Stochastic grammars and grammatical evolution

I’ve been wondering how to use grammatical evolution to generate signaling networks. So first we have to think up some sort of grammar for signaling networks. What would be appropriate start symbols? Productions? Terminals?

Start: Gene

Transcription: Gene > Gene + RNA (constitutive expression) | Gene*TF | Gene*Inhibitor

Transcription: Gene*TF > Gene + RNA | Gene*TF[*Cofactor]^n | Gene*TF*Inhibitor

Transcription: Gene*TF*Cofactor > Gene + RNA

Signaling: Receptor > Receptor*SIgnal | Receptor*Blocker

Degradation: Any > Nothing

and so on

People have done this sort of thing before, obviously, but I’m wondering about how applying genetic mutation operators to a string of such productions will lead to the same sort of changes to gene networks that are actually observed. Not obvious to me …

What happens if you use a stochastic grammar? What’s the difference between a stochastic grammar applied many times to a fixed genome vs a deterministic grammar applied to a population of genomes? In biology, the binding of TFs may actually be stochastic, so perhaps we should encode the probability of a symbol in the genome going to a particular production in the genome itself.


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