GENOMIC ORGANIZATION AND HOPFIELD'S MODEL OF ASSOCIATIVE MEMORY

We consider a description of genomic organization based on a neural network model of associative memory (Hopfield's model). This description is consonant with the view that the cell cycle involves transitions between successive phases, each entailing an assortment of metabolically distinct states. A picture emerges where a cell at a particular state in its life cycle fulfills its metabolic needs through a specific set of genetic patterns, comprising a characteristic, stable one, plus those belonging to its basin of attraction in the sense of the neural network metaphor. A gene may belong to more than one stable configuration, so its function will depend on its context as defined by the particular pattern playing a dominant role at any given moment. The model provides a conceptual framework in genomic analysis and yields quantitative results in some cases, that can be assessed using observable data. For example, it allows consideration of a genome's resilience under variation of its genetic interactions. It also suggests an upper bound on the number of stable genetic patterns in prokaryotes and archaeons, close to 14% of their total endowment of genes. We test this estimate against available information from sequenced genomes.Genomics, neural network, gene network