Erratum: Spectral renormalization group for the Gaussian model and ψ^{4} theory on nonspatial networks [Phys. Rev. E 92, 022106 (2015)].

This corrects the article DOI: 10.1103/PhysRevE.92.

Spectral renormalization group for the Gaussian model and ψ4 theory on nonspatial networks.

We implement the spectral renormalization group on different deterministic nonspatial networks without translational invariance. We calculate the thermodynamic critical exponents for the Gaussian model on the Cayley tree and the diamond lattice and find that they are functions of the spectral dimension, d[over ̃]. The results are shown to be consistent with those from exact summation and finite-size scaling approaches.

Motif statistics of artificially evolved and biological networks.

Topological features of gene regulatory networks can be successfully reproduced by a model population evolving under selection for short dynamical attractors. The evolved population of networks exhibit motif statistics, summarized by significance profiles, which closely match those of E. coli, S.

An ensemble approach to the evolution of complex systems.

Adaptive systems frequently incorporate complex structures which can arise spontaneously and which may be nonadaptive in the evolutionary sense. We give examples from phase transition and fractal growth to develop the themes of cooperative phenomena and pattern formation. We discuss RNA interference and transcriptional gene regulation networks, where a major part of the topological properties can be accounted for by mere combinatorics.

Tracking tumor evolution via prostate-specific antigen: an individual post-operative study.

Background: The progress of the prostate-specific antigen (PSA) level after radical prostatectomy is observed for a patient in order to extract information about the mode of tumor cell growth. Although PSA values are determined routinely to find the doubling time of the prostate marker, to our knowledge, this analysis is the first in the literature.

Results: The prostate tumor marker values were determined regularly after the surgery and plotted on a logarithmic scale against time.

Information content based model for the topological properties of the gene regulatory network of Escherichia coli.

Gene regulatory networks (GRN) are being studied with increasingly precise quantitative tools and can provide a testing ground for ideas regarding the emergence and evolution of complex biological networks. We analyze the global statistical properties of the transcriptional regulatory network of the prokaryote Escherichia coli, identifying each operon with a node of the network. We propose a null model for this network using the content-based approach applied earlier to the eukaryote Saccharomyces cerevisiae (Balcan et al.

Content-based networks: a pedagogical overview.

Complex interactions call for the sharing of information between different entities. In a recent paper, we introduced a combinatoric model which concretizes this idea via a string-matching rule. The model was shown to lend itself to analysis regarding certain topological features of the network.

The information coded in the yeast response elements accounts for most of the topological properties of its transcriptional regulation network.

The regulation of gene expression in a cell relies to a major extent on transcription factors, proteins which recognize and bind the DNA at specific binding sites (response elements) within promoter regions associated with each gene. We present an information theoretic approach to modeling transcriptional regulatory networks, in terms of a simple "sequence-matching" rule and the statistics of the occurrence of binding sequences of given specificity in random promoter regions. The crucial biological input is the distribution of the amount of information coded in these cognate response elements and the length distribution of the promoter regions.