AIM 1

我们的总体目标是开发计算方法来阐明生物控制电路的设计原则。然而，我们将采取相反的方法，询问可以执行特定目标功能的所有电路架构是什么，而不是采用传统的方法来询问任何一个电路是如何工作的。我们的目标是： 1)开发算法来计算枚举和分类核心电路架构，可以稳健地实现给定的目标功能。这些方法将涉及电路的粗粒度表示，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架构相关的兼容参数集和功能权衡。 3)通过使用结果作为合成电路（AIM 2）的设计和能够执行目标功能的自然电路（AIM 3）的识别和分类的指导，来测试上述分析的有效性。 我们的努力将首先集中在分析一个测试平台的目标行为-完美适应。完美适应 是生物学上重要的感觉功能，其中感觉系统响应于 输入，但如果输入持续，则自身恢复到初始基础输出水平。这种适应行为 允许在宽动态范围内对小输入变化作出响应。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架构的方法。

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.