Floyd Carlos, Dinner Aaron R, Murugan Arvind, Vaikuntanathan Suriyanarayanan
The Chicago Center for Theoretical Chemistry, The University of Chicago, Chicago, IL, USA.
The James Franck Institute, The University of Chicago, Chicago, IL, USA.
Nat Commun. 2025 Aug 5;16(1):7184. doi: 10.1038/s41467-025-61873-0.
Many biological decision-making tasks require classifying high-dimensional chemical states. The biophysical and computational mechanisms that enable classification remain enigmatic. In this work, using Markov jump processes as an abstraction of general biochemical networks, we reveal several unanticipated and universal limitations on the classification ability of generic biophysical processes. These limits arise from a fundamental non-equilibrium thermodynamic constraint that we have derived. Importantly, we show that these limitations can be overcome using common biochemical mechanisms that we term input multiplicity, examples of which include enzymes acting on multiple targets. Analogous to how increasing depth enhances the expressivity and classification ability of neural networks, our work demonstrates how tuning input multiplicity can potentially enable an exponential increase in a biological system's ability to classify and process information.
许多生物决策任务需要对高维化学状态进行分类。实现分类的生物物理和计算机制仍然是个谜。在这项工作中，我们将马尔可夫跳跃过程用作一般生化网络的抽象，揭示了通用生物物理过程在分类能力方面的几个意外且普遍的限制。这些限制源于我们推导出的一个基本的非平衡热力学约束。重要的是，我们表明可以使用我们称为输入多样性的常见生化机制来克服这些限制，其示例包括作用于多个靶点的酶。类似于增加深度如何增强神经网络的表现力和分类能力，我们的工作展示了调整输入多样性如何有可能使生物系统的分类和处理信息的能力呈指数级增长。
