探索利用量子计算促进生物细胞的时空分辨模型的构建
Exploring the use of quantum computing for facilitating spatially and temporally resolved models of a biological cell
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中文总结 AI 辅助
该研究针对经典架构无法实现全细胞模拟的问题，探讨量子计算在生物细胞多尺度建模中的潜力，分析算法加速场景，指出实际瓶颈并勾勒出相关路线图。
中文摘要 AI 辅助
全细胞模拟是计算生物学中的一项重大挑战，需要对细胞生命周期内的所有功能系统进行建模。即使是最简单的活细胞也包含数千种相互作用的蛋白质和代谢物（约数万亿个原子），其完整功能动力学在空间上跨越约五个数量级（从纳米到微米），在时间上跨越近十九个数量级（从飞秒到小时）。此外，许多关键的物理和化学性质仍未被充分表征。在经典架构上以全原子分辨率模拟此类复杂系统的完整细胞周期在计算上是不可行的，这引发了一个核心问题：量子计算能否为整合分子和系统层面复杂性的全细胞模拟提供可行路径？本文作为一篇观点文章，从原子-分子建模、代谢与调控网络、全细胞空间建模三个层次尺度探讨了量子计算的潜力。我们对经典和量子算法在代表性生物学问题上进行了复杂性分析，确定了在特定算法假设下具有显著理论加速比的场景。我们强调了旨在利用近期探索性和容错量子架构的算法进展，并讨论了实际瓶颈：数据编码开销、系统条件、测量约束以及量子-高性能计算（HPC）混合集成。这些结果共同勾勒出量子加速全细胞建模的路线图，以及这类多尺度框架最终可能带来的生物学见解。
英文摘要
Whole-cell simulation, modeling all of a cell's functional systems over its life cycle, is an outstanding challenge in computational biology. Even the simplest living cell contains thousands of interacting proteins and metabolites (on the order of trillions of atoms) whose full functional dynamics spans roughly five orders of magnitude in space (nm to $μ$m) and nearly nineteen in time (fs to hours). Further, many of the governing physical and chemical properties remain incompletely characterized. Simulating such complex systems at fully atomistic resolution over a full cell cycle is computationally intractable on classical architectures, raising a central question: Can quantum computing offer a viable path to whole-cell simulations that integrate molecular- and systems-level complexity? This Perspective examines the potential of quantum computing across three hierarchical scales: atomistic-molecular modeling, metabolic and regulatory networks, and whole-cell spatial modeling. We present a complexity analysis comparing classical and quantum algorithms for representative biological problems, identifying regimes of substantial theoretical speedup under specified algorithmic assumptions. We highlight algorithmic developments designed to leverage both near-term exploratory and fault-tolerant quantum architectures, and discuss practical bottlenecks: data encoding overhead, system conditioning, measurement constraints, and hybrid quantum-HPC integration. Together, these results outline a roadmap for quantum-accelerated whole-cell modeling and the biological insights such multiscale frameworks may eventually enable.
