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Register a new userAn asymptotic maximum principle for essentially linear evolution models
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Recent work on mutation-selection models has revealed that, under specific assumptions on the fitness function and the mutation rates, asymptotic estimates for the leading eigenvalue of the mutation-reproduction matrix may be obtained through a low-dimensional maximum principle in the limit N to infinity (where N is the number of types). In order to extend this variational principle to a larger class of models, we consider here a family of reversible N by N matrices and identify conditions under which the high-dimensional Rayleigh-Ritz variational problem may be reduced to a low-dimensional one that yields the leading eigenvalue up to an error term of order 1/N. For a large class of mutation-selection models, this implies estimates for the mean fitness, as well as a concentration result for the ancestral distribution of types.
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Tuffley and Steel (1997) proved that Maximum Likelihood and Maximum Parsimony methods in phylogenetics are equivalent for sequences of characters under a simple symmetric model of substitution with no common mechanism. This result has been widely cited ever since. We show that small changes to the model assumptions suffice to make the two methods inequivalent. In particular, we analyze the case of bounded substitution probabilities as well as the molecular clock assumption. We show that in these cases, even under no common mechanism, Maximum Parsimony and Maximum Likelihood might make conflicting choices. We also show that if there is an upper bound on the substitution probabilities which is `sufficiently small, every Maximum Likelihood tree is also a Maximum Parsimony tree (but not vice versa).
Cooperation is prevalent in nature, not only in the context of social interactions within the animal kingdom, but also on the cellular level. In cancer for example, tumour cells can cooperate by producing growth factors. The evolution of cooperation has traditionally been studied for well-mixed populations under the framework of evolutionary game theory, and more recently for structured populations using evolutionary graph theory. The population structures arising due to cellular arrangement in tissues however are dynamic and thus cannot be accurately represented by either of these frameworks. In this work we compare the conditions for cooperative success in an epithelium modelled using evolutionary graph theory, to those in a mechanical model of an epithelium =- the Voronoi tessellation model. Crucially, in this latter model cells are able to move, and birth and death are not spatially coupled. We calculate fixation probabilities in the Voronoi tessellation model through simulation and an approximate analytic technique and show that this leads to stronger promotion of cooperation in comparison with the evolutionary graph theory model.
The coexistence between different types of templates has been the choice solution to the information crisis of prebiotic evolution, triggered by the finding that a single RNA-like template cannot carry enough information to code for any useful replicase. In principle, confining $d$ distinct templates of length $L$ in a package or protocell, whose survival depends on the coexistence of the templates it holds in, could resolve this crisis provided that $d$ is made sufficiently large. Here we review the prototypical package model of Niesert et al. 1981 which guarantees the greatest possible region of viability of the protocell population, and show that this model, and hence the entire package approach, does not resolve the information crisis. This is so because to secure survival the total information content of the protocell, $Ld$, must tend to a constant value that depends only on the spontaneous error rate per nucleotide of the template replication mechanism. As a result, an increase of $d$ must be followed by a decrease of $L$ to ensure the protocell viability, so that the net information gain is null.
A matrix Lie algebra is a linear space of matrices closed under the operation $ [A, B] = AB-BA $. The Lie closure of a set of matrices is the smallest matrix Lie algebra which contains the set. In the context of Markov chain theory, if a set of rate matrices form a Lie algebra, their corresponding Markov matrices are closed under matrix multiplication; this has been found to be a useful property in phylogenetics. Inspired by previous research involving Lie closures of DNA models, it was hypothesised that finding the Lie closure of a codon model could help to solve the problem of mis-estimation of the non-synonymous/synonymous rate ratio, $ omega $. We propose two different methods of finding a linear space from a model: the first is the emph{linear closure} which is the smallest linear space which contains the model, and the second is the emph{linear version} which changes multiplicative constraints in the model to additive ones. For each of these linear spaces we then find the Lie closures of them. Under both methods, it was found that closed codon models would require thousands of parameters, and that any partial solution to this problem that was of a reasonable size violated stochasticity. Investigation of toy models indicated that finding the Lie closure of matrix linear spaces which deviated only slightly from a simple model resulted in a Lie closure that was close to having the maximum number of parameters possible. Given that Lie closures are not practical, we propose further consideration of the two variants of linearly closed models.
It was recently proposed to encode the one-sided exponential source X via K parallel channels, Y1, ..., YK , such that the error signals X - Yi, i = 1,...,K, are one-sided exponential and mutually independent given X. Moreover, it was shown that the optimal estimator hat{Y} of the source X with respect to the one-sided error criterion, is simply given by the maximum of the outputs, i.e., hat{Y} = max{Y1,..., YK}. In this paper, we show that the distribution of the resulting estimation error X - hat{Y} , is equivalent to that of the optimum noise in the backward test-channel of the one-sided exponential source, i.e., it is one-sided exponentially distributed and statistically independent of the joint output Y1,...,YK.
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