk, Michael G Surette & Uri
Alon, Just-in-time transcription program in metabolic pathways, Nature Genetics 36, 486 - 491 (2004). Pdf.
U. Alon, M.G. Surette, N. Barkai, S. Leibler, Robustness in Bacterial Chemotaxis, Nature 397,168-171 (1999). Pdf
M Ronen, R Rosenberg, B Shraiman & U Alon, Assigning numbers to the arrows: Parameterizing a gene regulation
network by using accurate expression kinetics. PNAS, 99:10555–10560 (2002). Pdf.
N Rosenfeld, M Elowitz & U Alon, Negative Autoregulation Speeds the Response Times of Transcription Networks, JMB,
323:785-793 (2002). Pdf.
N Rosenfeld & U Alon, Response Delays and the Structure of Transcription Networks, JMB, 329:645–654 (2003). Pdf.
S. Kalir, J. McClure, K. Pabbaraju, C. Southward, M. Ronen, S. Leibler, M.G. Surette, U. Alon, Ordering genes in a
flagella pathway by analysis of expression kinetics from living bacteria. Science, 292:2080-2083 (2001). Pdf
Y. Setty, A. E. Mayo, M. G. Surette, and U. Alon, Detailed map of a cis-regulatory input function, PNAS, 100:7702-7707
(2003). Pdf.
Shiraz Kalir and Uri Alon, Using a Quantitative Blueprint to Reprogram the Dynamics of the Flagella Gene Network, Cell,
117:713–720, (2004). Pdf.
Small world phenomena
(http://smallworld.columbia.edu)
P(k) ~ k-
R. Albert, H. Jeong, A-L Barabasi,
Nature, 401 130 (1999).
Other Observations:
•
•
•
•
•
•
•
•
•
•
•
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Scientific citations
Paper coauthorship/collaboration
Organization structure
Social structure
Actor joint casting in movies
Online communities
Websites linkage
…
Protein networks
Gene networks
Cell function networks
…
Scale-Free Networks
Metabolic network
Archaea
Bacteria
Eukaryotes
H. Jeong, B. Tombor, R. Albert, Z.N. Oltvai, and A.L. Barabasi, Nature, 407 651 (2000)
Organisms from all three domains of life are
scale-free networks!
Power Law
Small World
Rich Get Richer
(preferential
attachment)
Self-similarity
HUBS!
Preferential attachment in protein Interaction networks
ki
 ( ki ) 
 jk j
k vs. k : increase in the No. of links in a unit time
No PA: k is independent of k
PA: k ~k
Eisenberg E, Levanon EY, Phys. Rev. Lett. 2003
Jeong, Neda, A.-L.B, Europhys. Lett. 2003
Nature Biotechnology 18, 1257 1261 (2000) doi:10.1038/82360
A network of protein−protein
interactions in yeast
Benno Schwikowski, Peter Uetz
& Stanley Fields
Nature Biotechnology 18, 1257 - 1261 (2000) doi:10.1038/82360
A network of protein−protein interactions in yeast
Benno Schwikowski, Peter Uetz & Stanley Fields
C. Elegans
Li et al. Science 2004
Drosophila M.
Giot et al. Science 2003
Consequence 1 : Hubs and Robustness
Nature 408 307 (2000)
…
“One way to understand the p53 network
is to compare it to the Internet.
The cell, like the Internet, appears to
be a ‘scale-free network’.”
Consequence 1 : Hubs and Robustness
Complex systems maintain their basic functions
even under errors and failures
(cell  mutations; Internet  router breakdowns)
1
S
fc
0
1
Fraction of removed nodes, f
node failure
Hubs and Robustness
Complex systems maintain their basic functions
even under errors and failures
(cell  mutations; Internet  router breakdowns)
R. Albert, H. Jeong, A.L. Barabasi, Nature 406 378 (2000)
Achilles’ Heel of complex networks
Internet
failure
attack
R. Albert, H. Jeong, A.L. Barabasi, Nature 406 378 (2000)
Yeast protein network
- lethality and topological position
Highly connected proteins are more essential
(lethal)...
H. Jeong, S.P. Mason, A.-L. Barabasi, Z.N. Oltvai, Nature 411, 41-42 (2001)
Subgraphs
•
•
Subgraph: a connected graph
consisting of a subset of the nodes and
links of a network
Subgraph properties:
n: number of nodes
m: number of links
(n=3,m=3)
(n=3,m=2)
(n=4,m=4)
(n=4,m=5)
.
R Milo et al., Science 298, 824-827 (2002).
System biology
• Integration
• Computation
• Theory
Prediction!!!