1. Nietzsche called truth “a mobile army of metaphors.” That’s no less true in science. (Like turtles, metaphors go all the way down.) Can we mobilize other equation-defying language resources to serve science?
4.“The toggle-switch model of disease” yields to “seed-and-soil” thinking—local tissue ecology can enable cancer seeds to thrive or be throttled (swapping relational-ecological for technomorphic-reductionist metaphors).
5. “Cancer is no more a disease of cells than a traffic jam is a disease of cars,” declared DW Smithers). Studying cars alone won’t work—they’re necessary, but not sufficient—jams are higher-level relational contextual phenomena. Likewise gene-level thinking can “mistake the music for the piano.”
7. Like jam-resilient cities, biochemical “circuitry” often offers high-redundancy routes (multiple “sufficient but not necessary” paths and pathologies).
8. Mukherjee grumbles, “Ecologists… talk about webs of nutrition, predation, climate…[with] complex feedback loops, all context-dependent. To them, invasion is an equation, even a set of simultaneous equations.”
9. That equation-as-best-way-to-know metaphor can limit or mislead. The algebraic moves of equations thrive in reductionist, essentialist domains (physics, engineering) where every X’s reliably isolatable intrinsic traits beget stable behaviors.
10. But, as Mitchell notes, biology’s basic laws differ. They’re emergent, process-oriented, relational, systemic, with polysemic parameters often only interpretable relative to complex contexts: whole cells, tissue ecologies, organisms… you must zoom out to the proper level
11. Mukherjee’s second essay reverts to human-tech metaphors (cardiologists = plumbers, oncologists = exterminators). A single inflammation-influencing molecule’s role in heart disease and cancer is like a fuse-box switch that impacts two disparate disease circuits.
12. Our 20,000+ “gene-switches” aren’t monofunctional on/off elements. Biochemistry’s players are ensemble casts (one mutation can re-orchestrate hundreds of genes). As with musical notes or words, it’s specific sequences that count. The meanings (effects) of genes mostly emerge in higher-level structures—like tunes or texts, requiring precise sequences, synchronization, syntax, and grammar.
13. Biology’s patterns—molecular melodies played on 20,000 keys, cytoplasmic scripts or cell-spanning sentences in a 20,000-word vocabulary, choreographed across trillions of cells—challenge current concepts, vocabulary, metaphors, and methods.
14. Mukherjee mentions “gene-expression signatures,” but can word-count signatures explicate text? We can’t just jettison “gene grammars,“ or cellular syntax without leaking meaning.
15. Grammars express richer part-to-whole relationships (recipe-like algorithmic patterns) than geometric/algebraic essentialism. “What Euclid is to Europe, Panini is to India” (Staal). Panini’s rigorous Sanskrit grammar shaped less algebra-and-geometry-intoxicated minds.
17. E.O. Wilson says “scientists should think like poets”—new metaphors mobilize new thinking, but other language tools can provide parts-of-speech models for parts-of-reality interactions.
Illustration by Julia Suits, The New Yorker cartoonist & author of The Extraordinary Catalog of Peculiar Inventions