66,886 research outputs found
Expected degree for RNA secondary structure networks
Consider the network of all secondary structures of a given RNA sequence,
where nodes are connected when the corresponding structures have base pair
distance one. The expected degree of the network is the average number of
neighbors, where average may be computed with respect to the either the uniform
or Boltzmann probability. Here we describe the first algorithm, RNAexpNumNbors,
that can compute the expected number of neighbors, or expected network degree,
of an input sequence. For RNA sequences from the Rfam database, the expected
degree is significantly less than the CMFE structure, defined to have minimum
free energy over all structures consistent with the Rfam consensus structure.
The expected degree of structural RNAs, such as purine riboswitches,
paradoxically appears to be smaller than that of random RNA, yet the difference
between the degree of the MFE structure and the expected degree is larger than
that of random RNA. Expected degree does not seem to correlate with standard
structural diversity measures of RNA, such as positional entropy, ensemble
defect, etc. The program {\tt RNAexpNumNbors} is written in C, runs in cubic
time and quadratic space, and is publicly available at
http://bioinformatics.bc.edu/clotelab/RNAexpNumNbors.Comment: 25 pages, 5 figures, 5 table
Comparison of Modules of Wild Type and Mutant Huntingtin and TP53 Protein Interaction Networks: Implications in Biological Processes and Functions
Disease-causing mutations usually change the interacting partners of mutant
proteins. In this article, we propose that the biological consequences of
mutation are directly related to the alteration of corresponding protein
protein interaction networks (PPIN). Mutation of Huntingtin (HTT) which causes
Huntington's disease (HD) and mutations to TP53 which is associated with
different cancers are studied as two example cases. We construct the PPIN of
wild type and mutant proteins separately and identify the structural modules of
each of the networks. The functional role of these modules are then assessed by
Gene Ontology (GO) enrichment analysis for biological processes (BPs). We find
that a large number of significantly enriched (p<0.0001) GO terms in mutant
PPIN were absent in the wild type PPIN indicating the gain of BPs due to
mutation. Similarly some of the GO terms enriched in wild type PPIN cease to
exist in the modules of mutant PPIN, representing the loss. GO terms common in
modules of mutant and wild type networks indicate both loss and gain of BPs. We
further assign relevant biological function(s) to each module by classifying
the enriched GO terms associated with it. It turns out that most of these
biological functions in HTT networks are already known to be altered in HD and
those of TP53 networks are altered in cancers. We argue that gain of BPs, and
the corresponding biological functions, are due to new interacting partners
acquired by mutant proteins. The methodology we adopt here could be applied to
genetic diseases where mutations alter the ability of the protein to interact
with other proteins.Comment: 35 pages, 10 eps figures, (Supplementary material and Datasets are
available on request
Neutral Evolution of Mutational Robustness
We introduce and analyze a general model of a population evolving over a
network of selectively neutral genotypes. We show that the population's limit
distribution on the neutral network is solely determined by the network
topology and given by the principal eigenvector of the network's adjacency
matrix. Moreover, the average number of neutral mutant neighbors per individual
is given by the matrix spectral radius. This quantifies the extent to which
populations evolve mutational robustness: the insensitivity of the phenotype to
mutations. Since the average neutrality is independent of evolutionary
parameters---such as, mutation rate, population size, and selective
advantage---one can infer global statistics of neutral network topology using
simple population data available from {\it in vitro} or {\it in vivo}
evolution. Populations evolving on neutral networks of RNA secondary structures
show excellent agreement with our theoretical predictions.Comment: 7 pages, 3 figure
Effects of neutral selection on the evolution of molecular species
We introduce a new model of evolution on a fitness landscape possessing a
tunable degree of neutrality. The model allows us to study the general
properties of molecular species undergoing neutral evolution. We find that a
number of phenomena seen in RNA sequence-structure maps are present also in our
general model. Examples are the occurrence of "common" structures which occupy
a fraction of the genotype space which tends to unity as the length of the
genotype increases, and the formation of percolating neutral networks which
cover the genotype space in such a way that a member of such a network can be
found within a small radius of any point in the space. We also describe a
number of new phenomena which appear to be general properties of neutrally
evolving systems. In particular, we show that the maximum fitness attained
during the adaptive walk of a population evolving on such a fitness landscape
increases with increasing degree of neutrality, and is directly related to the
fitness of the most fit percolating network.Comment: 16 pages including 4 postscript figures, typeset in LaTeX2e using the
Elsevier macro package elsart.cl
A tractable genotype-phenotype map for the self-assembly of protein quaternary structure
The mapping between biological genotypes and phenotypes is central to the
study of biological evolution. Here we introduce a rich, intuitive, and
biologically realistic genotype-phenotype (GP) map, that serves as a model of
self-assembling biological structures, such as protein complexes, and remains
computationally and analytically tractable. Our GP map arises naturally from
the self-assembly of polyomino structures on a 2D lattice and exhibits a number
of properties: (genotypes vastly outnumber phenotypes),
(genotypic redundancy varies greatly between
phenotypes), (phenotypes consist
of disconnected mutational networks) and (most
phenotypes can be reached in a small number of mutations). We also show that
the mutational robustness of phenotypes scales very roughly logarithmically
with phenotype redundancy and is positively correlated with phenotypic
evolvability. Although our GP map describes the assembly of disconnected
objects, it shares many properties with other popular GP maps for connected
units, such as models for RNA secondary structure or the HP lattice model for
protein tertiary structure. The remarkable fact that these important properties
similarly emerge from such different models suggests the possibility that
universal features underlie a much wider class of biologically realistic GP
maps.Comment: 12 pages, 6 figure
A complex adaptive systems approach to the kinetic folding of RNA
The kinetic folding of RNA sequences into secondary structures is modeled as
a complex adaptive system, the components of which are possible RNA structural
rearrangements (SRs) and their associated bases and base pairs. RNA bases and
base pairs engage in local stacking interactions that determine the
probabilities (or fitnesses) of possible SRs. Meanwhile, selection operates at
the level of SRs; an autonomous stochastic process periodically (i.e., from one
time step to another) selects a subset of possible SRs for realization based on
the fitnesses of the SRs. Using examples based on selected natural and
synthetic RNAs, the model is shown to qualitatively reproduce characteristic
(nonlinear) RNA folding dynamics such as the attainment by RNAs of alternative
stable states. Possible applications of the model to the analysis of properties
of fitness landscapes, and of the RNA sequence to structure mapping are
discussed.Comment: 23 pages, 4 figures, 2 tables, to be published in BioSystems (Note:
updated 2 references
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