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Evolutionary Systems Biology

Overview of attention for book
Cover of 'Evolutionary Systems Biology'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Evolutionary Systems Biology: Historical and Philosophical Perspectives on an Emerging Synthesis
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    Chapter 2 Metabolic networks and their evolution.
  4. Altmetric Badge
    Chapter 3 Evolutionary Systems Biology
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    Chapter 4 Evolution of regulatory networks: nematode vulva induction as an example of developmental systems drift.
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    Chapter 5 Life's Attractors : Understanding Developmental Systems Through Reverse Engineering and In Silico Evolution.
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    Chapter 6 Evolutionary characteristics of bacterial two-component systems.
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    Chapter 7 Comparative interaction networks: bridging genotype to phenotype.
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    Chapter 8 Evolution In Silico: From Network Structure to Bifurcation Theory
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    Chapter 9 On the search for design principles in biological systems.
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    Chapter 10 Toward a theory of multilevel evolution: long-term information integration shapes the mutational landscape and enhances evolvability.
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    Chapter 11 Evolutionary Principles Underlying Structure and Response Dynamics of Cellular Networks
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    Chapter 12 Phenotypic plasticity and robustness: evolutionary stability theory, gene expression dynamics model, and laboratory experiments.
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    Chapter 13 Genetic redundancies and their evolutionary maintenance.
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    Chapter 14 Evolution of Resource and Energy Management in Biologically Realistic Gene Regulatory Network Models
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    Chapter 15 Reverse ecology: from systems to environments and back.
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    Chapter 16 Bacteria-virus coevolution.
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    Chapter 17 The genotype-phenotype maps of systems biology and quantitative genetics: distinct and complementary.
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    Chapter 18 How evolutionary systems biology will help understand adaptive landscapes and distributions of mutational effects.
  20. Altmetric Badge
    Chapter 19 Building Synthetic Systems to Learn Nature's Design Principles.
  21. Altmetric Badge
    Chapter 20 The robustness continuum.
Attention for Chapter 19: Building Synthetic Systems to Learn Nature's Design Principles.
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Good Attention Score compared to outputs of the same age and source (65th percentile)

Mentioned by

twitter
2 tweeters
patent
1 patent

Citations

dimensions_citation
30 Dimensions

Readers on

mendeley
27 Mendeley
citeulike
1 CiteULike
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Chapter title
Building Synthetic Systems to Learn Nature's Design Principles.
Chapter number 19
Book title
Evolutionary Systems Biology
Published in
Advances in experimental medicine and biology, June 2012
DOI 10.1007/978-1-4614-3567-9_19
Pubmed ID
Book ISBNs
978-1-4614-3566-2, 978-1-4614-3567-9
Authors

Eric A. Davidson, Oliver P. F. Windram, Travis S. Bayer

Editors

Orkun S. Soyer

Abstract

Evolution undoubtedly shapes the architecture of biological systems, yet it is unclear which features of regulatory, metabolic, and signalling circuits have adaptive significance and how the architecture of these circuits constrains or promotes evolutionary processes, such as adaptation to new environments. Experimentally rewiring circuits using genetic engineering and constructing novel circuits in living cells allows direct testing and validation of hypotheses in evolutionary systems biology. Building synthetic genetic systems enables researchers to explore regions of the genotype-phenotype and fitness landscapes that may be inaccessible to more traditional analysis. Here, we review the strategies that allow synthetic systems to be constructed and how evolutionary design principles have advanced these technologies. We also describe how building small genetic regulatory systems can provide insight on the trade-offs that constrain adaptation and can shape the structure of biological networks. In the future, the possibility of building biology de novo at the genome scale means that increasingly sophisticated models of the evolutionary dynamics of networks can be proposed and validated, and will allow us to recreate ancestral systems in the lab. This interplay between evolutionary systems theory and engineering design may illuminate the fundamental limits of performance, robustness, and evolvability of living systems.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 7%
Slovenia 1 4%
Unknown 24 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 22%
Student > Master 5 19%
Researcher 4 15%
Other 3 11%
Student > Postgraduate 3 11%
Other 5 19%
Unknown 1 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 37%
Biochemistry, Genetics and Molecular Biology 4 15%
Engineering 3 11%
Social Sciences 2 7%
Computer Science 2 7%
Other 3 11%
Unknown 3 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 22 May 2014.
All research outputs
#2,941,996
of 11,888,373 outputs
Outputs from Advances in experimental medicine and biology
#467
of 3,160 outputs
Outputs of similar age
#26,240
of 112,237 outputs
Outputs of similar age from Advances in experimental medicine and biology
#8
of 23 outputs
Altmetric has tracked 11,888,373 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 3,160 research outputs from this source. They receive a mean Attention Score of 3.3. This one has done well, scoring higher than 84% 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 112,237 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 23 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 65% of its contemporaries.