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Thank you so much for your post!

Here are some links and sources which may help to explain why I say that the genetic code contains algorithms:

Molecular Information Theory and the Theory of Molecular Machines

adaptation has been achieved by the process, already mentioned, which hinges on the gaining of information by means of genetic change and natural selection, as well as on the storing of knowledge in the code of the chain molecules in the genome.
-- Konrad Lorenz, Nobel Prize lecture, 1973

Examples of Molecular Machines

Theory of Molecular Machines

Molecular Machine Operations

Physics and Life - Lecture in honour of Abdus Salam - Paul Davies, Physics Department Imperial College London

The belief that life is ‘written into the laws of nature is sometimes called biological determinism (Shapiro, 1986). In its most extreme form, as advocated for example by Sidney Fox (Fox, 1988), it asserts that the laws of the universe are cunningly rigged to coax life into being from lifeless chemicals, by favouring the production of just those molecules that life needs. On this manifestly teleological view, life’s information content derives from the physical laws that generate the informational molecules. This view is hard to sustain, since the information content, as measured algorithmically (Chaitin, 1990), of the known laws of physics at least, is demonstrably low (Yockey, 1992). That is why crystals, which are determined by those laws, have low information content, being just regular arrays of atoms. By contrast, DNA is a random string of atoms - the ‘aperiodic crystal famously predicted by Schrödinger (1944) - and so has high algorithmic information content; it is then hard to see how such an entity could be a product of law alone. In this respect it is worth noting that although the backbone structure of DNA is determined by the laws of physics and chemistry, the precise sequence of nucleotides the ‘letters of the genetic alphabet are not. There are no chemical bonds between successive nucleotides; chemistry is indifferent to the sequence chosen.,,,

In the living cell, nucleic acids and proteins, which are scarcely on nodding terms chemically, deal with each other via an information channel, i.e. using software rather than hardware, written in a triplet mathematical code. The advantage of life ‘going digital in this way is much greater flexibility and fidelity (as is also the case with digitization in electronic devices). The situation can be likened to flying a kite versus a radio-controlled plane. A kite is hard-wired to the controller, and is clumsy to control by pulling on the strings. By contrast, a radio-controlled plane is easier to fly because the controller’s instructions are digitized and transmitted to the plane, where they are decoded and used to harness local energy sources. The radio waves themselves do not push and pull the plane around; they merely convey the information. Analogously, nucleic acids do not themselves assemble proteins, they relay the instructions for ribosomes to do it. This frees protein assembly from the strictures of chemistry, and permits life to choose whatever amino acid sequences it needs. So, far from deriving from physics and chemistry, biological information is quasi-independent of it. To explain the origin of this information-based control, we need to understand how mere hardware (atoms) wrote its own software.

Note that we must do more than simply explain where information per se came from. A gene is a set of coded instructions (e.g. for the manufacture of a protein). To be effective, there must exist a molecular milieu that can decode and interpret the instructions, and carry them out, otherwise the sequence information in the DNA is just so much gobbledygook. The information is therefore semantic in content, i.e. it must mean something (KEpers, 1985). So we are faced with the task of understanding the nature and origin of semantic, or meaningful, information. Since the very concept of information emerged from communication theory in the realm of human discourse, this is no trivial matter. Information is not like mass or energy: you can’t tell by looking whether a molecule has it or not. As yet, there is no ‘info-dynamics comparable to the dynamics of matter, let alone an understanding of how ‘meaning emerges in nature…

Can molecular Darwinism explain biogenesis? Maybe, but we have scant idea what those first replicating molecules might be. Examination of real organic replicator systems like RNA/proteins indicates that even the simplest replicators are extremely large and complex molecules, unlikely to form by chance. Moreover, the smaller the molecules the sloppier they copy, suggesting that molecules small enough to form by chance would be very bad at replicating information, and thus subject to Eigen’s error catastrophe (Eigen & Schuster, 1979), whereby information is eroded by the inaccurate copying process faster than natural selection can inject it.

I concede that if something like the RNA world (Cech, 1986) were given to us ready-made, it has the capacity to evolve into life as we know it. But it strains credulity to suppose that the RNA world sprang into being in one huge chemical transformation. Likely it would be the product of a long series of steps. We can liken the situation to a vast decision tree of chemical reactions, with the RNA world as one tiny twig on the tree. (There is the question of whether there are other twigs that could lead to life, but I shall assume here that the RNA route is the only one.) So we need to understand how a hypothetical class of simple, small replicators navigated through that decision tree and ‘found the RNA twig. Was this just a lucky fluke, or is there something other than a random walk involved?…

Yockey comments

The Physics of Symbols: Bridging the Epistemic Cut

Syntactic Autonomy: Or Why There is no Autonomy Without Symbols and how Self-Organizing Systems Systems Might Evolve Them

Complexity International – Brief Comments on Junk DNA (pdf)

Language Like Features in Junk DNA

The Genetic Algorithms archive

International Society for Genetic and Evolutionary Computation – ISGEC