Can machine learning design living organisms?

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SUMMARY

Machine learning can now design artificial proteins using Boltzmann machine learning to create enzymes with evolutionary compatible statistical constraints, as demonstrated in the study of chorismate mutase enzymes. However, the complexity of biological systems presents significant challenges, including the need for complete data on DNA and enzyme interactions. Current limitations highlight the necessity of understanding allosteric regulation and the potential for unintended consequences in biological design. The consensus is that designing living organisms through machine learning remains a distant goal, akin to science fiction.

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  • Understanding of Boltzmann machine learning techniques
  • Knowledge of enzyme design and allosteric regulation
  • Familiarity with evolutionary biology principles
  • Basic concepts of protein synthesis and function
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  • Study allosteric regulation in enzyme functionality
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Biochemists, synthetic biologists, machine learning practitioners, and researchers interested in the intersection of artificial intelligence and biological design.

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My Dad was a Reliability Engineer for Boeing, Lockheed, etc. and couldn't understand Software Reliability Engineering. Software is just a mathematical algorithm that is either works or it doesn't. Problem with complex interactions is there will always be some flaw, somewhere. "The law of unintended consequences." Many of the "flaws" in biological systems happen through trial and error, and are crucial for the feedback that keeps the system stable. At the rate of DNA and enzyme discovery you would be using incomplete data. You would have to build your enzymes in accordance with current allosreic regulation and make sure your enzyme has no fatal allosteric weakness. And we need to take into account the multiple interactions we still don't know about for each enzyme. SARS has only 8 simple genes on a single strand, and we don't even yet know how it works let alone duplicate it. Still Science Fiction for the near future.
 

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