design of bio-chips from theory and multiscale simulation

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Design Design of Bio-Chips from of Bio-Chips from Theory Theory and Multiscale and Multiscale Simulation Simulation

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Design of Bio-Chips from Theory and Multiscale Simulation. Prominent Technologies. DNA and Peptide/protein. DNA & Protein Microarrays are useful for a variety of tasks. Genetic analysis Disease detection Synthetic Biology Computing. What tools are required?. $1000 Genome at birth - PowerPoint PPT Presentation

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Page 1: Design  of Bio-Chips from Theory and  Multiscale Simulation

Design Design of Bio-Chips from of Bio-Chips from

TheoryTheoryand Multiscaleand Multiscale

SimulationSimulation

Page 2: Design  of Bio-Chips from Theory and  Multiscale Simulation
Page 3: Design  of Bio-Chips from Theory and  Multiscale Simulation

Prominent TechnologiesProminent Technologies

DNA and Peptide/protein

Page 4: Design  of Bio-Chips from Theory and  Multiscale Simulation

DNA & Protein MicroarraysDNA & Protein Microarrays

are useful for a variety of tasks

• Genetic analysis• Disease detection• Synthetic Biology• Computing

Page 5: Design  of Bio-Chips from Theory and  Multiscale Simulation

What tools are required?What tools are required?

• $1000 Genome at birth

• $100 Diagnostic exam

• Proteome at birth?

• Universal standards

Page 6: Design  of Bio-Chips from Theory and  Multiscale Simulation

Problems in Surface Mounted Problems in Surface Mounted technologies: Microarrays, Next-gen technologies: Microarrays, Next-gen

……Cross platform comparisons

• Controls• Validation• Data bases for comparisons

Nearly impossible due differences in the physics and chemistry at the surface

Page 7: Design  of Bio-Chips from Theory and  Multiscale Simulation

A Central Theoretical A Central Theoretical Issue:Issue:

Binding (recognition) is different in the presence of a surface than in homogeneous solution.

The surface determines:

Polarization fields

Ionic screening layers

Ultimately: Device response

Page 8: Design  of Bio-Chips from Theory and  Multiscale Simulation

Salt Water

Prepared Hard Surface

--

-- -

--

- - --- - --

-- -

--

--

--- - -

-- -

GCTA

AT

CGGC

AT

Probes

Targets- ---

-------

T

TT A A

A

CC C

G G G

G

5nm

---

- - --

- --

-

-

--

-

Page 9: Design  of Bio-Chips from Theory and  Multiscale Simulation

Materials, Preparation Materials, Preparation and Size Matterand Size Matter

• DNA prep

10 μm to bulk solid

10nm

100nm

Page 10: Design  of Bio-Chips from Theory and  Multiscale Simulation

What length scales are What length scales are required?required?

• Solid Substrate Roughness

• Linkers

• Surface Probes

• Targets

• Solution Correlations

Page 11: Design  of Bio-Chips from Theory and  Multiscale Simulation

ChemistryChemistry

Na Cl .1 to .8 M

ProbeTarget

Page 12: Design  of Bio-Chips from Theory and  Multiscale Simulation

Simulations and Theories of Bio ChipsSet up must include

• Substrate (Au, Si, SiO2 …)• Electrostatic fields• Surface modifications• Spacers (organic)• Probe and Target Bio (DNA or peptide) strands• Salt and lots of Water• Force field, …

Wong & Pettitt CPL 326, 193 (2000)Wong & Pettitt TCA 106, 233 (2001)Wong & Pettitt Biopoly 73, 570 (2004)Pettitt, Vainrub, Wong NATO ASI (2005)Qamhieh, Wong Lynch Pettitt IJNAM (2009)

Page 13: Design  of Bio-Chips from Theory and  Multiscale Simulation

Simulate a simple classical force fieldSimulate a simple classical force field

Model the interactions between atoms• Bonds - 2 body term

– harmonic, Hooke's law spring

• Angles - 3 body term

• Dihedrals - 4 body term

bonds

eb rrK 2)(

angles

eK 2)(

torsions

nK )cos(1 Source: http://www.ch.embnet.org/MD_tutorial/

Page 14: Design  of Bio-Chips from Theory and  Multiscale Simulation

Nonbonded Terms• 2 body terms

• van der Waals (short range) & Coulomb (long range)

• Coulomb interaction consumes > 90% computing timeEwald Sum electrostatics to mimic condensed phase

screening• Periodic Boundary Conditions

r

qq

rrji

atomsofpairsNonbonded

ijijij

612

4

Electrostatic Forces Dominate Behavior

Page 15: Design  of Bio-Chips from Theory and  Multiscale Simulation

F = ma or

With a classical Molecular Mechanics potential, V(r)

These potentials have only numerical solutions.

t must be small, 10-15s

rmrV )(

)(0)()()()( 32!2

1 tttrttrtrttr

Page 16: Design  of Bio-Chips from Theory and  Multiscale Simulation
Page 17: Design  of Bio-Chips from Theory and  Multiscale Simulation
Page 18: Design  of Bio-Chips from Theory and  Multiscale Simulation

Correlations• Neither Vertical nor Flat on surface

(tilt)

• Nonuniform Structures

• Linker/spacer effects

Wong & Pettitt CPL 326, 193 (2000)Wong & Pettitt TCA 106, 233 (2001)

Page 19: Design  of Bio-Chips from Theory and  Multiscale Simulation

ImplicationsImplications• Colloidal behavior affects

– synthesis / fabrication – and binding

• Tilt restricts possible geometries of pairing

• Affinity and specificity

G & G

Page 20: Design  of Bio-Chips from Theory and  Multiscale Simulation

Forces: Surface and SolutionForces: Surface and Solution

±±±±

±

Water

+[salt]

Substrate

–– – ––– –

––

––––

– ––

–––––

– –

––

––

±±±±±±±

±±

±

Page 21: Design  of Bio-Chips from Theory and  Multiscale Simulation

TheoryTheoryCalculate the free energy difference of

hybridization between that in solution and on a surface

Gsol-hybrid -Gsur-hybrid= Gss-att-Gdup-att

Target + Probe Target : Probe

Target + Probe:Surface Target :Probe:Surface

Gsol-hybrid

Gsur-hybrid

Gdup-attachGss-attach

Page 22: Design  of Bio-Chips from Theory and  Multiscale Simulation

TheoryTheoryTo get the work to form a single duplex on the

surface we need:

•ΔGsol which we can take from thermodynamic tables: solution reference state (John Santa-Lucia).

•ΔGatt from a consideration of the surface effects (chemistry and physics)

Page 23: Design  of Bio-Chips from Theory and  Multiscale Simulation

Coarse graining DNA Coarse graining DNA electrostatic fieldelectrostatic field

(Poisson-Boltzmann(Poisson-Boltzmann)

Vainrub, Pettitt, Chem. Phys. Lett. 323 160 (2000)

Ambia-Garrido, Pettitt, Comp Phys Com 3, 1117 (2008)

J. Ambia-Garrido

Page 24: Design  of Bio-Chips from Theory and  Multiscale Simulation

The ions and water penetrate the grooves.The ions and water penetrate the grooves.

Na+

Cl-

H2O

Feig & Pettitt BJ 77, 1769 (1999)

Page 25: Design  of Bio-Chips from Theory and  Multiscale Simulation
Page 26: Design  of Bio-Chips from Theory and  Multiscale Simulation

A Simple Model

• Ion permeable, 20 Å sphere over a plane/surface– 8 bp in aqueous saline solution over a surface

• Linear Poisson-Boltzmann has an

analytic solution

h

DNA - Vainrub and Pettitt, CPL ’00Ellipse - Garrido and Pettitt, CPC, 08

Page 27: Design  of Bio-Chips from Theory and  Multiscale Simulation

The shift of the dissociation free energy or temperature for an immobilized 8 base pair

oligonucleotide duplex at 0.01M NaCl as a function of the distance from a charged dielectric surface

q=0 or 0.36e-/nm2

S

HTm

linker

Vainrub and Pettitt, CPL ’00Vainrub and Pettitt, Biopoly ’03

Page 28: Design  of Bio-Chips from Theory and  Multiscale Simulation

Surface at a constant potential for a metal coated substrate @ .01 M NaCl

Vainrub and Pettitt, CPL ’00Vainrub and Pettitt, Biopoly ’03

Page 29: Design  of Bio-Chips from Theory and  Multiscale Simulation

Response to E-fieldsResponse to E-fieldsSalt and Substrate Material Effects on 8-bp DNA

Vainrub & Pettitt, Biopoly 2004

Page 30: Design  of Bio-Chips from Theory and  Multiscale Simulation

Finite Concentration and CoverageFinite Concentration and Coverage 

outside the sphere and plane,

inside the sphere,

|r =a+ = |r =a- , r |r =a+ = r |r =a- on the sphere,

|z =0+ = |r =0- , z |z =0+ - r |z =0- = - on the plane.

 

h

Different from O & K

Vainrub and Pettitt, Biopolymers 2003Vainrub & Pettitt et al , NATO Sci , 2005

Page 31: Design  of Bio-Chips from Theory and  Multiscale Simulation

Coulomb Blockage Dominates Coulomb Blockage Dominates Optimum DNA spacingOptimum DNA spacing

High negative charge density repels target

surface binding

Langmuir

On Chip

Target Conc

Bin

ding

Probe surface density

RT

wn

RT

STH

θ

θ TPPP000

ZZZC

(exp

1exp

hybridization efficiency θ (0 θ 1) target concentration C0 Vainrub &Pettitt, PRE (2002) Vainrub &Pettitt, JACS (2003)

Page 32: Design  of Bio-Chips from Theory and  Multiscale Simulation

Experimental Isotherm for perfectly Experimental Isotherm for perfectly matched lengthsmatched lengths

Explains some experiments:• Low on-array hybridization efficiency on glass (Guo et al 1994, Shchepinov et al 1995)• Broadening and down-temperature shift of melting curve (Forman et al 1998, Lu et al

2002)• Surface probe density effects (Peterson et al 2001, Steel et al 1998, Watterson 2000)

0 5x1012 1x1013 2x1013-15

-14

-13

-12

-11

ln[(

1-

)C/]

(1+)Np

Page 33: Design  of Bio-Chips from Theory and  Multiscale Simulation

0.0

0.2

0.4

0.6

0.8

1.0

200 250 300 350

Parameter:[(V

d-V

p)/RT

0]*qp

*Np

12 10 8 6 4 2 1 0

Temperature (K)

Hyb

rid

iza

tion

eff

icie

ncy

Melting curve temperature and widthMelting curve temperature and width Analytic wrt surface probe density (coverage)

*1012 nP probes/cm2

TmTm - Tm

W + W W In solutionTm = S – R ln C)

W = 4RTm2/

On-array: Isotherms

m

p2

0

p2

m

T3

2 W

n3wZ H2

n3wZ T

dA20 /dT20

duplex

Critical for SNP detection design Pettitt et al NATO Sci 206, 381 (2005)

Page 34: Design  of Bio-Chips from Theory and  Multiscale Simulation

Strength and linearity of hybridization signalStrength and linearity of hybridization signal

109

1010

1011

1012

10-2 10-1 100 101 102 103 104

0.186421

Target concentration (*exp[G0/RT

0] Moles)

Hyb

rid

s d

en

sity

(1

/cm

2 )

0 5x1012 1x1013

0

2x1010

Probe density (1/cm2)

x1012

Vainrub and Pettitt JACS (2003); ibid Biopolymers, (2004)

Page 35: Design  of Bio-Chips from Theory and  Multiscale Simulation

Peak of sensitivityPeak of sensitivity

0.0 2.0x1012 4.0x10120.0

0.2

0.4

0.6

0.8

1.0

T = 360 K

T = 340 K

T = 334 K

T = 332 K

T = 330 K

Nor

mal

ized

hyb

ridiz

atio

n si

gnal

Probe surface density (cm-2)

2P

pwZ

RTn

Vainrub and Pettitt, JACS (2003)

Page 36: Design  of Bio-Chips from Theory and  Multiscale Simulation

Linear range of hybridizationLinear range of hybridization

10-4

10-3

10-2

10-1

100

10-13 10-11 1x10-9 1x10-7 1x10-5 1x10-3

4

4

4

43

3

3

2

3

2

2

21

1

1

1

Target concentration (mol/L)

Hyb

ridiz

atio

n ef

ficie

ncy

RT

ZwnSTH

RT

θZZwn

RTθ

RTθθZZwnδ

PP

TPPTPP

200

2

exp

1exp)1(

)1(

Non-linear error is below

RT

ZZZwn

RT

STH

θ

θ

VN

SnC

TPPP00

A

P0

(expexp

1

Beyond the linear range use the on-array binding isotherm

Page 37: Design  of Bio-Chips from Theory and  Multiscale Simulation
Page 38: Design  of Bio-Chips from Theory and  Multiscale Simulation

Probe vs. Target lengths often Probe vs. Target lengths often mismatch in various waysmismatch in various ways

vs.. . .

Page 39: Design  of Bio-Chips from Theory and  Multiscale Simulation

Longer sequences are the expectedLonger sequences are the expectedand require true mesoscale models

Perfect pairing Pairing w/ an overhangGarrido Vainrub &Pettitt CPC ‘10

Page 40: Design  of Bio-Chips from Theory and  Multiscale Simulation

To Accommodate To Accommodate Longer Sequences and Longer Sequences and Secondary Structure Secondary Structure

Near a SurfaceNear a Surface

Page 41: Design  of Bio-Chips from Theory and  Multiscale Simulation

Mesoscale:Mesoscale: Allow for variations

inLinker chemistry

Over-hangUnder-hangon probes

and targetsin all combinations.

Parameterize ssDNA as well as dsDNA.

Garrido Vainrub &Pettitt JPCM submitted

Page 42: Design  of Bio-Chips from Theory and  Multiscale Simulation

DNA Entropic contributionsDNA Entropic contributions

• Sample with Monte-Carlo

• via Meirovitch (hypothetical scan)

• via Quasi Harmonic

• Correlations of sequence mismatch with physical corrections

• Data mining for equilibrium thermodynamics

Page 43: Design  of Bio-Chips from Theory and  Multiscale Simulation

ΔG

Page 44: Design  of Bio-Chips from Theory and  Multiscale Simulation

ΔG

Page 45: Design  of Bio-Chips from Theory and  Multiscale Simulation

Information vs efficiencyInformation vs efficiency

• ssDNA has the same spatial average as dsDNA but not fluctuations.

• Longer strands have more information 4n.– Kinetic problem

• Shorter strands pair more efficiently.– Kinetic balance

Page 46: Design  of Bio-Chips from Theory and  Multiscale Simulation

High Salt is Far Beyond Analytic High Salt is Far Beyond Analytic PB (Mean Field) ElectrostaticsPB (Mean Field) Electrostatics

Gouy-Chapman ~1910 Double layer at charged interfaces.

How good is this?

Add all the atoms via integral equations in 3D (renorm.- HNC)

Page 47: Design  of Bio-Chips from Theory and  Multiscale Simulation

Solvent and ion charge densities

HNC-IEMF-PB

w/ Ions

H2Oε=80

Page 48: Design  of Bio-Chips from Theory and  Multiscale Simulation

Charge-Charge correlationsCharge-Charge correlations

• Multi Layer effects not Bilayer

• Dominate at interfaces

• Determine the thermodynamics

• Change the kinetics

Page 49: Design  of Bio-Chips from Theory and  Multiscale Simulation

To design for To design for Affinity and SpecificityAffinity and Specificity

Coverage and Surface effects•Use Electric fields

– Effects of DNA with poly cations

•Use surface effects– Layered hard materials

•Use quantitative theories – Explicit polymer entropy

– Non m.f. coverage and electrostatics

Page 50: Design  of Bio-Chips from Theory and  Multiscale Simulation
Page 51: Design  of Bio-Chips from Theory and  Multiscale Simulation

Arnold VainrubClem Wong

Joaquin Ambia-GarridoJesse Howard

Khawla QamhiehAlex Micu

Keck Center for Computational BiologyMike Hogan, Lian Gao,

Rosina Georgiadis,Yuri Fofanov

Thanks to NIH, NASA, DOE, Welch Foundation, ARP&SDSC, PNNL, PSC, NCI, MSI

Page 52: Design  of Bio-Chips from Theory and  Multiscale Simulation