15 research outputs found
One Hub-One Process: A Tool Based View on Regulatory Network Topology
The relationship between the regulatory design and the functionality of
molecular networks is a key issue in biology. Modules and motifs have been
associated to various cellular processes, thereby providing anecdotal evidence
for performance based localization on molecular networks. To quantify
structure-function relationship we investigate similarities of proteins which
are close in the regulatory network of the yeast Saccharomyces Cerevisiae. We
find that the topology of the regulatory network show weak remnants of its
history of network reorganizations, but strong features of co-regulated
proteins associated to similar tasks. This suggests that local topological
features of regulatory networks, including broad degree distributions, emerge
as an implicit result of matching a number of needed processes to a finite
toolbox of proteins.Comment: 18 pages, 3 figures, 5 supplementary figure
Parameters of proteome evolution from histograms of amino-acid sequence identities of paralogous proteins
Background: The evolution of the full repertoire of proteins encoded in a given genome is mostly driven by gene duplications, deletions, and sequence modifications of existing proteins. Indirect information about relative rates and other intrinsic parameters of these three basic processes is contained in the proteome-wide distribution of sequence identities of pairs of paralogous proteins. Results: We introduce a simple mathematical framework based on a stochastic birth-and-death model that allows one to extract some of this information and apply it to the set of all pairs of paralogous proteins in H. pylori, E. coli, S. cerevisiae, C. elegans, D. melanogaster, and H. sapiens. It was found that the histogram of sequence identities p generated by an all-to-all alignment of all protein sequences encoded in a genome is well fitted with a power-law form ∼ p−γ with the value of the exponent γ around 4 for the majority of organisms used in this study. This implies that the intra-protein variability of substitution rates is best described by the Gamma-distribution with the exponent α ≈ 0.33. Different features of the shape of such histograms allow us to quantify the ratio between the genome-wide average deletion/duplication rates and the amino-acid substitution rate. 1 Conclusions: We separately measure the short-term (“raw”) duplication and deletion rates r ∗ dup, r ∗ del whic
Species–area relationships always overestimate extinction rates from habitat loss : comment
Author Posting. © Ecological Society of America, 2013. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Ecology 94 (2013): 761–763, doi:10.1890/12-0047.1.The species–area relationship summarizes the relationship
between the average number of species in a
region and its area. This relationship provides a basis for
predicting the loss of species associated with loss of
habitat (e.g., Pimm and Raven 2000). The approach
involves two steps. First, as discussed in more detail
below, the species–area relationship is used to predict the
number of species that are endemic to the habitat at risk
based on its area. Second, these endemic species are
assumed to become extinct should this habitat be lost. In
a controversial paper, He and Hubbell (2011) argued
that the way in which the species–area relationship is
used to predict the number of endemic species is incorrect
when individual organisms are aggregated in space and
argued that this explains a discrepancy between predicted
and observed extinction rates associated with habitat
loss. The controversy surrounding the paper focused
primarily on the second part of their argument (Brooks
2011, Evans et al. 2011, He and Hubbell 2012, Pereira et
al. 2012, Thomas and Williamson 2012). Here, we focus
on the details underlying the first part.U. Roll is
supported by the Adams Fellowship Program of the Israel
Academy of Sciences and Humanities. L. Stone is supported by
the Israeli Science Foundation
Degree Landscapes in Scale-Free Networks
We generalize the degree-organizational view of real-world networks with
broad degree-distributions in a landscape analogue with mountains (high-degree
nodes) and valleys (low-degree nodes). For example, correlated degrees between
adjacent nodes corresponds to smooth landscapes (social networks), hierarchical
networks to one-mountain landscapes (the Internet), and degree-disassortative
networks without hierarchical features to rough landscapes with several
mountains. We also generate ridge landscapes to model networks organized under
constraints imposed by the space the networks are embedded in, associated to
spatial or, in molecular networks, to functional localization. To quantify the
topology, we here measure the widths of the mountains and the separation
between different mountains.Comment: 4 pages, 5 figure
Cost and Capacity of Signaling in the Escherichia coli Protein Reaction Network
In systems biology new ways are required to analyze the large amount of
existing data on regulation of cellular processes. Recent work can be roughly
classified into either dynamical models of well-described subsystems, or
coarse-grained descriptions of the topology of the molecular networks at the
scale of the whole organism. In order to bridge these two disparate approaches
one needs to develop simplified descriptions of dynamics and topological
measures which address the propagation of signals in molecular networks. Here,
we consider the directed network of protein regulation in E. coli,
characterizing its modularity in terms of its potential to transmit signals. We
demonstrate that the simplest measure based on identifying sub-networks of
strong components, within which each node could send a signal to every other
node, indeed partitions the network into functional modules. We then suggest
measures to quantify the cost and spread associated with sending a signal
between any particular pair of proteins. Thereby, we address the signalling
specificity within and between modules, and show that in the regulation of
E.coli there is a systematic reduction of the cost and spread for signals
traveling over more than two intermediate reactions.Comment: 21 pages, 6 figure
Degree landscapes in scale-free networks
We generalize the degree-organizational view of real-world networks with broad degree distributions in a landscape analog with mountains ͑high-degree nodes͒ and valleys ͑low-degree nodes͒. For example, correlated degrees between adjacent nodes correspond to smooth landscapes ͑social networks͒, hierarchical networks to one-mountain landscapes ͑the Internet͒, and degree-disassortative networks without hierarchical features to rough landscapes with several mountains. To quantify the topology, we here measure the widths of the mountains and the separation between different mountains. We also generate ridge landscapes to model networks organized under constraints imposed by the space the networks are embedded in, associated to spatial or in molecular networks to functional localization