Cell Modeling

四、TR和D2--摘要 本项目的总体目标是开发新一代空间环境计算机仿真平台 细胞和亚细胞生物化学的逼真模拟和分析。蜂窝系统，特别是在 神经元，因为空间、生物化学和 发生在多个组织层次上的分子复杂性，从大分子组装到 神经回路的突触结构。生物复杂性令人望而生畏，科学研究人员必须 坚持不懈地寻找克服它的方法。这很重要，因为科学发现是由可测试性驱动的 源自我们直觉的假设和围绕我们当前对现实的理解的问题。但 当令人望而生畏的复杂性混淆了我们的直觉时，我们很难构思新的假设和 发现号逐渐停了下来。计算模型使研究人员能够探索复杂的关系 在生物成分之间，获得新的洞察和直觉--新假说的起源。这个 我们正在开发的用于细胞建模的Mcell/CellBlender平台就是专门为满足这一需求而设计的， 提供对复杂蜂窝系统的洞察和理解。我们在这里开发的单元建模工具包括 设计为与分子、网络和基于图像的建模工具相结合，如1、3和4。 我们的驾驶生物医学项目研究伙伴将使用工具来研究神经元和突触结构 以及大脑中涉及学习和记忆的复杂的生化途径。详细的 通过对这些系统进行计算建模所提供的对这些系统的理解程度将提供新的 可能适用于多种类型的细胞信号通路的见解，尤其应该有助于 阐明细胞信号转导功能障碍对神经和精神病理的影响。

IV. TR&D2 - Abstract The overall goal of this project is to develop the next generation computer simulation platform for spatially realistic simulation and analysis of cellular and subcellular biochemistry. Cellular systems, especially in neurons, are profoundly difficult to understand because of the interplay between spatial, biochemical and molecular complexity that occurs on multiple levels of organization, from macromolecular assemblies to synapse architecture to neural circuits. Biological complexity is daunting and scientific investigators must persevere to finds ways to overcome it. This is important because Scientific Discovery is driven by testable hypotheses which derive from our intuition and questions surrounding our current understanding of reality. But when daunting complexity confounds our intuition we struggle to conceive new hypotheses and the cycle of discovery grinds to a halt. Computational models allow investigators to probe the complex relationships between biological components, obtain new insights and intuition -- the genesis of new hypotheses. The MCell/CellBlender platform for cell modeling we are developing is expressly designed to fulfill this need, providing insight and understanding of complex cellular systems. The cell modeling tools we develop here are designed to mesh with the molecular, network, and image-derived modeling tools of TR&Ds 1, 3 and 4. The tools will be used by our Driving Biomedical Project research partners to study neuronal and synaptic structure and function and the intricate biochemical pathways involved in learning and memory in the brain. The detailed level of understanding of these systems afforded by computational modeling of these systems will provide new insights that may be applicable to many types of cell signaling pathways, and in particular should help to elucidate how dysfunctions in cell signaling may contribute to neurological and psychiatric pathology.