Emergent Properties of Complex Systems: From Atoms to Macromolecules; from Humans to Societies

项目摘要(30行) 其修订后的标题为《复杂系统的涌现特性：从原子到大分子》 人与社会“这一建议通过增加对一个问题的数据分析和模拟而扩大了范围 当前令人严重关切的问题：即像SARS-2-CoV这样的空气传播病毒如何在人类群体中传播。 偶然卷入其中，我开始着迷于每天的病例和死亡人数是如何与 时间以及使数据遵循Gompertz函数的物理机制是什么。 迈克尔·莱维特，首席调查员，长期从事独立科学研究，始于 1967年，他是最早从事计算生物学研究的人之一。他的早期工作建立了概念上的， 蛋白质和DNA结构细化、结构分析和分析的理论和计算框架 大分子模拟。他使计算机代码变得可用，并在科学上卓有成效 半个世纪以来，严谨而有影响力。这种方法在这里由致力于指导的PI继续进行 青年科学家以及从事持续的研究--社区服务和公众宣传。 1.基于深度等变网络的蛋白质结构精化。我们建议使用深度学习 精炼蛋白质模型的技术。我们预计，这样的方法结合了 现代神经网络结构和硬件的计算性能将使高效采样成为可能 接近天然状态的蛋白质构象空间，并将系统地为结构提供 精确度对于药物开发目的非常有用。 2.核糖体的功能动力学。我们在结构管理方面的经验将导致一台有用的计算机 给其他人打包。我们在核糖体动力学方面的工作将提供一个模型，说明SECM等多肽是如何 可以延缓核糖体的形成。从我们的MD模拟中采样的结构也可以被用作潜在的 药物发现的目标。 3.疫情分析、曲线拟合与仿真。应用于SARS-CoV-2和新冠肺炎，我们展示了 这种病毒传播遵循Gompertz增长函数，而不是通常认为的物流或 指数函数。这意味着传播感染的人口并不统一。网络 病毒传播的模拟表明，只有当连接网络是无尺度的时，模拟的 传染病服从Gompertz函数。我们将使用以下命令为具有无比例连接的物理系统建模 分子动力学来模拟具有广泛质量范围的粒子的2D气体。这部小说集多部作品于一体 纪律处分也可能适用于未来的呼吸道病毒，以更好地控制其传播。 与实验同事合作研究具有生物医学意义的系统将揭示令人着迷的 生物学的细节在起作用。我们希望这项工作将有助于阐明潜在的 复杂系统中的结构和功能，从大分子机器延伸到人类社会。

Project Summary (30 lines) With its revised title “Emergent Properties of Complex Systems: From Atoms to Macromolecules; from Humans to Societies” this proposal has been broadened by adding data-analysis & simulation on a problem of grave current concern: namely how an air-borne virus like SARS-2-CoV spread in human population. Getting involved by accident, I became fascinated with how the numbers of daily cases & deaths group with time and what is the physical mechanism that make the data follow the Gompertz function. Michael Levitt, the Principal Investigator has a long career of independent scientific research that started in 1967 when he was one of the first to work in computational biology. His early work set up the conceptual, theoretical and computational framework for protein and DNA structure refinement, structure analysis and macromolecular simulations. He makes computer codes available and has been productive, scientifically rigorous and impactful for half a century. This approaches is continued here by a PI committed to mentoring young scientists as well as engaging in sustained research-community service and public outreach. 1. Protein Structure Refinement with Deep Equivariant Networks. We propose to use Deep Learning technique to refine models of proteins. We anticipate that such an approach, combined with the power of modern neural net architectures and computational performance of hardware will enable efficient sampling of the protein conformational space near the native state and will systematically provide structures with accuracy useful for drug development purposes. 2. Functional Dynamics of Ribosome. Our experience with structure curation will lead to a useful computer package for others. Our work on Ribosome dynamics will provide a model of how peptides such as SecM can stall the ribosome. Structures sampled from our MD simulations could also be used as potential targets for drug discovery. 3. Epidemic Analysis, Curve-Fitting and Simulation. Applied to SARS-Cov-2 and COVID-19, we show that viral spread follows the Gompertz growth function rather than commonly assumed Logistics or Exponential functions. This means that the population transmitting the infection is not uniform. Network simulation of viral spread shows that only when the connection network is scale-free does the simulated epidemic follow the Gompertz function. We will model a physical system with scale-free connectivity using molecular dynamics to simulate a 2D gas of particles with a wide range of masses. This novel multi- disciplinary approach may also apply to future respiratory viruses to enable better control of their spread. Studying biomedically significant systems in collaboration with experimental colleagues will reveal fascinating details of biology in action. We expect this work will help elucidate the relationship between underlying structure and function in complex systems, extending from macromolecular machines to human societies.