Stochastic Dynamic MOdeling of Cellular Protein Interactions

分子-蛋白质相互作用的随机建模方法是研究分子-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。虽然在这方面取得了很大的进展， 在过去的15年里，计算生物学家们一直致力于这个方向， 互动仍然很遥远。更准确地说，目前的模型现实主义水平导致了一个高原， 分子间相互作用的预测精度。这激发了新的动态模型的发展 它包含了更广泛的生物学细节，可以为模拟增加真实感。 然而，这种模型在计算上是令人生畏的，因此开发高效且准确的模型是至关重要的。 计算方法其目的是增强分子水平的理解和模拟 生物相互作用。特别是，通过利用新的发展随机优化？阿申 研究人员应通过增加一个新的现实主义维度来显著改进对相互作用的预测。 然而，对于许多实际情况，随机目标函数将成为高维的，非高斯的， 非线性随机场，这将是非常具有挑战性的计算优化。这是一个很难 研究人员计划通过开发新的不确定性量化（UQ）来解决这个问题 数学理论具体目标是：（一）开发一个紧凑的动态代理模型， 结合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Poisson Boltzmann（PB）方程的静电场。随机 利用代理模型有效地解决了优化问题。(ii)分析复解析规律 非线性Poisson-Boltzmann方程解的性质（以及其他分子性质） 相对于概率分子构象模型。(iii)提高 代理模型从复杂的分析规律性方面的实现的数量， 计算复杂度（Computational Complexity）大多数蛋白质相互作用模型都是基于分子水平的。 结构呈现刚性形状，从而导致错误预测。研究人员建议， 通过结合蛋白质相互作用的动态不确定性， 分子构象形状UQ理论及其在蛋白质相互作用中的应用尚处于起步阶段。

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.