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Evolutionary Systems Biology

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

Table of Contents

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    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.
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    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 14: Evolution of Resource and Energy Management in Biologically Realistic Gene Regulatory Network Models
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

Mentioned by

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Citations

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30 Dimensions

Readers on

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12 Mendeley
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Chapter title
Evolution of Resource and Energy Management in Biologically Realistic Gene Regulatory Network Models
Chapter number 14
Book title
Evolutionary Systems Biology
Published in
Advances in experimental medicine and biology, June 2012
DOI 10.1007/978-1-4614-3567-9_14
Pubmed ID
Book ISBNs
978-1-4614-3566-2, 978-1-4614-3567-9
Authors

Dov J. Stekel, Dafyd J. Jenkins

Editors

Orkun S. Soyer

Abstract

We describe the use of computational models of evolution of artificial gene regulatory networks to understand the topologies of biological gene regulatory networks. We summarize results from three complementary approaches that explicitly represent biological processes of transcription, translation, metabolism and gene regulation: a fine-grained model that allows detailed molecular interactions, a coarse-grained model that allows rapid evolution of many generations, and a fixed-architecture model that allows for comparison of different hypotheses. In the first two cases, we are able to evolve networks towards the biological fitness objectives of survival and reproduction. A theme that emerges is that the control of cell energy and resources is a major driver of gene network topology and function. This is demonstrated in the fine-grained model with the emergence of biologically realistic mRNA and protein turnover rates that optimize energy usage and cell division time, and the evolution of basic repressor activities; in the fixed architecture model with a negative self-regulating gene evolving major efficiencies in mRNA usage; and in the coarse-grained model by the need for the inclusion of basal gene expression to obtain biologically plausible networks and the emergence of global regulators keeping all cellular systems under negative control. In summary, we demonstrate the value of biologically realistic computer evolution techniques, and the importance of energy and resource management in driving the topology and function of gene regulatory networks.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Slovenia 1 8%
Unknown 11 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 33%
Researcher 3 25%
Student > Master 2 17%
Student > Bachelor 1 8%
Professor > Associate Professor 1 8%
Other 0 0%
Unknown 1 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 42%
Computer Science 2 17%
Biochemistry, Genetics and Molecular Biology 1 8%
Immunology and Microbiology 1 8%
Medicine and Dentistry 1 8%
Other 1 8%
Unknown 1 8%

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 30 July 2012.
All research outputs
#1,806,645
of 3,634,284 outputs
Outputs from Advances in experimental medicine and biology
#404
of 971 outputs
Outputs of similar age
#28,773
of 72,157 outputs
Outputs of similar age from Advances in experimental medicine and biology
#9
of 25 outputs
Altmetric has tracked 3,634,284 research outputs across all sources so far. This one is in the 45th percentile – i.e., 45% of other outputs scored the same or lower than it.
So far Altmetric has tracked 971 research outputs from this source. They receive a mean Attention Score of 2.2. This one has gotten more attention than average, scoring higher than 53% 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 72,157 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 54% of its contemporaries.
We're also able to compare this research output to 25 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 60% of its contemporaries.