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INTRODUCTION Cells respond to their environment, make decisions, build structures, and coordinate tasks. Underlying these processes are computational operations performed by networks of regulatory proteins that integrate signals and control the timing of gene expression. Harnessing this capability is critical for biotechnology projects that require decision-making, control, sensing, or spatial organization. It has been shown that cells can be programmed using synthetic genetic circuits composed of regulators organized to generate a desired operation. However, the construction of even simple circuits is time-intensive and unreliable. RATIONALE Electronic design automation (EDA) was developed to aid engineers in the design of semiconductor-based electronics. In an effort to accelerate genetic circuit design, we applied principles from EDA to enable increased circuit complexity and to simplify the incorporation of synthetic gene regulation into genetic engineering projects. We used the hardware description language Verilog to enable a user to describe a circuit function. The user also specifies the sensors, actuators, and “user constraints file” (UCF), which defines the organism, gate technology, and valid operating conditions. Cello (www.cellocad.org) uses this information to automatically design a DNA sequence encoding the desired circuit. This is done via a set of algorithms that parse the Verilog text, create the circuit diagram, assign gates, balance constraints to build the DNA, and simulate performance. RESULTS Cello designs circuits by drawing upon a library of Boolean logic gates. Here, the gate technology consists of NOT/NOR logic based on repressors. Gate connection is simplified by defining the input and output signals as RNA polymerase (RNAP) fluxes. We found that the gates need to be insulated from their genetic context to function reliably in the context of different circuits. Each gate is isolated using strong terminators to block RNAP leakage, and input interchangeability is improved using ribozymes and promoter spacers. These parts are varied for each gate to avoid breakage due to recombination. Measuring the load of each gate and incorporating this into the optimization algorithms further reduces evolutionary pressure. Cello was applied to the design of 60 circuits for Escherichia coli , where the circuit function was specified using Verilog code and transformed to a DNA sequence. The DNA sequences were built as specified with no additional tuning, requiring 880,000 base pairs of DNA assembly. Of these, 45 circuits performed correctly in every output state (up to 10 regulators and 55 parts). Across all circuits, 92% of the 412 output states functioned as predicted. CONCLUSION Our work constitutes a hardware description language for programming living cells. This required the co-development of design algorithms with gates that are sufficiently simple and robust to be connected by automated algorithms. We demonstrate that engineering principles can be applied to identify and suppress errors that complicate the compositions of larger systems. This approach leads to highly repetitive and modular genetics, in stark contrast to the encoding of natural regulatory networks. The use of a hardware-independent language and the creation of additional UCFs will allow a single design to be transformed into DNA for different organisms, genetic endpoints, operating conditions, and gate technologies.

Protein-protein interactions are critical for many biological processes, and over the past several decades, this importance has prompted researchers to investigate the physical and chemical bases of protein binding. How much do we now understand about how proteins interact? Perhaps the most rigorous way to answer this question is to endeavor to design, at an atomic level of detail, such an interaction from scratch. On page 816 of this issue, Fleishman et al. ( 1 ) take on this challenge. They used a computational method that enabled them to design two proteins that bind to a preselected surface on an influenza virus. This work demonstrates how far we have come in understanding and being able to predict protein-protein interactions, and the technique could prove useful for developing biosensors, reagents, and more effective drugs.