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Synthetic evolving systems that implement a user-specified genetic code of arbitrary design.

ABSTRACT: A synthetic genetic system, based on cross-replicating RNA enzymes, provides a means to evaluate alternative genetic codes that relate heritable information to corresponding molecular function. A special implementation of encoded combinatorial chemistry was used to construct complex populations of cross-replicating RNA enzymes in accordance with a user-specified code that relates genotype and phenotype on a molecule-by-molecule basis. The replicating enzymes were made to undergo self-sustained Darwinian evolution, resulting in the emergence of the most advantageous variants. These included both highly active enzymes that sustained the population as a whole and poorly active enzymes that survived as parasites of the active molecules. This evolutionary outcome was a consequence of the information capacity and fidelity of the genetic code, suggesting how these parameters should be adjusted to implement codes tailored to particular applications.

SUBMITTER: Sczepanski JT

PROVIDER: S-EPMC3483592 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature

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Synthetic evolving systems that implement a user-specified genetic code of arbitrary design.

Sczepanski Jonathan T JT Joyce Gerald F GF

Chemistry & biology 20121001 10

A synthetic genetic system, based on cross-replicating RNA enzymes, provides a means to evaluate alternative genetic codes that relate heritable information to corresponding molecular function. A special implementation of encoded combinatorial chemistry was used to construct complex populations of cross-replicating RNA enzymes in accordance with a user-specified code that relates genotype and phenotype on a molecule-by-molecule basis. The replicating enzymes were made to undergo self-sustained D ...[more]

PMID: 23102225

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Project description:ObjectiveThe objective of this qualitative study is to gauge physician sentiment about an emergency department (ED) clinical decision support (CDS) system implemented in multiple adult EDs within a university hospital system. This CDS system focuses on predicting patients' likelihood of ED recidivism and/or adverse opioid-related events.MethodsThe study was conducted among adult emergency physicians working in three EDs of a single academic health system in Rhode Island. Qualitative, semistructured interviews were conducted with ED physicians. Interviews assessed physicians' prior experience with predictive analytics, thoughts on the alert's placement, design, and content, the alert's overall impact, and potential areas for improvement. Responses were aggregated and common themes identified.ResultsTwenty-three interviews were conducted (11 preimplementation and 12 postimplementation). Themes were identified regarding each physician familiarity with predictive analytics, alert rollout, alert appearance and content, and on alert sentiments. Most physicians viewed these alerts as a neutral or positive EHR addition, with responses ranging from neutral to positive. The alert placement was noted to be largely intuitive and nonintrusive. The design of the alert was generally viewed positively. The alert's content was believed to be accurate, although the decision to respond to the alert's call-to-action was physician dependent. Those who tended to ignore the alert did so for a few reasons, including already knowing the information the alert contains, the alert offering information that is not relevant to this particular patient, and the alert not containing enough information to be useful.ConclusionUltimately, this alert appears to have a marginally positive effect on ED physician workflow. At its most beneficial, the alert reminded physicians to deeply consider the care provided to high-risk populations and to potentially adjust their care and referrals. At its least beneficial, the alert did not affect physician decision-making but was not intrusive to the point of negatively impacting workflow.

Dataset Information

Synthetic evolving systems that implement a user-specified genetic code of arbitrary design.

Publications

Synthetic evolving systems that implement a user-specified genetic code of arbitrary design.

Similar Datasets

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