Systems Biology through Data Analysis and Simulation

U.S. Department of Energy

Pacific Northwest National Laboratory

5/30/03 ₁

Biomolecular Networks Initiative

William Cannon

Computational Biosciences

Pacific Northwest National Laboratory

Systems Biology through Data Analysis

and Simulation

RAF P MEK P P P NARP PKA

Gene

Expression

Change in Protein

state of cell

Post-translational modifications

Cellular

Dynamics

Data Mining

NARX

Nitrate

P P ATP Binding Domain ADP ATP PO4 Transfer Domain NARQ

Nitrate

P P ATP Binding Domain ADP ATP PO4 Transfer Domain P P NARL

Microbial Cell

Dynamics

U.S. Department of Energy Pacific Northwest National Laboratory

5/30/03 3

Biomolecular Networks Initiative

Deciphering Cell Dynamics

■ Peptide State: analyze peptide MS/MS data de novo with capability to obtain

knowledge not captured in sequence databases.

•

Develop a reliable, statistically-based identification method.

•

Account for sequence variations relative to sequence database.

■ Protein State and State of the cell: High-throughput proteomics must paint a picture of

the state of the cell

•

MS data needs to be pieced together into a picture of the state of the protein.

•

All expressed proteins must be analyzed.

•

Microarrays have so far set a bar that must be surpassed if the expense of proteomics is to be justified.

■ Cellular networks: Determination from high-throughput technologies:

•

Account for hidden variables

•

Make use of existing pathway information

-80 -60 -40 -20 0 20 40 60 80 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 C7

NH₃-HC_α-CO- NH-HC_α-CO- NH-HC_α-CO- NH-HC_α-CO- NH-HC_α-COOH Learn the fragmentation patterns and frequency of occurrence from

MS/MS spectra of known peptides

U.S. Department of Energy Pacific Northwest National Laboratory

5/30/03 5 (a ) C a nd id a te fin g e rp r in t for P G ID F T N D P L L Q G R 2 0 0 4 0 0 6 0 0 8 0 0 1 0 0 0 1 2 0 0 1 4 0 0 0 1 2 3 4 M o le c u la r W e ig ht R elat iv e I nte ns it y

(b ) E x tra c tio n a n d c o m p a r is o n o f c a nd id a te fin g e rp r in t p e a ks fro m a s p e c tru m for P G ID F T N D P L L Q G R L o g -L ik e lih o o d R a tio = 2 1 .3 P rob a b ility S c ore = 0 .9 9 9

F ig u r e 3 . 2 0 0 4 0 0 6 0 0 8 0 0 1 0 0 0 1 2 0 0 1 4 0 0 0 0 .2 0 .4 0 .6 0 .8 1 F re que nc y o f Ap pe ar an ce C u t- o ff fre q u e nc y q0fo r s c or in g

For peptide in question, construct a MS/MS fingerprint: • peak location, lr,i

• variance in location, ,sr,i,

• frequency of appearance, pr,i

Peptide Fingerprint

MS/MS Spectrum:

• fingerprint peaks (black) • unassigned peaks (aqua)

Statistical hypothesis test for identifying a peptide: • H_A: the spectrum resulted from a known peptide.

• H₀: the association between the spectrum and the peptide is no better than random.

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U.S. Department of Energy Pacific Northwest National Laboratory

5/30/03 7 -30 -20 -10 0 10 20 30 40 50 60 70 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Log-Likelihood Ratio Relat iv e F re quenc y 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Probability Score R e la ti v e F requ enc y Mismatches Matches 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0 1 2 3 4 5 x 10-3 Probability Score R e la ti v e F requ enc y 6 Figure 6. Peptide LR P(H_a) P chg mass Protein

1 VVLEISPYDTSR 15.66 1.00 0.87 1 1378.56 DR2123 initiation factor 1 2 LSLLPEHLESDK 2.25 0.01 0.13 1 1380.55 DRB0143 McrB-related protein 3 VVHSMWTPLPGR -1.07 0.00 -0.06 1 1379.57 DR1838 GTP pyrophosphokinase 4 PELPWGYNGTPF -1.44 0.00 -0.08 1 1377.53 DR1795 hypothetical protein 5 VVPDFNCQDGGTK -2.05 0.00 -0.11 1 1379.50 DRC0030 hypothetical protein 6 PQLTVTFDDEGR -2.51 0.00 -0.14 1 1377.51 DR2194 ribosomal protein S6 7 TTEQTFNVEIAK -2.57 0.00 -0.14 1 1380.53 DR2429 hypothetical protein 8 PVDLVTLSEHLR -2.77 0.00 -0.15 1 1378.60 DR0549 DNA helicase (dnaB) 9 QFDSHIEVIHR -3.12 0.00 -0.17 1 1380.55 DRB0110 thioredoxin-related protein 10 LSLDLALGVGGIPR -3.52 0.00 -0.20 1 1380.65 DR2340 recA protein

What is a “Genetic Regulatory Network” ?

G₁ G₂

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•What its not:

a metabolic or synthetic pathway What it is:

• A network inferred from transcriptional data (microarrays); hence the name “genetic”

• The network connections are supposed to show which genes control which other genes; hence the name “regulatory”

U.S. Department of Energy Pacific Northwest National Laboratory

5/30/03 9

Transcription is regulated at multiple levels

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G₂

G₃

G4 mRNA1

Protein₁ mRNA_{2 Protein}

2

The statistical inference on microarray data will ideally account for hidden variableshidden

• Methylation of DNA • Regulation by proteins

(transcription factors) • RNA interference • RNA loop structures • etc.

Approaches for network inference

•

Cluster analysis: coarse level analysis. Hypothesize function,

regulation, etc.

•

Relevance network

:

connect all associated genes – used by some

for drug target identification.

•

Graphical model:

identify ‘causal pathways’ based on conditional

covariation patterns (e.g., Bayesian network)

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5/30/03 11

Using Graphical Models for Analyzing Biological

Data

■

Resolution:

Inferred networks will be low resolution maps of the cellular networks

à

Fine structure will not be resolved.

■

Sample size problem:

Typically trying to infer relationships between 6,000+ genes from only

10-100 microarray experiments.

■

Solution: Invert the object/variable space

I

nstead, infer relations between 10-100 experiments from

One approach: Invert the object/variable space

Knockout data Observational data Gene 1 Gene 2 Gene 3 Gene i Gene j Gene p Gene 1 Gene 2 Gene 3 Gene i Gene j Gene p E xp 1 E xp 2 E xp n Gen e M1 ∆ Gen e M2 ∆ Gen e Mn ∆ Gen e Mi ∆ Gen e Mj ∆ … … … …

Ideal intervention and Experimental Design:

Each experiment is a gene-knock-out (knock-in, RNAi, etc)

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5/30/03 13

Inferring the connections

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₁

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₂

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₃

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₄

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₅ True network: ÷ ÷ ÷ ÷ ÷ ÷ ø ö ç ç ç ç ç ç è æ = 1 95 . 1 95 . 82 . 1 94 . 91 . 90 . 1 84 . 81 . 80 . 89 . 1 R

Correlation matrix Partial Correlation matrix

G₁ G₂

G₃

G₄

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Resulting graph

Partial Correlations

■ Partial correlations are correlations between two variables when other variables are

are controlled for.

■ Partial correlations remove the effect of confounding variables

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U.S. Department of Energy Pacific Northwest National Laboratory

5/30/03 15

Partial Correlations

Ø Pairwise partial correlations when the effect of all other genes are removed:

. 1 90 . 1 90 . 1 27 . 24 . 1 53 . 1 ~ . ÷ ÷ ÷ ÷ ÷ ÷ ø ö ç ç ç ç ç ç è æ − = .81 R₁₂

Partial Correlation matrix

G₁ G₂

G₃

G₄

G₅

Partial Correlations using subsets and patterns

ØPairwise partial correlations when the effect of subsets of other genes are removed:

ØSearch for specific correlation patterns when constructing the graphs

•Directionality G₁ G₂ G₃ G₄ G₅ Inferred Pattern G₁ G₂ G₃ G₄ G₅ True Network

U.S. Department of Energy Pacific Northwest National Laboratory

5/30/03 17

Using Graphical Models

Determining Causal influence

Ø

(1) Gene C causes A and B, or (2) A causes C which causes B:

C

A

B

A

C

B

A and B conditionally independent on C

Ø Inference algorithms work from conditional dependence/independence relationships backwards to causal influence.

repressor

lactose lac operon repressor

Results for 273 yeast knock-out mutants

U.S. Department of Energy Pacific Northwest National Laboratory

5/30/03 19 Cellular Networks:

Comparison of inferred Network with Synthetic

Pathway

■

Genetic Networks are not metabolic/synthetic pathways

■

Genes in a pathway can be ordered by their time of

appearance.

■

Causal networks provide information on order

■

Partial order between inferred networks and metabolic

pathways may be possible

Erg10 > Erg13 > HMG1, HMG2 > … … > Erg3 > Erg5 > Erg4.

U.S. Department of Energy Pacific Northwest National Laboratory

5/30/03 21

Partial Order Comparison between synthetic

pathway and inferred network

Agreement:

■

IDI1 > ERG4

■

HMG2 > ERG11 > yer044c (ERG28) > ERG2, ERG3

■

MGG2 > ERG11 > ERG2

■

ERG6 > ERG2

■

ERG11 > ERG2

Disagreement:

■

Erg2 > Erg3

Regulation of Erg 11 by Swi4

■

There has been speculation that Swi4 regulates erg11:

•

Swi4 binding motif:

CACGAAA

•

URS1ERG11 motif:

CACGAAAAACGAGACAAACGAA

•

Swi4 weakly binds to the ERG11 URS as determined by chromatin

immunoprecipitation and microarray analysis (Iyer, et al, Nature 409,

p. 533)

■

This appears to be the first evidence of functional correlation

between erg11 and swi4.

U.S. Department of Energy Pacific Northwest National Laboratory

5/30/03 23

Biomolecular Networks

Deciphering Cellular Networks:

Future

■

Use known relationships as constraints:

•

Genes in the same operon

•

Regulators and their regulated genes

•

Genes in known metabolic pathways

■

Use protein expression data including post-translational modifications

Acknowledgments

Current Funding DOE OBER ■Microbial Genome ■Microbial Cell ■Genomes-to-Life Macromolecular Structure and Dynamics Gordon Anderson Ken Auberry Dick Smith Statistics and Appl. Math

Kris Jarman Alan Willse Sharon Wunschel Alejandro Heredia-Langner Biogeosciences Jim Fredrickson Shewanella Federation