The Experts below are selected from a list of 4509 Experts worldwide ranked by ideXlab platform

Christopher A. Voigt - One of the best experts on this subject based on the ideXlab platform.

  • Genetic Circuit characterization by inferring RNA polymerase movement and ribosome usage.
    Nature communications, 2020
    Co-Authors: Amin Espah Borujeni, Jing Zhang, Hamid Doosthosseini, Alec A. K. Nielsen, Christopher A. Voigt
    Abstract:

    To perform their computational function, Genetic Circuits change states through a symphony of Genetic parts that turn regulator expression on and off. Debugging is frustrated by an inability to characterize parts in the context of the Circuit and identify the origins of failures. Here, we take snapshots of a large Genetic Circuit in different states: RNA-seq is used to visualize Circuit function as a changing pattern of RNA polymerase (RNAP) flux along the DNA. Together with ribosome profiling, all 54 Genetic parts (promoters, ribozymes, RBSs, terminators) are parameterized and used to inform a mathematical model that can predict Circuit performance, dynamics, and robustness. The Circuit behaves as designed; however, it is riddled with Genetic errors, including cryptic sense/antisense promoters and translation, attenuation, incorrect start codons, and a failed gate. While not impacting the expected Boolean logic, they reduce the prediction accuracy and could lead to failures when the parts are used in other designs. Finally, the cellular power (RNAP and ribosome usage) required to maintain a Circuit state is calculated. This work demonstrates the use of a small number of measurements to fully parameterize a regulatory Circuit and quantify its impact on host.

  • Genetic Circuit dynamics hazard and glitch analysis
    ACS Synthetic Biology, 2020
    Co-Authors: Pedro Fontanarrosa, Christopher A. Voigt, Amin Espah Borujeni, Hamid Doosthosseini, Yuval Dorfan, Chris J Myers
    Abstract:

    Multiple input changes can cause unwanted switching variations, or glitches, in the output of Genetic combinational Circuits. These glitches can have drastic effects if the output of the Circuit ca...

  • Genetic Circuit design automation for yeast.
    Nature microbiology, 2020
    Co-Authors: Ye Chen, Shuyi Zhang, Douglas Densmore, Eric M. Young, Timothy S. Jones, Christopher A. Voigt
    Abstract:

    Cells can be programmed to monitor and react to their environment using Genetic Circuits. Design automation software maps a desired Circuit function to a DNA sequence, a process that requires units of gene regulation (gates) that are simple to connect and behave predictably. This poses a challenge for eukaryotes due to their complex mechanisms of transcription and translation. To this end, we have developed gates for yeast (Saccharomyces cerevisiae) that are connected using RNA polymerase flux as the signal carrier and are insulated from each other and host regulation. They are based on minimal constitutive promoters (~120 base pairs), for which rules are developed to insert operators for DNA-binding proteins. Using this approach, we constructed nine NOT/NOR gates with nearly identical response functions and 400-fold dynamic range. In Circuits, they are transcriptionally insulated from each other by placing ribozymes downstream of terminators to block nuclear export of messenger RNAs resulting from RNA polymerase readthrough. Based on these gates, Cello 2.0 was used to build Circuits with up to 11 regulatory proteins. A simple dynamic model predicts the Circuit response over days. Genetic Circuit design automation for eukaryotes simplifies the construction of regulatory networks as part of cellular engineering projects, whether it be to stage processes during bioproduction, serve as environmental sentinels or guide living therapeutics. This study describes design automation and predictable gene regulatory network engineering in a eukaryotic microorganism.

  • Author Correction: Genetic Circuit design automation for the gut resident species Bacteroides thetaiotaomicron
    Nature Biotechnology, 2020
    Co-Authors: Mao Taketani, Jianbo Zhang, Shuyi Zhang, Alexander J. Triassi, Yu-ja Huang, Linda G. Griffith, Christopher A. Voigt
    Abstract:

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

  • Genetic Circuit design automation for the gut resident species Bacteroides thetaiotaomicron.
    Nature Biotechnology, 2020
    Co-Authors: Mao Taketani, Jianbo Zhang, Shuyi Zhang, Alexander J. Triassi, Yu-ja Huang, Linda G. Griffith, Christopher A. Voigt
    Abstract:

    Bacteroides thetaiotaomicron is a human-associated bacterium that holds promise for delivery of therapies in the gut microbiome1. Therapeutic bacteria would benefit from the ability to turn on different programs of gene expression in response to conditions inside and outside of the gut; however, the availability of regulatory parts, and methods to combine them, have been limited in B. thetaiotaomicron2-5. We report implementation of Cello Circuit design automation software6 for this species. First, we characterize a set of genome-integrated NOT/NOR gates based on single guide RNAs (CRISPR-dCas9) to inform a Bt user constraint file (UCF) for Cello. Then, logic Circuits are designed to integrate sensors that respond to bile acid and anhydrotetracycline (aTc), including one created to distinguish between environments associated with bioproduction, the human gut, and after release. This Circuit was found to be stable under laboratory conditions for at least 12 days and to function in bacteria associated with a primary colonic epithelial monolayer in an in vitro human gut model system.

Anil Wipat - One of the best experts on this subject based on the ideXlab platform.

  • sbol owl an ontological approach for formal and semantic representation of synthetic biology information
    ACS Synthetic Biology, 2019
    Co-Authors: Goksel Misirli, Chris J Myers, Renee Taylor, James Alastair Mclaughlin, John H. Gennari, Phillip Lord, Angel Gonimoreno, Anil Wipat
    Abstract:

    Standard representation of data is key for the reproducibility of designs in synthetic biology. The Synthetic Biology Open Language (SBOL) has already emerged as a data standard to represent information about Genetic Circuits, and it is based on capturing data using graphs. The language provides the syntax using a free text document that is accessible to humans only. This paper describes SBOL-OWL, an ontology for a machine understandable definition of SBOL. This ontology acts as a semantic layer for Genetic Circuit designs. As a result, computational tools can understand the meaning of design entities in addition to parsing structured SBOL data. SBOL-OWL not only describes how Genetic Circuits can be constructed computationally, it also facilitates the use of several existing Semantic Web tools for synthetic biology. This paper demonstrates some of these features, for example, to validate designs and check for inconsistencies. Through the use of SBOL-OWL, queries can be simplified and become more intuitive. Moreover, existing reasoners can be used to infer information about Genetic Circuit designs that cannot be directly retrieved using existing querying mechanisms. This ontological representation of the SBOL standard provides a new perspective to the verification, representation, and querying of information about Genetic Circuits and is important to incorporate complex design information via the integration of biological ontologies.

  • SBOL-OWL: An ontological approach for formal and semantic representation of synthetic Genetic Circuits
    2018
    Co-Authors: Goksel Misirli, Chris J Myers, Renee Taylor, Angel Goñi-moreno, James Alastair Mclaughlin, John H. Gennari, Phillip Lord, Anil Wipat
    Abstract:

    Abstract Standard representation of data is key for the reproducibility of designs in synthetic biology. The Synthetic Biology Open Language (SBOL) has already emerged as a data standard to represent Genetic Circuit designs, and it is based on capturing data using graphs. The language provides the syntax using a free text document which is accessible to humans only. Here, we provide SBOL-OWL, an ontology for a machine understandable definition of SBOL. This ontology acts as a semantic layer for Genetic Circuit designs. As a result, computational tools can understand the meaning of design entities in addition to parsing structured SBOL data. SBOL-OWL not only describes how Genetic Circuits can be constructed computationally, it also facilitates the use of several existing Semantic Web tooling for synthetic biology. Here, we demonstrate some of these features, for example, to validate designs and check for inconsistencies. Through the use of SBOL-OWL, queries are simplified and become more intuitive. Moreover, existing reasoners can be used to infer information about Genetic Circuit designs that can’t be directly retrieved using existing querying mechanisms. This ontological representation of the SBOL standard provides a new perspective to the verification, representation and querying of information about synthetic Genetic Circuits and is important to incorporate complex design information via the integration of biological ontologies.

  • Compiling Combinatorial Genetic Circuits with Semantic Inference
    ACS synthetic biology, 2018
    Co-Authors: William Waites, Goksel Misirli, Matteo Cavaliere, Vincent Danos, Anil Wipat
    Abstract:

    A central strategy of synthetic biology is to understand the basic processes of living creatures through engineering organisms using the same building blocks. Biological machines described in terms of parts can be studied by computer simulation in any of several languages or robotically assembled in vitro. In this paper we present a language, the Genetic Circuit Description Language (GCDL) and a compiler, the Genetic Circuit Compiler (GCC). This language describes Genetic Circuits at a level of granularity appropriate both for automated assembly in the laboratory and deriving simulation code. The GCDL follows Semantic Web practice and the compiler makes novel use of the logical inference facilities that are therefore available. We present the GCDL and compiler structure as a study of a tool for generating $\kappa$-language simulations from semantic descriptions of Genetic Circuits.

  • a Genetic Circuit compiler generating combinatorial Genetic Circuits with web semantics and inference
    ACS Synthetic Biology, 2018
    Co-Authors: William Waites, Goksel Misirli, Matteo Cavaliere, Vincent Danos, Anil Wipat
    Abstract:

    A central strategy of synthetic biology is to understand the basic processes of living creatures through engineering organisms using the same building blocks. Biological machines described in terms of parts can be studied by computer simulation in any of several languages or robotically assembled in vitro. In this paper we present a language, the Genetic Circuit Description Language (GCDL) and a compiler, the Genetic Circuit Compiler (GCC). This language describes Genetic Circuits at a level of granularity appropriate both for automated assembly in the laboratory and deriving simulation code. The GCDL follows Semantic Web practice, and the compiler makes novel use of the logical inference facilities that are therefore available. We present the GCDL and compiler structure as a study of a tool for generating κ-language simulations from semantic descriptions of Genetic Circuits.

  • Evolutionary computation for the design of a stochastic switch for synthetic Genetic Circuits
    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Inte, 2010
    Co-Authors: Jennifer Hallinan, Goksel Misirli, Anil Wipat
    Abstract:

    Biological systems are inherently stochastic, a fact which is often ignored when simulating Genetic Circuits. Synthetic biology aims to design Genetic Circuits de novo, and cannot therefore afford to ignore the effects of stochastic behavior. Since computational design tools will be essential for large-scale synthetic biology, it is important to develop an understanding of the role of stochasticity in molecular biology, and incorporate this understanding into computational tools for Genetic Circuit design. We report upon an investigation into the combination of evolutionary algorithms and stochastic simulation for Genetic Circuit design, to design regulatory systems based on the Bacillus subtilis sin operon.

Shuyi Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Genetic Circuit design automation for yeast.
    Nature microbiology, 2020
    Co-Authors: Ye Chen, Shuyi Zhang, Douglas Densmore, Eric M. Young, Timothy S. Jones, Christopher A. Voigt
    Abstract:

    Cells can be programmed to monitor and react to their environment using Genetic Circuits. Design automation software maps a desired Circuit function to a DNA sequence, a process that requires units of gene regulation (gates) that are simple to connect and behave predictably. This poses a challenge for eukaryotes due to their complex mechanisms of transcription and translation. To this end, we have developed gates for yeast (Saccharomyces cerevisiae) that are connected using RNA polymerase flux as the signal carrier and are insulated from each other and host regulation. They are based on minimal constitutive promoters (~120 base pairs), for which rules are developed to insert operators for DNA-binding proteins. Using this approach, we constructed nine NOT/NOR gates with nearly identical response functions and 400-fold dynamic range. In Circuits, they are transcriptionally insulated from each other by placing ribozymes downstream of terminators to block nuclear export of messenger RNAs resulting from RNA polymerase readthrough. Based on these gates, Cello 2.0 was used to build Circuits with up to 11 regulatory proteins. A simple dynamic model predicts the Circuit response over days. Genetic Circuit design automation for eukaryotes simplifies the construction of regulatory networks as part of cellular engineering projects, whether it be to stage processes during bioproduction, serve as environmental sentinels or guide living therapeutics. This study describes design automation and predictable gene regulatory network engineering in a eukaryotic microorganism.

  • Author Correction: Genetic Circuit design automation for the gut resident species Bacteroides thetaiotaomicron
    Nature Biotechnology, 2020
    Co-Authors: Mao Taketani, Jianbo Zhang, Shuyi Zhang, Alexander J. Triassi, Yu-ja Huang, Linda G. Griffith, Christopher A. Voigt
    Abstract:

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

  • Genetic Circuit design automation for the gut resident species Bacteroides thetaiotaomicron.
    Nature Biotechnology, 2020
    Co-Authors: Mao Taketani, Jianbo Zhang, Shuyi Zhang, Alexander J. Triassi, Yu-ja Huang, Linda G. Griffith, Christopher A. Voigt
    Abstract:

    Bacteroides thetaiotaomicron is a human-associated bacterium that holds promise for delivery of therapies in the gut microbiome1. Therapeutic bacteria would benefit from the ability to turn on different programs of gene expression in response to conditions inside and outside of the gut; however, the availability of regulatory parts, and methods to combine them, have been limited in B. thetaiotaomicron2-5. We report implementation of Cello Circuit design automation software6 for this species. First, we characterize a set of genome-integrated NOT/NOR gates based on single guide RNAs (CRISPR-dCas9) to inform a Bt user constraint file (UCF) for Cello. Then, logic Circuits are designed to integrate sensors that respond to bile acid and anhydrotetracycline (aTc), including one created to distinguish between environments associated with bioproduction, the human gut, and after release. This Circuit was found to be stable under laboratory conditions for at least 12 days and to function in bacteria associated with a primary colonic epithelial monolayer in an in vitro human gut model system.

  • Genetic Circuit design automation for the gut resident species Bacteroides thetaiotaomicron
    Nature Biotechnology, 2020
    Co-Authors: Mao Taketani, Jianbo Zhang, Shuyi Zhang, Alexander J. Triassi, Yu-ja Huang, Linda G. Griffith, Christopher A. Voigt
    Abstract:

    Bacteroides thetaiotaomicron is a human-associated bacterium that holds promise for delivery of therapies in the gut microbiome^ 1 . Therapeutic bacteria would benefit from the ability to turn on different programs of gene expression in response to conditions inside and outside of the gut; however, the availability of regulatory parts, and methods to combine them, have been limited in B. thetaiotaomicron ^ 2 – 5 . We report implementation of Cello Circuit design automation software^ 6 for this species. First, we characterize a set of genome-integrated NOT/NOR gates based on single guide RNAs (CRISPR–dCas9) to inform a Bt user constraint file (UCF) for Cello. Then, logic Circuits are designed to integrate sensors that respond to bile acid and anhydrotetracycline (aTc), including one created to distinguish between environments associated with bioproduction, the human gut, and after release. This Circuit was found to be stable under laboratory conditions for at least 12 days and to function in bacteria associated with a primary colonic epithelial monolayer in an in vitro human gut model system. A platform for Genetic Circuit design in a human gut commensal bacterium brings microbiome therapies that respond to gastrointestinal signals a step closer.

  • engineered dcas9 with reduced toxicity in bacteria implications for Genetic Circuit design
    Nucleic Acids Research, 2018
    Co-Authors: Shuyi Zhang, Christopher A. Voigt
    Abstract:

    Large synthetic Genetic Circuits require the simultaneous expression of many regulators. Deactivated Cas9 (dCas9) can serve as a repressor by having a small guide RNA (sgRNA) direct it to bind a promoter. The programmability and specificity of RNA:DNA basepairing simplifies the generation of many orthogonal sgRNAs that, in theory, could serve as a large set of regulators in a Circuit. However, dCas9 is toxic in many bacteria, thus limiting how high it can be expressed, and low concentrations are quickly sequestered by multiple sgRNAs. Here, we construct a non-toxic version of dCas9 by eliminating PAM (protospacer adjacent motif) binding with a R1335K mutation (dCas9*) and recovering DNA binding by fusing it to the PhlF repressor (dCas9*_PhlF). Both the 30 bp PhlF operator and 20 bp sgRNA binding site are required to repress a promoter. The larger region required for recognition mitigates toxicity in Escherichia coli, allowing up to 9600 ± 800 molecules of dCas9*_PhlF per cell before growth or morphology are impacted, as compared to 530 ± 40 molecules of dCas9. Further, PhlF multimerization leads to an increase in average cooperativity from n = 0.9 (dCas9) to 1.6 (dCas9*_PhlF). A set of 30 orthogonal sgRNA-promoter pairs are characterized as NOT gates; however, the simultaneous use of multiple sgRNAs leads to a monotonic decline in repression and after 15 are co-expressed the dynamic range is <10-fold. This work introduces a non-toxic variant of dCas9, critical for its use in applications in metabolic engineering and synthetic biology, and exposes a limitation in the number of regulators that can be used in one cell when they rely on a shared resource.

Mao Taketani - One of the best experts on this subject based on the ideXlab platform.

  • Author Correction: Genetic Circuit design automation for the gut resident species Bacteroides thetaiotaomicron
    Nature Biotechnology, 2020
    Co-Authors: Mao Taketani, Jianbo Zhang, Shuyi Zhang, Alexander J. Triassi, Yu-ja Huang, Linda G. Griffith, Christopher A. Voigt
    Abstract:

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

  • Genetic Circuit design automation for the gut resident species Bacteroides thetaiotaomicron.
    Nature Biotechnology, 2020
    Co-Authors: Mao Taketani, Jianbo Zhang, Shuyi Zhang, Alexander J. Triassi, Yu-ja Huang, Linda G. Griffith, Christopher A. Voigt
    Abstract:

    Bacteroides thetaiotaomicron is a human-associated bacterium that holds promise for delivery of therapies in the gut microbiome1. Therapeutic bacteria would benefit from the ability to turn on different programs of gene expression in response to conditions inside and outside of the gut; however, the availability of regulatory parts, and methods to combine them, have been limited in B. thetaiotaomicron2-5. We report implementation of Cello Circuit design automation software6 for this species. First, we characterize a set of genome-integrated NOT/NOR gates based on single guide RNAs (CRISPR-dCas9) to inform a Bt user constraint file (UCF) for Cello. Then, logic Circuits are designed to integrate sensors that respond to bile acid and anhydrotetracycline (aTc), including one created to distinguish between environments associated with bioproduction, the human gut, and after release. This Circuit was found to be stable under laboratory conditions for at least 12 days and to function in bacteria associated with a primary colonic epithelial monolayer in an in vitro human gut model system.

  • Genetic Circuit design automation for the gut resident species Bacteroides thetaiotaomicron
    Nature Biotechnology, 2020
    Co-Authors: Mao Taketani, Jianbo Zhang, Shuyi Zhang, Alexander J. Triassi, Yu-ja Huang, Linda G. Griffith, Christopher A. Voigt
    Abstract:

    Bacteroides thetaiotaomicron is a human-associated bacterium that holds promise for delivery of therapies in the gut microbiome^ 1 . Therapeutic bacteria would benefit from the ability to turn on different programs of gene expression in response to conditions inside and outside of the gut; however, the availability of regulatory parts, and methods to combine them, have been limited in B. thetaiotaomicron ^ 2 – 5 . We report implementation of Cello Circuit design automation software^ 6 for this species. First, we characterize a set of genome-integrated NOT/NOR gates based on single guide RNAs (CRISPR–dCas9) to inform a Bt user constraint file (UCF) for Cello. Then, logic Circuits are designed to integrate sensors that respond to bile acid and anhydrotetracycline (aTc), including one created to distinguish between environments associated with bioproduction, the human gut, and after release. This Circuit was found to be stable under laboratory conditions for at least 12 days and to function in bacteria associated with a primary colonic epithelial monolayer in an in vitro human gut model system. A platform for Genetic Circuit design in a human gut commensal bacterium brings microbiome therapies that respond to gastrointestinal signals a step closer.

Chris J Myers - One of the best experts on this subject based on the ideXlab platform.

  • Genetic Circuit dynamics hazard and glitch analysis
    ACS Synthetic Biology, 2020
    Co-Authors: Pedro Fontanarrosa, Christopher A. Voigt, Amin Espah Borujeni, Hamid Doosthosseini, Yuval Dorfan, Chris J Myers
    Abstract:

    Multiple input changes can cause unwanted switching variations, or glitches, in the output of Genetic combinational Circuits. These glitches can have drastic effects if the output of the Circuit ca...

  • sbol owl an ontological approach for formal and semantic representation of synthetic biology information
    ACS Synthetic Biology, 2019
    Co-Authors: Goksel Misirli, Chris J Myers, Renee Taylor, James Alastair Mclaughlin, John H. Gennari, Phillip Lord, Angel Gonimoreno, Anil Wipat
    Abstract:

    Standard representation of data is key for the reproducibility of designs in synthetic biology. The Synthetic Biology Open Language (SBOL) has already emerged as a data standard to represent information about Genetic Circuits, and it is based on capturing data using graphs. The language provides the syntax using a free text document that is accessible to humans only. This paper describes SBOL-OWL, an ontology for a machine understandable definition of SBOL. This ontology acts as a semantic layer for Genetic Circuit designs. As a result, computational tools can understand the meaning of design entities in addition to parsing structured SBOL data. SBOL-OWL not only describes how Genetic Circuits can be constructed computationally, it also facilitates the use of several existing Semantic Web tools for synthetic biology. This paper demonstrates some of these features, for example, to validate designs and check for inconsistencies. Through the use of SBOL-OWL, queries can be simplified and become more intuitive. Moreover, existing reasoners can be used to infer information about Genetic Circuit designs that cannot be directly retrieved using existing querying mechanisms. This ontological representation of the SBOL standard provides a new perspective to the verification, representation, and querying of information about Genetic Circuits and is important to incorporate complex design information via the integration of biological ontologies.

  • SBOL-OWL: An ontological approach for formal and semantic representation of synthetic Genetic Circuits
    2018
    Co-Authors: Goksel Misirli, Chris J Myers, Renee Taylor, Angel Goñi-moreno, James Alastair Mclaughlin, John H. Gennari, Phillip Lord, Anil Wipat
    Abstract:

    Abstract Standard representation of data is key for the reproducibility of designs in synthetic biology. The Synthetic Biology Open Language (SBOL) has already emerged as a data standard to represent Genetic Circuit designs, and it is based on capturing data using graphs. The language provides the syntax using a free text document which is accessible to humans only. Here, we provide SBOL-OWL, an ontology for a machine understandable definition of SBOL. This ontology acts as a semantic layer for Genetic Circuit designs. As a result, computational tools can understand the meaning of design entities in addition to parsing structured SBOL data. SBOL-OWL not only describes how Genetic Circuits can be constructed computationally, it also facilitates the use of several existing Semantic Web tooling for synthetic biology. Here, we demonstrate some of these features, for example, to validate designs and check for inconsistencies. Through the use of SBOL-OWL, queries are simplified and become more intuitive. Moreover, existing reasoners can be used to infer information about Genetic Circuit designs that can’t be directly retrieved using existing querying mechanisms. This ontological representation of the SBOL standard provides a new perspective to the verification, representation and querying of information about synthetic Genetic Circuits and is important to incorporate complex design information via the integration of biological ontologies.

  • iBioSim 3: A Tool for Model-Based Genetic Circuit Design.
    ACS synthetic biology, 2018
    Co-Authors: Leandro H. Watanabe, Nicholas Roehner, Curtis Madsen, Tramy Nguyen, Michael Zhang, Zach Zundel, Zhen Zhang, Chris J Myers
    Abstract:

    The iBioSim tool has been developed to facilitate the design of Genetic Circuits via a model-based design strategy. This paper illustrates the new features incorporated into the tool for DNA Circuit design, design analysis, and design synthesis, all of which can be used in a workflow for the systematic construction of new Genetic Circuits.

  • directed acyclic graph based technology mapping of Genetic Circuit models
    ACS Synthetic Biology, 2014
    Co-Authors: Nicholas Roehner, Chris J Myers
    Abstract:

    As engineering foundations such as standards and abstraction begin to mature within synthetic biology, it is vital that Genetic design automation (GDA) tools be developed to enable synthetic biologists to automatically select standardized DNA components from a library to meet the behavioral specification for a Genetic Circuit. To this end, we have developed a Genetic technology mapping algorithm that builds on the directed acyclic graph (DAG) based mapping techniques originally used to select parts for digital electronic Circuit designs and implemented it in our GDA tool, iBioSim. It is among the first Genetic technology mapping algorithms to adapt techniques from electronic Circuit design, in particular the use of a cost function to guide the search for an optimal solution, and perhaps that which makes the greatest use of standards for describing Genetic function and structure to represent design specifications and component libraries. This paper demonstrates the use of our algorithm to map the specifica...