Building smarter synthetic biological circuits Synthetic genetic and biological regulatory circuits can enable logic functions to form the basis of biological computing; synthetic biology can also be used to control cell behaviors (see the Perspective by Glass and Alon). Andrews et al. used mathematical models and computer algorithms to combine standardized components and build programmable genetic sequential logic circuits. Such circuits can perform regulatory functions much like the biological checkpoint circuits of living cells. Circuits composed of interacting proteins could be used to bypass gene regulation, interfacing directly with cellular pathways without genome modification. Gao et al. engineered proteases that regulate one another, respond to diverse inputs that include oncogene activation, process signals, and conditionally activate responses such as those leading to cell death. This platform should facilitate development of “smart” therapeutic circuits for future biomedical applications. Science, this issue p. eaap8987, p. 1252; see also p. 1199 Progress has been made toward enabling synthetic biological computing via programmable genetic sequential logic circuits in bacteria. INTRODUCTION Modern computing is based on sequential logic, in which the state of a circuit depends both on the present inputs as well as the input history (memory). Implementing sequential logic inside a living cell would enable it to be programmed to progress through discrete states. For example, cells could be designed to differentiate into a multicellular structure or order the multistep construction of a material. A key challenge is that sequential logic requires the implementation of regulatory feedback, which has proven difficult to design and scale. RATIONALE We present a quantitative method to design regulatory circuits that encode sequential logic. Our approach uses NOT gates as the core unit of regulation, in which an input promoter drives the expression of a repressor protein that turns off an output promoter. Each gate is characterized by measuring its response function, in other words, how changing the input affects the output at steady state. Mathematically, the response functions are treated as nullclines, and tools from nonlinear dynamics (phase plane and bifurcation analyses) are applied to predict how combining gates leads to multiple steady states and dynamics. The circuits can be connected to genetic sensors that respond to environmental information. This is used to implement checkpoint control, in which the cell waits for the right signals before continuing to the next state. Circuits are built that instruct Escherichia coli to proceed through a linear or cyclical sequence of states. RESULTS First, pairs of repressors are combined to build the simplest unit of sequential logic: a set-reset (SR) latch, which records a digital bit of information. The SR latches can be easily connected to each other and to sensors because they are designed such that the inputs and outputs are both promoters. Each latch requires two repressors that inhibit each other’s expression. A total of 11 SR latches were designed by using a phase plane analysis. The computation accurately predicts the existence of multiple steady states by using only the empirical NOT gate response functions. A set of 43 circuits was constructed that connects these latches to different combinations of sensors that respond to small molecules in the media. These circuits are shown to reliably hold their state for >48 hours over many cell divisions, only switching states in response to the sensors that connect to the set and reset inputs of the latch. Larger circuits are constructed by combining multiple SR latches and additional feedback loops. A gated data (D) latch, common in electronic integrated circuits, is constructed where one input sets the state of the circuit and the second input locks this state. Up to three SR latches (based on six repressors) are combined in a single cell, thus allowing three bits to be reversibly stored. The performances of these circuits closely match those predicted by the responses of the component gates and a bifurcation analysis. Circuits are designed to implement checkpoint control, in which cells wait indefinitely in a state until the correct signals are received to progress to the next state. The progression can be designed to be cyclical, analogous to cell cycle phases, during which cells progress through a series of states until returning to the starting state. The length of time in each state is indefinite, which is confirmed by demonstrating stability for days when the checkpoint conditions are not met. CONCLUSION This work demonstrates the implementation of sequential logic circuits in cells by combining reliable units of regulation according to simple rules. This approach is conducive to design automation software, which can use these rules to combine gates to build larger circuits. This provides a designable path to building regulatory networks with feedback loops, critical to many cellular functions and ubiquitous in natural networks. This represents a critical step toward performing advanced computing inside of cells. Quantitative design of sequential logic in living cells. Cells can be genetically programmed to respond to temporal stimuli by using complex sequential logic circuits. (Left) Checkpoint control is one such example in which the circuit state (s0 and s1) transitions when the specified input signals are presented. (Middle) Sequential logic circuits can be designed from simple steady-state response functions measured in relative promoter units by using principles of nonlinear dynamics. Bistable latches are used as rewritable memory. The colored symbols represent gates. (Right) The circuit output (Y) was measured for cells that were grown in inputs that were varied over time. The square waveforms indicate the presence or absence of the input signals. Over multiple days, the cells can be cycled through the circuit states or held waiting for the next checkpoint. Biological processes that require orderly progression, such as growth and differentiation, proceed via regulatory checkpoints where the cell waits for signals before continuing to the next state. Implementing such control would allow genetic engineers to divide complex tasks into stages. We present genetic circuits that encode sequential logic to instruct Escherichia coli to proceed through a linear or cyclical sequence of states. These are built with 11 set-reset latches, designed with repressor-based NOR gates, which can connect to each other and sensors. The performance of circuits with up to three latches and four sensors, including a gated D latch, closely match predictions made by using nonlinear dynamics. Checkpoint control is demonstrated by switching cells between multiple circuit states in response to external signals over days.

中文翻译：

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使用可编程时序逻辑的蜂窝检查点控制

构建更智能的合成生物电路 合成基因和生物调节电路可以使逻辑功能形成生物计算的基础；合成生物学也可用于控制细胞行为（参见 Glass 和 Alon 的观点）。安德鲁斯等人。使用数学模型和计算机算法组合标准化组件并构建可编程遗传时序逻辑电路。这样的电路可以像活细胞的生物检查点电路一样执行调节功能。由相互作用的蛋白质组成的电路可用于绕过基因调控，直接与细胞通路连接，无需基因组修饰。高等人。相互调节的工程化蛋白酶对多种输入做出反应，包括致癌基因激活、处理信号、并有条件地激活反应，例如导致细胞死亡的反应。该平台应促进未来生物医学应用的“智能”治疗电路的开发。科学，这个问题 p。eaap8987，第。1252; 另见第。1199 在通过细菌中的可编程遗传序列逻辑电路实现合成生物计算方面取得了进展。简介现代计算基于时序逻辑，其中电路的状态取决于当前输入以及输入历史（内存）。在活细胞内实施时序逻辑将使其能够被编程为在离散状态中前进。例如，细胞可以被设计成分化成多细胞结构或订购材料的多步构造。一个关键挑战是时序逻辑需要实施监管反馈，这已被证明难以设计和扩展。基本原理我们提出了一种设计对时序逻辑进行编码的调节电路的定量方法。我们的方法使用非门作为核心调控单元，其中输入启动子驱动阻遏蛋白的表达，关闭输出启动子。每个门都通过测量其响应函数来表征，换句话说，改变输入如何影响稳态下的输出。在数学上，响应函数被视为零斜线，并应用来自非线性动力学（相平面和分叉分析）的工具来预测组合门如何导致多个稳态和动力学。这些电路可以连接到对环境信息做出反应的基因传感器。这用于实现检查点控制，其中单元在继续下一个状态之前等待正确的信号。构建的电路指示大肠杆菌通过线性或循环状态序列进行。结果 首先，组合成对的抑制器来构建最简单的时序逻辑单元：设置-复位 (SR) 锁存器，它记录信息的数字位。SR 锁存器可以很容易地相互连接并连接到传感器，因为它们的设计使得输入和输出都是启动器。每个闩锁需要两个抑制彼此表达的阻遏物。通过相平面分析设计了总共 11 个 SR 锁存器。该计算仅使用经验非门响应函数来准确预测多个稳态的存在。构建了一组 43 个电路，将这些锁存器连接到对介质中的小分子做出响应的不同传感器组合。这些电路被证明可以在许多细胞分裂中可靠地保持其状态 > 48 小时，只有响应连接到锁存器的设置和重置输入的传感器才会切换状态。更大的电路是通过组合多个 SR 锁存器和额外的反馈回路来构建的。电子集成电路中常见的门控数据 (D) 锁存器被构造为其中一个输入设置电路的状态，第二个输入锁定该状态。最多三个 SR 锁存器（基于六个抑制器）组合在一个单元中，从而允许可逆地存储三位。这些电路的性能与组件门的响应和分叉分析所预测的非常匹配。电路设计用于实现检查点控制，其中单元在一个状态中无限期等待，直到接收到正确的信号以进入下一个状态。进程可以设计为循环的，类似于细胞周期阶段，在此期间细胞通过一系列状态进行直到返回到起始状态。每个状态的时间长度是不确定的，这通过在不满足检查点条件的情况下证明稳定性的天数来确认。结论这项工作通过根据简单的规则组合可靠的调节单元，展示了在单元中实现时序逻辑电路。这种方法有利于设计自动化软件，它可以使用这些规则来组合门以构建更大的电路。这提供了一条可设计的路径来构建具有反馈回路的调节网络，这对许多细胞功能至关重要并且在自然网络中无处不在。这是在细胞内执行高级计算的关键一步。活细胞中时序逻辑的定量设计。通过使用复杂的时序逻辑电路，可以对细胞进行基因编程以响应时间刺激。（左）检查点控制就是这样一个例子，其中当出现指定的输入信号时，电路状态（s0 和 s1）会发生转换。（中）利用非线性动力学原理，可以从相对启动子单元中测量的简单稳态响应函数设计顺序逻辑电路。双稳态锁存器用作可重写存储器。彩色符号代表门。（右）针对在随时间变化的输入中生长的细胞测量电路输出 (Y)。方波表示输入信号的存在或不存在。在多日内，细胞可以循环通过电路状态或等待下一个检查点。需要有序进展的生物过程，如生长和分化，通过监管检查点进行，细胞在那里等待信号，然后继续进入下一个状态。实施这种控制将使基因工程师能够将复杂的任务划分为多个阶段。我们提出了编码顺序逻辑的遗传电路，以指示大肠杆菌通过线性或循环状态序列进行。这些内置有 11 个设置复位锁存器，设计有基于抑制器的 NOR 门，可以相互连接和传感器。具有多达三个锁存器和四个传感器（包括门控 D 锁存器）的电路的性能与使用非线性动力学所做的预测非常匹配。检查点控制通过在多个电路状态之间切换单元以响应外部信号来证明。