AIM 1

目标1

• 批准号: 8378582
• 负责人: Chao Tang
• 金额: $ 43.95万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8378582
• 关键词: Adaptive Behaviors; Algorithms; Architecture; Behavior; Biological; Cereals; Classification; Goals; Methods; Output; Resolution; Sensory; System; Testing; Work; design; response

Our overall goal is to develop computational approaches to elucidate the design principles underlying biological control circuits. Rather than taking a traditional approach of asking how any one circuit works, however, we will take the inverse approach of asking what are all circuit architectures that could perform a particular target function. Our aims are to: 1) Develop algorithms to computationally enumerate and classify core circuit architectures that can robustly achieve a given target function. These methods will involve coarse-grained representations of circuits, which can be efficiently searched and evaluated. 2) Use high resolution circuit analysis methods to identify compatible parameter sets and functional tradeoffs associated with specific architectures. 3) Test the validity the above analysis by using the results as a guide for both design of synthetic circuits (AIM 2) and the identification and classification of natural circuits (AIM 3) that can perform the target function. Our efforts will initially focus on analyzing one testbed target behavior ¿ Perfect Adaptation. Perfect Adaptation is a biologically important sensory function in which a sensing system produces an output spike in response to an input, but restores itself to the initial basal output level if the input is sustained. This adaptation behavior allows responses to small input changes over a wide dynamic range. Subsequently we will apply these approaches to understand and design circuit architectures compatible with other target functions.

我们的总体目标是开发计算方法，以阐明生物控制电路的设计原理。但是，我们将采用一种逆向方法来询问所有可以执行特定目标功能的电路体系结构是什么。我们的目标是： 1）开发算法以列举计算，并对可以鲁棒性实现给定目标函数的核心电路体系结构进行分类。这些方法将涉及电路的粗粒表示，可以有效地搜索和评估。 2）使用高分辨率电路分析方法来识别与特定体系结构相关的兼容参数集和功能折衷。 3）通过使用结果作为合成电路设计（AIM 2）以及可以执行目标函数的自然电路的识别和分类来测试上述分析。 我们的努力最初将专注于分析一个测试型目标行为»完美适应。完美的适应 是一种在生物学上重要的感觉函数，其中感应系统会产生输出尖峰。 输入，但如果持续输入，则将自己恢复到初始的基本输出级别。这种适应行为 允许对广泛的动态范围内的小输入更改做出响应。随后我们将应用这些 理解和设计与其他目标功能兼容的电路体系结构的方法。