Networks and Systems Biology

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Oct 23, 2013 (3 years and 7 months ago)

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Networks and Systems Biology

BMI 730


Kun Huang

Department of Biomedical Informatics

Ohio State University

Review of Pathways
and
Resources


Challenges in system biology


Large data


New computation and modeling methods


Kinetics vs. dynamics


Scale
-
Free Network


Network Motifs

Genes

Functions, pathways and networks

Pathway


What’s out there?

320

Pathway software


GenMapp

(Free)


CytoScape

(Free)


GESA (Free)


DAVID (Free)


Pathway Architect (Commercial)


Pathway Studio (Commercial)


Ingenuity Pathway Analysis (Commercial)


Manually
curated


On
-
demand computation

Review of Pathways
and
Resources


Challenges in system biology


Large data


New computation and modeling methods


Kinetics vs. dynamics


Scale
-
Free Network


Network Motifs

“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
.”

Biology

Domain knowledge


Hypothesis testing

Experimental work


Genetic manipulation


Quantitative measurement


Validation

System Sciences

Theory

Analysis

Modeling


Synthesis/prediction


Simulation


Hypothesis generation

Informatics

Data management


Database

Computational infrastructure


Modeling tools


High performance computing

Visualization

System Biology

Understanding!

Prediction!

Feedback is ubiquitous; it is
essential for the stabilization of
any system (biological,
engineering, social …)

Control System

Input

Output

Open Loop

Control System

Input

Output

Feedback

+

±

Closed Loop

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


A

B

Oscillation

Maeda et al., Science, 304(5672):875
-
878, 2004

Simple Two Nodes Pattern

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).

Chang et al., Multistable
and multistep dynamics in
neutrophil differentiation,
BMC Cell Biology 2006,
7:11

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

Review of Pathways
and
Resources


Challenges in system biology


Large data


New computation and modeling methods


Kinetics vs. dynamics


Scale
-
Free Network


Network Motifs

A Tale of Two Groups


A.
-
L. Barabasi

Ten 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 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
, S
uperfamilies of designed and
evolved networks,
Science
, 303:1538
-
42 (2004).
Pdf
.

R Milo, S Shen
-
Orr, S Itzkovitz, N Kashtan, D Chklovskii & U Alon
, N
etwork Motifs: Simple Building Blocks of Complex
Networks,
Science,

298:824
-
827 (2002).
Pdf
.

S Shen
-
Orr, R Milo, S Mangan & U Alon
, N
etwork 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
, T
he 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
, R
ules 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:



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

Organisms from all three domains of life are
scale
-
free

networks!

H. Jeong, B. Tombor, R. Albert, Z.N. Oltvai, and A.L. Barabasi,
Nature
,
407

651 (2000)

Archaea

Bacteria

Eukaryotes

Power Law

Small World

Rich Get Richer

(preferential
attachment)


Self
-
similarity



HUBS!

Preferential attachment in protein Interaction networks


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

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

Hubs and Robustness

Complex systems maintain their basic functions
even under errors and failures
(cell


mutations; Internet


router breakdowns)

node failure

f
c

0

1

Fraction of removed nodes,
f

1

S

Consequence 1 : 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)


Review of Pathways
and
Resources


Challenges in system biology


Large data


New computation and modeling methods


Kinetics vs. dynamics


Scale
-
Free Network


Network Motifs

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).

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

S
x

T
on

Sign sensitive delay for ON signal

S
x

Coherent

Feed Forward Loop (FFL)

X

Y

Z

X

Y

Z

AND

S
x

Sign sensitive delay for ON signal

S
x

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

S
x

S
y

Nature Genetics


31
, 64
-

68 (2002)

Network motifs in the transcriptional regulation network of
Escherichia coli

Shai S. Shen
-
Orr, Ron Milo, Shmoolik Mangan &

Uri Alon

Noise (low
-
pass) filter

Coherent

Feed Forward Loop (FFL)

A coherent feed
-
forward loop with a SUM input
function prolongs flagella expression in
Escherichia coli

Shiraz 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 coli

Shiraz Kalir, Shmoolik Mangan and Uri Alon, Mol. Sys. Biol., Mar.2005.

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

Systems
biology



Integration



Computation



Theory



Prediction!!!