Computational Biology and Bioinformatics Lab, School of Computing, The University of Southern Mississippi, USA.
BMC Genomics. 2010 Dec 1;11 Suppl 3(Suppl 3):S3. doi: 10.1186/1471-2164-11-S3-S3.
The challenge today is to develop a modeling and simulation paradigm that integrates structural, molecular and genetic data for a quantitative understanding of physiology and behavior of biological processes at multiple scales. This modeling method requires techniques that maintain a reasonable accuracy of the biological process and also reduces the computational overhead. This objective motivates the use of new methods that can transform the problem from energy and affinity based modeling to information theory based modeling. To achieve this, we transform all dynamics within the cell into a random event time, which is specified through an information domain measure like probability distribution. This allows us to use the "in silico" stochastic event based modeling approach to find the molecular dynamics of the system.
In this paper, we present the discrete event simulation concept using the example of the signal transduction cascade triggered by extra-cellular Mg2+ concentration in the two component PhoPQ regulatory system of Salmonella Typhimurium. We also present a model to compute the information domain measure of the molecular transport process by estimating the statistical parameters of inter-arrival time between molecules/ions coming to a cell receptor as external signal. This model transforms the diffusion process into the information theory measure of stochastic event completion time to get the distribution of the Mg2+ departure events. Using these molecular transport models, we next study the in-silico effects of this external trigger on the PhoPQ system.
Our results illustrate the accuracy of the proposed diffusion models in explaining the molecular/ionic transport processes inside the cell. Also, the proposed simulation framework can incorporate the stochasticity in cellular environments to a certain degree of accuracy. We expect that this scalable simulation platform will be able to model more complex biological systems with reasonable accuracy to understand their temporal dynamics.
今天的挑战是开发一种建模和模拟范例，将结构、分子和遗传数据集成在一起，以便在多个尺度上对生理和生物过程的行为进行定量理解。这种建模方法需要能够保持生物过程合理准确性同时降低计算开销的技术。这一目标促使我们采用新的方法，将问题从基于能量和亲和力的建模转变为基于信息论的建模。为了实现这一目标，我们将细胞内的所有动力学都转化为随机事件时间，通过信息域度量（如概率分布）来指定。这使我们能够使用“计算机模拟”随机事件建模方法来找到系统的分子动力学。
在本文中，我们以沙门氏菌 Typhimurium 的双组分 PhoPQ 调控系统中细胞外 Mg2+浓度触发的信号转导级联为例，介绍了离散事件模拟的概念。我们还提出了一种模型，通过估计作为外部信号进入细胞受体的分子/离子的到达时间之间的统计参数，来计算分子输运过程的信息域度量。该模型将扩散过程转化为随机事件完成时间的信息论度量，以获得 Mg2+释放事件的分布。使用这些分子输运模型，我们接下来研究了外部触发对 PhoPQ 系统的计算机模拟效应。
我们的结果说明了所提出的扩散模型在解释细胞内分子/离子输运过程方面的准确性。此外，所提出的模拟框架可以在一定程度上准确地纳入细胞环境中的随机性。我们期望这个可扩展的模拟平台能够以合理的准确性来模拟更复杂的生物系统，以了解它们的时间动态。
