BIC: Collaborative Research: Evolutionary Optimization of Biological Circuits: Towards Cellular Programming

重新设计甚至构建全新生物实体的能力为利用生物学解决人类问题提供了革命性的机会。然而，梦想远远超出了现实：设计新的生物机器，对细胞行为进行编程，以及建立新的生命形式，都带来了巨大的技术和概念挑战。我们的研究旨在通过开发普遍适用的工程方法来建立合成基因网络，从而迈出一些重要的第一步。即使是在生物系统中实现最简单的电路，也需要对大量难以理解的参数进行乏味的优化，其中许多参数既不能测量，也不容易操作。模拟有时可以指导优化，但我们相信生物系统最好是使用自然的编辑策略--进化--来优化。我们相信，基于计算预测的合理设计与定向进化相结合的方法--在实验室进行突变并选择那些表现出预期行为的生物体--将是合成生物学进步的基础。我们实际上将学习如何‘培育’有用的合成基因网络，就像我们已经学习如何培育有用的植物和动物一样。这种方法模仿自然进化，探索组成工程调控途径的一组分子可用的巨大而复杂的功能图景。重要的是，它绕过了我们对DNA序列如何编码一组特定的生物功能的近乎完全的无知，这是任何“理性”设计方法所必需的详细理解。通过将随机突变引入DNA并筛选可能由突变电路表达的不同功能，我们可以确定哪些功能是可能的，以及特定搜索过程可用的功能范围(例如，针对特定基因的随机点突变)。通过进一步的分析，例如鉴定突变的测序和电路元件的生化分析，我们可以深入了解实现或修改整体功能的分子机制。在这个项目中，我们有三个具体目标。首先是验证“选择模块”，通过它，我们可以在实验室中高效地进化元件和电路。这个模块将适当的电路功能与表达它的细胞的生存和生长能力联系起来。具有正常电路的细胞能够存活和生长；那些没有解决问题的细胞就不会。要对复杂的行为进行编程，我们还需要响应预定义范围的输入参数和可预测输出参数的组件。因此，我们的第二个目标是使用定向进化来创建一系列基于特征良好的框架蛋白LuxR的转录激活剂。实验室进化的LuxR变异体将在DNA上不同的非自然启动子位置激活基因转录。最后，我们建议通过进化探索来研究一组预定义组件可用的电路功能范围。具体地说，我们将进化出一系列能响应预先指定的酰基-HSL浓度范围的“波段检测”电路。这些电路将被用来构建在固相中形成基因表达模式的合成系统。我们的最终目标是开发一种基本的使能技术，用于合成生物学以及开发用于计算的生物启发模式和体系结构。我们设想，进化的电路和可以用它们构建的合成多细胞系统将有助于研究人员开发基因调控、群体感应和细胞计算的其他方面的定量模型。

The ability to redesign and even build completely new biological entities offers revolutionary opportunities for using biology to solve human problems. The dream, however, far outstrips the reality: engineering new biological machines, programming cells behaviors, and building new forms of life pose huge technical and conceptual challenges. Our research is designed to make some important first steps, by developing generally-applicable engineering methods for building synthetic gene networks. Implementation of even the most simple circuits in a biological system requires tedious optimization of a large number of poorly-understood parameters, many of which can neither be measured nor easily manipulated. Simulations can sometimes guide optimization, but we believe that biological systems are best optimized using nature's editing strategy, evolution. We believe that a combined approach of rational design based on computational predictions coupled with directed evolution-making mutations in the laboratory and selecting those organisms exhibiting the desired behaviors--will be fundamental to the progress of synthetic biology. We will in effect learn how to 'breed' useful synthetic gene networks, just as we have learned how to breed useful plants and animals.This approach mimics natural evolution in exploring the vast and complex landscape of functions available to a set of molecules making up an engineered regulatory pathway. Importantly, it circumvents our near-complete ignorance of how a DNA sequence encodes a specific set of biological functions, a detailed understanding that is required for any 'rational' design approach. By introducing random mutations into the DNA and screening for different functions that might be expressed by the mutant circuits, we can identify which functions are possible as well as the ranges of function available to the specific search process (e.g. random point mutation targeted to a specific gene). With further analysis, e.g. sequencing to identify the mutations and biochemical analysis of circuit components, we gain insights into the molecular mechanisms by which the overall function is achieved or modified. In this project we have three specific aims. The first is to validate a 'selection module' by which we can efficiently evolve components and circuits in the laboratory. This module connects proper circuit function to the ability of cells that express it to survive and grow. Cells with functioning circuits survive and grow; those that have not solved the problem do not. To program complex behaviors, we will also need components that respond to predefined ranges of input parameters with predictable output parameters. Thus our second aim is to use directed evolution to create a range of transcriptional activators based on the well-characterized framework protein, LuxR. Laboratory-evolved LuxR variants will activate gene transcription at different, nonnatural promoter sites on the DNA. Finally, we propose to investigate the range of circuit functions available to a predefined set of components via evolutionary exploration. Specifically, we will evolve a series of 'band detect' circuits that respond to a prespecified range of acyl-HSL concentrations. These circuits will be used to construct synthetic systems that form patterns of gene expression in the solid phase.Our ultimate goal is to develop a fundamental enabling technology for synthetic biology as well as for developing bio-inspired modes and architectures for computing. We envision that evolved circuits and the synthetic multicellular systems that can be constructed from them will be useful to researchers developing quantitative models of gene regulation, quorum sensing, and other aspects of cellular computing.