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      Genetic programs can be compressed and autonomously decompressed in live cells

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      1 , 1 ,
      Nature nanotechnology

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          Abstract

          Fundamental computer science concepts inspired novel information-processing molecular systems in test tubes 113 and genetically-encoded circuits in live cells 1421 . Recent research showed that digital information storage in DNA, implemented using deep sequencing and conventional software, can approach the maximum Shannon information capacity 22 of 2 bits per nucleotide 23 . DNA is used in nature to store genetic programs, but the information content of natural encoding rarely approaches this maximum 24 . We hypothesize that the biological function of a genetic program can be preserved while reducing the length and increasing the information content of its DNA encoding. Here we support this hypothesis by describing an experimental procedure for compressing a genetic program and its subsequent autonomous decompression and execution in human cells. As a test-bed we choose an RNAi cell classifier circuit 25 that comprises redundant DNA sequence and is therefore amenable for compression, as are many other complex gene circuits 15, 18, 2628 . In one example, we implement a compressed encoding of a ten-gene four-input AND gate circuit using only four genetic constructs. The compression principles applied to gene circuits can enable fitting complex genetic programs into DNA delivery vehicles with limited cargo capacity, and storing compressed and biologically inert programs in vivo for on-demand activation.

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          Most cited references23

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          Time-dependent ROC curves for censored survival data and a diagnostic marker.

          ROC curves are a popular method for displaying sensitivity and specificity of a continuous diagnostic marker, X, for a binary disease variable, D. However, many disease outcomes are time dependent, D(t), and ROC curves that vary as a function of time may be more appropriate. A common example of a time-dependent variable is vital status, where D(t) = 1 if a patient has died prior to time t and zero otherwise. We propose summarizing the discrimination potential of a marker X, measured at baseline (t = 0), by calculating ROC curves for cumulative disease or death incidence by time t, which we denote as ROC(t). A typical complexity with survival data is that observations may be censored. Two ROC curve estimators are proposed that can accommodate censored data. A simple estimator is based on using the Kaplan-Meier estimator for each possible subset X > c. However, this estimator does not guarantee the necessary condition that sensitivity and specificity are monotone in X. An alternative estimator that does guarantee monotonicity is based on a nearest neighbor estimator for the bivariate distribution function of (X, T), where T represents survival time (Akritas, M. J., 1994, Annals of Statistics 22, 1299-1327). We present an example where ROC(t) is used to compare a standard and a modified flow cytometry measurement for predicting survival after detection of breast cancer and an example where the ROC(t) curve displays the impact of modifying eligibility criteria for sample size and power in HIV prevention trials.
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            Multi-input RNAi-based logic circuit for identification of specific cancer cells.

            Engineered biological systems that integrate multi-input sensing, sophisticated information processing, and precisely regulated actuation in living cells could be useful in a variety of applications. For example, anticancer therapies could be engineered to detect and respond to complex cellular conditions in individual cells with high specificity. Here, we show a scalable transcriptional/posttranscriptional synthetic regulatory circuit--a cell-type "classifier"--that senses expression levels of a customizable set of endogenous microRNAs and triggers a cellular response only if the expression levels match a predetermined profile of interest. We demonstrate that a HeLa cancer cell classifier selectively identifies HeLa cells and triggers apoptosis without affecting non-HeLa cell types. This approach also provides a general platform for programmed responses to other complex cell states.
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              Programmable chemical controllers made from DNA.

              Biological organisms use complex molecular networks to navigate their environment and regulate their internal state. The development of synthetic systems with similar capabilities could lead to applications such as smart therapeutics or fabrication methods based on self-organization. To achieve this, molecular control circuits need to be engineered to perform integrated sensing, computation and actuation. Here we report a DNA-based technology for implementing the computational core of such controllers. We use the formalism of chemical reaction networks as a 'programming language' and our DNA architecture can, in principle, implement any behaviour that can be mathematically expressed as such. Unlike logic circuits, our formulation naturally allows complex signal processing of intrinsically analogue biological and chemical inputs. Controller components can be derived from biologically synthesized (plasmid) DNA, which reduces errors associated with chemically synthesized DNA. We implement several building-block reaction types and then combine them into a network that realizes, at the molecular level, an algorithm used in distributed control systems for achieving consensus between multiple agents.
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                Author and article information

                Journal
                101283273
                34218
                Nat Nanotechnol
                Nat Nanotechnol
                Nature nanotechnology
                1748-3387
                1748-3395
                28 September 2017
                13 November 2017
                April 2018
                13 May 2018
                : 13
                : 4
                : 309-315
                Affiliations
                [1 ]Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Mattenstrasse 26, Switzerland
                Article
                NIHMS907412
                10.1038/s41565-017-0004-z
                5895506
                29133926
                82ac4935-7c62-4f47-becd-db4a5415e5b4

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                Nanotechnology
                Nanotechnology

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