Hi Emmanuel and FISers,
Thank you, Emmanuel, for your generous remarks. It is heartening to know that our ideas converge, although we carried out our research independently of each other, a clear example of consilience. (1) I like and agree with the Kolomogorov quote you cited in [1]: "Information is a linguistic description of structures in a given data set." It seems to me that there are 4 key concepts embedded in the above quote, which we may view as the definition of what may be called the "Komogorov information" or the "Kolmogorov-Bateson information" for the convenience of reference: i) data set (e.g., ACAGTCAACGGTCCAA) ii) linguistic description (e.g., Threonine, Valine, Asparagine, Glycine) iii) structure (e.g., 16 mononucdotide, 8 dinucldotides, 5 trinucleotides plus 1) iv) mathematical description (e.g., tensor product of two 2x2 matrices of 4 nucleotides) [2, 3]. The first three elements are obvious, but the 4th is not so obvious but justified in view of the recent work of Petoukhov [2, 3]. (2) Based on these ideas, I have constructed Table 1 below of the various names applied to the two kinds of information which I described as I(-) and I(+) in my previous post. Table 1. The arbitrariness of the signs referring to ‘information’. It doesn’t matter what you call it, as long as your chosen label refers to the right reality, thing, process, mechanisms, etc. 1 Type I Information Type II information 2 Physical Information Sematic information 3 Shannon information Kolmogorov information, or Kolmogorov-Bateson information 4 ‘Meaningless’ information ‘Meaningful’ information 5 I(-) information, or simply I(-) I(+) information, or simply I(+) 6 Quantitative information Qualitative information 7 Mathematical information Linguistic information (see Statement (1)) 8 Formal information Phenomenological information 9 Interpretant-less sign [4] Triadic sign [4] (3) One practical application of the dual theory of information under discussion is in deducing the structure of cell language, or the structure of the linguistics of DNA, in a much more rigorous manner than was possible in 1997 [5]. It is the common practice in biology to use the terms "letters", "words", "sentences", and "texts" without any rigorous definitions. The general rule is to follow the rules of concatenations used in linguistics literally and say that i) just as 26 letters in the English alphabet are combined to form words (the process being called the second articulation [5]), so the 4 letters of the genetic alphabets, A, C, G and T/U, combine in triplets to form genetic codons. Similarly, just as words form sentences and sentences form texts by the same concatenation procedure (or tensor multiplication, mathematically speaking , i.e, linearly arranging words and sentences, respectively (see the second column in Table 2), so the 64 nucleotide triplets combine to form proteins and proteins combine to form metabolic pathways by continuing the concatenation process, or the tensor multiplication of matrices of larger and larger sizes (see the fourth column, which is based on the physical theory of information, i.e., without any involvement of semantics or the first articulation). ii) In contrast to the fourth column just described, we can justify an alternative structural assignments based on the semantic theory of information as shown in the fifth column of Table 2. Here the letters of the cell language alphabet are not always mononucloetoides but thought to be n-nucleotides, such as dinucleotides (when n = 2), trinucleotides (when n =3), tetranucleotides (when n = 4), penta-nucelotides (when n = 5), etc. That is, unlike in human language where the letters of an alphabet usually consist of one symbol, e.g., A, B, C, D, E, . . . , I am claiming that in cell language, the letters can be mononucloetides (i.e., A, G, C, T/U), dinucloeotides (i.e., AG, AC, . . . .) , trinucleotides (i.e., ACT, GTA, . . . ), tetranucleotides (i.e., ACTG, CCGT, . . . .), pentanucleotides (i.e., ACCTG, TCGAT, . . .) and, up to n-nucleotides (also called n-plets [2, 3]), where n is an unknown number whose upper limit is not yet known (at least to me). If this conjecture turns out to be true, then the size of the cell language alphabet can be much larger (10^3 - 10^9 ?) than the size of a typical human linguistic alphabet which is usually less than 10^2, probably due to the limitation of the memory capacity of the human brain. (iii) From linguistics, we learn that there are at least 4 levels of organization, each level characterized by a unique function (see the second column). Without presenting any detailed argument, I just wish to suggest that the linguistic structures deduced based on the semantic information theory (i.e., the fifth column) agree with the human linguistic structures (i.e., the second column) better than does the linguistic structures based on the physical/mathematical/quantitative information theory (i.e., the fourth column), when the functional hierarchy given in the third column is taken into account. Table 2. Two versions of the linguistics of DNA based on (i) the physical information theory, and (ii) the semantic information theory [1]. M stands for a 2x2 matrix whose elements are the 4 genetic nucleotides, A, C, G and T/U, i.e., M = [C A; T G] (see Figure 16 in [2]). The symbol, (x), indicates tensor multiplication [2, 3]. The I to II transition is known in linguistics as the second articulation; the II to III transition as the first articulation [4]; the III to IV transition was referred to as the third articulation [5]. Organization level Human Language Cell Language Structure Function/Semantics Structure based on the Physical Information Theory (PIT) [1] Structure based on the Semantic Information Theory (SIT) [1] I Letters Basic building blocks or basic physical signals 4 Nucleotides (A, C, G, T/U); M = [C A;T G]* mono-, di-, trinucleotides, 4-plets, 5-plets, . . . , n-plets of nucleotides, . . . II Words To denote 16 dinucleotides; M(x)M or M^2 Any combinations of the n-plets/ genes/proteins III Sentences To decide 64 trinucleotides /amino acids; M(x)M(x)M or M^3 Assembly of genes/proteins; or metabolic pathways (MP) IV Texts To argue/compute/ reason (e.g., syllogism) 254 tetranucleotides; Metabolic pathways (?); M(x)M(x)M(x)M or M^4 Networks of MP’s characterized by a unique function (see the second column). Without presenting any detailed argument, I would like to suggest that the linguistic structures deduced based on the semantic information theory (i.e., the fifth column) agree with the human linguistic structures (i.e., the second column) better than does the linguistic structures based on the physical/mathematical/quantitative information theory (i.e., the fourth column). In other words, the structure of cell language deduced based on the semantic information theory agrees better, functionally, with that of the human language than the structure of cell language deduced based on the physical information theory, thus further supporting the 1997 postulate that cell and human languages are isomorphic [5, 6]. If you have any questions or suggestions for improvements on the above tables, I would appreciate hearing from you. All the best. Sung References: [1] Emanuel Diamant, The brain is processing information, not data. Does anybody care?, ISIS Summit Vienna 2015, Extended Abstract. http://sciforum.net/conference/isis-summit-vienna-2015/paper/2842<https://na01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fsciforum.net%2Fconference%2Fisis-summit-vienna-2015%2Fpaper%2F2842&data=02%7C01%7Csji%40pharmacy.rutgers.edu%7C89f81861ee684f05e46b08d559d86fe1%7Cb92d2b234d35447093ff69aca6632ffe%7C1%7C1%7C636513708497810284&sdata=bMlZ324OoEHA5XMQibKiEFsm75NhcpkfIcSRUJbQZNg%3D&reserved=0> [2] Petoukhov, S. (2017). Genetic coding and united-hypercomplex systems in the models of algebraic biology. BioSystems 158: 31-46. [3] Petoukhov, S. (2016). The system-resonance approach in modeling genetic structures. BiosySystems 139:1-11. [4] Ji, S. (2017).Neo-Semiotics: Introducing Zeroness into Peircean Semiotics May Bridge the Knowable and the Unknowable. Prog. Biophys. Mol. Biol. 131:387-401. PDF at http://www.sciencedirect.com/science/article/pii/S0079610717300858?via%3Dihub [5] Ji, S. (1997). Isomorphism between cell and human languages: molecualr biological, bioinformatic and linguistic implications. <http://www.conformon.net/wp-content/uploads/2012/05/Isomorphism1.pdf> BioSystems 44:17-39. PDF at http://www.conformon.net/wp-content/uploads/2012/05/Isomorphism1.pdf [6] Ji, S. (2017). The Cell Language Theory: Connecting Mind and Matter. World Scientific, New Jersey. Chapter 5. ________________________________ From: Fis <fis-boun...@listas.unizar.es> on behalf of Emanuel Diamant <emanl....@gmail.com> Sent: Friday, January 12, 2018 11:20 AM To: fis@listas.unizar.es Subject: [Fis] I salute to Sungchul Dear FISers, I would like to express my pleasure with the current state of our discourse – an evident attempt to reach a more common understanding about information issues and to enrich preliminary given assessments. In this regard, I would like to add my comment to Sungchul’s post of January 12, 2018. Sungchul proposes “to recognize two distinct types of information which, for the lack of better terms, may be referred to as the "meaningless information" or I(-) and "meaningful information" or I(+)”. That is exactly what I am trying to put forward for years, albeit under more historically rooted names: Physical and Semantic information [1]. Never mind, what is crucially important here is that the duality of information becomes publicly recognized and accepted by FIS community. I salute to Sungchul’s suggestion! Best regards, Emanuel. [1] Emanuel Diamant, The brain is processing information, not data. Does anybody care?, ISIS Summit Vienna 2015, Extended Abstract. http://sciforum.net/conference/isis-summit-vienna-2015/paper/2842<https://na01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fsciforum.net%2Fconference%2Fisis-summit-vienna-2015%2Fpaper%2F2842&data=02%7C01%7Csji%40pharmacy.rutgers.edu%7C89f81861ee684f05e46b08d559d86fe1%7Cb92d2b234d35447093ff69aca6632ffe%7C1%7C1%7C636513708497810284&sdata=bMlZ324OoEHA5XMQibKiEFsm75NhcpkfIcSRUJbQZNg%3D&reserved=0>
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