engineering systems biology lots of questions...03/31/2008 ece690 bio-signal processing (sokhansanj)...

Engineering Systems BiologyEngineering Systems BiologyLots of Questions...Lots of Questions...

Bahrad A. Sokhansanj,Bahrad A. Sokhansanj, PhDPhDMolecular Health Engineering LaboratoryMolecular Health Engineering Laboratory

School of Biomedical Engineering, Science &School of Biomedical Engineering, Science &Health Systems, Drexel UniversityHealth Systems, Drexel University

ECE690 Biological Signal Processing IIECE690 Biological Signal Processing IIMarch 31, 2008March 31, 2008

03/31/2008 ECE690 Bio-Signal Processing (Sokhansanj) 2

Engineering Quantitative Biology

( )

12233

23112

1211

PPdt

dP

PPdt

dP

Pdt

dP

µµ

µµ

µµ

+=

!=

+!=

Building Models Quantitative Measurement

Molecular Health Engineering Laboratory

Problem: Genetic / Disease-Related Variation in Cellular Regulation

Solution: Integrate quantitative biomeasurement and modeling


Understanding Biological Regulation:The Central Dogma

Genes(DNA)

OtherCells

Function/Environment

Message(RNA)

Proteins

Regulation

A coordinated network of complex biochemicalprocesses involving RNA, DNA, proteins, andother chemicals.


Central Dogma


Stimulus, e.g. Reactive Oxygen Species (ROS; oxidative stress)

Cell Death? Survival?

Biomolecular Networks are Complicated


Growth Signaling Network


The λ Phage Biological “Circuit”: A Role forElectrical Engineering?


The Landscape of “Post-Genome” Biology

Epidemiological Data Comparative Genomics/Proteomics

Microarrays & DNA chips

Proteomics (MS & 2DGE)

Quantitative blood & tissue analysis

1000 2000 3000 4000

0

10

20

30

40

1000 2000 3000 4000

1192.36

1878.07

2572.61

2734.72

0

10

20

30

40

1000 2000 3000 4000

1246.64

1482.071689.18

2150.5

2376.01

Multiscale Dynamic Imaging


Microarray Experiments

Microarrrays measure the relative change in mRNAconcentration (gene expression) under a change inconditions.

Amino Propyl Silane

Glass Microscope Slide

cDNA targets

cDNA probes

Two Samples

“Targets”

mRNA mRNA

cDNA cDNA

red green

dye dye

Mix & Hybridize

massagemassage


Microarrays Produce Ugly Data

• Microarray fabrication isinconsistent: experiments havepoor repeatibility– size, shape, and alignment of

spots varies from array to array– defects in the slide and different

washing protocols result in intra- &inter-slide variations inbackground

• Results are highly sensitive toimage analysis– spot recognition, intensity

quantification and normalization,background subtraction

• DNA chips are more consistent,but still not perfect - and they costmuch more


Operon Signals in Expression Data

-3

-2

-1

0

1

2

3

0 20 40 60 80 100 120 140 160 180 200

ORF

2/1

4/1

4/2

Log Expression


Example of proteomic data from 2D gels


Metabolic gene pathways of Y. pestis

Low Ca

Metabolomic datatypes - Popular Platforms

1H Nuclear MagneticResonance Spectroscopy

(NMR)

Liquid Chromatography –Mass Spectrometry

(LC-MS)

Metabolomic datatypes - Problem

• Metabolomic datasets (e.g.NMR, MS-based) are large,open systems

• Physical interpretation of thedata is challenging

Transcriptomics – relatively straightforward interpretation

timem/z

DB/+DB/DB

relative abundance


Molecular Signatures – Genome, Proteome,Metabolome, Glycome, etc.

• New technologies allow large scale, parallel measurementof cell state:– transcription (mRNA expression; gene chips, RT-PCR)– translation (protein expression; gels, MS, protein chips)– protein modification (gels, MS)– protein-protein interaction (2-hybrid, protein chips, MS)– metabolites (MS, NMR)– carbohydrates and glycosylation (MS, ?)– (also large scale phenotypic changes)

• At first order, we can either/both– identify groups of cells/tissues/populations that share

common patterns– detect patterns of gene/protein/metabolites/etc. that correlate

with previously identified phenotypic groups


Many Ways to Identify Signatures

• Identifying major “components” of variation (potentiallysomething that has to be removed from data, such as afundamental difference between sampled groups)– singular value decomposition (SVD), principle component

analysis (PCA), etc.


Singular Value Decomposition

http://public.lanl.gov/mewall/kluwer2002.html


Complex Regulation Drives Yeast Cell CycleYeast cyclins are proteins responsible forregulating cell cycle transitions. Cyclin gene(mRNA) expression data is taken fromSpellman, et al., Molec. Biol. Cell, 9:3273, 1998.

http://cyberia.cfdrc.com/datab/Applications/cell_tissue_bio/cellcycle/cellcycle.html


Singular Value Decomposition(Yeast Cell Cycle Microarray Data)

(based on 38 genes, elu expression dataset)




analysis (PCA), etc.• Finding groups within data

– clustering– self-organizing maps– support vector machines


Clustering (k-means)

http://rana.lbl.gov/FuzzyK/images/figure3.html




analysis (PCA), etc.• Finding groups within data

– clustering– self-organizing maps– support vector machines

• Separating known groups– univariate methods (i.e. B-Tests, T-Tests on each gene,

ANOVA on each gene)– horrible “capitalization on chance” problems– linear discriminant analysis / canonical variate analysis

• these methods can be generalized for undetermined data, thoughthe relative magnitudes of variables becomes significant in thatcase (but that filters out potentially noisy data) OR you getcapitalization by chance by using stepwise methods


Linear Separation – Group Classification

NonlinearClassification?(Kernel methods)

Canonical Variate Analysis(Linear Discriminant Analysis)

Find optimal linearcombinations ofvariables that maximizeinter-group differenceswhile minimizingintra-group differences.


What Do We Get From Signatures

• Pattern for discrimination between groups (responders,non-responders, different genetic populations, etc.)– therapeutic design– diagnostics

• Lists of Genes– what genes appear to be the most significant in determining

the difference between groups or cause the formation ofdistinct patterns within data?

• you get long lists when you “capitalize on chance” usingunivariate methods

• shorter lists from multivariate methods or when you use “honest”statistics modified for variable selection


Model-Driven Experimental Design

Genome Sequence (DNA)Homology Analysis

Competing HypotheticalGene Networks

Gene Knockout /Overexpression

Gene Expression Microarrayand/or

Protein Expression (MS / 2DGE) Design Optimal Experiment(s)

COMPUTATIONCOMPUTATION

EXPERIMENTEXPERIMENT


SimulationTime scale

BiologicalDetail

Many Cells

SingleGene

SingleMolecules

Atoms

fsec µsec seconds min hrs

Single Cell

10-100 GeneNetwork Stochastic Simulation

Metabolic networks

System of ODEsEvolution of population

statistics

days

Molecular DynamicsBinding constants

Structural effects of proteinmutations

Finite StateSystem Simulation

Homology-basedstructure prediction

Quantum ChemistryReactive mechanismsActive site chemistry

Modeling at Different Scales


Oxidative Stress & Human Health

Lung

Joints

Heart

SkinKidney

Eye

GI

Vessels

Multi-organ Brain

TraumaStrokeAlzheimer’s Disease

COPDAsthmaARDSHyperoxia

RheumatoidArthritis

AngioplastyKeshandisease(seleniumdeficiency)

BurnDermatitisPsoriasis

Renal graftGlomerulonephritisDegenerative retinal damage

Cataractogenesis

Ischemic BowelEndotoxinLiverInjury

VasospasmArtherosclerosis

RadiationAgingCancerInflammatory-Immune injuryIschemia-ReflowDiabetes

OXIDATIVE STRESS

03/31/2008 ECE690 Bio-Signal Processing (Sokhansanj) 29http://greengenes.llnl.gov/repair/html/overview.html

DNA Damage:Oxidative StressAdduct FormationChromosome Break

Oxidative Stress:Low Dose RadiationEndogenous MetabolismEnvironmental ToxinsHypoxiaIschemiaTraumaNeurodegeneration


DNA Repair Mechanisms (in E. coli)

http://www.web-books.com/MoBio/Free/Ch7G.htm

Base Excision Repair(Single Strand Break Repair) Nucleotide Excision Repair Mismatch Repair


Disease

Cancer

Aging

Environmentionizing radiation,sunlight, pollution

Lifestyleobesity, smoking,alcohol

Biologyinflammation, trauma,ischemia, aging,metabolism

Input

DNA Base Modifications

8-oxoguanineuracilthymine glycol5-hydroxyuracil3-methyladenineabasic sites... etc.

Mathematical Equations

LESION

3’flap-gap

5’nicked

Nicked

ABASIC

5’flap-gap

Gapped

Nicked

REPAIRED

LPR, flap

NickedGapped

Base Excision Repair Pathways

TranscriptionalDefects

Mutations

Cell Death

Output

Persistent DNA Damage

DNA Base Excision RepairMolecular Health Engineering


U.S. Department of Energy Human Genome Program, http://www.ornl.gov/hgmis.


Table 2 Amino acid substitution variants identified in DNA repair and repair-related genes

Gene

name Exon Codon

Common

residue

Variant

residue

Allele

frequenc

y

Mouse

residue cDNA sequence 5'3'

APE1 3 51 Gln His 0.03 Gln GAT CA(G/C) AAAAC

APE1 3 64 Ile Val 0.01 Ile TCAAG (A/G)TC TGC

APE1 5 148 Asp Glu 0.33 Glu GGC GA(T/G)GAGGA

APE1 5 241 Gly Arg 0.01 Gly GCTTC (G/A)GGGAA

FEN1 No

variantsLIG1 3 24 Ala Val 0.01 Thr GGAG G(C/T)A TCCA

LIG1 4 62 Arg Trp 0.01 Gln CGGCC (C/T)GG GTC

LIG1 9 249 Gly Glu 0.01 Gly GCCA G(G/A)GGCTC

LIG1 10 267 Asn Ser 0.02 Asn TTAC A(A/G)TCCTG

LIG1 13 369 Val Ile 0.01 Ile AGTCC (G/A)TC CGG

LIG1 13 409 Arg His 0.01 Cys GTTC C(G/A)C GACA

LIG1 16 480 Met Val 0.01 Val CAGCC (A/G)TG GTG

LIG1 20 614 Thr Ile 0.01 Thr GGTC A(C/T)A TCCT

LIG1 22 673 Glu Asp 0.01 Gln CGT GA(G/T)CCCCT

LIG1 22 677 Arg Leu 0.01 Arg TTCC C(G/T)G CGCC

LIG3 18 780 Arg His 0.03 Cys GTCC C(G/A)C AAGG

LIG3 19 811 Lys Thr 0.01 Lys TGCA A(A/C)GCCTT

LIG3 21 899 Pro Ser 0.01 Thr AGAAC (C/T)CT GCG

POLB 1 8 Gln Arg 0.01 Gln GCCG C(A/G)G GAGA

POLB 7 137 Arg Gln 0.006 Arg TCAG C(G/A)AATTG

POLB 12 242 Pro Arg 0.005 Pro GCTT C(C/G)C AGTA

POLD1 1 19 Arg His 0.12 Arg GGCC C(G/A)T GGGG

POLD1 1 30 Arg Trp 0.006 Ser CACCT (C/T)GG CCA

POLD1 3 119 Arg His 0.15 Arg ATCC C(G/A)C GGCT

POLD1 4 173 Ser Asn 0.05 Ser CATC A(G/A)CCGGG

POLD1 4 177 Arg His 0.003 Arg CAGT C(G/A)CGGGG

POLD1 19 849 Arg His 0.011 Arg ACTG C(G/A)CCGCC

POLD1 26 1086 Arg Gln 0.01 Arg GGTG C(G/A)GAAGG

Mohrenweiser HW, Xi T, Vazquez-Matias J, Jones IM. Identification of 127 amino acid substitution variants in screening 37 dna repair genes in humans. Cancer Epidemiol. Biomarkers & Prev., 11: 1054-1064, 2002.


R237A

L104R

E126D

Molecular Modeling of Amino Acid Variants (Ape1)

03/31/2008 ECE690 Bio-Signal Processing (Sokhansanj) 37Hadi, M. Z., Coleman, M. A., Fidelis, K., Mohrenweiser, H. W. and Wilson, D. M. III, Nucleic Acids Res., 28, 3871-3879, 2000.


• Differential equations for eachenzymatic activity: kcat, KM andprotein concentrations taken fromexperimental data• Based on physical measurementsof cell: assume well-mixed proteins,but kcat/KM for slowed diffusion in thenucleus• Model is consistent withexperimental mechanistic data (i.e.predominance of short patch BERand coordination between proteins)

Sokhansanj et al. NAR 2002

Predictive BER System Model


Percentage increase in Steady State Damage (for continuous formation of damage) and Repair Time (foran initial amount of damage to be cleared) given sub-functional, potentially non-lethal variants

% Increase in …

Protein Variant

% of Wild Type

Enzyme Activity

Steady State

Damage

Time to

Repair

Ogg1 (excision) S236C 63% 3% 9%

Ogg1 (excision) Hypothetical 10% 29% 341%

Ape1 (5’ -incision) D148E 95% 0% 0%

Ape1 (5' -incision) R237A 35% 2% 1%

Pol! (gap-filling) Hypothetical 50% 4% 2%

Pol! (gap-filling) Hypothetical 10% 13% 7%

Pol! (5'-dRp lyase) Hypothetical 50% 4% 1%

Pol! (5'-dRp lyase) Hypothetical 10% 32% 56%

Lig1 Hypothetical 50% 21% 34%

Sokhansanj and Wilson CEBP 2006

Pathway Impact of Variants


Acknowledgments

• Students– Andrew Atkins– He Zhao– Chris Abdullah– Krista Szymborski– Suman Datta, MS (Merrimack)– Geoff Gipson, PhD (with Drs. Sue

Connor and Kay Tatsuoka of GSK)

• Website:http://www.pages.drexel.edu/~bas44

• Email:[email protected]

• Drexel Biosciences– E. Gardner– A. Saunders– M. Howett– M. Lechner

• Collaborations– D. M. Wilson, III (NIH)– X. Hu (Drexel IST)– G. Rose (Drexel ECE)– K. Pourrezaie (Drexel Biomed)– H. Nilsen (Oslo)

engineering systems biology lots of questions...03/31/2008 ece690 bio-signal processing (sokhansanj)...

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