Abstract
We show how random Boolean networks, a well-known model of the interaction among genes within a living cell, can be generalized by relaxing the Boolean approximation, therefore defining a class of continuous genetic networks, and by explicitly describing gene–chemical interactions. It is also shown that population growth can be modelled by describing in detail the interactions among a limited number of genes, while the effects of the remaining genes are represented by a collective variable. The model can describe specific metabolic systems, as shown by the case of a microorganism able to degrade aromatic compounds, and it can also be used to study the generic properties of genetic networks. For this purpose, theoretical and simulation results are given in the case where all the genes are arranged on a 2-D regular square topology, with connections among neighbouring sites, which represents a cellular-automata like model. Parallel simulation on a MIMD machine shows a good speed-up, thus confirming that these models are amenable to efficient parallel implementation.
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