AI 中文总结
研究生物分子分布代码的解码问题，利用集体物理不稳定性，通过信息论匹配条件得出几何条件，经模拟发现相分离等不稳定性能有效读取分布信息，相比质量作用结合更优，为细胞解码分子分布代码提供新方式。
AI 中文摘要
生物信息通常编码在仅在少数化学特征上不同的分子变体中，而非任意不同的物种中。这些分子分布携带细胞状态信息，但用传统分子电路读取它们需要大量不同的传感器。相比之下，研究表明集体物理不稳定性可自然整合此类分布中编码的信息。通过编码分布与物理读出之间的信息论匹配条件，得出任何良好解码器必须满足的几何条件，发现相分离、渗流和膜曲率不稳定性对生物自然分布接近该条件，而简单的质量作用结合则不然。使用平均场理论和晶格蒙特卡罗模拟发现，相分离能读取分布形状而非仅均值，能稳健捕捉方差和偏度，在相边界附近读出几乎能捕捉分子群体中的所有信息。有限价通过网络形成阈值增加了平均场理论不可见的辨别力。这些结果表明细胞可利用集体物理不稳定性作为自然、紧凑且接近最优的传感器来解码分子分布代码。
英文摘要
Biological information is often encoded in molecular variants that differ in just a few chemical traits, such as the number of phosphorylated sites or ubiquitin chain length, rather than in arbitrarily distinct species. These molecular distributions carry information about cellular state, yet reading them with conventional molecular circuits requires prohibitively many distinct sensors. In contrast, we show that collective physical instabilities can naturally integrate the information encoded in such distributions. Using an information-theoretic matching condition between an encoded distribution and a physical readout, we derive a geometric condition that any good decoder must satisfy, and establish that phase separation, percolation, and membrane curvature instabilities all approach it for biologically natural distributions while simple mass-action binding does not. Using mean-field theory and lattice Monte Carlo simulations, we find that phase separation reads the shape of a distribution beyond its mean, robustly capturing its variance and, more weakly, its skewness, whereas mass-action binding detects only the mean. Near phase boundaries the readout captures nearly all the information present in the molecular population. Finite valency, through the threshold for network formation, adds discriminatory power invisible to mean-field theory. These results suggest that cells can exploit collective physical instabilities as natural, compact, yet near-optimal sensors for decoding molecular distributional codes.
