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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 3: Computational design of RNA parts, devices, and transcripts with kinetic folding algorithms implemented on multiprocessor clusters.
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Chapter title
Computational design of RNA parts, devices, and transcripts with kinetic folding algorithms implemented on multiprocessor clusters.
Chapter number 3
Book title
Computational Methods in Synthetic Biology
Published in
Methods in molecular biology, January 2015
DOI 10.1007/978-1-4939-1878-2_3
Pubmed ID
Book ISBNs
978-1-4939-1877-5, 978-1-4939-1878-2
Authors

Tim Thimmaiah, William E Voje, James M Carothers, William E. Voje, James M. Carothers, Thimmaiah, Tim, Voje, William E., Carothers, James M.

Abstract

With progress toward inexpensive, large-scale DNA assembly, the demand for simulation tools that allow the rapid construction of synthetic biological devices with predictable behaviors continues to increase. By combining engineered transcript components, such as ribosome binding sites, transcriptional terminators, ligand-binding aptamers, catalytic ribozymes, and aptamer-controlled ribozymes (aptazymes), gene expression in bacteria can be fine-tuned, with many corollaries and applications in yeast and mammalian cells. The successful design of genetic constructs that implement these kinds of RNA-based control mechanisms requires modeling and analyzing kinetically determined co-transcriptional folding pathways. Transcript design methods using stochastic kinetic folding simulations to search spacer sequence libraries for motifs enabling the assembly of RNA component parts into static ribozyme- and dynamic aptazyme-regulated expression devices with quantitatively predictable functions (rREDs and aREDs, respectively) have been described (Carothers et al., Science 334:1716-1719, 2011). Here, we provide a detailed practical procedure for computational transcript design by illustrating a high throughput, multiprocessor approach for evaluating spacer sequences and generating functional rREDs. This chapter is written as a tutorial, complete with pseudo-code and step-by-step instructions for setting up a computational cluster with an Amazon, Inc. web server and performing the large numbers of kinefold-based stochastic kinetic co-transcriptional folding simulations needed to design functional rREDs and aREDs. The method described here should be broadly applicable for designing and analyzing a variety of synthetic RNA parts, devices and transcripts.

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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 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
China 2 12%
United States 1 6%
Iran, Islamic Republic of 1 6%
Unknown 13 76%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 59%
Student > Ph. D. Student 2 12%
Student > Master 1 6%
Student > Doctoral Student 1 6%
Professor > Associate Professor 1 6%
Other 1 6%
Unknown 1 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 53%
Engineering 3 18%
Computer Science 2 12%
Physics and Astronomy 1 6%
Biochemistry, Genetics and Molecular Biology 1 6%
Other 0 0%
Unknown 1 6%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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
#14,206,722
of 22,774,233 outputs
Outputs from Methods in molecular biology
#4,175
of 13,091 outputs
Outputs of similar age
#186,553
of 352,917 outputs
Outputs of similar age from Methods in molecular biology
#268
of 996 outputs
Altmetric has tracked 22,774,233 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% 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 64% 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 352,917 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
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 gotten more attention than average, scoring higher than 70% of its contemporaries.