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

类似于复杂的工程系统，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，生物学家已经构建了“模型”来捕捉这种复杂性，并验证他们的直觉，以及交流特定生物系统或子系统的实际工作方式。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可用性，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多层数值信息中。生物模型通常以图形和流程图的形式呈现，其中包含许多组成部分，每个组成部分对应于某个生化反应。这样的图表也与数学模型联系在一起，但并不总是如此，主要是以微分方程的形式。当这些方程具有一组定义良好的动力学参数时，这些方程用于对系统进行模拟。根据模拟的痕迹是否与生物数据相符，对模型进行驳斥或验证。人们经常会遇到这样的情况，没有数学模型，或者模型不完整，缺乏一套完整的参数，然而生物学家确实对许多组成部分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，但没有呈现出全局和动态的视角（例如，不变量）。当从实验中推断出本体不变量时（使用GOALIE重新描述工具），可以将这些不变量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。该项目通过提供自动推理工具来解决这两种情况，该工具在生物学中架起了计算模型和描述模型的桥梁。这些工具和实验分析的结果暗示了有效可测试预测的构建。然后利用湿室实验的结果来完善和修正正式模型。建模和实验之间的这种反馈循环对于获得对潜在细胞机制的过程级理解非常重要。进一步表征哺乳动物细胞周期行为的特定部分（例如，一个可能未知的因素如何允许Cdk2在G1/ s时磷酸化Cdk抑制剂p27）。从长远来看，理解细胞周期的复杂调控和代谢结构的更广泛含义将为生物学和高级计算的新应用提供重要的见解。此外，它们将通过利用生物驱动的隐喻，为计算提供新的视角。更重要的是，在混合系统（HS）模型和生物本体的背景下开发的方法将应用于群体机器人，社交软件，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，尽管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.