Bauer Marianne, Bialek William, Goddard Chase, Holmes Caroline M, Krishnamurthy Kamesh, Palmer Stephanie E, Pang Rich, Schwab David J, Susman Lee
Joseph Henry Laboratories of Physics, Princeton University.
Lewis-Sigler Institute for Integrative Genomics, Princeton University.
ArXiv. 2025 May 29:arXiv:2505.23398v1.
Many biological systems perform close to their physical limits, but promoting this optimality to a general principle seems to require implausibly fine tuning of parameters. Using examples from a wide range of systems, we show that this intuition is wrong. Near an optimum, functional performance depends on parameters in a "sloppy" way, with some combinations of parameters being only weakly constrained. Absent any other constraints, this that we should observe widely varying parameters, and we make this precise: the entropy in parameter space can be extensive even if performance on average is very close to optimal. This removes a major objection to optimization as a general principle, and rationalizes the observed variability.
许多生物系统都在接近其物理极限的状态下运行，但要将这种最优性推广为一个普遍原则，似乎需要对参数进行精细到难以置信的微调。通过来自广泛系统的示例，我们表明这种直觉是错误的。在最优状态附近，功能性能对参数的依赖方式“很宽松”，某些参数组合受到的约束很弱。在没有任何其他约束的情况下，这意味着我们应该观察到参数有很大差异，我们对此进行了精确表述：即使平均性能非常接近最优，参数空间中的熵也可能是广泛存在的。这消除了将优化作为一个普遍原则的一个主要障碍，并使观察到的变异性变得合理。
