“…Because the replication of instructions is noisy (giving rise to mutations), populations of programs evolve and adapt to user-specified fitness landscapes. The Ne=36171 sequences that we analyse here represent the complete set of all possible self-replicating programs of length L=9 (out of a possible 269≈5.43×1012) obtained via an exhaustive search [31]. ‘Self-replication’ is defined here as the capacity to form a growing colony of programs (in the absence of mutations) and does not necessarily require perfect copying of information.…”
Section: Application To Self-replicating Sequencesmentioning
confidence: 99%
“…We can immediately calculate the information content of these 9-mers using Szostak’s formula (1.1), which gives 6 I9=log2636171269≈5.77mers, that is, about 64% of the sequence is information. The smallest possible replicator in this world has L=8, with an even more compressed code [31]. Figure 1The four largest connected components of the network of self-replicators comprise 31 592 sequences (about 87% of the total).…”
Section: Application To Self-replicating Sequencesmentioning
The information content of symbolic sequences (such as nucleic or amino acid sequences, but also neuronal firings or strings of letters) can be calculated from an ensemble of such sequences, but because information cannot be assigned to single sequences, we cannot correlate information to other observables attached to the sequence. Here we show that an information
score
obtained from multivariate (multiple-variable) correlations within sequences of a ‘training’ ensemble can be used to predict observables of out-of-sample sequences with an accuracy that scales with the complexity of correlations, showing that functional information emerges from a hierarchy of multi-variable correlations.
This article is part of the theme issue ‘Emergent phenomena in complex physical and socio-technical systems: from cells to societies’.
“…Because the replication of instructions is noisy (giving rise to mutations), populations of programs evolve and adapt to user-specified fitness landscapes. The Ne=36171 sequences that we analyse here represent the complete set of all possible self-replicating programs of length L=9 (out of a possible 269≈5.43×1012) obtained via an exhaustive search [31]. ‘Self-replication’ is defined here as the capacity to form a growing colony of programs (in the absence of mutations) and does not necessarily require perfect copying of information.…”
Section: Application To Self-replicating Sequencesmentioning
confidence: 99%
“…We can immediately calculate the information content of these 9-mers using Szostak’s formula (1.1), which gives 6 I9=log2636171269≈5.77mers, that is, about 64% of the sequence is information. The smallest possible replicator in this world has L=8, with an even more compressed code [31]. Figure 1The four largest connected components of the network of self-replicators comprise 31 592 sequences (about 87% of the total).…”
Section: Application To Self-replicating Sequencesmentioning
The information content of symbolic sequences (such as nucleic or amino acid sequences, but also neuronal firings or strings of letters) can be calculated from an ensemble of such sequences, but because information cannot be assigned to single sequences, we cannot correlate information to other observables attached to the sequence. Here we show that an information
score
obtained from multivariate (multiple-variable) correlations within sequences of a ‘training’ ensemble can be used to predict observables of out-of-sample sequences with an accuracy that scales with the complexity of correlations, showing that functional information emerges from a hierarchy of multi-variable correlations.
This article is part of the theme issue ‘Emergent phenomena in complex physical and socio-technical systems: from cells to societies’.
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