arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2205.06313q-bio.MNcond-mat.stat-mechcs.LG
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作为广义Boltzmann机的详细平衡化学反应网络

Detailed Balanced Chemical Reaction Networks as Generalized Boltzmann Machines

California Institute of Technology(加州理工学院);Imperial College London(帝国理工学院)

William Poole, Thomas Ouldridge, Manoj Gopalkrishnan, Erik Winfree

摘要:

一个微米大小的相互作用的分子袋能理解并适应不断波动的环境吗?细胞生命提供了一个肯定的存在证据,但允许生命存在的原则还远未得到证明。工程和理解生化计算的一个挑战是由于化学波动引起的固有噪声。在本文中,我们从机器学习理论、化学反应网络理论和统计物理学中汲取了一些见解,以表明广泛的和生物相关的详细平衡化学反应网络能够表示和调节复杂的分布。这些结果说明了生物化学计算机如何使用固有的化学噪声来执行复杂的计算。此外,我们使用我们明确的物理模型推导出热力学成本的推理。

英文摘要:

Can a micron sized sack of interacting molecules understand, and adapt to a constantly-fluctuating environment? Cellular life provides an existence proof in the affirmative, but the principles that allow for life's existence are far from being proven. One challenge in engineering and understanding biochemical computation is the intrinsic noise due to chemical fluctuations. In this paper, we draw insights from machine learning theory, chemical reaction network theory, and statistical physics to show that the broad and biologically relevant class of detailed balanced chemical reaction networks is capable of representing and conditioning complex distributions. These results illustrate how a biochemical computer can use intrinsic chemical noise to perform complex computations. Furthermore, we use our explicit physical model to derive thermodynamic costs of inference.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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