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Computational Methods in Synthetic Biology

Overview of attention for book
Cover of 'Computational Methods in Synthetic Biology'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Computational protein design methods for synthetic biology.
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    Chapter 2 Computer-aided design of DNA origami structures.
  4. Altmetric Badge
    Chapter 3 Computational design of RNA parts, devices, and transcripts with kinetic folding algorithms implemented on multiprocessor clusters.
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    Chapter 4 Regulatory RNA design through evolutionary computation and strand displacement.
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    Chapter 5 Programming Languages for Circuit Design
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    Chapter 6 Kappa Rule-Based Modeling in Synthetic Biology
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    Chapter 7 Modular Design of Synthetic Gene Circuits with Biological Parts and Pools
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    Chapter 8 Computationally Guided Design of Robust Gene Circuits
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    Chapter 9 Chemical Master Equation Closure for Computer-Aided Synthetic Biology
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    Chapter 10 Feedback loops in biological networks.
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    Chapter 11 Efficient Analysis Methods in Synthetic Biology
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    Chapter 12 Using computational modeling and experimental synthetic perturbations to probe biological circuits.
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    Chapter 13 In silico control of biomolecular processes.
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    Chapter 14 Stochastic modular analysis for gene circuits: interplay among retroactivity, nonlinearity, and stochasticity.
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    Chapter 15 Distributed Model Construction with Virtual Parts
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    Chapter 16 The synthetic biology open language.
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    Chapter 17 Computational Methods for the Construction, Editing, and Error Correction of DNA Molecules and Their Libraries
Attention for Chapter 4: Regulatory RNA design through evolutionary computation and strand displacement.
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

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Chapter title
Regulatory RNA design through evolutionary computation and strand displacement.
Chapter number 4
Book title
Computational Methods in Synthetic Biology
Published in
Methods in molecular biology, January 2015
DOI 10.1007/978-1-4939-1878-2_4
Pubmed ID
Book ISBNs
978-1-4939-1877-5, 978-1-4939-1878-2
Authors

William Rostain, Thomas E. Landrain, Guillermo Rodrigo, Alfonso Jaramillo, Rostain, William, Landrain, Thomas E., Rodrigo, Guillermo, Jaramillo, Alfonso

Abstract

The discovery and study of a vast number of regulatory RNAs in all kingdoms of life over the past decades has allowed the design of new synthetic RNAs that can regulate gene expression in vivo. Riboregulators, in particular, have been used to activate or repress gene expression. However, to accelerate and scale up the design process, synthetic biologists require computer-assisted design tools, without which riboregulator engineering will remain a case-by-case design process requiring expert attention. Recently, the design of RNA circuits by evolutionary computation and adapting strand displacement techniques from nanotechnology has proven to be suited to the automated generation of DNA sequences implementing regulatory RNA systems in bacteria. Herein, we present our method to carry out such evolutionary design and how to use it to create various types of riboregulators, allowing the systematic de novo design of genetic control systems in synthetic biology.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
China 1 5%
Unknown 20 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 29%
Student > Ph. D. Student 4 19%
Student > Master 3 14%
Professor 3 14%
Other 2 10%
Other 2 10%
Unknown 1 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 38%
Biochemistry, Genetics and Molecular Biology 4 19%
Computer Science 2 10%
Chemistry 2 10%
Medicine and Dentistry 1 5%
Other 1 5%
Unknown 3 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 August 2015.
All research outputs
#13,344,161
of 22,775,504 outputs
Outputs from Methods in molecular biology
#3,564
of 13,091 outputs
Outputs of similar age
#171,519
of 352,928 outputs
Outputs of similar age from Methods in molecular biology
#234
of 996 outputs
Altmetric has tracked 22,775,504 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,091 research outputs from this source. They receive a mean Attention Score of 3.3. This one has gotten more attention than average, scoring higher than 72% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 352,928 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 996 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.