进行性与分配性非平衡网络在交替极限下的区分能力
Processive and Distributive Non-Equilibrium Networks Discriminate in Alternate Limits
AI总结：
本文研究非平衡生化网络的产物区分能力，提出一个由速率常数直接计算的必要条件，并揭示能量型与动力学型区分约束的对立关系，进一步引入混合网络，展示仅通过消耗自由能即可实现灵敏产物切换。
摘要：
我们研究受生物校对机制启发的、能够进行产物区分的生化反应网络。在平衡态下，产物区分，即相对于“错误产物”选择性形成“正确产物”，从根本上受到两种产物之间自由能差的限制。然而，生物系统往往远远超过这一限制，它们利用消耗自由能以维持非平衡稳态的区分网络。非平衡系统 notoriously 难以分析，目前不存在确定使区分最大化的参数区域的系统性方法。在此，我们引入一个可直接从生化速率常数计算得到的量，该量为校对提供了一个必要条件。非平衡系统可以利用结合能（能量型）差异或活化能（动力学型）差异进行区分，但在这两种情况下，只要满足我们引入的必要条件，当耗散最大化时校对是最优的。我们表明这些约束可能相互对立，这解释了为什么在 Hopfield 和 Ninio 的原始校对方案中，错误率是不可逆驱动的非单调函数。最后，我们引入混合网络，其中一种产物在能量上受青睐，另一种在动力学上受青睐。在此类网络中，仅通过消耗自由能即可实现灵敏的产物切换。在生物学上，这对应于无需网络微调即可通过驱动单个反应在产物之间进行选择的能力。这可用于在具有挑战性的环境中探索交替的产物空间。
英文摘要：
We study biochemical reaction networks capable of product discrimination inspired by biological proofreading mechanisms. At equilibrium, product discrimination, the selective formation of a "correct" product with respect to an "incorrect product", is fundamentally limited by the free energy difference between the two products. However, biological systems often far exceed this limit, by using discriminatory networks that expend free energy to maintain non-equilibrium steady states. Non-equilibrium systems are notoriously difficult to analyze and no systematic methods exist for determining parameter regimes which maximize discrimination. Here we introduce a measure that can be computed directly from the biochemical rate constants and which provides a necessary condition for proofreading. Non-equilibrium systems can discriminate using binding energy (energetic) differences or using activation energy (kinetic) differences, but in both cases, proofreading is optimal when dissipation is maximized, so long as the necessary condition we introduce is satisfied. We show that these constraints can be in opposition and that this explains why the error rate is a non-monotonic function of the irreversible drive in the original proofreading scheme of Hopfield and Ninio. Finally, we introduce mixed networks, in which one product is favored energetically and the other kinetically. In such networks, sensitive product switching can be achieved simply by spending free energy. Biologically, this corresponds to the ability to select between products by driving a single reaction without network fine tuning. This may be used to explore alternate product spaces in challenging environments.
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