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Susanna Manrubia

Publications and source records attributed to Susanna Manrubia.

23 records · Page 2Linked to original sources

Enumerating secondary structures and structural moieties for circular RNAs

A quantitative characterization of the relationship between molecular sequence and structure is essential to improve our understanding of how function emerges. This particular genotype-phenotype map has been often studied in the context of RNA sequences, with the folded configurations standing as a proxy for the phenotype. Here, we count the secondary structures of circular RNAs of length $n$ and calculate the asymptotic distributions of different structural moieties, such as stems or hairpin loops, by means of symbolic combinatorics. Circular RNAs differ in essential ways from their linear counterparts. From the mathematical viewpoint, the enumeration of the corresponding secondary structures demands the use of combinatorial techniques additional to those used for linear RNAs. The asymptotic number of secondary structures for circular RNAs grows as $a^nn^{-5/2}$, with a depending on particular constraints applied to the secondary structure. The abundance of any structural moiety is normally distributed in the limit $n\to\infty$, with a mean and a variance that increase linearly with $n$.

q-bio.PE↗

Demography-based adaptive network model reproduces the spatial organization of human linguistic groups

The distribution of human linguistic groups presents a number of interesting and non-trivial patterns. The distributions of the number of speakers per language and the area each group covers follow log-normal distributions, while population and area fulfill an allometric relationship. The topology of networks of spatial contacts between different linguistic groups has been recently characterized, showing atypical properties of the degree distribution and clustering, among others. Human demography, spatial conflicts, and the construction of networks of contacts between linguistic groups are mutually dependent processes. Here we introduce an adaptive network model that takes all of them into account and successfully reproduces, using only four model parameters, not only those features of linguistic groups already described in the literature, but also correlations between demographic and topological properties uncovered in this work. Besides their relevance when modeling and understanding processes related to human biogeography, our adaptive network model admits a number of generalizations that broaden its scope and make it suitable to represent interactions between agents based on population dynamics and competition for space.

q-bio.PE↗

Evolution on neutral networks accelerates the ticking rate of the molecular clock

Large sets of genotypes give rise to the same phenotype because phenotypic expression is highly redundant. Accordingly, a population can accept mutations without altering its phenotype, as long as thegenotype mutates into another one on the same set. By linking every pair of genotypes that are mutually accessible through mutation, genotypes organize themselves into neutral networks (NN). These networks are known to be heterogeneous and assortative, and these properties affect the evolutionary dynamics of the population. By studying the dynamics of populations on NN with arbitrary topology we analyze the effect of assortativity, of NN (phenotype) fitness, and of network size. We find that the probability that the population leaves the network is smaller the longer the time spent on it. This progressive "phenotypic entrapment" entails a systematic increase in the overdispersion of the process with time and an acceleration in the fixation rate of neutral mutations. We also quantify the variation of these effects with the size of the phenotype and with its fitness relative to that of neighbouring alternatives.

q-bio.PE↗

toyLIFE: a computational framework to study the multi-level organization of the genotype-phenotype map

The genotype-phenotype map is an essential object in our understanding of organismal complexity and adaptive properties, determining at once genomic plasticity and those constraints that may limit the ability of genomes to attain evolutionary innovations. An exhaustive experimental characterization of the relationship between genotypes and phenotypes is at present out of reach. Therefore, several models mimicking that map have been proposed and investigated, leading to the identification of a number of general features: genotypes differ in their robustness to mutations, phenotypes are represented by a broadly varying number of genotypes, and simple point mutations seem to suffice to navigate the space of genotypes while maintaining a phenotype. However, most current models address only one level of the map (sequences and folded structures in RNA or proteins; networks of genes and their dynamical attractors; sets of chemical reactions and their ability to undergo molecular catalysis), such that many relevant questions cannot be addressed. Here we introduce toyLIFE, a multi-level model for the genotype-phenotype map based on simple genomes and interaction rules from which a complex behavior at upper levels emerges, remarkably plastic gene regulatory networks and metabolism. toyLIFE is a tool that permits the investigation of how different levels are coupled, in particular how and where do mutations affect phenotype or how the presence of certain metabolites determines the dynamics of toyLIFE gene regulatory networks. The possibilities of this model are not exhausted by the results presented in this contribution. It can be easily generalized to incorporate evolution through mutations that change genome length or through recombination, to consider gene duplication or deletion, and therefore to explore further properties of extended genotype-phenotype maps.

q-bio.PE↗

Simple Model of Large Scale Organization in Evolution

A mathematical model of interacting species filling ecological niches left by the extinction of others is introduced. Species organize themselves into genera of all sizes. The size of a genus on average grows linearly with its age, confirming a general relation between Age and Area proposed by Willis. The ecology exhibits punctuated equilibrium. Analytic and numerical results show that the probability distribution of genera sizes, genera lifetimes, and extinction event sizes are the same power law $P(x) \sim 1/x^2$, consistent with paleontological data.

cond-mat↗