<i>Owl</i>: Electronic Datasheet Generator

Appleton, Evan; Tao, Jenhan; Wheatley, F. Carter; Desai, Devina H.; Lozanoski, Thomas; Shah, Pooja D.; Awtry, Jake A.; Jin, Shawn S; Haddock, Traci L.; Densmore, Douglas

doi:10.1021/sb500053j

Cited by 3 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A key activity in synthetic biology is the characterization of bioparts to facilitate the utilization and manipulation in applications. Extensive efforts have been done to ease the effective transferability between part repositories and between repositories and BioCAD applications. ,− Datasheets representation is one key channel in the transfer process which enables the incorporation of detailed descriptions, spanning from parts identity, qualitative and quantitative information, to the contextual experimental details. − To this end, BMSS could extend the functionality of the data model system in generating biopart datasheets by supplementing more associated quantitative information (model formulations, kinetic parameters, and data features) to be reported and transferred for a particular biopart or device. Apart from generating the SBML files and the SBOL-compliant gene circuit visual graphics, more tool-independent structured formats such as the simulation experiment description markup language (SED-ML), SBOL, and the complete computational modeling in biological network (COMBINE) archive that includes all of the above-mentioned files could be incorporated into the BMSS platform to ensure the standardization for information exchange and reproducibility.…”

Section: Discussionmentioning

confidence: 99%

An Automated Biomodel Selection System (BMSS) for Gene Circuit Designs

Yeoh

Teh

et al. 2019

ACS Synth. Biol.

View full text Add to dashboard Cite

Constructing a complex functional gene circuit composed of different modular biological parts to achieve the desired performance remains challenging without a proper understanding of how the individual module behaves. To address this, mathematical models serve as an important tool toward better interpretation by quantifying the performance of the overall gene circuit, providing insights, and guiding the experimental designs. As different gene circuits might require exclusively different mathematical representations in the form of ordinary differential equations to capture their transient dynamic behaviors, a recurring challenge in model development is the selection of the appropriate model. Here, we developed an automated biomodel selection system (BMSS) which includes a library of pre-established models with intuitive or unintuitive features derived from a vast array of expression profiles. Selection of models is built upon the Akaike information criteria (AIC). We tested the automated platform using characterization data of routinely used inducible systems, constitutive expression systems, and several different logic gate systems (NOT, AND, and OR gates). The BMSS achieved a good agreement for all the different characterization data sets and managed to select the most appropriate model accordingly. To enable exchange and reproducibility of gene circuit design models, the BMSS platform also generates Synthetic Biology Open Language (SBOL)compliant gene circuit diagrams and Systems Biology Markup Language (SBML) output files. All aspects of the algorithm were programmed in a modular manner to ease the efforts on model extensions or customizations by users. Taken together, the BMSS which is implemented in Python supports users in deriving the best mathematical model candidate in a fast, efficient, and automated way using part/circuit characterization data.

show abstract

Section: Discussionmentioning

confidence: 99%

An Automated Biomodel Selection System (BMSS) for Gene Circuit Designs

Yeoh

Teh

et al. 2019

ACS Synth. Biol.

View full text Add to dashboard Cite

show abstract

“…Tools have subsequently been built to help with the creation of similar datasheets. Examples are: OWL [40], (that integrates with existing registries such as JBEI ICE); and tools such as Pigeon [41] and Raven [42]; that give users a flexible way to describe component behaviour (consistent with the variety of parts and possible associated metrics) – while standardising the output (e.g. HTML page with standard typesetting, and/or a PDF file).…”

Section: Introductionmentioning

confidence: 99%

Data model for biopart datasheets

Murieta

Bultelle

Kitney

2018

Engineering Biology

View full text Add to dashboard Cite

This study introduces a new data model, based on the DICOM-SB (see glossary of terms for definition of acronyms) standard for synthetic biology, that is capable of describing/incorporating the data, metadata and ancillary information from detailed characterisation experimentsto present DNA components (bioparts) in datasheets. The data model offers a standardised mechanism to associate bioparts with data and information about component performancein a particular biological context (or a range of contexts, e.g. chassis). The data model includes the raw, experimental data for each characterisation run, and the protocol details needed to reliably reproduce the experiment. In addition, it provides metrics (e.g. relative promoter units, synthesis/growth rates etc.) that constitute the main content of a biopart datasheet. The data model has been developed to directly link to DICOM-SB, but also to be compatible with existing data standards, e.g. SBOL and SBML. It has been implemented within the latest version of the API that enables access to the SynBIS information system. The work should contribute significantly to the current standardisation effort in synthetic biology. The standard data model for datasheets is seen as a necessary step towards effective interoperability between part repositories, and between repositories and BioCAD applications.

show abstract

Design Automation in Synthetic Biology

Appleton

Madsen

Roehner

et al. 2017

Cold Spring Harb Perspect Biol

Self Cite

View full text Add to dashboard Cite

Design automation refers to a category of software tools for designing systems that work together in a workflow for designing, building, testing, and analyzing systems with a target behavior. In synthetic biology, these tools are called bio-design automation (BDA) tools. In this review, we discuss the BDA tools areas-specify, design, build, test, and learn-and introduce the existing software tools designed to solve problems in these areas. We then detail the functionality of some of these tools and show how they can be used together to create the desired behavior of two types of modern synthetic genetic regulatory networks.S ynthetic biology as a discipline has concerned itself with "forward engineering" living systems. This is a purposefully grand goal and is faced with numerous scientific, engineering, political, and ethical challenges. These engineering challenges include the storage of biological information, the design of complex, interacting biological systems using that information, the physical creation of these systems, and the dissemination of foundational principles as abstractions on which the next technical advances can be made. What should be clear to even the most casual observer is that computers, and the computational capabilities they bring with them, are going to be required if the field is going to succeed going forward. Debates remain regarding the framing of the computational approaches (Andrianantoandro et al. 2006;Lux et al. 2012;Densmore and Bhatia 2014), but there are few who debate the important and increasingly prominent role of computation in the field.Synthetic biology and its associated promise have attracted a wide variety of participants. In particular, computer engineers and computer scientists began developing computational tools for engineering these genetic systems using techniques from their disciplines. Electronic design automation (EDA) showed a clear process by which design formalisms could be built, software tools created, and a larger, separate industry developed (see latticeautomation.com; teselagen.com; deskgen.com; benchling.com). This pursuit in the synthetic biology field has recently been coined bio-design automation (BDA) (Densmore 2012). BDA often uses a "divide and conquer" approach for solving small parts of a larger problem one piece at a time and allows for specialists to tackle specific narrow problems in which they have considerable ex-

show abstract

Owl: Electronic Datasheet Generator

Cited by 3 publications

References 8 publications

An Automated Biomodel Selection System (BMSS) for Gene Circuit Designs

An Automated Biomodel Selection System (BMSS) for Gene Circuit Designs

Data model for biopart datasheets

Design Automation in Synthetic Biology

Contact Info

Product

Resources

About