network biology bmi 730
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Network Biology BMI 730. Kun Huang Department of Biomedical Informatics Ohio State University. Understanding!. Systems Sciences Theory Analysis Modeling Synthesis/prediction Simulation Hypothesis generation. Prediction!. Systems Biology. Biology Domain knowledge Hypothesis testing - PowerPoint PPT PresentationTRANSCRIPT
Network BiologyBMI 730
Kun HuangDepartment of Biomedical Informatics
Ohio State University
BiologyDomain knowledge
• Hypothesis testingExperimental work
• Genetic manipulation• Quantitative measurement• Validation
Systems SciencesTheoryAnalysisModeling
• Synthesis/prediction• Simulation• Hypothesis generation
InformaticsData management
• DatabaseComputational infrastructure
• Modeling tools• High performance computing
Visualization
Systems Biology
Understanding! Prediction!
Review of Network Topology – Scale Free and Modularity
Elements of Dynamical Modeling
Network Motif Analysis
Integration of Multiple Networks – Several Examples
Course Projects
A Tale of Two GroupsA.-L. Barabasi at University of Notre DameTen Most Cited Publications:
Albert-László Barabási and Réka Albert, Emergence of scaling in random networks , Science 286, 509-512 (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 networksNature 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 networksNature 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 ]
Power Law Small World
Rich Get Richer(preferential attachment) Self-similarity
HUBS!
Modularity
Scale-free and Modularity/Hierarchy are thought to be exclusive.
Scale-free(a)
Modular(b)
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).
Review of Network Topology – Scale Free and Modularity
Elements of Dynamical Modeling
Network Motif Analysis
Integration of Multiple Networks – Several Examples
Course Projects
Genetic Network – Transcription Network• Regulation of protein expression is mediated by
transcription factors
DNA
Promoter
Gene Y
DNA
RNA polymerase
Gene Y
mRNA
Protein Y
Transcription
Translation
Genetic Network – Transcription Network• TF factor X regulates protein (gene) Y
DNAGene Y
mRNA
Protein Y
X*
X*XSX
Y
Y
Y
YY
Y
X Y
Activation / positive control, X is called activator.
Genetic Network – Transcription Network• TF factor X regulates protein (gene) Y
DNAGene Y
mRNA
X
Y
Y
YY
DNAGene Y
X*
X*XNo transcription
Repression / negative control, X is called repressor.
X Y
Genetic Network – Transcription Network• How to model the input-output relationship?
Concentration of active TF X*
Rate of production of protein Y
Concentration of protein Y
F(X*) is usually monotonic, S-shaped function.
Genetic Network – Transcription Network• Hill function• Derived from the equilibrium binding of the TF to its target
site.Activator
K – activation coefficient – maximal expression leveln – Hill coefficient (1<n<4 for most cases)F(X*) approximates step function (logic) for large n
X*>>K, F(X*) = X* = K, F(X*) = /2
X*/K
n=1
n=4
n=2
0 1 2
Genetic Network – Transcription Network
Repressor
X*/K
n=1
n=4n=2
0 1 2
F(X*) approximates step function (logic) for large n
Genetic Network – Transcription Network• TF factor X regulates protein (gene) Y• Timescale for E. Coli
1.Binding of signaling molecule to TF and changing its activity~1msec
2.Binding of active TF to DNA ~1sec3.Transcription + translation of gene ~5min4.50% change of target protein concentration
~1h
Genetic Network – Transcription Network• Logic function approximation• Hill function is for detailed modeling. Logic
function is for simplicity and mathematical clarity.
Activator
K – threshold – maximal expression level
Repressor
t0
Genetic Network – Transcription Network• Logic function approximation• Multiple input
X* AND Y*
X* OR Y*
SUM
Genetic Network – Transcription Network• The dynamics• Change over time• Degradation• Dilution (cell growth and volume increase)• Response time (characteristics)
Dynamical equation
Equilibrium (steady state)
Genetic Network – Transcription Network• The dynamics• Response time (characteristics)• Sudden removal of production
1
0.5
Genetic Network – Transcription Network• The dynamics• Response time (characteristics)• Sudden initiation of production
1
0.5
Motif Statistics and Dynamics• Autoregulation• Self-edge in the transcription network
Motif Statistics and Dynamics• Autoregulation
DNAGene Y
mRNA
X
A
Negative autoregulation
Motif Statistics and Dynamics• Autoregulation
DNAGene Y
mRNA
XA
10 Time (t)
X(t
)/K
1
Motif Statistics and Dynamics• Autoregulation
10Time (t)
X(t
)/K
1
Short response time
Motif Statistics and Dynamics• Autoregulation
Robustness / stabilization
If fluctuates, Xss is stable for negative autoregulation but not for simple regulation.
Review of Network Topology – Scale Free and Modularity
Elements of Dynamical Modeling
Network Motif Analysis
Integration of Multiple Networks – Several Examples
Course Projects
Motif Topology
Each edge has 4 choices (why?). Three edges 4X4X4 = 64 choices. There are symmetry redundancy. Despite the choices of activation and repression, there are 13 types.
X
Y
Z
X
Y
Z
X
Y
Z
X
Y
Z
X
Y
Z
X
Y
Z
X
Y
Z
X
Y
Z
Coherent Feed Forward Loop (FFL)
Incoherent Feed Forward Loop
Coherent Feed Forward Loop (FFL)
X
Y
Z
X
Y
Z
AND
Sx
Ton
Sign sensitive delay for ON signal
Sx
Coherent Feed Forward Loop (FFL)
X
Y
Z
X
Y
Z
AND
Sx
Sign sensitive delay for ON signal
Sx
Coherent Feed Forward Loop (FFL)
The Coherent Feedforward Loop Serves as a Sign-sensitive Delay Element in Transcription Networks Mangan, S.; Zaslaver, A.; Alon, U. J. Mol. Biol., 334:197-204, 2003.
Coherent Feed Forward Loop (FFL)
Timing instrument
Coherent Feed Forward Loop (FFL)
X
Y
Z
X
Y
Z
AND
Sx
Sy
Nature Genetics 31, 64 - 68 (2002) Network motifs in the transcriptional regulation network of Escherichia coliShai S. Shen-Orr, Ron Milo, Shmoolik Mangan & Uri Alon
Noise (low-pass) filter
Coherent Feed Forward Loop (FFL)
X
Y
Z
X
Y
Z
OR
Sx
Sign sensitive delay for OFF signal
Sx
Coherent Feed Forward Loop (FFL)
A coherent feed-forward loop with a SUM input function prolongs flagella expression in Escherichia coliShiraz Kalir, Shmoolik Mangan and Uri Alon, Mol. Sys. Biol., Mar.2005.
Coherent Feed Forward Loop (FFL)
A coherent feed-forward loop with a SUM input function prolongs flagella expression in Escherichia coliShiraz Kalir, Shmoolik Mangan and Uri Alon, Mol. Sys. Biol., Mar.2005.
Incoherent Feed Forward Loop (FFL)
X
Y
Z
X
Y
Z
AND
Sx
Fast response time to steady state
Sx
Table 3. Summary of functions of the FFLs
* In incoherent FFL with basal level, Sy modulates Z between two nonzero levels.
Steady-state logic is sensitive to both Sx and Sy
Coherent and incoherent* Types 1, 2 AND Types 3, 4 OR
Sign-sensitive delay upon Sx steps Coherent Types 1, 2, 3, 4
Sy-gated pulse generator upon Sx steps
Incoherent with no basal Y level
Types 3, 4 AND Types 1,2 OR
Sign-sensitive acceleration upon Sx steps
Incoherent with basal Y level
Types 1,2,3,4
Mangan, S. and Alon, U. (2003) Proc. Natl. Acad. Sci. USA 100, 11980-11985
Review of Network Topology – Scale Free and Modularity
Elements of Dynamical Modeling
Network Motif Analysis
Integration of Multiple Networks – Several Examples
Course Projects
Barabasi A-L, Network medicine – from obesity to “Diseasome”, NEJM, 357(4): 404-407, 2007.
Integration of Multi-Modal Data
Tissue-Tissue Network
Dobrin et al. Genome Biology 2009 10:R55 doi:10.1186/gb-2009-10-5-r55
Tissue-Tissue Network
Dobrin et al. Genome Biology 2009 10:R55 doi:10.1186/gb-2009-10-5-r55
Genotype-Phenotype Network
Scoring scheme of CIPHER. First, the human phenotype network, protein network, and gene–phenotype network are assembled into an integrated network. Then, to score a particular phenotype–gene pair (p, g), the phenotype similarity profile for p is extracted and the gene closeness profile for g is computed from the integrated network. Finally, the linear correlation of the two profiles is calculated and assigned as the concordance score between the phenotype p and the gene g.
Wu et al. Molecular Systems Biology, 2009 4:189, Network-based global inference of human disease genes
Genotype-Phenotype Network
Known disease
geneRank in 8919 candidates
CIPHER-SP % CIPHER-DN %
BRCA1 1 0.01 2 0.02AR 3 0.03 3 0.03
ATM 19 0.21 4 0.04CHEK2 66 0.74 19 0.21BRCA2 139 1.56 49 0.54STK11 150 1.69 21 0.23RAD51 174 2.00 36 0.40PTEN 188 2.10 24 0.26
BARD1 196 2.20 41 0.45TP53 287 3.22 45 0.50
RB1CC1 798 8.95 6360 71.30NCOA3 973 10.91 343 3.84PIK3CA 1644 18.43 367 4.11PPM1D 1946 21.82 7318 82.04CASP8 4978 55.81 2397 26.87TGF1 7116 79.78 3502 39.26
Wu et al. Molecular Systems Biology, 2009 4:189, Network-based global inference of human disease genes
Kelley and Ideker, Nature Biotechnology, 2005 23:561-566, Systematic interpretation of genetic interactions using protein networks
Review of Network Topology – Scale Free and Modularity
Elements of Dynamical Modeling
Network Motif Analysis
Integration of Multiple Networks – Several Examples
Course Projects