The Ski-Lift Pathway: Thermodynamically Unique, Biologically Ubiquitous
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Transcript of The Ski-Lift Pathway: Thermodynamically Unique, Biologically Ubiquitous
The Ski-Lift Pathway: Thermodynamically Unique,
Biologically Ubiquitous
Goren GordonWeizmann Institute of Science
Rehovot
Avshalom C. Elitzurwww.a-c-elitzur.co.il
Outline
1. The Goal: A Unified Physical Set of Principles Underlying all Forms of Life
2. Entropy, Information and Complexity3. The new Question: How do Transitions from High-
to-High-Entropy States Take Place?4. The Ski-Lift Model
Ordered, Random, ComplexMeasures of Orderliness
1. Divergence from equiprobability (Gatlin)
(Are there any digits in the sequence that are more common?)
2. Divergence from independence (Gatlin)
(Is there any dependence between the digits?)
3. Redundancy (Chaitin)
(Can the sequence be compressed into any shorter algorithm?)
a. 3333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333
b. 1860271194945955774038867706591873856869843786230090655440136901425331081581505348840600451256617983
c. 1234567890123456789012345678901234567890123456789012345678901234567890123456789012345678901234567890
d. 6180339887498948482045868343656381177203091798057628621354486227052604628189024497072072041893911374
2
15
Sequence d is complex
Sequence d is highly informative
Bennett’s Measure of Complexity
Given A sequence’s shortest algorithm, how much computation is needed to produce it from the algorithm, or conversely to compress it back into it?
Complexity is not directly related to Order/Entropy
High order
complexity
Low order
Maxwell’s Demon
A Lawful Maxwell’s Demon in a Complex Environment
A Lawful Maxwell’s Demon in a Complex Environment
Interim Summary
Thermodynamics offers a ubiquitous physical basis for the understanding of numerous biological phenomena, through the introduction of concepts like entropy/order, information and complexity.
How does Complexity Emerge?And How is it Maintained?
Order Information/Complexity Disorder
The Hypothesis: Ski-Lift
High Order
Low Order
RequiresEnergy
Spontaneous
X Desired state
High Order
Low Order
RequiresEnergy
Spontaneous
The Hypothesis: Ski-Lift
Step 1:Use Ski-
Lift, get to the top
X Desired state
High Order
Low Order
RequiresEnergy
Spontaneous
The Hypothesis: Ski-Lift
Step 2:Ski down
Step 1:Use Ski-
Lift, get to the top
The Ski-Lift Conjecture:
Life approaches complexity “from above,” i.e., from
the high-order state, and not “from below,” from the
low-order state. Though the former route seems to
require more energy, the latter requires immeasurable
information, hence unrealistic energy.
Dynamical evolution of complex states
How to reach a complex state?
Initial state at equilibrium (unknown, high entropy)
Final complex state, defined by environment
1. Direct path
1. Probabilistic
2. Deterministic
2. Ski-lift theorem
Initial state Final state
Ent
ropy
Direct path
Ski-lift
Definitions
– stateN – equivalent microstates of Entropy of state: S()=log(N)
Initial state, i – high entropy,Ni À 1
Final state, f – high complexity, specific, S(f)=S(i)
Operations allowed:
1. S-: Decrease entropy.1. Uncontrolled2. Energy cost: E=S
2. T: Transformation.• Controlled, requires information• Does not change entropy on average, <S(T) – S()>=0• Energy cost: E=
Numerical example=a0a1a2….an
i=18602711949459557740 (or any other random number)
f=61803398874989484820 (a specific, complex number)
order=00000000000000000000
Operations:1. S-: Decrease entropy.
Uncontrolled.
18602711949459557740
10602001040050500740
00000000000000000000
E= S
E= S
Numerical example=a0a1a2….an
i=18602711949459557740 (or any other random number)
f=61803398874989484820 (a specific, complex number)
order=00000000000000000000
Operations:1. S-: Decrease entropy.
Uncontrolled
2. T: Transformations.Addition.<S(T)-S()>=0due to symmetry
T1=(+4)(+2)(+0)(+6)….(+1)T2=(+1)(+7)(+8)(+3)….(+9)…
T1I =50662711949459557741 T2order=17830000000000000009
Perform a transformation on the initial state to arrive at the final state
Ti!f (???)
Initial state unknown
For each transformation only one initial state transforms to final state
Hilbert Space
Initial stateFinal state
Direct Path:
Perform a transformation on the initial state to arrive at the final state
Ti!f (???)
Initial state unknown
For each transformation only one initial state transforms to final state
Perform transformation once
Energy cost:E=
Probability of success:P=1/Ni
=e-S(i)¿ 1
Hilbert Space
Initial stateFinal state
Direct Path: Probabilistic
Perform a transformation on the initial state to arrive at the final state
Ti!f (???)
Initial state unknown
For each transformation only one initial state transforms to final state
Repeat transformation until finalstate is reached
Probability of success:P=1
Average energy cost:E= eS(i)À 1
Direct Path: Deterministic
Hilbert Space
Initial stateFinal state
Perform a transformation on the initial state to arrive at the final state
Ti!f
If one has information about initial stateIi=S(i)
And information about final state (environment)If=S(f)
Then can perform the right transformation once
Probability of success:P=1
Energy cost:E=
Information required:I=S(i)+S(f)
Direct Path: Information
Hilbert Space
Initial stateFinal state
Two stages path:
Stage 1: Increase orderS-i! order
Ends with a specific, known stateProbability of success: P1=1Energy cost: E1=S(i)
Ski-lift Path:
Hilbert Space
Initial stateFinal state
Two stages path:
Stage 1: Increase orderS-i! order
Ends with a specific, known stateProbability of success: P1=1Energy cost: E1=S(i)
Stage 2: Controlled transformationTorder!f
Ends with the specific, final stateProbability of success: P2=1Energy cost: E2=
Ski-lift Path:
Hilbert Space
Initial stateFinal state
Requires information on final state (environment), in order to apply the right transformation on ordered-state
Probability of success: P=1
Energy cost: Eski-lift=S(i)+
Information required:I=S(f)
Hilbert Space
Initial stateFinal state
Ski-lift Path: Information
Comparison between paths
Direct Path
1. Probabilistic1. Low probability
2. Low energy
2. Deterministic:1. High probability
2. High energy
3. Information:1. Requires much information
2. Low energy
Ski-lift• Deterministic• Controlled• Reproducible• Costs low energy• Requires only
environmental information
Ski-lift uses ordered-state and environmental information to obtain controllability and reproducibility
“What is life?” revisitedHilbert Space
High orderRedundancy
High entropyHigh informationHigh complexity
(specific environment)
Requires energy
Requires information
Biological examples
• Cell formation
• Embryonic development
• Natural selection
• Ecological development
Cell formation
Initial state: free molecules in primordial pool
Ski-lift model
1. Increased order: compartmentalization
2. Controlled transformation: specialization
Direct path
Improbable, Irreproducible
Embryonic development
Initial state: fertilized ovum + nutrientsSki-lift model1. Increased order: mitosis, Blastocyte 2. Controlled transformation: differentiation
Direct pathDifferentiation to final organismImprobable, irreproducible due to highsusceptibility to environmental variations
The Morphotropic State as the Embryonic Progenitor of
Complexity
The Morphotropic State as the Cellular Progenitor of Complexity
Minsky A, Shimoni E, Frenkiel-Krispin D. (2002) “Stress, order and survival.” Nat. Rev. Mol. Cell Biol. Jan;3(1):50-60.
Natural selection
Initial state: Individual + resourcesSki-lift model1. Increased order: reproduction 2. Controlled transformation: minor mutations
Direct pathLarge mutations. Attempts to reach “optimized”
organism at “one go”.Improbable, irreproducible due to highsusceptibility to environmental variations
Ecological development
Initial state: Natural complexity
Ski-lift model
1. Increased order: accumulate resources
2. Controlled transformation: build cities
Direct path
Develop technology without a controlled environment
The Morphotropic State as the Ecological Progenitor of
Complexity