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 ### AI in Synthetic Biology
 
-Synthetic biology offers significant potential to address important societal challenges. However, a major obstacle is our current inability to predict biological systems as precisely as we can predict and simulate physical or chemical ones. This limitation has both practical and fundamental implications. Practically, we cannot design biological systems (e.g., proteins, pathways, cells) to specific requirements (e.g., binding affinity, production rates). Fundamentally, we lack a deep understanding of the mechanisms that produce observable characteristics or traits of organisms. Artificial intelligence and machine learning<Term t="AI-ML">machine learning</Term> (ML) hold promise in enhancing the predictive power needed in synthetic biology and can be applied throughout the synthetic biology process.
+Synthetic biology offers significant potential to address important societal challenges. However, a major obstacle is our current inability to predict biological systems as precisely as we can predict and simulate physical or chemical ones. This limitation has both practical and fundamental implications. Practically, we cannot design biological systems (e.g., proteins, pathways, cells) to specific requirements (e.g., binding affinity, production rates). Fundamentally, we lack a deep understanding of the mechanisms that produce observable characteristics or traits of organisms. Artificial intelligence and <Term t="AI-ML">machine learning</Term> (ML) hold promise in enhancing the predictive power needed in synthetic biology and can be applied throughout the synthetic biology process.
 
 For example, in the past two decades, various machine learning tools have been developed to aid enzyme engineering by simplifying approaches and reducing the screening efforts required. Machine learning can process information about enzyme sequences and properties, inferring novel information that is likely to enhance or refine these properties. These algorithms have numerous applications in synthetic biology, including optimizing genetic and metabolic networks, directing enzyme evolution, predicting kinetic properties of uncharacterized enzymes, and even the de novo design of entire proteins.