Quantifying evolutionary trade‑offs between performance optimization and mechanistic costs

Develop a quantitative evolutionary framework that computes the trade‑off between functional performance optimization (e.g., single‑photon detection in vision) and mechanistic costs (e.g., energy dissipation in biochemical amplification), and estimates the timescale for evolution to discover optimal solutions in realistic biological systems.

Background

Optimization principles are used to explain near‑limit performance of biological systems, but evaluating their evolutionary plausibility requires explicit calculations that weigh functional benefits against biophysical costs and assess search timescales for selection to find optima.

Such a framework would help reconcile optimization arguments with evolutionary dynamics and constraints.

References

Nobody knows how to do a calculation that weighs the benefits of optimizing performance (e.g., counting single photons in vision) against the costs of the underlying mechanisms (energy dissipation in the biochemical amplification of single molecular events), and we certainly don't know enough to calculate how long it would take evolution to find the optimal tradeoff.

— Ambitions for theory in the physics of life  (2401.15538 - Bialek, 2024) in Section Agenda

To earn the term the framework needs a fitness or flux functional, a space of accessible compensatory states---isodecoder upregulation, modification-enzyme upregulation, codon drift, reduced initiation rate---and a demonstration that no low-cost escape path exists within it. Section~\ref{sec:roadmap} lists this as open, not settled.

— Kinetic Interference in Translational Control: A Path-Measure Framework for Collision-Triggered Transcript Decay  (2609.25536 - Segal, 22 Sep 2026) in Section 1.2, “Kinetic traps”