The Core of the Computational Biology

数学建模涉及使用数学方程和关系来表示生物 现象。对这类建模的补充是使用计算机模拟来表示这些 多维建模方法。这些方法有两个目的。首先，它们提供了一个基本的 数据询问和集成的框架，通常提供对数据所需的类型和质量的洞察 提出假说或实验设计。此功能在尝试集成或 分析通常与系统生物学相关的大型数据集。第二，也是更重要的是，这些 模型或模拟应该允许人们预测调查中的生物状态，并预测 自然过程会在不同的情况下表现出来。 这些问题集中在对生物系统行为的理解上，生物系统的功能是 由多个相互作用组件在分子、细胞和 组织水平。我们还将开发生物信息学和生物成像模型来整合和分析数据 从组件1生成，并利用从数据分析中获得的信息，生物 具有建立电子计算机模型以模拟TIC行为、癌细胞凋亡、细胞迁移、细胞周期的知识 变化和药物治疗反应。此组件的目标是利用我们的组合 在细胞生物学和计算建模方面的专业知识，以开发连贯的实验方案和构建 用于理解乳腺癌干细胞进化机制的生物数学模型，即， 一个干细胞是如何在细胞微环境中进化成不同大小和成分的乳腺肿瘤的。我们的 假设肿瘤发展过程中的TIC行为可以用健壮的、多尺度的 乳腺癌发展过程中TIC行为的数学/计算模型。此外，这些都是 建立模型不仅可以反映分子、细胞和组织水平的动态，而且还可以 TIC对实验疗法反应的预测。

Mathematical modeling involves the use of mathematical equations and relationships to represent biological phenomena. Complementary to this type of modeling is the use of computer simulations to represent these modeling approaches in multiple dimensions. These approaches serve two purposes. First, they provide a basic framework for the interrogation and integration of data, often providing insight into the type and quality needed for addressing a hypothesis or experimental design. This feature is especially useful when trying to integrate or analyze the large datasets generally associated with systems biology. Second, and more importantly, these models or simulations should allow one to predict the biological state under investigation and predict how the natural process will behave in various circumstances. These problems center on the understanding of the behavior of biological systems whose function is governed by the spatial and temporal ordering of multiple interacting components at the molecular, cellular, and tissue levels. We will also develop bioinformatics and bioimaging models to integrate and analyze the data generated from Component 1, and make use of the information obtained from data analysis, biological knowledge to build in silico models to model TIC behavior, cancer cell apoptosis, cell migration, cell cycle changes and drug treatment response. The goal of this component is to take advantage of our combined expertise in cell biology and computational modeling to develop coherent experimental protocols and construct biomathematical models for understanding the mechanism underiying breast cancer stem cell evolution, i.e., how one stem cell evolves into breast tumor with various sizes and compositions in cell microenvironment. Our hypothesis is that that TIC behavior during tumor development can be simulated using a robust, multiscale mathematical/computational model of TIC behavior during breast cancer development. Further, that these models can be built to reflect not only the molecular, cellular, and tissue-level dynamics, but also to allow prediction of the response of TIC to experimental therapeutics.