Phylo.io: interactive viewing and comparison of large phylogenetic trees on the web
Oscar Robinson, David Dylus, Christophe Dessimoz
Phylogenetic mixtures and linear invariants for equal input models
Marta Casanellas, Mike Steel
The reconstruction of phylogenetic trees from molecular sequence data relies on modelling site substitutions by a Markov process, or a mixture of such processes. In general, allowing mixed processes can result in different tree topologies becoming indistinguishable from the data, even for infinitely long sequences. However, when the underlying Markov process supports linear phylogenetic invariants, then provided these are sufficiently informative, the identifiability of the tree topology can be restored. In this paper, we investigate a class of processes that support linear invariants once the stationary distribution is fixed, the `equal input model’. This model generalizes the `Felsenstein 1981′ model (and thereby the Jukes–Cantor model) from four states to an arbitrary number of states (finite or infinite), and it can also be described by a `random cluster’ process. We describe the structure and dimension of the vector space of phylogenetic mixtures (and the complementary space of linear invariants) for any fixed phylogenetic tree (and for all trees — the so called `model invariants’), on any number n of leaves. We also provide a precise description of the space of mixtures and linear invariants for the special case of n=4 leaves. By combining techniques from discrete random processes and (multi-) linear algebra, our results build on a classic result that was first established by James Lake in 1987.
Competing metabolic strategies in a multilevel selection model
André Amado, Lenin Fernández, Weini Huang, Fernando F. Ferreira, Paulo R. A. Campos
The interplay between energy efficiency and evolutionary mechanisms is addressed. One important question is how evolutionary mechanisms can select for the optimised usage of energy in situations where it does not lead to immediate advantage. For example, this problem is of great importance to improve our understanding about the major transition from unicellular to multicellular form of life. The immediate advantage of gathering efficient individuals in an energetic context is not clear. Although this process increases relatedness among individuals, it also increases local competition. To address this question, we propose a model of two competing metabolic strategies that makes explicit reference to the resource usage. We assume the existence of an efficient strain, which converts resource into energy at high efficiency but displays a low rate of resource consumption, and an inefficient strain, which consumes resource at a high rate with a low efficiency in converting it to energy. We explore the dynamics in both well-mixed and structured populations. The selection for optimised energy usage is measured by the likelihood of that an efficient strain can invade a population only comprised by inefficient strains. It is found that the region of the parameter space at which the efficient strain can thrive in structured populations is always larger than observed in well-mixed populations. In fact, in well-mixed populations the efficient strain is only evolutionarily stable in the domain whereupon there is no evolutionary dilemma. We also observe that small group sizes enhance the chance of invasion by the efficient strain in spite of increasing the competition among relatives. This outcome corroborates the key role played by kin selection and shows that the group dynamics relied on group expansion, overlapping generations and group split can balance the negative effects of local competition.
Combinatorial Scoring of Phylogenetic Networks
Nikita Alexeev, Max A. Alekseyev
Construction of phylogenetic trees and networks for extant species from their characters represents one of the key problems in phylogenomics. While solution to this problem is not always uniquely defined and there exist multiple methods for tree/network construction, it becomes important to measure how well constructed networks capture the given character relationship across the species.
In the current study, we propose a novel method for measuring the specificity of a given phylogenetic network in terms of the total number of distributions of character states at the leaves that the network may impose. While for binary phylogenetic trees, this number has an exact formula and depends only on the number of leaves and character states but not on the tree topology, the situation is much more complicated for non-binary trees or networks. Nevertheless, we develop an algorithm for combinatorial enumeration of such distributions, which is applicable for arbitrary trees and networks under some reasonable assumptions.