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