Dear Sung and Francesco,

Information and Energy are not only separable but quite different.

More, the Energy exists without Information.
To create and process Information, Energy is needed.

Please see the next publication with example just from the economics   

Krassimir Markov
page 122-125 in:

Friendly greetings

From: Francesco Rizzo 
Sent: Monday, May 07, 2018 10:42 AM
To: Sungchul Ji 
Cc: FIS FIS ; 
Subject: Re: [Fis] Are there 3 kinds of motions in physics and biology?

Caro Sung e cari tutti, 

"I think information and energy are inseparable in reality": è vero anche in 

La Parte Terza--Teoria del valore: energia e informazione--  di "Valore e 
valutazioni. La scienza dell'economia o l'economia della scienza" 
(FrancoAngeli, Milano, 1995-1999) è costituita dalle pagine 451-646  contenenti 
questa interessante e significativa problematica. 

Grazie e auguri.


2018-05-07 4:08 GMT+02:00 Sungchul Ji <>:

  Hi FISers,

  I think information and energy are inseparable in reality.  Hence to 
understand what information is, it may be helpful to understand what energy 
(and the associated concept of motion) is.  In this spirit, I am forwarding the 
following email that I wrote motivated by the lecture given by Dr. Grossberg 
this afternoon at the 119th Statistical Mechanics Conference.  In Table 1 in 
the email, I divided particle motions studied in physics and biology into three 
classes -- (i) random, (ii) passive, and (iii) active, and identified the field 
of specialization wherein these motions are studied as (i) statistical 
mechanics, (ii) stochastic mechanics, and (iii) info-statistical mechanics.  
The last term was coined by me in 2012  in [1].  I will be presenting a short 
talk (5 minutes) on Info-statistical mechanics on Wednesday, May 9, at the 
above meeting.   The abstract of the short talk is given below:

  Short talk to be presented at the 119th Statistical Mechanics Conference, 
Rutgers University, Piscataway, N.J., May 6-9, 2018).

  Planckian Information may be to Info-Statistical Mechanics what Boltzmann 
Entropy is to Statistical Mechanics. 

  Sungchul Ji, Department of Pharmacology and Toxicology, Ernest Mario School 
of Pharmacy, Rutgers University, Piscataway, N.J. 08854

  Traditionally, the dynamics of any N-particle systems in statistical 
mechanics is completely described in terms of the 6-dimensional phase space 
consisting of the 3N positional coordinates and 3N momenta, where N is the 
number of particles in the system [1]. Unlike the particles dealt with in 
statistical mechanics which are featureless and shapeless, the particles in 
biology have characteristic shapes and internal structures that determine their 
biological properties.  The particles in physics are completely described in 
terms of energy and matter in the phase space but the description of the 
particles in living systems require not only the energy and matter of the 
particle but also their genetic information, consistent with the 
information-energy complementarity (or gnergy) postulate discussed in [2, 
Section 2.3.2].  Thus, it seems necessary to expand the dimensionality of the 
traditional phase space to accommodate the information dimension, which 
includes the three coordinates encoding the amount (in bits), meaning (e.g., 
recognizability), and value (e.g., practical effects) of information [2, 
Section 4.3]. Similar views were expressed by Bellomo et al. [3] and Mamontov 
et al. [4].  The expanded “phase space” would comprise the 6N phase space of 
traditional statistical mechanics plus the 3N information space entailed by 
molecular biology.  The new space (to be called the “gnergy space”) composed of 
these two subspaces would have 9N dimensions as indicated in Eq. (1).  This 
equation also makes contact with the concepts of  synchronic and diachronic 
informations discussed in [2, Section 4.5].  It was suggested therein that the 
traditional 6N-dimensional phase space deals with  the synchronic information 
and hence was referred to as the Synchronic Space while the 3N-dimensional 
information space is concerned with the consequences of history and evolution 
encoded in each particle and thus was referred to as the Diachronic Space.  The 
resulting space was called the gnergy space (since it encodes not only energy 
but also information).  

             Gnergy Space =  6N-D Phase Space  +  3N-D  Information Space       

                                          (Synchronic Space)           
(Diachronic Space)         

  The study of both energy and information was defined as “info-statistical 
mechanics” in 2012 [2, pp. 102-106, 297-301].  The Planckian information of the 
second kind, IPS, [5] was defined as the negative of the binary logarithm of 
the skewness of the long-tailed histogram that fits the Planckian Distribution 
Equation (PDE) [6].   In Table 1, the Planckian information is compared to the 
Boltzmann entropy in the context of the complexity theory of Weaver [8]. The 
inseparable relation between energy and information that underlies 
“info-statistical mechanics” may be expressed by the following aphorism:  

  “Information without energy is useless;
  Energy without information is valueless.”

        Table 1.  A comparison between Planckian Information (of the second 
kind) and Boltzmann entropy.  Adopted from [6, Table 8.3].
        IPS = - log2 [(µ - mode)/σ] 

       S = k log W 

        Planckian Information 
       Boltzmann entropy [7]
        Organized Complexity [8]
       Disorganized Complexity [8]
        Info-Statistical Mechanics [2, pp. 102-106]
       Statistical Mechanics [1]


     [1] Tolman, R. C. (1979). The Principles of Statistical Mechanics,  Dover 
Publications, Inc.,

  New York, pp. 42-46.

     [2] Ji, S. (2012) Molecular Theory of the Living Cell: Concepts, Molecular 
Mechanisms, and 

  Biomedical Applications.  Springer, New York.

     [3] Bellomo, N., Bellouquid, A. and Harrero, M. A. (2007).  From 
microscopic to macroscopic 

  descriptions of multicellular systems and biological growing tissues. Comp. 
Math. Applications 

  53: 647-663. 

     [4] Mamontov, E., Psiuk-Maksymowitcz, K. and Koptioug, A. (2006).  
Stochastic mechanics 

  in the context of the properties of living systems. Math. Comp. Modeling 
44(7-8): 595-607. 

      [5] Ji, S. (2018). Mathematical (Quantitative) and Cell Linguistic 
(Qualitative) Evidence for 

  Hypermetabolic Pathways as [SJ1] Potential Drug Targets. J. Mol. Genet. 
Medicine (in press). 

      [6] Ji, S. (2018).  The Cell Language Theory: Connecting Mind and Matter. 
World Scientific 

  Publishing, New Jersey.  Chapter 8. 

      [7] Boltzmann distribution law.

      [8] Weaver, W. (1948) Science and Complexity. American Scientist 


  - - - - - - - - - - - - - -the Email to Dr. Grossberg --------------------- - 
- - --- - -- - - - - - - - - - - - - - 


   Hi Dr. Grossberg,

  Thank you for your thought-provoking lecture (entitled "From Sisyphus to 
Boltzmann: an example of repulsive depletion interaction") this afternoon at 
the 119th Statistical Mechanics Conference at Rutgers. 

  Your lecture prompted me to construct the following table based on my 
experience as a theoretical cell biologist over the last 4 decades as 
summarized in [1, 2].

  According to Table 1, we can recognize three distinct kinds of particle 
motions in physics and biology that may be studied in 3 distinct fields of 
specialization, tentatively identified with (i) statistical mechanics, (ii) 
stochastic mechanics, and (iii) what I recently referred to as 
"info-statistical mechanics" [1,pp. 102-107; 2, pp. 371-374]. 

        Table 1.  The trichotomy of particle motions in physics and biology
        Particle Motions
       Random motion (1)

        (Statistical mechanics)
       Passive motion (2)
        (Stochastic mechanics ?)
       Active motion (3)

        (Info-statistical mechanics ?)

        [1, 2]
        Energy Source
       homogeneous thermal environment

        (e.g., magnetic field)
        (e.g., chemical reactions)

       dust particles in water

        at equilibrium
       dust particles in water in flow
       a piece of sodium metal in water at equilibrium
        Brownian motion
       e.g., Orenstein-Uhlenbeck process
       artificial molecular machines

       dead bacteria in water at equilibrium
       live bacteria moving down a gradient
       live bacteria swimming against a gradient
        Brownian motion
       diffusion driven by gradient
       Chemotaxis driven by ATP hydroly

    a.. In your lecture today,. you referred to "active motions" of inanimate 
particles, which seems to correspond to "passive motions" in the above table, 
since the driving force for your particle motion is external to the particle. 
If you wish to preserve the term "active motion", it seems to me necessary to 
differentiate between the "externally driven active motion" and the "internally 
driven active motion".  On the other hand, if we adopt the terms,"active" vs. 
"passive" particle motions, we have a precedence in biology where "active 
transport" (e.g., the Na/K pump) and "passive transport" (e.g., the Na/Ca 
exchange channel) are well known.  In fact, the triadic classification of 
particle motions defined in Table 1 can be applied to ion movements across 
biomembranes with an equal force. 

  If you have any questions or comments on the suggestions made in Table 1, I 
would appreciate hearing from you. 

  All the best.



     [1] Ji, S. (2012). Molecular Theory of the Living Cell: Concepts, 
Molecular Mechanisms, and Biomedical Applications. Springer, New York.

     [2] Ji, S. (2018).  The Cell Language Theory: Connecting Mind and Matter.  
World Scientific Publishing, New Jersey. 

  With all the best.


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