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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.
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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.
  18. Altmetric Badge
    Chapter 17 Computational Methods for the Construction, Editing, and Error Correction of DNA Molecules and Their Libraries
Attention for Chapter 13: In silico control of biomolecular processes.
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Chapter title
In silico control of biomolecular processes.
Chapter number 13
Book title
Computational Methods in Synthetic Biology
Published in
Methods in molecular biology, January 2015
DOI 10.1007/978-1-4939-1878-2_13
Pubmed ID
Book ISBNs
978-1-4939-1877-5, 978-1-4939-1878-2
Authors

Jannis Uhlendorf, Agnès Miermont, Thierry Delaveau, Gilles Charvin, François Fages, Samuel Bottani, Pascal Hersen, Gregory Batt, Uhlendorf, Jannis, Miermont, Agnès, Delaveau, Thierry, Charvin, Gilles, Fages, François, Bottani, Samuel, Hersen, Pascal, Batt, Gregory

Abstract

By implementing an external feedback loop one can tightly control the expression of a gene over many cell generations with quantitative accuracy. Controlling precisely the level of a protein of interest will be useful to probe quantitatively the dynamical properties of cellular processes and to drive complex, synthetically-engineered networks. In this chapter we describe a platform for real-time closed-loop control of gene expression in yeast that integrates microscopy for monitoring gene expression at the cell level, microfluidics to manipulate the cells environment, and original software for automated imaging, quantification, and model predictive control. By using an endogenous osmo-stress responsive promoter and playing with the osmolarity of the cells environment, we demonstrate that long-term control can indeed be achieved for both time-constant and time-varying target profiles, at the population level, and even at the single-cell level.

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X Demographics

The data shown below were collected from the profile of 1 X user 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 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 71%
Student > Ph. D. Student 1 14%
Professor 1 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 2 29%
Arts and Humanities 1 14%
Computer Science 1 14%
Physics and Astronomy 1 14%
Engineering 1 14%
Other 0 0%
Unknown 1 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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
#20,246,428
of 22,774,233 outputs
Outputs from Methods in molecular biology
#9,866
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Outputs of similar age
#295,638
of 352,917 outputs
Outputs of similar age from Methods in molecular biology
#635
of 996 outputs
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So far Altmetric has tracked 13,091 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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