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The central dogma
How genetic information flows: DNA is transcribed to RNA, which is translated to protein.
The central dogma is the flow of genetic information in a cell: DNA → (transcription) → RNA → (translation) → protein. DNA is the archive — a stable double helix that stores the instructions. To act on those instructions, the cell copies a gene into RNA, then reads the RNA to build a protein, the molecular machine that actually does the work. First stated by Francis Crick in 1958, it names the direction information travels.
How it works
Transcription copies one gene’s DNA into a matching strand of messenger RNA. Translation then reads that RNA three letters at a time: each triplet — a codon — specifies one amino acid, following the near-universal genetic code, and the ribosome links those amino acids into a protein. Sixty-one codons encode amino acids; a few act as stop signals.
The clean arrow hides real complexity. Retroviruses run information backward with reverse transcription (RNA → DNA). In our cells, freshly made RNA is spliced — non-coding stretches cut out, coding pieces rejoined — so one gene can yield many proteins. Which genes transcribe at all is tightly regulated, and much of the genome makes non-coding RNA that never becomes protein but still does jobs of its own. The dogma is the backbone, not the whole story.
Why it matters (for bio × AI)
This is why sequence is the substrate AI reads. DNA, RNA, and protein are all written in short alphabets, and the central dogma is the grammar linking them — so biology hands machine learning exactly what it’s best at: language. Protein language models learn the statistics of amino-acid sequences; structure predictors like AlphaFold map sequence to shape. Both work because this flow makes sequence carry function, one rung of the sequence → structure → function ladder that modern bio-AI climbs.
Related
See protein language model for models that read the sequence alphabet directly, and AlphaFold for turning that sequence into 3D structure.