Systems Biology II

Roadmap

• Review from a long time ago when we last visited this topic.

• Review of some work we have done using a systems biology approach.

• Look at some research that benefited by adopting systems biology approaches.

“Inner life of a Cell”SIGGRAPH 2006 showcase

winner• Need to fight infection

– WBC

• Need to keep blood from leaking out

Two ways of looking a problem

• Top down or bottom up– Either look at the whole organism and

abstract large portions of it – Or try to understand each small piece and

then after understanding every small piece assemble into the whole

– Both are used, valid and complement each other

Theoretical types of control

Expression measurements

Visualizing the data

Blue line (pp)Yellow line (pd)

Graph theory, networks

• Two types of networks– Exponential and scale

free– Most cellular networks

are scale free– It makes the most

sense to study the interactions of the central nodes not the outer nodes

Using network properties of a large complex data-set to evaluate the

correlation of gene expression from a large microarray experiment

Design of initial experiment

Gene expression

SHR-SP SR/JR/HSD

120 ♂ F2 rats

Genotyping

…

mRNA of whole eyes

♂ ♀

F1 rats

Summary of eQTL linkages

Marker Location

Tra

nscr

ipt

Loca

tion

Cis Trans

NPCE: Non-Positional Correlation of Expression

Capture bio-relatednessPair-wise correlation

Macromolecular structuresMetabolic pathwaysDisease Genes

Devoid of marker informationMore information

not dependant on marker densitymore noise

Strongly correlated genes

Expression BBS3 (log2)

Exp

ress

ion

BB

S7

(log

2)

r2 = 0.78

Weak correlation

Expression BBS4 (log2)

Exp

ress

ion

AB

CA

4 (

log 2

) r2 = 0.16

Distribution of r2

0

1000000

2000000

3000000

4000000

5000000

6000000

7000000

8000000

-0.8

9-0

.83-0

.77-0

.71-0

.65-0

.59-0

.53-0

.47-0

.41-0

.35-0

.29-0

.23-0

.17-0

.11-0

.05

0.01

0.07

0.13

0.19

0.25

0.31

0.37

0.43

0.49

0.55

0.61

0.67

0.73

0.79

0.85

0.91

0.97

p=0.001

r2=0.78

p=0.01

r2=0.66

Combining pathway information with correlation

Median Correlations of Pathways

0

1

2

3

4

5

6

7

8

0.5

0.47

0.44

0.41

0.38

0.35

0.32

0.29

0.26

0.23 0.

20.

170.

140.

110.

080.

050.

02

Median Correlation

Nu

mb

er

All Pathways

Random

Shorter Pathways

Pairwise correlations are not enough?

• Looking at known pathways a simple cutoff value is not identifiable

• Partial correlation or multiple correlations – More feasible but, still difficult– May only work in a subset of pathways

• Most useful if you want to confirm membership to a known group?– Difference between random and known pathways is small

• Another way?

Networks

“Realworld” Networks

• Tend to be highly clustered

• Tend to have short path lengths

• Many nodes with few interactions– Few nodes with many interactions

Useful tools

• Cytoscape– Best for visualization– Limited (for us anyway) number of nodes– http://www.cytoscape.org/

• Networkx– Python module– Visualization and network discription

-https://networkx.lanl.gov/

Using network properties

• Can we use networks to identify “critical” genes?• Is it possible to determine a usable “cutoff” for

correlations used to make the network– What correlation value will give a usable, relevant network?– Is this value similar to the p value determined from the

distribution of correlations?

• Is it possible to use network properties to identify a grouping of interacting genes (ex. pathway, subunits or other interactions)

Highly Connected genesGene @ correlation # connect function

Glul .9,.8,.7 1498 Glutamine synthetase

Gnai3 .85,.8 832 Guanine nucleotide-binding protein

Smad4 .8 726 Common mediator of signal transduction

Syngap .8 672 Ras-GTPase activator

Orc4 .8,.75,.7 2521 directs DNA replication

Psma4 .7,.65,.6 7054 multicatalytic proteinase

Rabep1 .7,.65,.6 7037 Rab GTPase binding effector protein

Pcna .7,.65,.6 6799 proliferating cell nuclear antigen

Common ontologies

• Molecular function– Most common - none– glutamate-ammonia

ligase activity– GTPase activator

activity– carrier activity– structural molecule

activity– DNA binding

• Biological process– nitrogen fixation– Transport– vesicle fusion– cell motility– small GTPase

mediated signal transduction

What correlation level to useHighly Connected Nodes

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

0.9 0.85 0.8 0.75 0.7 0.65

Correlation level

Nu

mb

er

of

Nod

es

0

0.5

1

1.5

2

2.5

3

Perc

en

t of

tota

l ed

ges

Most connected

Percent of total

Other parameters

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.95 0.9 0.85 0.8 0.75 0.7 0.65

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

0.018

Clustering

Density

Validating a graph biological relevance

• Need to use information to pick a correlation level(s) used to construct a graph.

• After the graph is constructed– How well does it predict known bio-

interactions

Validating against pathways

• Kegg has a nice collection of pathway annotations (http://www.genome.jp/kegg/)– Also have a webservice interface– Allows programatic access to pathway annotations

(http://www.genome.jp/kegg/soap/)• By species• By pathway• By pathway type • Some problems kegg id vs affy probe id

– May be a many to many relationship

Rattus norvegicus (rat) metabolic pathways

• Kegg has 110 metabolic pathways

• Range in size from 3 members to 100’s of members

• Examples:– Novobiocin biosynthesis– ATP synthesis– Fructose and mannose metabolism

Path lengthPath length ratio

0

1

2

3

4

5

6

7

8

0.9

8

0.9

4

0.9

0.8

6

0.8

2

0.7

8

0.7

4

0.7

0.6

6

0.6

2

0.5

8

0.5

4

0.5

0.4

6

0.4

2

0.3

8

0.3

4

0.3

0.2

6

0.2

2

0.1

8

0.1

4

0.1

0.0

6

0.0

2

Rand/Pathway

Path coverage

Coverage of a .70 correlation network

0

1

2

3

4

5

6

7

8

2 8 14 20 26 32 38 44 50 56 62 68 74 80 86 92 98

Percent coverage

Nu

mb

er

of

path

ways

Kegg Pathways

Random

Different values

• Using a correlation of .9– No coverage for either pathway or random

set– Not enough connections, they may be

significant, but only a small fraction are present

• Lower correlations– Less clear– Much larger networks

Why networks != correlations

Bbs2

Bbs4

Bbs6

Bbs8

Bbs1

Bbs3

Bbs7

Bbs5

Bbs11

Bbs9

Abca4

0.45-0.540.55-0.640.65+

p < 0.001

p < 0.0002

Conclusions

• Network properties show promise as a way to look at this data

• Pair-wise correlations and networks are unable to predict pathways or other interactions with certainty– But they can help

• Using network tools and frameworks is a way to manage and simplify analysis

AcknowledgmentsMicroarray collaborators

Ed Stone

Val Sheffield

Jian Huang

Kwang-youn Kim

Ruth Swiderski

Kevin Knudtson

Rod Philp

CBCB

Todd ScheetzTom CasavantTerry BraunNathan Schulz

Example Studies

• Physicochemical modeling of cell signaling pathways. B.B. Aldridge et al. Nature Cell Biology. 8(11) Nov 2006. 1195-1203.

• Reverse engineering of regulatory networks in human B cells. K. Basso. Nature Genetics. 37(4) Apr 2005. 382-397.

• Dynamic proteomics in individual human cells uncovers widespread cell-cycle dependence of nuclear proteins. A. Sigal. Nature Methods. 3(7) Jul 2006. 525-532.

• Structural systems biology: modeling protein interactions. P. Aloy. Nature Reviews. Mar 2006. 188-198 .

Reverse engineering of regulatory networks in human B

cells• Have lots of microarrays, how can you

reconstruct the network of regulation.– Lower organisms, works– Higher, too much noise

• ARACNe algorithm for the reconstruction of accurate cellular networks– Find correlated genes– Remove indirect correlations

Mutual Information

• How much does value t1 tell you about value t2

• If MI = 0 there is no information if MI = 1 you have perfect information.

• Similar to correlation coefficient but able to capture more complex interactions.

Find direct interactions

• Use “data transmission theory” – Data processing inequality (DPI)– If (x,y) and (y,z) directly interact and (x,z)

indirectly interact• Mutual information of x,z will be less than x,y or

y,z• High MI values confound analysis• Three member loops are common, and difficult

to parse.

Assessing validity and coverage

Validation and conclusions

• Validated 34 candidates by chip-chip

• Make conclusions about hierarchical nature of the myc network

• Know important members of the network for further study.

Dynamic proteomics in individual human cells uncovers widespread cell-cycle

dependence of nuclear proteins

• Measure temporal and spatial relations in dividing cells of 20 fluorescently labeled proteins.

Keys

• New technique to introduce a fluorescent label that does not perturb the protein function (as much)

• In-silico synchronization

Results of the paper:

• Large number of proteins that probably are involved in cell cycle control

• A general, scalable technique for studying location and interaction of proteins.