The Dynamical Logic of Developmental Regulatory Networks

在生物体的发育过程中，指定细胞命运所必需的蛋白质的产生由相互作用的基因网络控制。 这些网络的数学模型是必要的，为进一步的实验产生有用的假设。 该项目解决了这样的模型应该采取什么形式的问题，重点是海胆胚胎发育早期阶段的典型案例。 工作假设是连续时间布尔模型可以有效地解释三个关键特征：网络的结构，每个元素执行的逻辑以及激活事件的时间。这些模型允许综合多个实验的结果，并检查解释的一致性，并揭示复杂网络执行给定任务所需的结构特征。 该项目的一个主要目标是构建一个网络模型，包括细胞间信号传导，以解释实验观察到的基因活动，这些基因活动参与了最初在海胆胚胎中形成的不同组织的形成。了解决定在不同位置产生何种类型细胞的机制，对于治疗或预防癌症以及胚胎发育科学的巨大挑战至关重要。 在这个项目中解决的数学建模问题可能会发现其他系统中的独立代理急剧切换行为的基础上产生的时间延迟的信息，如神经科学和交通控制的其他应用。 该项目直接为研究生提供系统生物学领域的培训，学术和工业的进步需要数学和生物学专业知识的结合。

During development of a living organism, the production of the necessary proteins for specifying cell fates is governed by a network of interacting genes. Mathematical models of these networks are necessary both for generating useful hypotheses for further experimentation. This project addresses the question of what form such models should take, focusing on the paradigmatic case of the early stages of development of the sea urchin embryo. The working hypothesis is that continuous-time Boolean models can efficiently account for three crucial features: the structure of the network, the logic carried out by each element, and the timing of activation events. The models allow the syntheses of results from multiple experiments and checks for consistency of interpretation, and reveals architectural features required for complex networks to perform given tasks. A major goal of the project is to construct a network model, including inter-cell signaling, that accounts for the experimentally observed gene activity involved in creating the distinct tissues first formed in sea urchin embryos.Understanding the mechanisms that determine what types of cells will be produced at different locations is central to the grand challenge of treating or preventing cancer as well as the science of embryonic development. The mathematical modeling issues addressed in this project may find additional application in other systems in which independent agents sharply switch behaviors based on time--delayed information generated by other agents, such as neuroscience and traffic control. The project directly provides training for graduate students in the rapidly emerging field of systems biology, where academic and industrial advances require a combination of mathematical and biological expertise.