A single scalar entropy-production-rate lower bound for the kinetochore DI cycle from trajectory-only estimators (short-time TUR / KL time-reversal asymmetry), gated by a synthetic-data power analysis and validated for cross-dataset reproducibility

Every time a cell divides, it must perfectly sort its chromosomes — one copy to each daughter cell. Getting this wrong even once can lead to cancer or birth defects. The machinery responsible is the kinetochore, a molecular structure that attaches chromosomes to the cellular 'ropes' (the mitotic spindle) that pull them apart. Intriguingly, chromosomes don't just sit still waiting to be pulled — they bounce back and forth in a jittery dance called directional instability before finally locking into the correct position. This oscillation isn't random noise; it's thought to be part of an active error-correction system. But how much energy does this process actually consume, and does that energy expenditure tell us something meaningful about its function? This hypothesis borrows a powerful idea from the physics of thermodynamics — specifically, a field called stochastic thermodynamics, which studies how tiny machines operating in noisy environments produce heat and consume energy. The key insight is that any process driven by energy (rather than just random thermal jostling) leaves a detectable fingerprint: its trajectory through time looks statistically different when played forwards versus backwards, like a movie of a bouncing ball versus one played in reverse. By analyzing the recorded positions of chromosomes over time using mathematical tools that detect this 'time-reversal asymmetry,' the hypothesis proposes calculating a single number — a lower bound on the rate of energy dissipation (in units of kT, the natural currency of molecular-scale energy) per oscillation cycle. Crucially, the researchers want to first test whether their analysis method can even detect a real signal given the short, noisy chromosome tracking data typically available — a built-in sanity check before claiming any result. What makes this particularly clever is the methodological care involved. Rather than making assumptions about the molecular machinery's internal states, the approach uses the chromosome's own position-versus-velocity trajectory as a 2D map, sidestepping known mathematical traps that plague simpler analyses. A cross-dataset check using chromosome tracking data from a different cell line adds a further layer of rigor, ensuring the energy estimate reflects real biology rather than a quirk of one experiment.

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