“Scientists should think like poets,” says E.O. Wilson, because new metaphors mobilize new thinking.
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
Does life work like our technology? Is life under the hood just like a car sporting souped-up complexity?
1. Does life work like our machines? Like cars sporting souped-up complexity? That tempting template hampers our tinkering under life’s hood.
2. E. coli’s single-celled life is “like a self-building, self-multiplying, self-healing race car that can run on kerosene [or] Coca-Cola,” says Andreas Wagner (Arrival of the Fittest: How Nature Innovates).
3. But cells have changing parts list. They’re like cars that build and recycle sparkplugs every firing cycle. Life builds umpteen temporary components, choreographing flashmob-like molecular fabricators for its transient machinery.
4. E. coli utilizes life’s ~60 molecular “building blocks,” its 4,000 - 5,500 genes orchestrate 1,300 densely interwoven fluctuating biochemical reaction circuits—>a dynamic complexity utterly unlike our machines.
6. "Nature doesn't just tolerate disorder. It needs some disorder” (Wagner). And complexity.
7. Biology lives in a region of reality that resists Occam’s Razor—life needs complexity-enabled robustness.
8. Robust solutions are like streetscapes offering many routes around roadblocks. Life’s biochemical circuits leverage similar re-routable-ness. Enabling E. coli to thrive on 80 different fuels (=diverse environments).
9. Why would our ~19,000 (microbiome supplemented) genes occupy ~2% of our DNA?
10. Partly because genes are like guitars, uselessly silent unless played (“mere presence of a guitar in your bedroom doesn't make you Slash”—Ed Yong). Detection ≠ usage details (≠ roles played ≠ where).
11. Our 30 trillion cells each, millions of times daily, unpack and play thousands of genes, exactly on cue.
12. Those cues often aren’t simple on-off switches, they’re tuned to the logic of many signals. For instance, the gene for crystallin has 5 regulators, each with off, low, medium, and high settings.
14. Our 98% non-gene DNA has ~3 million control elements, ~150,000 active per cell type.
15. This mind-boggling dynamic complexity means machine-like (stable parts-listed) thinking can mislead.
17. Gene-editing tools like CRISPR “will supposedly hack diseases out of our DNA, but… how do we know what to edit?” In which cell types? +Gene products often ≠ monofunctional (eye-lens crystallin is active in the pancreas and nervous system).
18. Indeed, “editing is a bit of a misnomer." CRISPR is like cut-and-paste on a vastly complex dynamic polysemic text or symphony or movie, where only snippets of the plot are known.
19. An Occam-preoccupied machinelike mindset can skew research, e.g. neuroscientists seeking “a gene for psychosis” or disease “neurosignatures.”
20. Few traits or illnesses are monogenetic. Few will be easily “editable.” Few illnesses will likely have simple (error code) signatures.
21. We’re babystep beginners at the dynamic logic and chemical semantics of biology’s teeming transient molecular machinery.
Illustration by Julia Suits, author of The Extraordinary Catalog of Peculiar Inventions, and The New Yorker cartoonist.