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We study genetic networks that produce many species of non-coding RNA molecules that are present at a moderate density, as typically exists in the cell. The associations of the many species of these RNA are modeled physically, taking into account the equilibrium constants between bound and unbound states. By including the pair-wise binding of the many RNA species, the network becomes highly interconnected and shows different properties than the usual type of genetic network. It shows much more robustness to mutation, and also rapid evolutionary adaptation in an environment that oscillates in time. This provides a possible explanation for the weak evolutionary constraints seen in much of the non-coding RNA that has been studied.
The majority of mammalian genomic transcripts do not directly code for proteins and it is currently believed that most of these are not under evolutionary constraint. However given the abundance non-coding RNA (ncRNA) and its strong affinity for inte
Mutation is a critical mechanism by which evolution explores the functional landscape of proteins. Despite our ability to experimentally inflict mutations at will, it remains difficult to link sequence-level perturbations to systems-level responses.
The evolution of the genome has led to very sophisticated and complex regulation. Because of the abundance of non-coding RNA (ncRNA) in the cell, different species will promiscuously associate with each other, suggesting collective dynamics similar t
Does regulation in the genome use collective behavior, similar to the way the brain or deep neural networks operate? Here I make the case for why having a genomic network capable of a high level of computation would be strongly selected for, and sugg
Rule-based modeling is a powerful way to model kinetic interactions in biochemical systems. Rules enable a precise encoding of biochemical interactions at the resolution of sites within molecules, but obtaining an integrated global view from sets of