5 ms·

This is quite the hot topic in chemistry and materials for the last few years. See https://arxiv.org/abs/2304.11120 https://arxiv.org/abs/2304.11120 for a curre

by rsfern 3y ago

This is quite the hot topic in chemistry and materials for the last few years. See https://arxiv.org/abs/2304.11120 https://arxiv.org/abs/2304.11120 for a current perspective-ish review

__MatrixMan__ 3y ago
I imagine a similar approach could be taken re: genomics / proteomics. Thousands of tiny bioreactors with slightly different genetics, temperature, nutrients, etc, all piped to chromatography equipment and optimizing for the metabolic pathway of some desirable. Maybe blast 'em with gamma and try to catch a lucky mutation, etc. Edit: I'm not the only one imagining such a thing: https://www.sciencedirect.com/science/article/pii/S0958166922002154 https://www.sciencedirect.com/science/article/pii/S095816692...
- ta988 3y ago
  Yes that's already existing. High throughout screening of enzymes, sequences or reactions is common.
- BioGeek 3y ago
  For a recently published example of this see [1]: an automated platform, called Self-driving Autonomous Machines for Protein Landscape Exploration (SAMPLE), can design and build proteins using AI agents and robotics. In an initial proof-of-concept, it was used to make glycoside hydrolase (sugar-cutting) enzymes that can withstand higher-than-normal temperatures. The SAMPLE system used four different autonomous agents, each of which designed slightly different proteins. These agents search the fitness landscape for a protein and then proceed to test and refine it over 20 cycles. The entire process took just under six months. It took one hour to assemble genes for each protein, one hour to run PCR, three hours to express the proteins in a cell-free system, and three hours to measure each protein’s heat tolerance. That’s nine hours per data point! The agents had access to a microplate reader and Tecan automation system, and some work was also done at the Strateos Cloud Lab. SAMPLE made sugar-cutting enzymes that could tolerate temperatures 10°C higher than even the best natural sequence, called Bgl3. The AI agents weren’t “told” to enhance catalytic efficiency, but their designs also had catalytic efficiencies that matched or exceeded Bgl3. [1] https://www.biorxiv.org/content/10.1101/2023.05.20.541582v1 https://www.biorxiv.org/content/10.1101/2023.05.20.541582v1 [2] https://www.readcodon.com/i/122504181/ai-agents-design-proteins https://www.readcodon.com/i/122504181/ai-agents-design-prote...
  - __MatrixMan__ 3y ago
    I recently started taking biology classes, the idea being that I might like to work with systems like this (writing code that solves code problems that are tenuously linked to real problems is not going to be satisfying forever). I'm taking bioinformatics next semester, which I hope will give me the lay of the land from a code perspective, but I really don't know what I'm getting into here. Any advice?

by rsfern 3y ago

This is quite the hot topic in chemistry and materials for the last few years. See https://arxiv.org/abs/2304.11120 https://arxiv.org/abs/2304.11120 for a current perspective-ish review

__MatrixMan__ 3y ago

I imagine a similar approach could be taken re: genomics / proteomics. Thousands of tiny bioreactors with slightly different genetics, temperature, nutrients, etc, all piped to chromatography equipment and optimizing for the metabolic pathway of some desirable. Maybe blast 'em with gamma and try to catch a lucky mutation, etc. Edit: I'm not the only one imagining such a thing: https://www.sciencedirect.com/science/article/pii/S0958166922002154 https://www.sciencedirect.com/science/article/pii/S095816692...

ta988 3y ago
Yes that's already existing. High throughout screening of enzymes, sequences or reactions is common.
BioGeek 3y ago
For a recently published example of this see [1]: an automated platform, called Self-driving Autonomous Machines for Protein Landscape Exploration (SAMPLE), can design and build proteins using AI agents and robotics. In an initial proof-of-concept, it was used to make glycoside hydrolase (sugar-cutting) enzymes that can withstand higher-than-normal temperatures. The SAMPLE system used four different autonomous agents, each of which designed slightly different proteins. These agents search the fitness landscape for a protein and then proceed to test and refine it over 20 cycles. The entire process took just under six months. It took one hour to assemble genes for each protein, one hour to run PCR, three hours to express the proteins in a cell-free system, and three hours to measure each protein’s heat tolerance. That’s nine hours per data point! The agents had access to a microplate reader and Tecan automation system, and some work was also done at the Strateos Cloud Lab. SAMPLE made sugar-cutting enzymes that could tolerate temperatures 10°C higher than even the best natural sequence, called Bgl3. The AI agents weren’t “told” to enhance catalytic efficiency, but their designs also had catalytic efficiencies that matched or exceeded Bgl3. [1] https://www.biorxiv.org/content/10.1101/2023.05.20.541582v1 https://www.biorxiv.org/content/10.1101/2023.05.20.541582v1 [2] https://www.readcodon.com/i/122504181/ai-agents-design-proteins https://www.readcodon.com/i/122504181/ai-agents-design-prote...
- __MatrixMan__ 3y ago
  I recently started taking biology classes, the idea being that I might like to work with systems like this (writing code that solves code problems that are tenuously linked to real problems is not going to be satisfying forever). I'm taking bioinformatics next semester, which I hope will give me the lay of the land from a code perspective, but I really don't know what I'm getting into here. Any advice?