A Synthetic Genetic Edge Detection Program

SummaryEdge detection is a signal processing algorithm common in artificial intelligence and image recognition programs. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E. coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. The algorithm is implemented using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks

PaLM: Scaling Language Modeling with Pathways

Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of task-specific training examples needed to adapt the model to a particular application. To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM. We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML system which enables highly efficient training across multiple TPU Pods. We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark. A significant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model. PaLM also has strong capabilities in multilingual tasks and source code generation, which we demonstrate on a wide array of benchmarks. We additionally provide a comprehensive analysis on bias and toxicity, and study the extent of training data memorization with respect to model scale. Finally, we discuss the ethical considerations related to large language models and discuss potential mitigation strategies

Spatiotemporal Control of Cellular Signalling with Light

Genetically-encodable optical reporters, such as Green Fluorescent Protein, have revolutionized the observation and measurement of cellular states. However, the inverse challenge of using light to precisely control cellular behavior has only recently begun to be addressed; in recent years, semi-synthetic chromophore-tethered receptors and naturally-occurring channel rhodopsins have been used to directly perturb neuronal networks. The difficulty of engineering light sensitive proteins remains a significant impediment to the optical control to most cell-biological processes. I have focused my work over the last five years on the production of genetically-encoded light-sensitive reagents for the control of both bacterial and eukaryotic signalling networks. I have demonstrated minute-timescale control of bacterial transcriptional networks with engineered light-sensitive histidine kinases. I have also demonstrated the use of a new genetically encoded light-control system based on an optimized reversible protein-protein interaction from the phytochrome signaling network of Arabidopsis thaliana. Because protein-protein interactions are one of the most general currencies of cellular information, this latter system can in principal be generically used to control diverse functions. I show that this system can be used to precisely and reversibly translocate target proteins to the membrane with micrometer spatial resolution and second time resolution. I show that light-gatedtranslocation of the upstream activators of rho-family GTPases, which control the actin cytoskeleton, can be used to precisely reshape and direct the cell morphology of mammalian cells. The light-gated protein-protein interaction that has been optimized in this latter work should be useful for the design of diverse light-programmable reagents, potentially enabling a new generation of perturbative, quantitative experiments in cell biology

Spatiotemporal control of cell signalling using a light-switchable protein interaction.

Genetically encodable optical reporters, such as green fluorescent protein, have revolutionized the observation and measurement of cellular states. However, the inverse challenge of using light to control precisely cellular behaviour has only recently begun to be addressed; semi-synthetic chromophore-tethered receptors and naturally occurring channel rhodopsins have been used to perturb directly neuronal networks. The difficulty of engineering light-sensitive proteins remains a significant impediment to the optical control of most cell-biological processes. Here we demonstrate the use of a new genetically encoded light-control system based on an optimized, reversible protein-protein interaction from the phytochrome signalling network of Arabidopsis thaliana. Because protein-protein interactions are one of the most general currencies of cellular information, this system can, in principle, be generically used to control diverse functions. Here we show that this system can be used to translocate target proteins precisely and reversibly to the membrane with micrometre spatial resolution and at the second timescale. We show that light-gated translocation of the upstream activators of Rho-family GTPases, which control the actin cytoskeleton, can be used to precisely reshape and direct the cell morphology of mammalian cells. The light-gated protein-protein interaction that has been optimized here should be useful for the design of diverse light-programmable reagents, potentially enabling a new generation of perturbative, quantitative experiments in cell biology