Emergence of Molecular Order in Nonequilibrium Systems and...
Transcript of Emergence of Molecular Order in Nonequilibrium Systems and...
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Emergence of Molecular Order in Nonequilibrium
Systems and the Physics of Information
David Andrieux
Center for Nonlinear Phenomena and Complex Systems
Université Libre de Bruxelles
Patras Summer School – July 20, 2011
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Macroscopic self-organization (non-equilibrium or dissipative structures)
Turing patterns
Collective order is developed through unstable fluctuations and far-from-equilibrium
successive bifurcations (Nicolis and Prigogine 1977)
Rayleigh-Bénard convection rolls
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Nonequilibrium states present long-range correlations
Li et al. 1994
Nicolis and Mansour 1984
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- Molecular motors: kinesin, myosin, RNA polymerase, ... - Electronic circuits: q-dots, single-electron transistors, ... - Chemical networks: catalysis, microfluidic, ... - Nanomachines, ....
length ~ nm - mm energy ~ kT N << N
Avogadro
Bustamante et al., Physics Today 2005
Nonequilibrium & noisy dymanics at the nanoscale
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From the macro to the microscopic scale Andrieux and Gaspard, JCP 2008
phase space
X(t) correlation function
Brusselator oscillating chemical network
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Voss and Kruse (1996):
NO2/H2/Pt catalytic reaction
Kinosita and coworkers (2001):
F1-ATPase + actin filament
Nonequilibrium systems manifest a dynamical order
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1. Dissipation and dynamical ordering
Finite Size Finite Time
Fluctuations
Out of equilibrium
How does self-organization emerge at the nanoscale?
2. Thermodynamics of information processing Copolymerizations, DNA replication, ...
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Origin of dynamical order?
Dissipation > 0
Out of equilibrium
Order (motion, transport,...)
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Dissipation > 0
Out of equilibrium
Time asymmetry Order (motion, transport,...)
Origin of dynamical order?
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Spontaneous symmetry breaking in phase space
Equations of motion are symmetric under time-reversal (micro-reversibility),
but their solutions are not:
w
wR
≠
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Spontaneous symmetry breaking in phase space
Equations of motion are symmetric under time-reversal (micro-reversibility),
but their solutions are not:
Can we relate irreversibility to the microscopic dynamics?
Equilibrium: P(w) = P(wR)
Out of equilibrium: P(w) ≠ P(wR)
w
wR
≠
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Dynamical randomness
Equilibrium: P(w) = P(wR)
Out of equilibrium: P(w) ≠ P(wR)
w
wR
≠
The probability of a typical path decays as
P(w) = P(w0 w1w2 ... wn) ~ exp( -n h )
h = Kolmogorov-Sinai entropy is a measure of dynamical randomness (predictability)
The probability of the time-reversed path decays as
P(wR) = P(wn … w2 w1 w0 ) ~ exp( -n hR )
hR = temporal disorder of the time-reversed paths Gaspard 2004
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Dissipation and out-of-equilibrium temporal ordering
The typical paths are more ordered in time
than the corresponding time-reversed paths:
hR = h equilibrium
hR ≥ h nonequilibrium
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The thermodynamic entropy production is expressed as
DiS = hR – h = temporal disorder of time reversed paths – temporal disorder of typical paths
= time asymmetry in the temporal disorder
⇒ Nonequilibrium processes can generate dynamical order and information
Dissipation and out-of-equilibrium temporal ordering
The typical paths are more ordered in time
than the corresponding time-reversed paths:
hR = h equilibrium
hR ≥ h nonequilibrium
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Driven Brownian motion
laser
Langevin equation
relative position z = x-ut
u > 0
u < 0
driving force F = -k (x-ut)
friction a
relaxation time t = 3 ms
trap stiffness k = 9.62 10-6 kg s-2
fluid velocity u = ± 4.24 μm/s
resolution ε = k x 0.558 nm (k=11-20)
2 mm diameter polystyrene particle in a 20% glycerol-water
solution trapped by optical tweezers and driven
by a moving fluid
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Entropy production and (e,t)-entropies per unit time
down to the nanoscale
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Path probabilities and dissipation
kBT
Heat dissipated along the random path:
Probabilities (±u) for a random path:
Andrieux et al., JSM 2008
with
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Erasure of (correlated) random bits and Landauer's principle
Consider the erasure process of a sequence of random bits with Shannon entropy D
⇒ Minimal dissipation is D kBT per bit.
Zurek 1989, Andrieux and Gaspard 2008
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Erasure of (correlated) random bits and Landauer's principle
Consider the erasure process of a sequence of random bits with Shannon entropy D
Erasure process generates no information: h = 0
In reversed time, information is generated at rate hR = D kBT
Erasure process dissipates hR - h = D kBT per bit.
Based on dynamical randomness and model independent
⇒ Minimal dissipation is D kBT per bit.
Zurek 1989, Andrieux and Gaspard 2008
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Information generation in polymerization processes
DNA transcription RNA Polymerase
Genetic information is encoded in DNA structure
Accuracy of transcription & replication?
Errors due to thermal & chemical fluctuations
How is information generated?
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examples: styrene-butadiene rubber atactic polypropylene
catalyst
AABAABAA AABAABAAB AABAABAA AABAABA AABAAB AABAA AABA AABAB AABA AAB AA A
space
Single polymer synthesis model
Polymer + A Polymer-A
Polymer + B Polymer-B
k+A
k-A
k-B
k+B
time
monomers
Growing copolymer
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catalyst
AABAABAA AABAABAAB AABAABAA AABAABA AABAAB AABAA AABA AABAB AABA AAB AA A
space
Single polymer synthesis model
Polymer + A Polymer-A
Polymer + B Polymer-B
k+A
k-A
k-B
k+B
time
monomers
Growing copolymer
where ω is a given sequence of the chain.
Master equation dynamics:
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What governs the polymer growth?
log (k+A [A] / k-A) = log (k+B [B] / k-B) = e
driving force (free energy difference):
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log (k+A [A] / k-A) = log (k+B [B] / k-B) = e
driving force (free energy difference):
→ fixed selectivity: k+A [A] /k+B [B] = k-A/k-B
What governs the polymer growth?
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log (k+A [A] / k-A) = log (k+B [B] / k-B) = e
driving force (free energy difference):
→ fixed selectivity: k+A [A] /k+B [B] = k-A/k-B
Thermodynamic equilibrium is not realized at zero driving force:
Ch
ain's len
gth
Steady growth at e = 0 or k+A [A] = k-A and k+B [B] = k-B
time
What governs the polymer growth?
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Thermodynamics of polymerization
The thermodynamic force (dissipation per step) is
driving force
A = ε A = 0 : equilibrium
A > 0 : polymer grows
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Thermodynamics of polymerization
The thermodynamic force (dissipation per step) is
driving force disorder
A = ε + D(polymer)
Shannon entropy: 0 ≤ D = limn → ∞ -(1/n) pn(ω) ln pn(ω) ≤ ln 2
A = 0 : equilibrium
A > 0 : polymer grows
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Thermodynamics of polymerization
The thermodynamic force (dissipation per step) is
driving force disorder
A = ε + D(polymer)
Randomness in the sequence acts as a positive driving force
Thermodynamic equilibrium at negative driving force: eeq < 0
Affinity is not fixed by the external conditions (concentrations, ...)
but emerges from the internal dynamics
Shannon entropy: 0 ≤ D = limn → ∞ -(1/n) pn(ω) ln pn(ω) ≤ ln 2
A = 0 : equilibrium
A > 0 : polymer grows
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Error-Dissipation Tradeoff
affinity per nucleotide A = e + D(e)
A single copolymer may grow by an entropic effect (-ln 2 < e < 0) coming from the
randomness D in the monomer sequence
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Error-Dissipation Tradeoff
affinity per nucleotide A = e + D(e)
A single copolymer may grow by an entropic effect (-ln 2 < e < 0) coming from the
randomness D in the monomer sequence
Error rate diminishes away from equilibrium even with fixed selectivity
percentage of errors (= [A]/[B])
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Error-Dissipation Tradeoff
affinity per nucleotide A = e + D(e)
A single copolymer may grow by an entropic effect (-ln 2 < e < 0) coming from the
randomness D in the monomer sequence
Error rate diminishes away from equilibrium even with fixed selectivity
e < - ln 2 ↔ Landauer's principle
percentage of errors (= [A]/[B])
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Thermodynamics of copying information
Polymer + X Polymer-X k+X|Y
k-X|Y
examples: DNA replication DNA-mRNA transcription mRNA-protein translation
Polymerization reactions depend on the template:
catalyst
template Y
growing copolymer monomers
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The thermodynamic force or dissipation per step is
driving force disorder mutual information
A = ε + D(polymer) – I (polymer, template)
Speed-Error-Dissipation-Information Tradeoff
D and I are information theoretic quantities that characterize information transmission
in communication channels
Disorder and information act as positive thermodynamic forces (D-I > 0)
Thermodynamic cost of copying information
Andrieux and Gaspard 2008
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mutual information I(X,Y) affinity per nucleotide A = e + D - I
percentage of errors velocity of replication
Fidelity in DNA replication is controlled by dissipation
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Out of equilibrium
Dynamical order
Disorder & Information
Information restored to the dynamics Memorization by the system
...AABBBAABA... errors act as a positive force
Self-sustained nonequilibrium organization
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Dissipation-information tradeoff in correlated processes
Mutual information is not always
maximal in the totally irreversible case:
Andrieux and Gaspard 2009
Spatial correlations increase out of equilibrium:
First-neighbor interaction:
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Summary
Irreversibility & temporal order
Nonequilibrium information processing
Nonequilibrium self-organization and molecular order
Δi S = hR - h
A= e + D - I
The typical paths are more ordered in time than the time-reversed ones
Irreversibility measures the time asymmetry in dynamical randomness Thermodynamic arrow of time revealed down to the nanoscale in
driven Brownian motion
Landauer principle
Constructive role of nonequilibrium fluctuations: DNA replication,...
Disorder and information as thermodynamics forces
Mechanism for nonequilibrium self-organization (≠ nonlinearities, ratchet effect, ...)
hR ≥ h
D kBT per bit
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Acknowledgments
P. Gaspard, Université Libre de Bruxelles
S. Ciliberto, Ecole Normale Supérieure de Lyon
N. Garnier, Ecole Normale Supérieure de Lyon
S. Joubaud, Ecole Normale Supérieure de Lyon
A. Petrosyan, Ecole Normale Supérieure de Lyon
Fonds National de la Recherche Scientifique - FNRS
Financial Support
Collaborators