BIC: EMT: Innovative Symbolic Hybrid Systems Models, Inspired by Biological Networks and Bio-Ontology

类似于一个复杂的工程系统，生物过程通过复杂网络中的许多同时相互作用来运作。传统上，生物学家构建了“模型”来捕捉这种复杂性，验证他们的直觉，并传达特定生物系统或子系统实际上是如何工作的。 为了建立这些模型，生物学家依赖于非常普遍和广泛的知识，并通过从少量范例系统中获得的深度和专业知识来增强它。 随着大量高通量实验数据的出现，生物学家也面临着从数据中重建模型的任务，其中相关信息可能深埋在数字信息层中。生物模型通常以图形和流程图的形式呈现，其中包含许多组件，每个组件对应于特定的生化反应。 这种图也与数学模型有关，但并不总是与数学模型有关，主要是微分方程的形式。 当它们具有一组定义良好的动力学参数时，方程用于执行系统的模拟。根据模拟的痕迹是否与生物学数据一致，模型被反驳或验证。人们经常面临这样的情况，没有数学模型，或者模型不完整，缺乏完整的参数集，但生物学家确实对许多组件及其相互作用有详细的描述性理解。例如，当前的微阵列数据分析技术将生物学家的注意力吸引到目标基因集上，但并没有呈现全局和动态的观点（例如，不变量）在数据集上共同推断。当从实验中推断出本体不变量时（使用GOALIE重描述工具），这些不变量可以与已知的描述信息进行比较，以确定我们是否有关于某些生物过程的完整和一致的理论。本项目通过提供连接生物学中计算模型和描述模型的自动推理工具来解决这两种情况。这些工具和实验分析的结果暗示了有效的可测试预测的构建。 湿实验室的实验结果，然后用于细化和修正的正式模型。 这种建模和实验之间的反馈循环已被证明是重要的，在获得一个过程水平的理解的基础细胞machinery.The哺乳动物细胞周期行为的特定部分的进一步表征（例如，一个可能未知的因素如何可能允许磷酸化Cdk抑制剂p27由Cdk 2在G1/S。从长远来看，了解细胞周期复杂的调控和代谢结构的更广泛的影响将为生物学和先进计算的新应用提供重要的见解。此外，他们还将通过利用生物驱动的隐喻来提供新的计算视角。更重要的是，在混合系统（HS）模型和生物本体论的背景下开发的方法将应用于群体机器人，社会软件，电子商务，复杂的交互式工程系统，计算机安全，自适应软件等，虽然从我们自己的历史角度来看，我们将继续致力于证明这种方法在生物医学应用中的首次成功。

Akin to a complex engineered system, biological processes operate through many simultaneous interactions within complex networks. Traditionally, biologists have constructed "models" to capture this complexity and verify their intuitions as well as communicate how a particular biological system or subsystem actually works. To build these models, biologists rely on a very general and broad array of knowledge, and also augment it with depth and expertise obtained from small number of exemplar systems. With availability of large amount of high-throughput experimental data, biologists are also faced with the task of reconstructing models from data where relevant information may be deeply buried in layers of numerical information.Biological models are often presented pictorially as graphs and flow charts with many components, each corresponding to a certain biochemical reaction. Such diagrams have also, but not always, been associated with mathematical models, mostly in the form of differential equations. The equations are used to perform simulations of the system, when they have a well-defined set of kinetic parameters. The model is refuted or validated depending on whether the simulated traces agree with biological data.Often one is faced with situations, where there is no mathematical model, or the model is incomplete and they lack a complete set of parameters, and yet biologists do have detailed descriptive understanding of many of the components and their interactions. For instance, current microarray data analysis techniques draw the biologist's attention to targeted sets of genes but do not otherwise present global and dynamic perspectives (e.g., invariants) inferred collectively over a dataset. When ontologically invariants are inferred from experiments (using GOALIE redescription tool), such invariants can be compared with the known descriptive information to determine if we have complete and consistent theories about certain biological processes.This project addresses these two scenarios by providing automated reasoning tools that bridge both computational and descriptive models in biology. The results from these tools and experimental analyses hint at the construction of efficiently testable predictions. The results of wet-lab experiments are then used to refine and amend the formal model. This feedback cycle between modeling and experimentation has proven important in obtaining a process-level understanding of the underlying cellular machinery.The further characterization of specific parts of the mammalian cell cycle behavior (e.g. how a possibly unknown factor may allow the phosphorlyzation Cdk inhibitor p27 by Cdk2 at G1/S.)In the longer run, understanding the wider implications of the complex regulatory and metabolic architecture of the cell cycle will provide significant insights into new applications of biology and advanced computing. In addition, they will provide new perspectives on computing by exploiting biologically driven metaphors. More importantly, the approaches developed in the context of hybrid-system (HS) models and bio-ontology will find applications to swarm robotics, social-software, e-commerce, complex interactive engineered systems, computer-security, adaptive software, etc., although from our own historical perspective, we will remain engaged in proving the first successes of this approach in biomedical applications.