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Saturday, May 07, 2022

AI and Synthetic Biology

Excellent topic we explored.  Here a very good,  largely non technical intro paper in the current ACM.  I see this as a very direction in the years to come.  

Artificial Intelligence for Synthetic Biology

By Mohammed Eslami, Aaron Adler, Rajmonda S. Caceres, Joshua G. Dunn, Nancy Kelley-Loughnane, Vanessa A. Varaljay, Hector Garcia Martin

Communications of the ACM, May 2022, Vol. 65 No. 5, Pages 88-97  10.1145/3500922

Biology has dramatically changed in the last two decades, enabling the effective engineering of biological systems. The genomic revolution,17 which provided the ability to sequence a cell's genetic code (DNA), is the primary driver of this dramatic change. One of the most recent discoveries and tools enabled by this genomic revolution is the ability to precisely edit DNA in vivo using CRISPR-based tools.11 Higher-level manifestations of the genetic code, such as the production of proteins, are known as phenotype (as shown in Figure 1 and the accompanying table). The combination of high-throughput phenotypic data with precision DNA editing provides a unique opportunity to link changes in the underlying code to phenotype.

Synthetic biology (synbio) aims to design biological systems to a specification3 (for example, cells that produce a desired amount of biofuel, or that react in a specific manner to an external stimulus). To this end, synthetic biologists leverage engineering design principles to use the predictability of engineering to control complex biological systems. These engineering principles include standardized genetic parts, and the Design-Build-Test-Learn (DBTL) cycle, iteratively used to achieve a desired outcome. The synbio DBTL cycle adapts the expected four stages to this discipline as follows:   .... ' 

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