A universal RNAi-based logic evaluator that operates in mammalian cells. Nat Biotechnol

FAS Center for Systems Biology, Harvard University, 7 Divinity Ave., Cambridge, Massachusetts 02138 USA.
Nature Biotechnology (Impact Factor: 41.51). 08/2007; 25(7):795-801. DOI: 10.1038/nbt1307
Source: PubMed


Molecular automata that combine sensing, computation and actuation enable programmable manipulation of biological systems. We use RNA interference (RNAi) in human kidney cells to construct a molecular computing core that implements general Boolean logic to make decisions based on endogenous molecular inputs. The state of an endogenous input is encoded by the presence or absence of 'mediator' small interfering RNAs (siRNAs). The encoding rules, combined with a specific arrangement of the siRNA targets in a synthetic gene network, allow direct evaluation of any Boolean expression in standard forms using siRNAs and indirect evaluation using endogenous inputs. We demonstrate direct evaluation of expressions with up to five logic variables. Implementation of the encoding rules through sensory up- and down-regulatory links between the inputs and siRNA mediators will allow arbitrary Boolean decision-making using these inputs.

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Available from: Rohan Maddamsetti, Sep 15, 2015
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    • "Targeted degradation of mRNA can be used to attenuate a signal, with strategies such as small RNA-mediated Hfq protein recruitment [32] and RNA interference [12] both requiring the input of small non-coding RNAs [33] [34]. Cys4-based RNA cleavage has been shown to be an effective regulator of signal strength that works well in different sequence contexts [35]: incorporation of the cleavage targeting sequence into a synthetic RNA allows either processing of RNAs for more efficient expression or disruption of coding sequences to prevent translation. "
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    ABSTRACT: Microorganisms are able to respond effectively to diverse signals from their environment and internal metabolism because they possess a sophisticated information processing capacity. A central aim of synthetic biology is to control and reprogramme the signal processing pathways within living cells so as to realise repurposed, beneficial applications ranging from disease diagnosis and environmental sensing to chemical bioproduction. Up to now most examples of synthetic biological signal processing have been built based on digital information flow, though analog computing is being developed to cope with more complex operations and larger sets of variables. Great progress has been made in expanding the categories of characterised biological components that can be used for cellular signal manipulation, thereby allowing synthetic biologists to more rationally programme increasingly complex behaviours into living cells. Here we provide an overview of the components and strategies that exist for designer cell signal processing and decision making, discuss how these have been implemented in prototype systems for therapeutic, environmental, and industrial biotechnological applications, and examine emerging challenges in this promising field. Copyright © 2015. Published by Elsevier B.V.
    New Biotechnology 01/2015; 20(6). DOI:10.1016/j.nbt.2014.12.009 · 2.90 Impact Factor
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    • "This programmability can be extended well beyond innate sensing capabilities. For example, synthetic gene networks have been designed to create digital signal processing capabilities such as cell-based memory units, data-loggers, counters, edge detectors, and multi-input logic circuits as well as analog processing functions such as filtering and timing [11], [12], [13], [14], [15], [16]. Furthermore, by employing existing mechanisms for quorum-sensing that enable communication between cells, capabilities can be extended to include population-level sensing and decision making [17]. "
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    ABSTRACT: Email Print Request Permissions Save to Project Microscale robots offer an unprecedented opportunity to perform tasks at resolutions approaching 1 μm, but the great majority of research to this point focuses on actuation and control. Potential applications for microrobots can be considerably expanded by integrating sensing, signal processing and feedback into the system. In this work, we demonstrate that technologies from the field of synthetic biology may be directly integrated into microrobotic systems to create cell-based programmable mobile sensors, with signal processors and memory units. Specifically, we integrate genetically engineered, ultraviolet light-sensing bacteria with magnetic microrobots, creating the first controllable biological microrobot that is capable of exploring, recording and reporting on the state of the microscale environment. We demonstrate two proof-of-concept prototypes: (a) an integrated microrobot platform that is able to sense biochemical signals, and (b) a microrobot platform that is able to deploy biosensor payloads to monitor biochemical signals, both in a biological environment. These results have important implications for integrated micro-bio-robotic systems for applications in biological engineering and research.
    Robotics and Automation (ICRA), 2014 IEEE International Conference on, Hong Kong; 05/2014
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    • "A more complex design, including both promoter activation and tRNA-mediated translation regulation, was applied to the implementation of an AND gate in E. coli[4]. Boolean gates were engineered in yeast via mRNA structures such as ribozymes and riboswitches [5,6] whereas mammalian cells hosted more complex digital circuits based on RNA interference [7,8]. Translation regulation through mRNA-binding proteins was employed in mammalian cells for the construction of single-cell-based logic gates [9]. "
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    ABSTRACT: In our previous computational work, we showed that gene digital circuits can be automatically designed in an electronic fashion. This demands, first, a conversion of the truth table into Boolean formulas with the Karnaugh map method and, then, the translation of the Boolean formulas into circuit schemes organized into layers of Boolean gates and Pools of signal carriers. In our framework, gene digital circuits that take up to three different input signals (chemicals) arise from the composition of three kinds of basic Boolean gates, namely YES, NOT, and AND. Here we present a library of YES, NOT, and AND gates realized via plasmidic DNA integration into the yeast genome. Boolean behavior is reproduced via the transcriptional control of a synthetic bipartite promoter that contains sequences of the yeast VPH1 and minimal CYC1 promoters together with operator binding sites for bacterial (i.e. orthogonal) repressor proteins. Moreover, model-driven considerations permitted us to pinpoint a strategy for re-designing gates when a better digital performance is required. Our library of well-characterized Boolean gates is the basis for the assembly of more complex gene digital circuits. As a proof of concepts, we engineered two 2-input OR gates, designed by our software, by combining YES and NOT gates present in our library.
    Journal of Biological Engineering 02/2014; 8(1):6. DOI:10.1186/1754-1611-8-6 · 2.48 Impact Factor
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