Descriptions are not universal insofar as they refer to some building blocks which cannot be changed without altering the significance of the descriptions. In the genetic system, the parts are amino acids. Computational parts might be for example the building blocks of neural networks coded by genetic algorithms. We can see that a self-reproducing organism following this scheme is an entanglement of symbolic controls and dynamic constraints which is closed on its semantics. Howard Pattee calls such a principle of self-organization semantic closure .
Semantic closure requires the parts problem discussed above to be explicitly taken into account.
The evolvability of a self-reproducing system, to be discussed below, depends on the dynamic parts used by the semantic code. If the parts are very simple, then the descriptions will have to be very complicated, whereas if the parts possess rich dynamic properties, the descriptions can be simpler since they will take for granted a lot of the dynamics that otherwise would have to be specified. Low programmability grants self-reproducing systems the ability to change without destroying their own organization, though it also reduces the space of possible evolvable configurations. We will discuss such systems later in this course. This ability to transmit mutations is precisely at the core of the principle of natural selection of modern Darwinism.