Commit 5c49e935 authored by Vladimir Reinharz's avatar Vladimir Reinharz
Browse files

update ref appeal

parent 53f34956
......@@ -299,3 +299,23 @@
Volume = {4},
Year = {2008},
Bdsk-Url-1 = {http://dx.doi.org/10.1371/journal.pcbi.1000124}}
@article{Aguirre:2011aa,
Abstract = {The evolution and adaptation of molecular populations is constrained by the diversity accessible through mutational processes. RNA is a paradigmatic example of biopolymer where genotype (sequence) and phenotype (approximated by the secondary structure fold) are identified in a single molecule. The extreme redundancy of the genotype-phenotype map leads to large ensembles of RNA sequences that fold into the same secondary structure and can be connected through single-point mutations. These ensembles define neutral networks of phenotypes in sequence space. Here we analyze the topological properties of neutral networks formed by 12-nucleotides RNA sequences, obtained through the exhaustive folding of sequence space. A total of 4(12) sequences fragments into 645 subnetworks that correspond to 57 different secondary structures. The topological analysis reveals that each subnetwork is far from being random: it has a degree distribution with a well-defined average and a small dispersion, a high clustering coefficient, and an average shortest path between nodes close to its minimum possible value, i.e. the Hamming distance between sequences. RNA neutral networks are assortative due to the correlation in the composition of neighboring sequences, a feature that together with the symmetries inherent to the folding process explains the existence of communities. Several topological relationships can be analytically derived attending to structural restrictions and generic properties of the folding process. The average degree of these phenotypic networks grows logarithmically with their size, such that abundant phenotypes have the additional advantage of being more robust to mutations. This property prevents fragmentation of neutral networks and thus enhances the navigability of sequence space. In summary, RNA neutral networks show unique topological properties, unknown to other networks previously described.},
Author = {Aguirre, Jacobo and Buld{\'u}, Javier M and Stich, Michael and Manrubia, Susanna C},
Date-Added = {2016-12-31 05:16:34 +0000},
Date-Modified = {2017-06-09 02:30:41 +0000},
Doi = {10.1371/journal.pone.0026324},
Journal = {PLoS One},
Journal-Full = {PloS one},
Mesh = {Base Sequence; Cluster Analysis; Computational Biology; Mutation; Nucleic Acid Conformation; Phenotype; Probability; RNA},
Number = {10},
Pages = {e26324},
Pmc = {PMC3196570},
Pmid = {22028856},
Pst = {ppublish},
Title = {Topological structure of the space of phenotypes: the case of {RNA} neutral networks},
Volume = {6},
Year = {2011},
Bdsk-Url-1 = {http://dx.doi.org/10.1371/journal.pone.0026324}}
\ No newline at end of file
......@@ -139,7 +139,7 @@ The reviewer is correct. It is has been fixed in the manuscript.
\begin{response}{
Section 4.1.2 - R has units of kcal mol-1 K-1 (which are not stated). Then RT = 0.06 kcal/mol. Although it seems reasonable to use this RT in the selection function, there is of course no reason why fitness has to depend on the Boltzmann factor. Use of beta in this equation is potentially confusing because often beta = 1/kT, and you have a 1/RT already. Maybe call beta something else, or just miss beta out of the equation. Later in this paragraph - shouldn't it be beta = +1 not -1, because more negative E means higher fitness? There is already a minus sign in the equation.}
The units of R are stated in the paragraph following the fitness function. The use of RT in the fitness function was to scale fitness in a way that could be readily compared with \texttt{RNAmutants}. The use of $\beta=-1$ is necessary as energy values are either zero or negative. If $\beta = +1$ then the exponent would remain negative and a more negative energy would then result in a lower fitness structure.
We added the units of R in the paragraph following the fitness function. The use of RT in the fitness function was to scale fitness in a way that could be readily compared with \texttt{RNAmutants}. The use of $\beta=-1$ is necessary as energy values are either zero or negative. If $\beta = +1$ then the exponent would remain negative and a more negative energy would then result in a lower fitness structure.
\end{response}
......@@ -246,7 +246,7 @@ We agree with the suggested change and will update the sentence.
\begin{response}{
Page 11. Here there are some very general statements based on very few simulations: ... variation to overcome energy barriers and obtain energies reached by RNAmutants.? The population used was quite small in comparison to natural ones, and no effort to study other population sizes has been made. It is known that, despite the canalization of evolutionary dynamics, the ability to overcome energy barriers is strongly dependent on the product of the population size times the mutation rate. The barrier disappears when this product is large enough.
Last paragraph. It is stated that their algorithm fails to generate the structural complexity found in real populations?. Which real populations are the authors comparing with? A single one? The complete database? Is this comparison sensible at sufficiently long evolutionary times (natural versus synthetic populations)? Why should selection for stability be the only pressure determining the observed natural structures?}
\experimentstag We are confronted with technical limitations were length 50 is the upper limit for which we can conduct such an extensive search, which we compare with smaller size as seen in other papers~\cite{???}.
\experimentstag We are confronted with technical limitations were length 50 is the upper limit for which we can conduct such an extensive search, which we compare with smaller size as seen in other papers~\cite{Stich:2008aa, Dingle:2015aa, Aguirre:2011aa}.
\claimstag We are comparing with the abundance of families with length around 50 which contain multiloops as shown in Fig.1. And we do not support the hypothesis that selection for stability is a good model since it does not correlate well with observations.
\end{response}
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