Neutral theory of chemical reaction networks

@article{Lee2012NeutralTO,
  title={Neutral theory of chemical reaction networks},
  author={Sang Hoon Lee and Sebastian Bernhardsson and Petter Holme and Beom Jun Kim and Petter Minnhagen},
  journal={New Journal of Physics},
  year={2012},
  volume={14},
  pages={033032}
}
To what extent do the characteristic features of a chemical reaction network reflect its purpose and function? In general, one argues that correlations between specific features and specific functions are key to understanding a complex structure. However, specific features may sometimes be neutral and uncorrelated with any system-specific purpose, function or causal chain. Such neutral features are caused by chance and randomness. Here we compare two classes of chemical networks: one that has… 

Figures from this paper

Beyond topology: coevolution of structure and flux in metabolic networks
TLDR
It is found that concentrations of metabolically derived, but not dietary carotenoids, depended on network structure, and the distribution of flux inCarotenoid metabolism closely follows network structure.
Topology of molecular interaction networks
TLDR
A move from a descriptive approach to a predictive one: rather than correlating biological network topology to generic properties such as robustness, it is used to predict specific functions or phenotypes, which points to new avenues of research.
Does Habitat Variability Really Promote Metabolic Network Modularity?
TLDR
It is determined that modularity in metabolic networks is dependent on species growth conditions, which highlights the need for a more suitable definition of habitat variability and a more careful examination of relationships of the network modularity with horizontal gene transfer, habitats, and environments.
Predicting effects of structural stress in a genome-reduced model bacterial metabolism
TLDR
Interestingly, metabolite motifs associated to reactions can predict the propagation of inactivation cascades and damage amplification effects arising in double knockouts, and genes controlling high-damage reactions tend to be expressed independently of each other, a functional switch mechanism that, simultaneously, acts as a genetic firewall to protect metabolism.
Prediction of social tag frequency's power law distribution with RGF model
  • Zhenyu Wu, Yu Liu, Yuying Wu
  • Computer Science
    2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
  • 2012
TLDR
RGF model is employed to predict the power law distribution of tag frequency and it is shown that the exponent of power law decreases with the increasing of the dataset's size.
Maximum Entropy, Word-Frequency, Chinese Characters, and Multiple Meanings
TLDR
It is shown that although the same Chinese text written in words and Chinese characters have quite differently shaped distributions, they are nevertheless both well predicted by their respective three a priori characteristic values.
Universal scaling in sports ranking
Ranking is a ubiquitous phenomenon in human society. On the web pages of Forbes, one may find all kinds of rankings, such as the world's most powerful people, the world's richest people, the
C L ] 1 7 D ec 2 01 7 Benford ’ s Law and First Letter of Words
A universal First-Letter Law (FLL) is derived and described. It predicts the percentages of first letters for words in novels. The FLL is akin to Benford’s law (BL) of first digits, which predicts
...
...

References

SHOWING 1-10 OF 22 REFERENCES
Atmospheric Reaction Systems as Null-Models to Identify Structural Traces of Evolution in Metabolism
TLDR
The possibility that reaction systems of planetary atmospheres can serve as a null-model against which to define metabolic structure and trace the influence of evolution is explored and it is found that the two types of data can be distinguished by their respective degree distributions.
The Blind Watchmaker Network: Scale-Freeness and Evolution
TLDR
It is found that the degree distribution of the blind watchmaker network agrees very precisely with that of the metabolic networks, suggesting that even a biological system, which due to natural selection has developed an enormous specificity like the cellular metabolism, nevertheless can, at the same time, display well defined characteristics emanating from the ubiquitous inherent random element of Darwinian evolution.
Substance graphs are optimal simple-graph representations of metabolism
TLDR
This paper investigates which representation reflects the functional organization of the metabolic system in the best way, according to the defined criteria, and finds that a “substance network”, where all metabolites participating in a reaction are connected, is better than others.
Dynamical evolution of ecosystems
TLDR
This model gives a very good description of the large quantity of data collected in Barro Colorado Island in Panama in the period 1990–2000 with just three ecologically relevant parameters and predicts the dynamics of extinction of the existing species.
From Physics to Pharmacology?
TLDR
This review focuses on the progress and future challenges of a systems biology approach to biology, an organized approach to quantitatively describe and elucidate the behavior of these complex networks.
KEGG: Kyoto Encyclopedia of Genes and Genomes
KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic
Robustness and Evolvability in Living Systems
TLDR
This book discusses robustness in Natural Systems and Self-Organization, as well as Robustness in Man-made Systems, and seven open questions for Systems Biology.
Selective pressure on metabolic network structures as measured from the random blind-watchmaker network
TLDR
A random null model termed the Blind Watchmaker network has been shown to reproduce the degree distribution found in metabolic networks, which might suggest that natural selection has had a role in shaping metabolic networks.
Just and unjust distributions
A single theory can explain how power-law distributions emerge from such wildly different areas as economy, cultural geography, ecology, linguistics, sociology, and biological chemistry. http://www
...
...