| Literature DB >> 25853854 |
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Abstract
[This corrects the article DOI: 10.1371/journal.pone.0117312.].Entities:
Year: 2015 PMID: 25853854 PMCID: PMC4390385 DOI: 10.1371/journal.pone.0124858
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Fig 3The flow v through the network depending on boundary species distance d.
All networks are simulated with a boundary concentration difference of ∣c 1−c 2∣ = 0.9 and a base concentration of min(c 1,c 2) = 0.1. Filled (grey) symbols represent linear networks, empty (white) the nonlinear ones. Error bars show the standard error of the mean.
Fig 4Varying flow through nonlinear networks.
Each data point is the average of all simulations with specific boundary species concentration (c 1 = 0.1 c 2 = 0.2…60) and a shortest path between boundary species of 3. (A) Dependency of flow from concentration difference. Pan-Sinha results are not shown as they overlap with the Erdős-Rényi ones. (B) Distribution of species chemical potential μ for different boundary condition strengths of BarabsiAlbert (BA) networks. (C) The fraction of dissipation in the network explained by the most dissipating 10 percent of reactions, f (0.1). (D) Standard deviation of chemical potentials σ normalized by difference between boundary species’ potentials Δμ = ∣μ −μ ∣ shows a more localized distribution of chemical potentials for larger flows.
Fig 5Number of 2- and 4-cycles in the (directed) substrate graphs of the nonlinear reaction networks.
The plots show the number of additional cycles depending on the flow through the network in comparison to the same network with random reaction directions (Table 1). Each data point is the average of all simulations with boundary points distance of 3 and fixed boundary concentrations (c 1 = 0.1 c 2 = 0.2…60).