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EE381V: Genomic Signal ProcessingEE381V: Genomic Signal Processing
Lecture #13Lecture #13
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The Course So FarThe Course So Far
Gene finding
DNA
Genome assembly
Regulatory motif discovery
Comparative genomics
Gene expression analysis Clusterdiscovery
Regulatory networks inference
Emerging network properties
Protein network analysis
SEQUENCES
INTERACTIONS
Sequence alignment
DONE
Next: Biomolecular Detection Systems
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ABI Prism ® 310 Genetic Analyzer Affymetrix GeneChip ® Roche LightCycler ®
DNA SequencingGene Expression
Profiling DNA Amplification
DONE
Fundamentals of Detection
Biological Assay
Transducer/Interface
DetectionCircuitry
Detection
BiochemicalUncertainties
Fabrication andProcess Variation
Electronic NoiseQuantization Noise
Molecular BiologyBiochemistry
Fabrication/Synthesis Processes
CircuitDesign
SignalProcessing
BiologicalSample Data
ADC
DNA Microarrays Photodiode Image Sensor Analog to Digital Conversion
Biomolecular Detection Systems
• Bio-molecular detection systems, in general, have the following sub-blocks:
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Example:
* There are different transduction methods for “counting” the binding events, e.g., fluorescence, electrochemical, chemi-luminescence …
1 2 IncubationIncubation 3 DetectionDetectionSample exposureSample exposure
Cap
ture
DN
A p
rob
es
CapturedDNA
Target DNA
Lin
ker
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Affinity-Based Biosensors
• Exploit the affinity of certain biomolecules for each other to capture and
detect:
Parallel Affinity-Based Sensing
Biological sample
Capturing sites
Planar solidsurface
Capturingprobe (A)
Capturingprobe (B)
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• For simultaneous detection of multiple targets, use affinity-based sensors in
parallel:
DNA Microarrays
DNAProbe (A)
DNAProbe (B)
TargetDNA (B)
TargetDNA (A)
Fluorescent Tag
20-100µm
50-300µm
A
B
10,0
00 s
po
ts
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• DNA microarrays are massively parallel affinity-based biosensor arrays:
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Applications of DNA Microarrays
• Recall central dogma:
• DNA microarrays interrupt the information flow and measure gene
expression levels
• frequently, the task is to measure relative changes in mRNA levels
• this gives information about the cell from which the mRNA is sampled
(e.g., cancer studies)
• Other applications:
• single nucleotide polymorphism (SNP) detection
• simultaneous detection of multiple viruses, biohazard / water testing
En
erg
y
ΔE
ACGTACGT
TGCATGCA
ACGTACGT
TGCATGCA
GACTGACT
CTGACTGA
GACTGACT
CTGACTGA
GACTGACT
CTGACTGA
GACTGACT
CTGACTGA
Hydrogenbonds
Sensing in DNA Microarrays
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• When complementary ssDNA molecules get close to each other, electrostatic
interactions (in form of hybridization bonds) may create dsDNA
• Because of thermal energy, the binding is a reversible stochastic process
• Relative stability of the dsDNA structures depend on the sequences
Stability of dsDNA: Melting Temperature
En
erg
yACGTACGT
TGCATGCA
ACGTACGT
TGCATGCA
Hydrogenbonds
dsDNA50%
ssDNA50%
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• The melting temperature (Tm) of a DNA fragment: the temperature at which
50% of the molecules form a stable double helix while the other 50% are
separated into single strand molecules
• Melting temperature is a function of DNA length, sequence content, salt
concentration, and DNA concentration
• For sequences shorter than 18 ntds, there is a simple (Wallace) heuristic:
)(4)(2 CGTATm
There are many different methods to approximate melting temperature:
Wallace method (DNA strands less than 18mers):
)(4)(2 CGTATm
%GC method:
])/625]GC[%4.0]Na[10(log6.165.81 NTm
1. Breslauer, K.J. et al. (1986) Proc. Natl. Acad. Sci. USA. 83: 3746-3750. 2. Rychlik, W. et al. (1990) Nucleic Acids Res. 18: 6409-6412.
Nearest neighbor method [1]:
]Na[10log6.1615.273)4/ln(
kcal4.3
CRSA
HTm
ΔH is the sum of nearest neighbor enthalpy changesA is the initiation constant of -10.8 cal/K° mole for non-self complementary sequences, -12.4 cal/Ko mole for complementary sequencesΔS is the sum of nearest neighbor entropy changesR is the gas constant 1.987 cal/K°moleC is the concentration of DNA (generally fixed at 250 pM) [2]
Computing Melting Temperature
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Temperature
Pro
ba
bil
ity
of
ss
DN
A
T1 T2 T3 T4 T5
DN
A (
1)
DN
A (
2)D
NA
(3)
DN
A (
4)D
NA
(5)
Melting temperature is a function of DNA length, sequence content, salt concentration, and DNA concentration
DNA Melting Curve
Computing Melting Temperature
12
En
erg
y
ΔE1
(Bond stability) 1 > (Bond stability) 2
ACGTTGCA
ACGTTGCA
ACGT
TGCA
ΔE2
AGGTTGCA
TGCA
AGGT
Not a perfectmatch
ΔE1 > ΔE2
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• Depending on sequences, non-specific binding may also happen:
Nonspecific Binding (Cross-hybridization)
• So, non-specific binding is not as stable as specific binding
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Probe (A)Probe (B) Probe (C)
(A) (B) (C)
),,( CBAfSA
Non-specific binding in microarrays
• Interference may lead to erroneous conclusions
• Useful signal is affected by the interfering molecules, false positives
possible
• Non-specific binding (cross-hybridization) manifests as interference:
The kinetics of reactions depends on:
1. The frequency that the reactive species (e.g., molecules) get close to each
other
2. If in close proximity, can thermal energy “facilitate” microscopic interactions
Ene
rgy
ΔE2
ΔE1
State 2Molecules activated
State 1 Molecules close
State 0Molecules bind
ssDNA (1) ssDNA (2)
ssDNA (1)
ssDNA (2)
dsDNA (1+2)
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Road to a Model: Underlying Physics
Arrhenius equation represents the dependence of the rate constant of areaction on temperature :
In its original form the pre-exponential factor and the activation energy are considered to be temperature-independent.
T
RTEaAek /
k
A
aE
)I(
Ene
rgy
ΔE2
ΔE1
kTEeAk /11
2
kTEEeAk /)(1 1
12
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Road to a Model: Underlying Physics
The reaction kinetics of two molecules has the following Markov model:
Ene
rgy
ΔE2
ΔE1
2 10
State 2Molecules activated
State 1 Molecules close
State 0Molecules bind
2/1
teAp kTE
/12,0
22
teAp kTEE /)(12,1
122
2/1
2,11,1 1 pp 2,00,0 1 pp
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Markov Model
2 10
State 2Molecules activated
State 1 Molecules close
State 0Molecules bind
2/1
teAp kTE
/12,0
22
teAp kTEE /)(12,1
122
2/1
2,11,1 1 pp 2,00,0 1 pp
A more practical model is the two-state Markov model
10
State 1 Molecules close
State 0Molecules bind
tkp 12,0
tkp 12,1tkp 11,1 1tkp 10,0 1
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Markov Model
A more practical model is the two-state Markov model
10
State 1 Molecules close
State 0Molecules bind
tkp 12,0
tkp 12,1tkp 11,1 1tkp 10,0 1
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Markov Model
11
1
11
1
11
11
11
1
11
1
1
1
kk
kkk
k
tktk
tktk
kk
kkk
k
kTEkk
k/
11
1
1Re1
1
kTEkk
k/
11
1
1Re1
1
)II( )III(
Steady-state distribution:
The Number of Captured Targets: A Random Process
Distribution of Captured Particles
CapturedTargets at
Time (hours)C
aptu
red
An
aly
tes
Cap
ture
d A
na
lyte
s)
(tx)]([ txE
)( 1txxσ2
1t
)]([ txE
xtxE )]([
xtxE )]([
The number of captured molecules forms a continuous-time Markov process
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Array Fabrication2
Sample Preparation1
Incubation3 Detection4
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The steps involved in an experiment:
Microarrays