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Computational Methods in Systems Biology

Overview of attention for book
Cover of 'Computational Methods in Systems Biology'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Modeling and Engineering Promoters with Pre-defined RNA Production Dynamics in Escherichia Coli
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    Chapter 2 Deep Abstractions of Chemical Reaction Networks
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    Chapter 3 Derivation of a Biomass Proxy for Dynamic Analysis of Whole Genome Metabolic Models
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    Chapter 4 Computing Diverse Boolean Networks from Phosphoproteomic Time Series Data
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    Chapter 5 Characterization of the Experimentally Observed Clustering of VEGF Receptors
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    Chapter 6 Synthesis for Vesicle Traffic Systems
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    Chapter 7 Formal Analysis of Network Motifs
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    Chapter 8 Buffering Gene Expression Noise by MicroRNA Based Feedforward Regulation
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    Chapter 9 Stochastic Rate Parameter Inference Using the Cross-Entropy Method
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    Chapter 10 Experimental Biological Protocols with Formal Semantics
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    Chapter 11 Robust Data-Driven Control of Artificial Pancreas Systems Using Neural Networks
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    Chapter 12 Programming Substrate-Independent Kinetic Barriers with Thermodynamic Binding Networks
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    Chapter 13 A Trace Query Language for Rule-Based Models
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    Chapter 14 Inferring Mechanism of Action of an Unknown Compound from Time Series Omics Data
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    Chapter 15 Composable Rate-Independent Computation in Continuous Chemical Reaction Networks
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    Chapter 16 ASSA-PBN 3.0 : Analysing Context-Sensitive Probabilistic Boolean Networks
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    Chapter 17 KaSa: A Static Analyzer for Kappa
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    Chapter 18 On Robustness Computation and Optimization in BIOCHAM-4
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    Chapter 19 LNA++: Linear Noise Approximation with First and Second Order Sensitivities
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    Chapter 20 Reparametrizing the Sigmoid Model of Gene Regulation for Bayesian Inference
  22. Altmetric Badge
    Chapter 21 On the Full Control of Boolean Networks
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    Chapter 22 Systems Metagenomics: Applying Systems Biology Thinking to Human Microbiome Analysis
Attention for Chapter 15: Composable Rate-Independent Computation in Continuous Chemical Reaction Networks
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2 X users

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Chapter title
Composable Rate-Independent Computation in Continuous Chemical Reaction Networks
Chapter number 15
Book title
Computational Methods in Systems Biology
Published in
arXiv, September 2018
DOI 10.1007/978-3-319-99429-1_15
Book ISBNs
978-3-31-999428-4, 978-3-31-999429-1
Authors

Cameron Chalk, Niels Kornerup, Wyatt Reeves, David Soloveichik, Chalk, Cameron, Kornerup, Niels, Reeves, Wyatt, Soloveichik, David

X Demographics

X Demographics

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

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 40%
Student > Bachelor 1 20%
Student > Master 1 20%
Unknown 1 20%
Readers by discipline Count As %
Computer Science 3 60%
Mathematics 1 20%
Unknown 1 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 28 January 2020.
All research outputs
#15,018,183
of 23,102,082 outputs
Outputs from arXiv
#326,154
of 949,646 outputs
Outputs of similar age
#201,783
of 337,664 outputs
Outputs of similar age from arXiv
#10,465
of 24,048 outputs
Altmetric has tracked 23,102,082 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 949,646 research outputs from this source. They receive a mean Attention Score of 3.9. This one has gotten more attention than average, scoring higher than 60% 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 337,664 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 24,048 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.