Stochastic Dynamic MOdeling of Cellular Protein Interactions

模拟分子-蛋白质相互作用的随机方法是研究分子-蛋白质相互作用的全新方法 计算机模拟细胞生物学的重要生物学目标。尽管在……方面取得了很大的进展 这一方向由计算生物学家在过去15年中提出，其目标是使细胞分子“硅化” 互动仍然遥不可及。更准确地说，目前的模型现实主义水平已经导致了 分子相互作用的预测准确性。这推动了新的动态模型的发展 这包含了更广泛的生物学细节，并可以为模拟增加几层真实感。 然而，这样的模型在计算上令人望而生畏，因此开发高效和准确的模型至关重要 计算方法。其目的是增强分子水平的理解和模拟 生物学上的相互作用。特别是，通过利用随机优化中的新发展， 研究人员将通过增加一个新的现实主义维度来显著提高对交互作用的预测。 然而，对于许多实际情况，随机目标函数将变成高维、非高斯 在计算上很难优化的非线性随机场。这是一个很难的 研究人员计划通过开发新的不确定度量化(UQ)来解决的问题 数学理论。具体目标是：(I)开发一种紧凑的动态代理模型 考虑分子结构不确定性和分子性质的随机目标函数 例如通过求解非线性泊松玻尔兹曼(PB)方程来计算静电场。随机性 采用代理模型，有效地解决了优化问题。(二)分析复杂解析规律 非线性Poisson-Boltzmann方程解的性质(及其他分子性质) 关于概率分子构象模型。(Iii)发展两地的衔接速度 代理模型的复解析规律关于实现的次数 蛋白质结构(计算复杂性)。大多数蛋白质相互作用模型都是基于分子的。 结构采用刚性形状，因此导致错误的预测。调查人员提议 通过加入蛋白质相互作用的动态不确定性，显著改进了蛋白质相互作用的预测 分子构象形状。UQ在蛋白质相互作用中的理论和应用还处于初级阶段。

Stochastic methods for modeling molecular-protein interactions are entirely new approaches to the important biological goal of simulating cellular biology in silico. Though great progress has been made in this direction by computational biologists over the past 15 years, the goal of "siliconizing" cellular molecular interactions still remains remote. More precisely, the current level of model realism has led to a plateau in the prediction accuracy of molecular interactions. This motivates the development of novel dynamic models that incorporate more extensive biological details and can add layers of realism to the simulations. However, such models are computationally daunting, so that it is critical to develop efficient and accurate computational methods. The purpose is to augment molecular-level understanding and simulation of biological interactions. In particular, by exploiting novel developments in stochastic optimi?ation, the investigators shall significantly improve the prediction of interactions by adding a new dimension of realism. However, for many practical cases the stochastic objective function will become a high dimensional, nonGaussian, nonlinear random field that will be computationally very challenging to optimize. This is a hard problem that the investigators plan to address by developing novel Uncertainty Quantification (UQ) mathematical theory. The specific aims are to: (i) Develop a compact dynamic surrogate model of the stochastic objective function that incorporates the molecular structure uncertainty and molecular properties such as the electrostatic fields by solving the nonlinear Poisson Boltzmann (PB) equation. The stochastic optimization is solved efficiently with a surrogate model. (ii) Analyze the complex analytic regularity properties of the solution of the nonlinear Poisson-Boltzmann equation (and the other molecular properties) with respect to the probabilistic molecular conformation model. (iii) Develop convergence rates of the surrogate model from the complex analytic regularity with respect to the number of realizations of the protein structure (computational complexity). Most protein interactions models based on molecular. structure assume a rigid shape thus leading to erroneous predictions. The investigators propose to significantly improve the prediction of protein interactions by incorporating dynamic uncertainty of the molecular conformational shape. The theory and application of UQ to protein interactions is at its infancy.