SI2-SSE: Algorithms and Tools for Data-Driven Executable Biology

该项目旨在了解控制细胞活动的信号机制，如细胞分裂、细胞生长和细胞分化。细胞信号错误会导致癌症、自身免疫和糖尿病等疾病。因此，精确的细胞信号模型对于合理的药物设计和其他对国家健康至关重要的应用是必要的。该项目侧重于从实验数据推断模型。具体来说，它对蛋白质信号模型感兴趣，因为蛋白质控制和介导活细胞中的绝大多数生物过程。该项目遵循可执行生物学的方法：细胞信号模型是计算机程序，它允许在计算机上执行模型，并将模型行为与实验室环境中观察到的活细胞行为进行比较。对于这个项目最重要的是，将模型视为程序将使团队能够利用计算机程序自动合成的最新进展，从细胞的实验测量中合成模型。该项目的目标是为生物学家提供一个从不同类型的实验数据中综合各种可执行模型的工具。为了便于从实验数据中合成机制模型，该项目将开发一系列建模语言，以捕获生物系统的复杂行为，如时间和并发性。这些语言将是更通用的布尔网络语言的实例。该团队将研究如何根据可用实验数据的性质调整建模抽象；抽象将被实例化为从其语言族中适当选择的语言。建模框架将通过利用编程语言和形式化方法（如元编程和约束求解）中的技术来构建。

This project seeks to understand the signaling mechanisms that control cellular activities such as cell division, cell growth, and cell differentiation. Errors in cellular signaling cause diseases such as cancer, autoimmunity, and diabetes. Accurate models of cellular signalling are thus necessary for rational drug design and other applications central to national health. This project focuses on inferring models from experimental data. Specifically, it is interested in models of protein signalling because proteins control and mediate the vast majority of biological processes in a living cell. The project follows of the approach of executable biology: models of cell signalling are computer programs, which allows executing the models on the computer and comparing the model behavior against the behavior of the living cell observed in the lab setting. Most importantly for this project, viewing models as programs will allow the team to harness the recent advances in automatic synthesis of computer programs for synthesis of models from experimental measurements of cells. The goal of the project is to provide biologists with a tool that synthesizes a variety of executable models from varied types of experimental data. To facilitate synthesis of mechanistic models from experimental data, the project will develop a family of modeling languages that will capture complex behaviors of biological systems, such as time and concurrency. The languages will be instances of the more general Boolean-Networks language. The team will investigate how to adjust the modeling abstraction based on the nature of available experimental data; the abstractions will be instantiated as suitably chosen languages from their language family. The modeling framework will be built by leveraging techniques from programming languages and formal methods such as meta-programming and constraint solving.