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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
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    Chapter 1 Computational protein design methods for synthetic biology.
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    Chapter 2 Computer-aided design of DNA origami structures.
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    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 6: Kappa Rule-Based Modeling in Synthetic Biology
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Chapter title
Kappa Rule-Based Modeling in Synthetic Biology
Chapter number 6
Book title
Computational Methods in Synthetic Biology
Published in
Methods in molecular biology, November 2014
DOI 10.1007/978-1-4939-1878-2_6
Pubmed ID
Book ISBNs
978-1-4939-1877-5, 978-1-4939-1878-2
Authors

Wilson-Kanamori, John, Danos, Vincent, Thomson, Ty, Honorato-Zimmer, Ricardo, John Wilson-Kanamori, Vincent Danos, Ty Thomson, Ricardo Honorato-Zimmer

Editors

Marchisio, Mario Andrea

Abstract

Rule-based modeling, an alternative to traditional reaction-based modeling, allows us to intuitively specify biological interactions while abstracting from the underlying combinatorial complexity. One such rule-based modeling formalism is Kappa, which we introduce to readers in this chapter. We discuss the application of Kappa to three modeling scenarios in synthetic biology: a unidirectional switch based on nitrosylase induction in Saccharomyces cerevisiae, the repressilator in Escherichia coli formed from BioBrick parts, and a light-mediated extension to said repressilator developed by the University of Edinburgh team during iGEM 2010. The second and third scenarios in particular form a case-based introduction to the Kappa BioBrick Framework, allowing us to systematically address the modeling of devices and circuits based on BioBrick parts in Kappa. Through the use of these examples, we highlight the ease with which Kappa can model biological interactions both at the genetic and the protein-protein interaction level, resulting in detailed stochastic models accounting naturally for transcriptional and translational resource usage. We also hope to impart the intuitively modular nature of the modeling processes involved, supported by the introduction of visual representations of Kappa models. Concluding, we explore future endeavors aimed at making modeling of synthetic biology more user-friendly and accessible, taking advantage of the strengths of rule-based modeling in Kappa.

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The data shown below were collected from the profiles of 2 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 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 13%
United States 1 7%
Unknown 12 80%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 40%
Researcher 6 40%
Professor > Associate Professor 1 7%
Unknown 2 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 33%
Computer Science 3 20%
Biochemistry, Genetics and Molecular Biology 2 13%
Materials Science 1 7%
Engineering 1 7%
Other 0 0%
Unknown 3 20%
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 10 December 2014.
All research outputs
#15,261,578
of 22,774,233 outputs
Outputs from Methods in molecular biology
#5,276
of 13,091 outputs
Outputs of similar age
#150,107
of 258,739 outputs
Outputs of similar age from Methods in molecular biology
#52
of 142 outputs
Altmetric has tracked 22,774,233 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% 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 59% 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 258,739 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 142 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 63% of its contemporaries.