Doubtful. You can to computing with DNA http://dna.caltech.edu but it is still debated if the actual role of DNA is being a code (some recently weasel out form that stance by saying it's an “app”, like there's a difference, machine code being a code). Coding theory applied to DNA yields inconclusive results. Galois theory usually has power over any kind of information encoding, cryptographic, computable or not. One constructed one for DNA convinced mathematicians it's not the way to go at all. If it's computation it's nothing like what we mean by any model computation, you may as well say it's magic instead of making stretched analogies.
About OpenBSD and evolution, that's so fetch... these folk invented the attack in the first place, not evolved a response to some market force in the early oughties. Broadly I don't think designed systems are in business of evolving, otherwise living organisms could perhaps evolved electro-hydrostatic instead of hydraulic power system with a pump being a single point of failure.
Linking biology to specific concepts in computer architecture or worse, with OS design, is indeed a stretched analogy but biology is rich with computation. I'll try to be short.
A cell must sense and respond to its environment. One aspect of this is regulation of gene activity by interactions of for example, transcription factors. When interaction types can be well enough approximated as either of inhibition or activation, you can model them with boolean networks and when levels matter, some have found moderate success with recurrent neural networks under a restriction on what NN nodes represent, to ease interpretability.
That is not the same thing as saying your genome unrolls neural networks. What it is actually saying is that the complexity of the best performing neural network indicates the richness of the underlying computation, and the predictive accuracy of the model captures the functional equivalence of the respective computations (in the cell and in the neural network). The learned model is the instance and neural networks are the class, it is a mistake to place emphasis on neural network, they are merely a way of packaging the computational model of interest.
There is a precise correspondence between that formulation and one done in terms of online sequential prediction or the so called experts algorithm. All of these are models and still quite limited at that but biology is complex and the future lies in the development and leveraging of these rich connections.
Can you explain how the analogy is stretched? It’s my personal belief that biologists tend to be fairly implementation focused from how they learn about the systems involved.
In biology there is 2 factor authentication to protect against parasitic spoofing, intrusion detection systems, protected kernel memory, memory hierarchy, policy based permission. It a very nice operating system IMO.
Analogies can help but if taken too far, will provide only illusionary knowledge from superficial similarities. Consider, if you trace out the hierarchies induced by interactions in biological networks and then do the same for an OS's call graph, you find quite different topologies reflecting their different priorities. In computers, in part, efficiency and reuse. In biology, robustness, minimized interdependencies, developmental stability and more.
There are things that concern our electronics that do not matter for biology and there are many things biology must allow for that our hardware cannot. Tying things down to the peculiarities of our systems too narrows the scope of applicable models and understanding. Sometimes it is more useful to think in terms of stochastic differential equations than in terms of operating systems.
Doubtful. You can to computing with DNA http://dna.caltech.edu but it is still debated if the actual role of DNA is being a code (some recently weasel out form that stance by saying it's an “app”, like there's a difference, machine code being a code). Coding theory applied to DNA yields inconclusive results. Galois theory usually has power over any kind of information encoding, cryptographic, computable or not. One constructed one for DNA convinced mathematicians it's not the way to go at all. If it's computation it's nothing like what we mean by any model computation, you may as well say it's magic instead of making stretched analogies.
About OpenBSD and evolution, that's so fetch... these folk invented the attack in the first place, not evolved a response to some market force in the early oughties. Broadly I don't think designed systems are in business of evolving, otherwise living organisms could perhaps evolved electro-hydrostatic instead of hydraulic power system with a pump being a single point of failure.
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