Recursion, Incursion, and Hyper-incursion

Recursion, Incursion, and Hyper-incursion

 

How do Past and Future inform the present?

What happens in the Present is not only determined by the Past but also by the Future.  Karma and Destiny both play a role as to what is going on in your life Now.

Key Terms

  • Recursion
  • Incursion
  • Hyper Incursion
  • Discrete Processes
  • Cellular Automata
  • Fractal Machine
  • Hypersets
  • Interpenetration
  • Turing Machine
  • Symmetry
  • Non Well Founded Set Theory
  • Sets as Graphs
  • Leela
  • Predetermined Future
  • Bhagya
  • Fate
  • Destiny
  • Karma
  • Anticipation
  • Four Causes of Aristotle
  • Material Cause
  • Formal Cause
  • Efficient Cause
  • Final Cause
  • Left Computer
  • Right Computer
  • Parallel Computing
  • Fifth and the Fourth in Music Theory
  • Bicameral Brain
  • Hemispheric Division of Brain
  • One, Two, Three.  Where is the Fourth?

From GENERATION OF FRACTALS FROM INCURSIVE AUTOMATA, DIGITAL DIFFUSION AND WAVE EQUATION SYSTEMS

The recursion consists of the computation of the future value of the variable vector X(t+l) at time t+l from the values of these variables at present and/or past times, t, t-l, t-2 ….by a recursive function :

X (t+ 1) =f(X(t), X(t-1) …p..)

where p is a command parameter vector. So, the past always determines the future, the present being the separation line between the past and the future.

Starting from cellular automata, the concept of Fractal Machines was proposed in which composition rules were propagated along paths in the machine frame. The computation is based on what I called “INclusive reCURSION”, i.e. INCURSION (Dubois, 1992a- b). An incursive relation is defined by:

X(t+l) =f(…, X (t+l), X(t), X(t-1) ..p..).

which consists in the computation of the values of the vector X(t+l) at time t+l from the values X(t-i) at time t-i, i=1, 2 …. , the value X(t) at time t and the value X(t+j) at time t+j, j=l, 2, …. in function of a command vector p. This incursive relation is not trivial because future values of the variable vector at time steps t+l, t+2 …. must be known to compute them at the time step t+ 1.

In a similar way to that in which we define hyper recursion when each recursive step generates multiple solutions, I define HYPERINCURSION. Recursive computational transformations of such incursive relations are given in Dubois and Resconi (1992, 1993a-b).

I have decided to do this for three reasons. First, in relativity theory space and time are considered as a four-vector where time plays a role similar to space. If time t is replaced by space s in the above definition of incursion, we obtain

X(s+ l) =f( …, X(s+ 1), X(s), X (s-l) …p.).

and nobody is astonished: a Laplacean operator looks like this. Second, in control theory, the engineers control engineering systems by defining goals in the future to compute their present state, similarly to our haman anticipative behaviour (Dubois, 1996a-b). Third, I wanted to try to do a generalisation of the recursive and sequential Turing Machine in looking at space-time cellular automata where the order in which the computations are made is taken into account with an inclusive recursion.

We have already proposed some methods to realise the design of any discrete systems with an extension of the recursion by the concept of incursion and hyperincursion based on the Fractal Machine, a new type of Cellular Automata, where time plays a central role. In this framework, the design of the model of any discrete system is based on incursion relations where past, present and future states variables are mixed in such a way that they define an indivisible wholeness invariant. Most incursive relations can be transformed in different sets of recursive algorithms for computation. In the same way, the hyperincursion is an extension of the hyper recursion in which several different solutions can be generated at each time step. By the hyperincursion, the Fractal Machine could compute beyond the theoretical limits of the Turing Machine (Dubois and Resconi, 1993a-b). Holistic properties of the hyperincursion are related to the Golden Ratio with the Fibonacci Series and the Fractal Golden Matrix (Dubois and Resconi, 1992). An incursive method was developed for the inverse problem, the Newton- Raphson method and an application in robotics (Dubois and Resconi, 1995). Control by incursion was applied to feedback systems (Dubois and Resconi, 1994). Chaotic recursions can be synchronised by incursion (1993b). An incursive control of linear, non- linear and chaotic systems was proposed (Dubois, 1995a, Dubois and Resconi, 1994, 1995). The hyperincursive discrete Lotka-Voiterra equations have orbital stability and show the emergence of chaos (Dubois, 1992). By linearisation of this non-linear system, hyperincursive discrete harmonic oscillator equations give stable oscillations and discrete solutions (Dubois, 1995). A general theory of stability by incursion of discrete equations systems was developed with applications to the control of the numerical instabilities of the difference equations of the Lotka-Volterra differential equations as well as the control of the fractal chaos in the Pearl-Verhulst equation (Dubois and Resconi, 1995). The incursion harmonic oscillator shows eigenvalues and wave packet like in quantum mechanics. Backward and forward velocities are defined in this incursion harmonic oscillator. A connection is made between incursion and relativity as well as the electromagnetic field. The foundation of a hyperincursive discrete mechanics was proposed in relation to the quantum mechanics (Dubois and Resconi, 1993b, 1995).

This paper will present new developments and will show that the incursion and hyper-incursion could be a new tool of research and development for describing systems where the present state of such systems is also a function of their future states. The anticipatory property of incursion is an incremental final cause which could be related to the Aristotelian Final Cause.

Aristotle identified four explicit categories of causation: 1. Material cause; 2. Formal cause; 3. Efficient cause; 4. Final cause. Classically, it is considered that modem physics and mechanics only deal with efficient cause and biology with material cause. Robert Rosen (1986) gives another interpretation and asks why a certain Newtonian mechanical system is in the state (phase) Ix(t) (position), v(t) (velocity)]:

1. Aristotle’s “material cause” corresponds to the initial conditions of the system [x(0), v(0)] at time t=0.

2. The current cause at the present time is the set of constraints which convey to the system an “identity”, allowing it to go by recursion from the given initial phase to the latter phase, which corresponds to what Aristotle called formal cause.

3. What we call inputs or boundary conditions are the impressed forces by the environment, called efficient cause by Aristotle.

As pointed out by Robert Rosen, the first three of Aristotle’s causal categories are tacit in the Newtonian formalism: “the introduction of a notion of final cause into the Newtonian picture would amount to allowing a future state or future environment to affect change of state in the present, and this would be incompatible with the whole Newtonian picture. This is one of the main reasons that the concept of Aristotelian finality is considered incompatible with modern science.

In modern physics, Aristotelian ideas of causality are confused with determinism, which is quite different…. That is, determinism is merely a mathematical statement of functional dependence or linkage. As Russell points out, such mathematical relations, in themselves, carry no hint as to which of their variables are dependent and which are independent.”

The final cause could impress the present state of evolving systems, which seems a key phenomenon in biological systems so that the classical mathematical models are unable to explain many of these biological systems. An interesting analysis of the Final Causation was made by Emst von Glasersfeld (1990). The self-referential fractal machine shows that the hyperincursive field dealing with the final cause could be also very important in physical and computational systems. The concepts of incursion and hyper-incursion deal with an extension of the recursive processes for which future states can determine present states of evolving systems. Incursion is defined as invariant functional relations from which several recursive models with interacting variables can be constructed in terms of diverse physical structures (Dubois & Resconi, 1992, 1993b). Anticipation, viewed as an Aristotelian final cause, is of great importance to explain the dynamics of systems and the semantic information (Dubois, 1996a-b). Information is related to the meaning of data. It is important to note that what is usually called Information Theory is only a communication theory dealing with the communication of coded data in channels between a sender and a receptor without any reference to the semantic aspect of the messages. The meaning of the message can only be understood by the receiver if he has the same cultural reference as the sender of the message and even in this case, nobody can be sure that the receiver understands the message exactly as the sender. Because the message is only a sequential explanation of a non-communicable meaning of an idea in the mind of the sender which can be communicated to the receiver so that a certain meaning emerges in his mind. The meaning is relative or subjective in the sense that it depends on the experiential life or imagination of each of us. It is well- known that the semantic information of signs (like the coding of the signals for traffic) are the same for everybody (like having to stop at the red light at a cross roads) due to a collective agreement of their meaning in relation to actions. But the semantic information of an idea, for example, is more difficult to codify. This is perhaps the origin of creativity for which a meaning of something new emerges from a trial to find a meaning for something which has no a priori meaning or a void meaning.

Mind dynamics seems to be a parallel process and the way we express ideas by language is sequential. Is the sequential information the same as the parallel information? Let us explain this by considering the atoms or molecules in a liquid. We can calculate the average velocity of the particles from in two ways. The first way is to consider one particular particle and to measure its velocity during a certain time. One obtains its mean velocity which corresponds to the mean velocity of any particle of the liquid. The sec- ond way is to consider a certain number of particles at a given time and to measure the velocity of each of them. This mean velocity is equal to the first mean velocity. So there are two ways to obtain the same information. One by looking at one particular element along the time dimension and the other by looking at many elements at the same time. For me, explanation corresponds to the sequential measure and understanding to the parallel measure. Notice that ergodicity is only available with simple physical systems, so in general we can say that there are distortions between the sequential and the parallel view of any phenomenon. Perhaps the brain processes are based on ergodicity: the left hemisphere works in a sequential mode while the right hemisphere works in a parallel mode. The left brain explains while the right brain understands. The two brains arecomplementary and necessary.

Today computer science deals with the “left computer”. Fortunately, the informaticians have invented parallel computers which are based on complex multiplication of Turing Machines. It is now the time to reconsider the problem of looking at the “right computer”. Perhaps it will be an extension of the Fractal Machine (Dubois & Resconi, 1993a).

I think that the sequential way deals with the causality principle while the parallel way deals with a finality principle. There is a paradox: causality is related to the successive events in time while finality is related to a collection of events at a simultaneous time, i.e. out of time.Causality is related to recursive computations which give rise to the local generation of patterns in a synchronic way. Finality is related to incursive or hyperincursive symmetry invariance which gives rise to an indivisible wholeness, a holistic property in a diachronic way. Recursion (and Hyper recursion) is defined in the Sets Theory and Incursion (and Hyperincursion) could be defined in the new framework of the Hypersets Theory (Aczel, 1987; Barwise, Moss, 1991).

If the causality principle is rather well acknowledged, a finality principle is still controversial. It would be interesting to re-define these principles. Causality is defined for sequential events. If x(t) represents a variable at time t, a causal rule x(t+l) = f(x(t)) gives the successive states of the variable x at the successive time steps t, t+l, t+2, … from the recursive functionf(x(t)), starting with an initial state x(0) at time t=0. Defined like this, the system has no degrees of freedom: it is completely determined by the function and the initial condition. No new things can happen for such a system: the whole future is completely determined by its past. It is not an evolutionary system but a developmental system. If the system tends to a stable point, x(t+l) = x(t) and it remains in this state for ever. The variable x can represent a vector of states as a generalisation.

In the same way, I think that determinism is confused with predictability, in modern physics. The recent fractal and deterministic chaos theory (Mandeibrot, 1982; Peitgen, Jurgens, Saupe, 1992) is a step beyond classical concepts in physics. If the function is non-linear, chaotic behaviour can appear, what is called (deterministic) chaos. In this case, determinism does not give an accurate prediction of the future of the system from its initial conditions, what is called sensitivity to initial conditions. A chaotic system loses the memory of its past by finite computation. But it is important to point out that an average value, or bounds within which the variable can take its values, can be known;

it is only the precise values at the successive steps which are not predictable. The local information is unpredictable while the global symmetry is predictable. Chaos can presents a fractai geometry which shows a self-similarity of patterns at any scale.

A well-known fractal is the Sierpinski napkin. The self-similarity of pattems at any scale can be viewed as a symmetry invariance at any scale. An interesting property of such fractals is the fact that the final global pattern symmetry can be completely independent of the local pattern symmetry given as the initial condition of the process from which the fractal is built. The symmetry of the fractal structure, a final cause, can be independent of the initial conditions, a material cause. The formal cause is the local symmetry of the generator of the fractal, independently of its material elements and the efficient cause can be related to the recursive process to generate the fractal. In this particular fractal geometry, the final cause is identical to the final cause. The efficient cause is the making of the fractal and the material cause is just a substrate from which the fractal emerges but this substrate doesn’t play a role in the making.

Finally, the concepts of incursion and hyperincursion can be related to the theory of hypersets which are defined as sets containing themselves. This theory of hypersets is an alternative theory to the classical set theory which presents some problems as the in- completeness of G6del: a formal system cannot explain all about itself and some propositions cannot be demonstrated as true or false (undecidability). Fundamental entities of systems which are considered as ontological could be explain in a non-ontological way by self-referential systems.

Please see my related posts

On Anticipation: Going Beyond Forecasts and Scenarios

Autocatalysis, Autopoiesis and Relational Biology

Key sources of Research

 

Computing Anticipatory Systems with Incursion and Hyperincursion

Daniel M. DUBOIS

 

Click to access cd554835f0ae367c3d3e3fa40f3e5e5f5f11.pdf

 

 

 

Anticipation in Social Systems:

the Incursion and Communication of Meaning

Loet Leydesdorff 

Daniel M. Dubois

Click to access casys03.pdf

 

 

 

GENERATION OF FRACTALS FROM INCURSIVE AUTOMATA, DIGITAL DIFFUSION AND WAVE EQUATION SYSTEMS

Daniel M. Dubois

 

Click to access dubois.pdf

 

 

 

Non-wellfounded Set Theory

https://plato.stanford.edu/entries/nonwellfounded-set-theory/

Hypersets

  • Jon Barwise &
  • Larry Moss

https://link.springer.com/article/10.1007/BF03028340

Non-well-founded set theory

https://en.wikipedia.org/wiki/Non-well-founded_set_theory

What is Code Biology?

What is Code Biology?

 

 

 

Key Terms

  • Code Biology
  • Biosemiotics
  • Charles Sanders Peirce
  • Genetic Code
  • Musical Harmony
  • Symmetry
  • Jay Kappraff
  • Gary Adamson
  • Pythagorean Triples
  • Harmonic Laws
  • Numbers
  • Geometry
  • Matrices
  • Self, Culture, Nature
  • I, We, It, Its
  • Sergey V. Petoukhov
  • Codes
  • Meaning
  • Value
  • Marcello Barbieri
  • RNA, DNA, Proteins, Cells
  • Code Semiotics
  • Ferdinand D Saussure

 

What is Code Biology?

Codes and conventions are the basis of our social life and from time immemorial have divided the world of culture from the world of nature. The rules of grammar, the laws of government, the precepts of religion, the value of money, the rules of chess etc., are all human conventions that are profoundly different from the laws of physics and chemistry, and this has led to the conclusion that there is an unbridgeable gap between nature and culture. Nature is governed by objective immutable laws, whereas culture is produced by the mutable conventions of the human mind.

In this millennia-old framework, the discovery of the genetic code, in the early 1960s, came as a bolt from the blue, but strangely enough it did not bring down the barrier between nature and culture. On the contrary, a protective belt was quickly built around the old divide with an argument that effectively emptied the discovery of all its revolutionary potential. The argument that the genetic code is not a real code because its rules are the result of chemical affinities between codons and amino acids and are therefore determined by chemistry. This is the ‘Stereochemical theory’, an idea first proposed by George Gamow in 1954, and re-proposed ever since in many different forms (Pelc and Welton 1966; Dunnil 1966; Melcher 1974; Shimizu 1982; Yarus 1988, 1998; Yarus, Caporaso and Knight 2005). More than fifty years of research have not produced any evidence in favour of this theory and yet the idea is still circulating, apparently because of the possibility that stereochemical interactions might have been important at some early stages of evolution (Koonin and Novozhilov 2009). The deep reason is probably the persistent belief that the genetic code must have been a product of chemistry and cannot possibly be a real code. But what is a real code?

The starting point is the idea that a code is a set of rules that establish a correspondence, or a mapping, between the objects of two independent worlds (Barbieri 2003). The Morse code, for example, is a mapping between the letters of the alphabet and groups of dots and dashes. The highway code is a correspondence between street signals and driving behaviours (a red light means ‘stop’, a green light means ‘go’, and so on).

What is essential in all codes is that the coding rules, although completely compatible with the laws of physics and chemistry, are not dictated by these laws. In this sense they are arbitrary, and the number of arbitrary relationships between two independent worlds is potentially unlimited. In the Morse code, for example, any letter of the alphabet could be associated with countless combinations of dots and dashes, which means that a specific link between them can be realized only by selecting a small number of rules. And this is precisely what a code is: a small set of arbitrary rules selected from a potentially unlimited number in order to ensure a specific correspondence between two independent worlds.

This definition allows us to make experimental tests because organic codes are relationships between two worlds of organic molecules and are necessarily implemented by a third type of molecules, called adaptors, that build a bridge between them. The adaptors are required because there is no necessary link between the two worlds, and a fixed set of adaptors is required in order to guarantee the specificity of the correspondence. The adaptors, in short, are the molecular fingerprints of the codes, and their presence in a biological process is a sure sign that that process is based on a code.

This gives us an objective criterion for discovering organic codes and their existence is no longer a matter of speculation. It is, first and foremost, an experimental problem. More precisely, we can prove that an organic code exists, if we find three things: (1) two independents worlds of molecules, (2) a set of adaptors that create a mapping between them, and (3) the demonstration that the mapping is arbitrary because its rules can be changed, at least in principle, in countless different ways.

 

Two outstanding examples

The genetic code

In protein synthesis, a sequence of nucleotides is translated into a sequence of amino acids, and the bridge between them is realized by a third type of molecules, called transfer-RNAs, that act as adaptors and perform two distinct operations: at one site they recognize groups of three nucleotides, called codons, and at another site they receive amino acids from enzymes called aminoacyl-tRNA-synthetases. The key point is that there is no deterministic link between codons and amino acids since it has been shown that any codon can be associated with any amino acid (Schimmel 1987; Schimmel et al. 1993). Hou and Schimmel (1988), for example, introduced two extra nucleotides in a tRNA and found that that the resulting tRNA was carrying a different amino acid. This proved that the number of possible connections between codons and amino acids is potentially unlimited, and only the selection of a small set of adaptors can ensure a specific mapping. This is the genetic code: a fixed set of rules between nucleic acids and amino acids that are implemented by adaptors. In protein synthesis, in conclusion, we find all the three essential components of a code: (1) two independents worlds of molecules (nucleotides and amino acids), (2) a set of adaptors that create a mapping between them, and (3) the proof that the mapping is arbitrary because its rules can be changed.

 

The signal transduction codes

Signal transduction is the process by which cells transform the signals from the environment, called first messengers, into internal signals, called second messengers. First and second messengers belong to two independent worlds because there are literally hundreds of first messengers (hormones, growth factors, neurotransmitters, etc.) but only four great families of second messengers (cyclic AMP, calcium ions, diacylglycerol and inositol trisphosphate) (Alberts et al. 2007). The crucial point is that the molecules that perform signal transduction are true adaptors. They consists of three subunits: a receptor for the first messengers, an amplifier for the second messengers, and a mediator in between (Berridge 1985). This allows the transduction complex to perform two independent recognition processes, one for the first messenger and the other for the second messenger. Laboratory experiments have proved that any first messenger can be associated with any second messenger, which means that there is a potentially unlimited number of arbitrary connections between them. In signal transduction, in short, we find all the three essential components of a code: (1) two independents worlds of molecules (first messengers and second messengers), (2) a set of adaptors that create a mapping between them, and (3) the proof that the mapping is arbitrary because its rules can be changed (Barbieri 2003).

 

A world of organic codes

In addition to the genetic code and the signal transduction codes, a wide variety of new organic codes have come to light in recent years. Among them: the sequence codes (Trifonov 1987, 1989, 1999), the Hox code (Paul Hunt et al. 1991; Kessel and Gruss 1991), the adhesive code (Redies and Takeichi 1996; Shapiro and Colman 1999), the splicing codes (Barbieri 2003; Fu 2004; Matlin et al. 2005; Pertea et al. 2007; Wang and Burge 2008; Barash et al. 2010; Dhir et al. 2010), the signal transduction codes (Barbieri 2003), the histone code (Strahl and Allis 2000; Jenuwein and Allis 2001; Turner 2000, 2002, 2007; Kühn and Hofmeyr 2014), the sugar code (Gabius 2000, 2009), the compartment codes (Barbieri 2003), the cytoskeleton codes (Barbieri 2003; Gimona 2008), the transcriptional code (Jessell 2000; Marquard and Pfaff 2001; Ruiz i Altaba et al. 2003; Flames et al. 2007), the neural code (Nicolelis and Ribeiro 2006; Nicolelis 2011), a neural code for taste (Di Lorenzo 2000; Hallock and Di Lorenzo 2006), an odorant receptor code(Dudai 1999; Ray et al. 2006), a space code in the hippocampus (O’Keefe and Burgess 1996, 2005; Hafting et al. 2005; Brandon and Hasselmo 2009; Papoutsi et al. 2009), the apoptosis code (Basañez and Hardwick 2008; Füllgrabe et al. 2010), the tubulin code (Verhey and Gaertig 2007), the nuclear signalling code (Maraldi 2008), the injective organic codes (De Beule et al. 2011), the molecular codes (Görlich et al. 2011; Görlich and Dittrich 2013), the ubiquitin code (Komander and Rape 2012), the bioelectric code (Tseng and Levin 2013; Levin 2014), the acoustic codes (Farina and Pieretti 2014), the glycomic code (Buckeridge and De Souza 2014; Tavares and Buckeridge 2015) and the Redox code (Jones and Sies 2015).

The living world, in short, is literally teeming with organic codes, and yet so far their discoveries have only circulated in small circles and have not attracted the attention of the scientific community at large.

 

Code Biology

Code Biology is the study of all codes of life with the standard methods of science. The genetic code and the codes of culture have been known for a long time and represent the historical foundation of Code Biology. What is really new in this field is the study of all codes that came after the genetic code and before the codes of culture. The existence of these codes is an experimental fact – let us never forget this – but also more than that. It is one of those facts that have extraordinary theoretical implications.

The first is the role that the organic codes had in the history of life. The genetic code was a precondition for the origin of the first cells, the signal transduction codes divided the descendants of the common ancestor into the primary kingdoms of Archaea, Bacteria and Eukarya, the splicing codes were instrumental to the origin of the nucleus, the histone code provided the rules of chromatin, and the cytoskeleton codes allowed the Eukarya to perform internal movements, including those of mitosis and meiosis (Barbieri 2003, 2015). The greatest events of macroevolution, in other words, were associated with the appearance of new organic codes, and this gives us a completely new understanding of the history of life.

The second great implication is the fact that the organic codes have been highly conserved in evolution, which means that they are the great invariants of life, the sole entities that have been perpetuated while everything else has been changed. Code Biology, in short, is uncovering a new history of life and bringing to light new fundamental concepts. It truly is a new science, the exploration of a vast and still largely unexplored dimension of the living world, the real new frontier of biology.

 

References

Alberts B, Johnson A, Lewis J, Raff M, Roberts K, Walter P (2007) Molecular Biology of the Cell. 5th Ed. Garland, New York.

Barash Y, Calarco JA, Gao W, Pan Q, Wang X, Shai O, Blencow BJ and Frey BJ (2010). Deciphering the splicing code. Nature, Vol 465, 53-59.

Barbieri M (2003) The Organic Codes. An Introduction to Semantic Biology. Cambridge University Press, Cambridge, UK.

Barbieri M (2015) Code Biology. A New Science of Life. Springer, Dordrecht.

Basañez G and Hardwick JM (2008) Unravelling the Bcl-2 Apoptosis Code with a Simple Model System. PLoS Biol 6(6): e154. Doi: 10.137/journal.pbio.0060154.

Berridge M (1985) The molecular basis of communication within the cell. Scientific American, 253, 142-152.

Brandon MP and Hasselmo ME (2009) Sources of the spatial code within the hippocampus. Biology Reports, 1, 3-7.

Buckeridge MS and De Souza AP (2014) Breaking the “Glycomic Code” of cell wall polysaccharides may improve second-generation bioenergy production from biomass. BioEnergy Research, 7, 1065-1073.

De Beule J, Hovig E and Benson M (2011) Introducing Dynamics into the Field of Biosemiotics. Biosemiotics, 4(1), 5-24.

Dhir A, Buratti E, van Santen MA, Lührmann R and Baralle FE, (2010). The intronic splicing code: multiple factors involved in ATM pseudoexon definition. The EMBO Journal, 29, 749–760.

Di Lorenzo PM (2000) The neural code for taste in the brain stem: Response profiles. Physiology and Behaviour, 69, 87-96.

Dudai Y (1999) The Smell of Representations. Neuron 23: 633-635.

Dunnill P (1966) Triplet nucleotide-amino-acid pairing; a stereochemical basis for the division between protein and non-protein amino-acids. Nature, 210, 1267-1268.

Farina A and Pieretti N (2014) Acoustic Codes in Action in a Soundscape Context. Biosemiotics, 7(2), 321–328.

Flames N, Pla R, Gelman DM, Rubenstein JLR, Puelles L and Marìn O (2007) Delineation of Multiple Subpallial Progenitor Domains by the Combinatorial Expression of Transcriptional Codes. The Journal of Neuroscience, 27, 9682–9695.

Fu XD (2004) Towards a splicing code. Cell, 119, 736–738.

Füllgrabe J, Hajji N and Joseph B (2010) Cracking the death code: apoptosis-related histone modifications. Cell Death and Differentiation, 17, 1238-1243.

Gabius H-J (2000) Biological Information Transfer Beyond the Genetic Code: The Sugar Code. Naturwissenschaften, 87, 108-121.

Gabius H-J (2009) The Sugar Code. Fundamentals of Glycosciences. Wiley-Blackwell.

Gamow G (1954) Possible relation between deoxyribonucleic acid and protein structures. Nature, 173, 318.

Gimona M (2008) Protein linguistics and the modular code of the cytoskeleton. In: Barbieri M (ed) The Codes of Life: The Rules of Macroevolution. Springer, Dordrecht, pp 189-206.

Görlich D, Artmann S, Dittrich P (2011) Cells as semantic systems. Biochim Biophys Acta, 1810 (10), 914-923.

Görlich D and Dittrich P (2013) Molecular codes in biological and chemical reaction networks. PLoS ONE 8(1):e54,694, DOI 10.1371/journal.pone.0054694.

Hafting T, Fyhn M, Molden S, Moser MB, Moser EI (2005) Microstructure of a spatial map in the entorhinal cortex. Nature, 436, 801-806.

Hallock RM and Di Lorenzo PM (2006) Temporal coding in the gustatory system. Neuroscience and Behavioral Reviews, 30, 1145-1160.

Hou Y-M and Schimmel P (1988) A simple structural feature is a major determinant of the identity of a transfer RNA. Nature, 333, 140-145.

Hunt P, Whiting J, Nonchev S, Sham M-H, Marshall H, Graham A, Cook M, Alleman R, Rigby PW and Gulisano M (1991) The branchial Hox code and its implications for gene regulation, patterning of the nervous system and head evolution. Development, 2, 63-77.

Jenuwein T and Allis CD (2001) Translating the histone code. Science, 293, 1074-1080.

Jessell TM (2000) Neuronal Specification in the Spinal Cord: Inductive Signals and Transcriptional Codes. Nature Genetics, 1, 20-29.

Jones DP and Sies H (2015) The Redox Code. Antioxidants and Redox Signaling, 23 (9), 734-746.

Kessel M and Gruss P (1991) Homeotic Tansformation of Murine Vertebrae and Concomitant Alteration of Hox Codes induced by Retinoic Acid. Cell, 67, 89-104.

Komander D and Rape M (2012), The Ubiquitin Code. Annu. Rev. Biochem. 81, 203–29.

Koonin EV and Novozhilov AS (2009) Origin and evolution of the genetic code: the universal enigma. IUBMB Life. 61(2), 99-111.

Kühn S and Hofmeyr J-H S (2014) Is the “Histone Code” an organic code? Biosemiotics, 7(2), 203–222.

Levin M (2014) Endogenous bioelectrical networks store non-genetic patterning information during development and regeneration. Journal of Physiology, 592.11, 2295–2305.

Maraldi NM (2008) A Lipid-based Code in Nuclear Signalling. In: Barbieri M (ed) The Codes of Life: The Rules of Macroevolution. Springer, Dordrecht, pp 207-221.

Marquard T and Pfaff SL (2001) Cracking the Transcriptional Code for Cell Specification in the Neural Tube. Cell, 106, 651–654.

Matlin A, Clark F and Smith C (2005) Understanding alternative splicing: towards a cellular code. Nat. Rev. Mol. Cell Biol., 6, 386-398.

Melcher G (1974) Stereospecificity and the genetic code. J. Mol. Evol., 3, 121-141.

Nicolelis M (2011) Beyond Boundaries: The New Neuroscience of Connecting Brains with Machines and How It Will Change Our Lives.Times Books, New York.

Nicolelis M and Ribeiro S (2006) Seeking the Neural Code. Scientific American, 295, 70-77.

O’Keefe J, Burgess N (1996) Geometric determinants of the place fields of hippocampal neurons. Nature, 381, 425-428.

O’Keefe J, Burgess N (2005) Dual phase and rate coding in hippocampal place cells: theoretical significance and relationship to entorhinal grid cells. Hippocampus, 15, 853-866.

Papoutsi M, de Zwart JA, Jansma JM, Pickering MJ, Bednar JA and Horwitz B (2009) From Phonemes to Articulatory Codes: An fMRI Study of the Role of Broca’s Area in Speech Production. Cerebral Cortex,19, 2156 – 2165.

Pelc SR and Weldon MGE (1966) Stereochemical relationship between coding triplets and amino-acids. Nature, 209, 868-870.

Pertea M, Mount SM, Salzberg SL (2007) A computational survey of candidate exonic splicing enhancer motifs in the model plant Arabidopsis thaliana. BMC Bioinformatics, 8, 159.

Ray A, van der Goes van Naters W, Shiraiwa T and Carlson JR (2006) Mechanisms of Odor Receptor Gene Choice in Drosophila. Neuron, 53, 353-369.

Redies C and Takeichi M (1996) Cadherine in the developing central nervous system: an adhesive code for segmental and functional subdivisions. Developmental Biology, 180, 413-423.

Ruiz i Altaba A, Nguien V and Palma V (2003) The emergent design of the neural tube: prepattern, SHH morphogen and GLI code.Current Opinion in Genetics & Development, 13, 513–521.

Schimmel P (1987) Aminoacyl tRNA synthetases: General scheme of structure-function relationship in the polypeptides and recognition of tRNAs. Ann. Rev. Biochem., 56, 125-158.

Schimmel P, Giegé R, Moras D and Yokoyama S (1993) An operational RNA code for amino acids and possible relationship to genetic code. Proceedings of the National Academy of Sciences USA, 90, 8763-8768.

Shapiro L and Colman DR (1999) The Diversity of Cadherins and Implications for a Synaptic Adhesive Code in the CNS. Neuron, 23, 427-430.

Shimizu M (1982) Molecular basis for the genetic code. J. Mol. Evol., 18, 297-303.

Strahl BD and Allis D (2000) The language of covalent histone modifications. Nature, 403, 41-45.

Tavares EQP and Buckeridge MS (2015) Do plant cells have a code? Plant Science, 241, 286-294.

Trifonov EN (1987) Translation framing code and frame-monitoring mechanism as suggested by the analysis of mRNA and 16s rRNA nucleotide sequence. Journal of Molecular Biology, 194, 643-652.

Trifonov EN (1989) The multiple codes of nucleotide sequences. Bulletin of Mathematical Biology, 51: 417-432.

Trifonov EN (1999) Elucidating Sequence Codes: Three Codes for Evolution. Annals of the New York Academy of Sciences, 870, 330-338.

Tseng AS and Levin M (2013) Cracking the bioelectric code. Probing endogenous ionic controls of pattern formation. Communicative & Integrative Biology, 6(1), 1–8.

Turner BM (2000) Histone acetylation and an epigenetic code. BioEssays, 22, 836–845.

Turner BM (2002) Cellular memory and the Histone Code. Cell, 111, 285-291.

Turner BM (2007) Defining an epigenetic code. Nature Cell Biology, 9, 2-6.

Verhey KJ and Gaertig J (2007) The Tubulin Code. Cell Cycle, 6 (17), 2152-2160.

Wang Z and Burge C (2008) Splicing regulation: from a part list of regulatory elements to an integrated splicing code. RNA, 14, 802-813.

Yarus M (1988) A specific amino acid binding site composed of RNA. Science, 240, 1751-1758.

Yarus M (1998) Amino acids as RNA ligands: a direct-RNA-template theory for the code’s origin. J. Mol. Evol.,47(1), 109–117.

Yarus M, Caporaso JG, and Knight R (2005) Origins of the Genetic Code: The Escaped Triplet Theory. Annual Review of Biochemistry, 74,179-198.

 

CODE BIOLOGY, PEIRCEAN BIOSEMIOTICS, AND ROSEN’S RELATIONAL BIOLOGY

The classical theories of the genetic code claimed that its coding rules were determined by chemistry—either by stereochemical affinities or by metabolic reactions—but the experimental evidence has revealed a totally different reality: it has shown that any codon can be associated with any amino acid, thus proving that there is no necessary link between them. The rules of the genetic code, in other words, obey the laws of physics and chemistry but are not determined by them. They are arbitrary, or conventional, rules. The result is that the genetic code is not a metaphorical entity, as implied by the classical theories, but a real code, because it is precisely the presence of arbitrary rules that divides a code from all other natural processes. In the past 20 years, furthermore, various independent discoveries have shown that many other organic codes exist in living systems, which means that the genetic code has not been an isolated case in the history of life. These experimental facts have one outstanding theoretical implication: they imply that in addition to the concept of information we must introduce in biology the concept of meaning, because we cannot have codes without meaning or meaning without codes. The problem is that at present we have two different theoretical frameworks for that purpose: one is Code Biology, where meaning is the result of coding, and the other is Peircean biosemiotics, where meaning is the result of interpretation. Recently, however, a third party has entered the scene, and it has been proposed that Robert Rosen’s relational biology can provide a bridge between Code Biology and Peircean biosemiotics.

 

 

Please see my related posts

Semiotics, Bio-Semiotics and Cyber Semiotics

Autocatalysis, Autopoiesis and Relational Biology

Geometry of Consciousness

Mind, Consciousness and Quantum Entanglement

 

 

Key Sources of Research:

 

Code Biology

http://www.codebiology.org

 

What is Code Biology?

Marcello Barbieri

https://www.researchgate.net/publication/320332986_What_is_Code_Biology

Code Biology, Peircean Biosemiotics, and Rosen’s Relational Biology

Marcello Barbieri

 

 

 

Why Biosemiotics? An Introduction to Our View on the Biology of Life Itself

Kalevi Kull, Claus Emmeche and Jesper Hoffmeyer

 

 

 

BIOSEMIOTICS AND SELF-REFERENCE FROM PEIRCE TO ROSEN

Eliseo Fernández

Click to access PRfinal.pdf

 

 

 

What Does it Take to Produce Interpretation? Informational, Peircean and Code-Semiotic Views on Biosemiotics

Søren Brier & Cliff Joslyn

https://www.researchgate.net/publication/255813854_What_Does_It_Take_to_Produce_Interpretation_Informational_Peircean_and_Code-Semiotic_Views_on_Biosemiotics

Naturalizing semiotics: The triadic sign of Charles Sanders Peirce as a systems property

https://www.ncbi.nlm.nih.gov/pubmed/26276466

 

 

 

BIOSEMIOSIS AND CAUSATION: DEFENDING BIOSEMIOTICS THROUGH ROSEN’S THEORETICAL BIOLOGY OR INTEGRATING BIOSEMIOTICS AND ANTICIPATORY SYSTEMS THEORY1

Arran Gare

http://cosmosandhistory.org/index.php/journal/article/viewFile/806/1396

 

 

 

GENERALIZED GENOMIC MATRICES, SILVER MEANS, AND PYTHAGOREAN TRIPLES

Jay Kappraff

Gary W. Adamson

 

Click to access report0809-12.pdf

https://pdfs.semanticscholar.org/f641/6a1d093e77df80173ed76add159b452924b1.pdf?_ga=2.121727499.1841123216.1571671914-1769689123.1571671914

 

 

The genetic code, 8-dimensional hypercomplex numbers and dyadic shifts

 

Sergey V. Petoukhov

 

Click to access 1102.3596.pdf

 

 

 

A Fresh Look at Number

Jay Kappraff

Gary Adomson

Click to access bridges2000-255.pdf

 

 

 

SYMMETRIES IN MOLECULAR-GENETIC SYSTEMS AND MUSICAL HARMONY

G. Darvas, A.A. Koblyakov, S.V.Petoukhov, I.V.Stepanian

 

Click to access GENETIC_CODE_AND_MUSICAL_HARMONY_2012_PETOUKHOV.pdf

 

 

 

On the Semio-Mathematical Nature of Codes

Yair Neuman & Ophir Nave

Click to access On-the-Semio-Mathematical-Nature-of-Codes.pdf

 

 

GENETIC CODE AS A HARMONIC SYSTEM

Miloje M. Rakočević

 

Click to access 0610044.pdf

 

 

 

Genetic Code Table: A note on the three splittings into amino acid classes

Miloje M. Rakočević

 

Click to access 0903.4110.pdf

 

 

 

GENETIC CODE AS A HARMONIC SYSTEM: THREE SUPPLEMENTS

Miloje M. Rakočević

 

Click to access 0703011.pdf

 

 

THE GENETIC CODE INVARIANCE: WHEN EULER AND FIBONACCI MEET

Tidjani Négadi

 

Click to access 1305.5103.pdf

 

 

 

Genetic Code as a Coherent System

Miloje Rakočević

 

Click to access Genetic-Code-as-a-Coherent-System.pdf

 

 

 

A NEW GENETIC CODE TABLE

Miloje M. Rakočević

 

Click to access A-New-Genetic-Code-Table.pdf

 

 

 

Harmonically Guided Evolution

Richard Merrick

 

Click to access a084ad5ca081cf5ac00c82c77d5857795745.pdf

 

 

 

Golden and Harmonic Mean in the Genetic Code

Miloje M. Rakočević

Click to access 35c07d4f0e09a12acc2d6822a16407a14ccd.pdf

 

Cyber-Semiotics: Why Information is not enough

Cyber-Semiotics: Why Information is not enough

 

CyberSemiotics is a framework developed by Prof. Soren Brier.  He is from Copenhagen Business School, Denmark.

 

 

From Cybersemiotics: a semiotic-systemic transdisciplinary approach

Since the critique of the logical positivists’ unity of science for being too reductionist to be transdisciplinary, most scientists have abandoned this model. But other candidates have emerged. The first was evolutionary system science and cybernetics, which was instrumental in producing the new information science supporting the development of cognitive science, with computation as the central process. But the new info-computational transdisciplinary framework still lacks a phenomenological and hermeneutical foundation just as system science and cybernetics did. Peircean semiotics has this foundation and includes a theory of information and has a transdisciplinary scope, but lacks the self-organization theory developed in autopoiesis theory, which Luhmann uses in his new communicatively based system theory. Cybersemiotics integrates Peirce and Luhmann’s paradigms into a new transdisciplinary framework encompassing the theory of mind as embodied, extended, enacted and embedded.

 

From Can Cybersemiotics Solve the Problem of Informational Transdisciplinarity?

A transdisciplinary theory for cognition and communication has at least been described from the following paradigms

  • An objective information processing view or info-mechanicism;
  • A social constructivist view;
  • A systemic cybernetic view of self-organization;
  • Semiotic paradigms of experience and interpretation (phenomenological and hermeneutical aspects) including biosemiotic going into animal, plant, bacterial and cellular living systems. They all have their transdisciplinary shortcomings.

A transdisciplinary framework called Cybersemiotics that
integrate phenomenological and hermeneutical aspect in Peircean semiotic logic with cybernetic and systemic autopoietic emergentist process-informational view, is suggested.

 

From Cybersemiotics: A New Foundation for Transdisciplinary Theory of Information, Cognition, Meaningful Communication and the Interaction Between Nature and Culture

Cybersemiotics constructs a non-reductionist framework in order to integrate third person knowledge from the exact sciences and the life sciences with first person knowledge described as the qualities of feeling in humanities and second person intersubjective knowledge of the partly linguistic communicative interactions, on which the social and cultural aspects of reality are based. The modern view of the universe as made through evolution in irreversible time, forces us to view man as a product of evolution and therefore an observer from inside the universe. This changes the way we conceptualize the problem and the role of consciousness in nature and culture. The theory of evolution forces us to conceive the natural and social sciences as well as the humanities together in one theoretical framework of unrestricted or absolute naturalism, where consciousness as well as culture is part of nature. But the theories of the phenomenological life world and the hermeneutics of the meaning of communication seem to defy classical scientific explanations. The humanities therefore send another insight the opposite way down the evolutionary ladder, with questions like: What is the role of consciousness, signs and meaning in the development of our knowledge about evolution?

Phenomenology and hermeneutics show the sciences that their prerequisites are embodied living conscious beings imbued with meaningful language and with a culture. One can see the world view that emerges from the work of the sciences as a reconstruction back into time of our present ecological and evolutionary self understanding as semiotic intersubjective conscious cultural and historical creatures, but unable to handle the aspects of meaning and conscious awareness and therefore leaving it out of the story. Cybersemiotics proposes to solve the dualistic paradox by starting in the middle with semiotic cognition and communication as a basic sort of reality in which all our knowledge is created and then suggests that knowledge develops into four aspects of human reality: Our surrounding nature described by the physical and chemical natural sciences, our corporality described by the life sciences such as biology and medicine, our inner world of subjective experience described by phenomenologically based investigations and our social world described by the social sciences. I call this alternative model to the positivistic hierarchy the cybersemiotic star. The article explains the new understanding of Wissenschaft that emerges from Peirce’s and Luhmann’s conceptions.

 

 

 

Please see my related posts:

Meta Integral Theories: Integral Theory, Critical Realism, and Complex Thought

Socio-Cybernetics and Constructivist Approaches

Truth, Beauty, and Goodness: Integral Theory of Ken Wilber

Semiotics, Bio-Semiotics and Cyber Semiotics

Society as Communication: Social Systems Theory of Niklas Luhmann

Autocatalysis, Autopoiesis and Relational Biology

Systems View of Life: A Synthesis by Fritjof Capra

Systems and Organizational Cybernetics

 

 

 

 

Key Sources of Research:

 

 

Soren Brier

http://cybersemiotics.com/content/søren-brier

 

 

Cybersemiotics:
A New Foundation for Transdisciplinary Theory of Information, Cognition, Meaningful Communication and the Interaction Between Nature and Culture

Søren Brier

 

Click to access cf50ffc5edbc110ccd08279d6d8b513bfbe2.pdf

A transdisciplinary and evolutionary framework encompassing information with meaningful cognition and communication through second order cybernetics and Peircean semiotics

Soren Brier

 

 

 

 

Cybersemiotics
Why information is not enough!

A Trans-Disciplinary Approach to Information, Cognition and Communication Studies, Through an Integration of Niklas Luhmann’s Communication Theory with C. S. Peirce’s Semiotics.

 

Click to access luhmann02.pdf

 

 

FROM FIRST TO THIRD VIA CYBERSEMIOTICS –

A Festschrift honoring Professor Søren Brier on the Occasion of his 60th Birthday

 

Click to access 9788770719964.pdf

 

 

 

Cybersemiotics: a semiotic-systemic transdisciplinary approach

Studia Kulturoznawcze nr 1 (7), 247-265 2015

 

Click to access Studia_Kulturoznawcze-r2015-t-n1_(7)-s247-265.pdf

 

 

 

 

The Paradigm of Peircean Biosemiotics

Soren Brier

https://tidsskrift.dk/signs/article/viewFile/26836/23600

Click to access Brier_2008_peircean_biosemiotics.pdf

 

 

 

Levels of Cybersemiotics: Possible ontologies of signification

Soren Brier

Click to access 2_Brier_v1_2.pdf

 

 

 

Cybersemiotics: An Evolutionary World View Going Beyond Entropy and Information into the Question of Meaning

Søren Brier

https://www.mdpi.com/1099-4300/12/8/1902/pdf

 

 

 

CYBERSEMIOTICS: MERGING THE SEMIOTIC AND CYBERNETIC EVOLUTIONARY VIEW OF REALITY AND CONSCIOUSNESS TO A TRANSDISCIPLINARY VISION OF REALITY

Soren Brier

 

Click to access Brier%20P26.pdf

 

 

 

Cybersemiotics: A New Foundation for Transdisciplinary Theory of Information, Cognition, Meaning, Communication and Consciousness

Soren Brier

Click to access 798.pdf

 

 

Cybersemiotics: A Semiotic-systemic Transdisciplinary Approach

 

Søren Brier

2015

Click to access s_ren_brier_cybersemiotics_a_semiotic_systemic_publishersversion.pdf

 

 

The riddle of the Sphinx answered: On how C. S. Peirce’s transdisciplinary semiotic philosophy of knowing links science and spirituality

 

Søren Brier

 

Click to access Brier_Peirce.pdf

 

Cybersemiotics and the Question of Knowledge

 

Soren Brier

https://www.researchgate.net/publication/242583114_1_Cybersemiotics_and_the_Question_of_Knowledge_1

 

Habit as a Connecting Nature, Mind and Culture in
C.S. Peirce’s Semiotic Pragmaticism †

Søren Brier

http://www.mdpi.com/2504-3900/1/3/226

How Peircean semiotic philosophy connects Western science with Eastern emptiness ontology

https://www.sciencedirect.com/science/article/pii/S0079610717301293

Cybersemiotics and the reasoning powers of the universe: philosophy of information in a semiotic-systemic transdisciplinary approach

Soren Brier

 

https://www.tandfonline.com/doi/abs/10.1080/14688417.2015.1070684

Cybersemiotics: An Evolutionary World View Going Beyond Entropy and Information into the Question of Meaning

Søren Brier

 

http://www.mdpi.com/1099-4300/12/8/1902/htm

 Cybersemiotic Pragmaticism and Constructivism

Søren Brier

 

Click to access 3b1ab5c1eda273c1d4a5f4974c63df847ede.pdf

Cognitive Semiotics

 

Click to access Zlatev2012-Paris1.pdf

 

Semiotics, Bio-Semiotics and Cyber Semiotics

Semiotics, Bio-Semiotics, and Cyber Semiotics

 

From The Biosemiotic Approach in Biology: Theoretical Bases and Applied Models

Biosemiotics is a growing field that investigates semiotic processes in the living realm in an attempt to combine the findings of the biological sciences and semiotics. Semiotic processes are more or less what biologists have typically referred to as “signals,” “codes,” and “information pro- cessing” in biosystems, but these processes are here understood under the more general notion of semiosis, that is, the production, action, and interpretation of signs. Thus, biosemiotics can be seen as biology interpreted as a study of living sign systems—which also means that semiosis or sign process can be seen as the very nature of life itself. In other words, biosemiotics is a field of research investigating semiotic processes (meaning, signification, communication, and habit formation in living systems) and the physicochemical preconditions for sign action and interpretation.

To treat biosemiotics as biology interpreted as sign systems study is to emphasize an important intertheoretical relation between biology as we know it (as a field of inquiry) and semiotics (the study of signs). Biosemiotics offers a way of understanding life in which it is considered not just from the perspectives of physics and chemistry, but also from a view of living systems that stresses the role of signs conveyed and inter- preted by other signs in a variety of ways, including by means of molecules. In this sense, biosemiotics takes for granted and preserves the complexity of living processes as revealed by the existing fields of biology, from molecular biology to brain science and behavioral studies. However, biosemiotics attempts to bring together separate findings of the various disciplines of biology (including evolutionary biology) into a sign- theoretical perspective concerning the central phenomena of the living world, from the ribosome to the ecosystem and from the beginnings of life to its ultimate meanings. From this perspective, no positivist (i.e., theory-reductionist) form of unification is implied, but simply a broader approach to life processes in general, paying attention to the location of biology between the psychological (the humanities) and the physical (natural) sciences.

Furthermore, by incorporating new concepts, models, and theories from biology into the study of signs, biosemiotics attempts to shed new light on some of the unsolved questions within the general study of sign processes (semiotics), such as the question about the origins of signification in the universe (e.g., Hoffmeyer 1996), and the major thresholds in the levels and evolution of semiosis (Sebeok 1997; Deacon 1997; Kull 2000; Nöth 2000). Here, signification (and sign action) is understood in a broad sense, that is, not simply as the transfer of information, but also as the generation of the very content and meaning of that information in all living sign producers and sign receivers.

Sign processes are thus taken as real: they are governed by regularities (habits, or natural rules) that can be discovered and explained. They are intrinsic in living nature, but we can access them—not directly, but indirectly through other sign processes (e.g., scientific measurements and qualitative distinction methods)—even though the human representation and understanding of these processes in the construction of explanations is built up as a separate scientific sign system distinct from the organisms’ own sign processes.

One of the central characteristics of living systems is the highly organized character of their physical and chemical processes, partly based upon informational and molecular properties of what has been described in the 1960s as the genetic code (or, more precisely, organic codes). Distinguished biologists, such as Ernst Mayr (1982), have seen these informational aspects as one of the emergent features of life, namely, as a set of processes that distinguishes life from everything else in the physical world, except perhaps human-made computers. However, while the informational teleology of computer programs are derived, qua being designed by humans to achieve specific goals, the teleology and informational characteristics of organisms are intrinsic, qua having evolved naturally, through adaptational and evolutionary processes. The reductionist and mechanistic tradition in biology (and philosophy of biology) has seen such processes as being purely physical and having to do with only efficient causation. Biosemiotics is an attempt to use the concepts of semiotics in the sense employed by Charles Sanders Peirce to answer questions about the biological emergence of meaning, intentionality, and a psychological world (CP 5:484).  Indeed, these are questions that are hard to answer within a purely mechanistic and reductionist framework.

 

From The Biosemiotic Approach in Biology: Theoretical Bases and Applied Models

The term “biosemiotic” was first used by F. S. Rothschild in 1962, but Thomas Sebeok has done much to popularize the term and the field.  Apart from Charles Peirce (1939–1914) and Charles Morris (1901– 1979), early pioneers of biosemiotics were Jakob von Uexküll (1864– 1944), Heini Hediger (1908–1992), and Giorgio Prodi (1928–1987), and the founding fathers were Thomas Sebeok (1920–2001) and Thure von Uexküll (1908–2004). After 2000, an institutionalization of biosemiotics can be noticed: since 2001, annual international meetings of biosemioticians have been taking place (initially organized by the Copenhagen and Tartu groups); in 2004, the International Society for Biosemiotic Studies was established (with Jesper Hoffmeyer as its first president; see Favareau 2005); the specialized publications Journal of Biosemiotics (Nova Science) and Biosemiotics (Springer) have appeared; several collections of papers have characterized the scope and recent projects in biosemiotics, such as a special issue of Semiotica 127 (1/4) (1999), Sign Systems Studies 30 (1) (2002), Sebeok and Umiker-Sebeok 1992, Witzany 2007, and Barbieri 2007.

Also, from the 1960s to the 1990s, the semiotic approach in biology was developed in various branches:

a. Zoosemiotics, the semiotics of animal behavior and communication

b. Cellular and molecular semiotics, the study of organic codes and protolinguistic features of cellular processes

c. Phytosemiotics, or sign processes in plant life

d. Endosemiotics, or sign processes in the organism’s body

e. Semiotics in neurobiology

f. Origins of semiosis and semiotic thresholds

 

From Cybersemiotics: A New Foundation for Transdisciplinary Theory of Information, Cognition, Meaningful Communication and the Interaction Between Nature and Culture

 

Cybersemiotics constructs a non-reductionist framework in order to integrate third person knowledge from the exact sciences and the life sciences with first person knowledge described as the qualities of feeling in humanities and second person intersubjective knowledge of the partly linguistic communicative interactions, on which the social and cultural aspects of reality are based. The modern view of the universe as made through evolution in irreversible time, forces us to view man as a product of evolution and therefore an observer from inside the universe. This changes the way we conceptualize the problem and the role of consciousness in nature and culture. The theory of evolution forces us to conceive the natural and social sciences as well as the humanities together in one theoretical framework of unrestricted or absolute naturalism, where consciousness as well as culture is part of nature. But the theories of the phenomenological life world and the hermeneutics of the meaning of communication seem to defy classical scientific explanations. The humanities therefore send another insight the opposite way down the evolutionary ladder, with questions like: What is the role of consciousness, signs and meaning in the development of our knowledge about evolution? Phenomenology and hermeneutics show the sciences that their prerequisites are embodied living conscious beings imbued with meaningful language and with a culture. One can see the world view that emerges from the work of the sciences as a reconstruction back into time of our present ecological and evolutionary self- understanding as semiotic intersubjective conscious cultural and historical creatures, but unable to handle the aspects of meaning and conscious awareness and therefore leaving it out of the story. Cybersemiotics proposes to solve the dualistic paradox by starting in the middle with semiotic cognition and communication as a basic sort of reality in which all our knowledge is created and then suggests that knowledge develops into four aspects of human reality: Our surrounding nature described by the physical and chemical natural sciences, our corporality described by the life sciences such as biology and medicine, our inner world of subjective experience described by phenomenologically based investigations and our social world described by the social sciences. I call this alternative model to the positivistic hierarchy the cybersemiotic star. The article explains the new understanding of Wissenschaft that emerges from Peirce’s and Luhmann’s conceptions.

 

Key People:

  • Thomas Sebeok
  • L M Rocha
  • Jesper Hoffmeyer
  • Charles Sanders  Pierce
  • Soren Brier
  • Marcello Barbieri
  • Howard Pattee
  • Jakob von Uexküll
  • Stanley Salthe
  • Claus Emmeche
  • M. Florkin
  • Kalevi Kull
  • Donald Favareau
  • Umberto Eco
  • Koichiro Matsuno
  • Thure von Uexküll
  • Gregory Bateson

 

Key Sources of Research:

 

A Short History of Biosemiotics

Marcello Barbieri

Click to access Marcello%20Barbieri%20(2009)%20A%20Short%20History%20of%20Biosemiotics.pdf

 

 

The Biosemiotic Approach in Biology : Theoretical Bases and Applied Models

Jo ã o Queiroz, Claus Emmeche, Kalevi Kull, and Charbel El-Hani

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.94.171&rep=rep1&type=pdf

 

 

Irreducible and complementary semiotic forms

Howard Pattee

 

Click to access irreducible_and_complementary_semiotic_howard_pattee.pdf

 

EVOLVING SELF-REFERENCE: Matter, Symbols, AND SEMANTIC CLOSURE 

Howard Pattee

http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=0E1C125F151B5165F839E8FAC5411A00?doi=10.1.1.17.6467&rep=rep1&type=pdf

 

Essential Readings in Biosemiotics: Anthology and Commentary

D. Favareau,

Essential Readings in Biosemiotics, Biosemiotics 3,

Springer Science+Business Media B.V. 2010

 

 

Introduction: An Evolutionary History of Biosemiotics

Donald Favareau

Essential Readings in Biosemiotics, Biosemiotics 3

 

Click to access Lesson_13_Favareau_History_biosemiotics.pdf

 

 

Introduction to Biosemiotics: The New Biological Synthesis

edited by Marcello Barbieri

 

Cybersemiotics:
A New Foundation for Transdisciplinary Theory of Information, Cognition, Meaningful Communication and the Interaction Between Nature and Culture

Søren Brier

 

Click to access Brier,%20Cybersemiotics,%20Vol.%209,%20No.%202.pdf

 

 

Levels of Cybersemiotics: Possible ontologies of signification

Søren Brier

 

Click to access 2_Brier_v1_2.pdf

 

Design and Information in Biology: From Molecules to Systems

By J. A. Bryant

 

Cognitive Biology: Dealing with Information from Bacteria to Minds

By Gennaro Auletta

 

The cell as the smallest DNA-based molecular computer

Sungchul Ji

 

Click to access The_cell_as_the_smallest_DNA_based_molecular_computer.pdf

 

Semiotics Web page of Umberto Eco

http://www.umbertoeco.com/en/semiotics-links.html

 

Biosemiotics in the twentieth century: A view from biology

KALEVI KULL

 

Click to access semi.1999.127.385.pdf

 

Biosemiotics: a new understanding of life

Marcello Barbieri

 

Click to access Bar08.pdf

 

What Does it Take to Produce Interpretation? Informational, Peircean and Code-Semiotic Views on Biosemiotics

Søren Brier & Cliff Joslyn

Click to access 02e7e529745b2b7e66000000.pdf

 

Spencer-Brown, G. (1972).

Laws of Form

New York: Crown Publishers

 

The Paradigm of Peircean Biosemiotics

Søren Brier

Click to access Brier_2008_peircean_biosemiotics.pdf

 

BIOSEMIOTICS AND BIOPHYSICS — THE FUNDAMENTAL APPROACHES TO THE STUDY OF LIFE

KALEVI KULL

Click to access BiosemBiophys.pdf

 

Biosemiotic Questions

Kalevi Kull & Claus Emmeche & Donald Favareau

Click to access a4414fbb4bdca11561d08cb4de0a0d6c.pdf