- 103 Views
- Uploaded on
- Presentation posted in: General

Maxwell's Demon: Implications for Evolution and Biogenesis

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Maxwell's Demon: Implications for Evolution and Biogenesis

Avshalom C. ElitzurIyar, The Israeli Institute for Advanced Research

Copyleft 2010

The Relevance of Thermodynamicsto Life Sciences

- Thermodynamics is a discipline that studies energy, entropy, and information

For several equally possible states, P0

With information reducing the possible states to P1:

Ideally, for P1=1:

Boltzmann’s Entropy

For all states being equiprobable:

Otherwise:

Information of one English letter:

For a string of G letters:

The Relevance of Thermodynamicsto Life Sciences

- Thermodynamics is a discipline that studies energy, entropy, and information
- Its jurisdiction is ubiquitous, regardless of the system’s chemical composition or type of energy

The Common Textbook Answer:

“Living organisms are open systems”

?

Rocks

Chairs

Blackboards

Trash cans (!)

etc.

The Thesis:

Adaptation = Information

- Kelvin: The devil is alive
- Von Smoluchowski: It’s intelligent
- Szilard, Brillouin: It uses information
- Bennett & Landauer: It erases information

Information Costs Energy

ergo

Information can Save Energy

With information, you can do work with less energy, applied at the right time and/or place

Minute chemical energy: Neurotoxin (cobrotoxin) moleculesreach the synapses with enormous precision

Considerable mechanical energy: Crushing the entire prey’s body

Ek

Et

Ec

Ee

Et

Ec + Ee

Ec + Ee

Ec'>Ec

Ek

The Demon Vs. the Living Organism: The Analogy

Life increases energy’s efficiency, up the thermodynamic scale

It does that with the aid of information

The Demon Vs. the Living Organism: The Disanalogy

The real environment is never completely disordered but complex

The organism does not create order but complexity

Measures of Orderliness

- Divergence from equiprobability (Gatlin) (Are there any digits in the sequence that are more common?)
- Divergence from independence (Gatlin) (Is there any dependence between the digits?)
- Redundancy (Chaitin) (Can the sequence be compressed into any shorter algorithm?)
- 3333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333
- 1860271194945955774038867706591873856869843786230090655440136901425331081581505348840600451256617983
- 0123456789012345678901234567890123456789012345678901234567890123456789012345678901234567890123456789
- 6180339887498948482045868343656381177203091798057628621354486227052604628189024497072072041893911374

Sequence d is

highly informative

Sequence d is

complex

Given the shortest algorithm, how much computation is required to produce the sequence from it?And conversely:How much computation is required to encode a sequence into its shortest algorithm?

complexity

Low order

High order

High Order

Requires

Energy

Spontaneous

Low Order

High Order

Step 1:

Use Ski-Lift, get to the top

Requires

Energy

Spontaneous

Initial State

Desired State

Low Order

High Order

Step 1:

Use Ski-Lift, get to the top

Requires

Energy

Spontaneous

Initial State

Desired State

Low Order

High Order

Step 1:

Use Ski-Lift, get to the top

Requires

Energy

Spontaneous

Step 2:

Ski down

Initial State

Desired State

Low Order

The Ski-Lift Conjecture (Gordon & Elitzur, 2009):

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?

- Direct path
- Probabilistic
- Deterministic

- Ski-lift theorem

Ski-lift

Entropy

Final state

Initial state

Direct path

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

Hilbert Space

Initial state

Final 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

Hilbert Space

Perform transformation once

Energy cost:

E=

Probability of success:

P=1/Ni=e-S(i)¿ 1

Initial state

Final state

Direct Path: Deterministic

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

Repeat transformation until final

state is reached

Probability of success:

P=1

Average energy cost:

E= eS(i)À 1

Initial state

Final state

Direct Path: Information

Perform a transformation on the initial state to arrive at the final state

Ti!f

If one has information about initial state

Ii=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)

Hilbert Space

Initial state

Final state

Ski-lift Path

Two stages path:

Stage 1: Increase order

S-i! order

Ends with a specific, known state

Probability of success: P1=1

Energy cost: E1=S(i)

Hilbert Space

Initial state

Final state

Ski-lift Path

Two stages path:

Stage 1: Increase order

S-i! order

Ends with a specific, known state

Probability of success: P1=1

Energy cost: E1=S(i)

Hilbert Space

Stage 2: Controlled transformation

Torder!f

Ends with the specific, final state

Probability of success: P2=1

Energy cost: E2=

Initial state

Final state

Ski-lift Path: Information

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 state

Final state

Direct Path

Probabilistic

Low probability

Low energy

Deterministic:

High probability

High energy

Information:

Requires much information

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

Disorder

Order

Information/Complexity

Given the shortest algorithm, how much computation is required to produce the sequence from it?And conversely:How much computation is required to encode a sequence into its shortest algorithm?

complexity

Low order

High order

- Cell formation
- Apoptosis
- Embryonic development
- Ecological development

Minsky A, Shimoni E, Frenkiel-Krispin D. (2002) “Stress, order and survival.”Nat. Rev. Mol. Cell Biol. Jan;3(1):50-60.

Order as the Ecological Progenitor of Complexity

Maintaining the complexity of civilization necessitates

huge reservoirs of order

Hilbert Space

Requires energy

High entropy

High information

High order

Redundancy

High complexity

(specific environment)

Requires information

BIBLIOGRAPHY

- Leff, H. S., & Rex, A. F. (2003) Maxwell’s Demon 2: Entropy, Classical and Quantum Information, Computing. Bristol: Institute of Physics Publishing.
- Dill, K.A. , & Bromberg, S. (2003) Molecular Driving Forces: Statistical Thermodynamics in Chemistry and Biology. New York: Garland Science.
- Di Cera, E., Ed. (2000) Thermodynamics in Biology” Oxford: Oxford University Press.
- Gordon, G., & Elitzur, A. C. (2008) The Ski-Lift Pathway: Thermodynamically unique, biologically ubiquitous. http://www.a-c-elitzur.co.il/site/siteArticle.asp?ar=214