Lecture 8: Random Walks and the Structure of …brigita/COURSES/BIOPHYS_2011...Random walk model of...

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PHYS 461 & 561, Fall 2011-2012 1 10/18/2011 Lecture 8: Random Walks and the Structure of Macromolecules Lecturer: Brigita Urbanc Office: 12-909 (E-mail: [email protected]) Course website: www.physics.drexel.edu/~brigita/COURSES/BIOPHYS_2011-2012/

Transcript of Lecture 8: Random Walks and the Structure of …brigita/COURSES/BIOPHYS_2011...Random walk model of...

Page 1: Lecture 8: Random Walks and the Structure of …brigita/COURSES/BIOPHYS_2011...Random walk model of a polymer: series of rigid rods (the Kuhn segments) connected by flexible hinges

PHYS 461 & 561, Fall 2011-2012 110/18/2011

Lecture 8: Random Walks and the Structure of

Macromolecules

Lecturer:Brigita Urbanc Office: 12­909(E­mail: [email protected])

Course website: www.physics.drexel.edu/~brigita/COURSES/BIOPHYS_2011­2012/

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PHYS 461 & 561, Fall 2011-2012 210/18/2011

Description of the macromolecular structure as random walks

­motivation: calculate entropic cost of DNA packed into a cell;   description of DNA stretching by optical tweezers

­ atomic coordinates deposited on databases such as Protein Data  Bank (pdb format of an ascii file with x,y,z coordinates of known  protein structures)

(r1, r

2, … r

N) with r

i = (x

i, y

i, z

i)

­ data derived based on X­ray crystallography or NMR

­ statistical measure of the size of a macromolecule: Radius of    Gyration R

G

­ example of DNA characterization r(s) where s is the distance  along the contour of the molecule

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PHYS 461 & 561, Fall 2011-2012 310/18/2011

Random walk model of a polymer:series of rigid rods (the Kuhn segments) connected by flexible hinges

lattice model in 1D                          lattice model in 3D

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PHYS 461 & 561, Fall 2011-2012 410/18/2011

DNA molecule as a random walk:Every macromolecular configuration is equally probable

AFM image

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PHYS 461 & 561, Fall 2011-2012 510/18/2011

What is a random walk?­ 1D random walker: p

R = p

L = ½  at every step, independently

­ for N random walk steps, there are 2N possible macromolecular  configurations

­ the mean distance of the walker from its point of departure:

<R> = < ∑i x

i> = ∑

i <x

i> = 0

­ the variance of the probability distribution:

<R2> = < ∑i∑

j x

i x

j > = ∑

i<x

i2> + ∑

i≠j<x

ix

j> 

<R2> = N a2

­ thus the size of the area covered by the random walk is:

√<R2> = √N a

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PHYS 461 & 561, Fall 2011-2012 610/18/2011

Derivation Based on the Probability Theory:­ a macroscopic state:  many microscopic states

­ N steps in 1D random walk:  N = n

R + n

L, each with

  a probability of ½ 

­ a probability associated with  each microstate of N steps  is:  (1/2)N

­ multiplicity W associated with  n

R steps is:

W = N!/[nR! (N ­ n

R)!]

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PHYS 461 & 561, Fall 2011-2012 710/18/2011

Probability of an overall departure  nR from the origin:

p(nR; N) = N!/[n

R! (N­ n

R)!] (1/2)N

This distribution is normalized:

 ∑nR

 p(nR; N) = 1,    n

R∈{0,N}

Probability distribution for the end­to­end distance: R = a (n

R­n

L) = a (2n

R ­N) ; n

R= N/2 + R/(2a)

such thatp(R; N) = (1/2)N N!/{[N/2 + R/(2a)]! [N/2 – R/(2a)]!}which takes the form of a Gaussian distribution for R « Na: p(R; N) =  2/√2N exp[­R2/(2Na2)]

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PHYS 461 & 561, Fall 2011-2012 810/18/2011

To obtain the probability distribution function for the end­to­enddistance of a freely jointed chain P(R; N), p(R; N) needs to be divided by the number of integer R values per unit length (=2a):

P(R; N) =  2/√2Na2 exp[­R2/(2Na2)]

Note that <R> = 0 and <R2> = Na2, independent of the spaceDimension. This can be used to derive P(R; N) in 3D space:

P(R; N) = A exp(R2);A = [3/(2Na2)]3/2;    = 3/(2Na2)

Use normalization condition in 3D and calculate the variance to determine A and   (integration in 3D).

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PHYS 461 & 561, Fall 2011-2012 910/18/2011

End­to­end distance probability distribution for 1D random walk:Binomial (dots) versus Gaussian (curve) distribution for N=100

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PHYS 461 & 561, Fall 2011-2012 1010/18/2011

Persistence Length versus the Kuhn Length

­ persistence length: the length scale P over which the polymer 

  remains approximately straight­ the Kuhn length: the length of a step a in the random walk model

  <R2> = La­ persistence length  

P can be calculated from the unit tangent 

  vector  t(s) where s is a distance along the polymer: <t(s) t(u)> = exp(­|s­u|/

P)

­ to find the relationship between a and  P we use R = ∫

0L ds t(s):  

<R2> = <∫0

L ds t(s) ∫0L du t(u)> = ∫

0L∫

0L ds du <t(s) t(u)> =

2 ∫0

L ds ∫sL du exp[­(s­u)/

P] = 2 ∫

0L ds ∫

0ꝏ dx exp(­x/

P) = 2L

P

 a = 2P

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PHYS 461 & 561, Fall 2011-2012 1110/18/2011

<R2> = 2LP   

<RG

2> = 1/3 LP 

(use definition)

L = 0.34 nm NBP

NBP …

 # of           basepairs

RG = 1/3 √N

BP 

P nm

Size of genomic DNA in solution