Trees from Functions as Processes

Levy-Longo Trees and Bohm Trees are the best known tree structures on the {\lambda}-calculus. We give general conditions under which an encoding of the {\lambda}-calculus into the {\pi}-calculus is sound and complete with respect to such trees. We apply these conditions to various encodings of the call-by-name {\lambda}-calculus, showing how the two kinds of tree can be obtained by varying the behavioural equivalence adopted in the {\pi}-calculus and/or the encoding

Trees from functions as processes

International audienceLévy-Longo Trees and Böhm Trees are the best known tree structures on the λ-calculus. We give general conditions under which an encoding of the λ-calculus into the π-calculus is sound and complete with respect to such trees. We apply these conditions to various encodings of the call-by-name λ-calculus, showing how the two kinds of tree can be obtained by varying the behavioural equivalence adopted in the π-calculus and/or the encoding

iteRates: An R Package for Implementing a Parametric Rate Comparison on Phylogenetic Trees

Patterns of diversification rate variation detected in phylogenetic hypotheses are frequently used to infer historical, ecological, and evolutionary processes. The parametric rate comparison (PRC) is a method for detecting rate variation in trees that models branch lengths as random variables drawn from familiar statistical distributions. iteRates is a library of functions for the R statistical computing environment for implementing PRC on phylogenetic trees. Here, we describe some of the functions in iteRates for subtree identification, tree manipulation, applying the PRC and K-clades PRC analyses, and conducting a whole-tree randomization test

The Origin, Succession, and Predicted Metabolism of Bacterial Communities Associated with Leaf Decomposition.

Intraspecific variation in plant nutrient and defensive traits can regulate ecosystem-level processes, such as decomposition and transformation of plant carbon and nutrients. Understanding the regulatory mechanisms of ecosystem functions at local scales may facilitate predictions of the resistance and resilience of these functions to change. We evaluated how riverine bacterial community assembly and predicted gene content corresponded to decomposition rates of green leaf inputs from red alder trees into rivers of Washington State, USA. Previously, we documented accelerated decomposition rates for leaves originating from trees growing adjacent to the site of decomposition versus more distant locales, suggesting that microbes have a "home-field advantage" in decomposing local leaves. Here, we identified repeatable stages of bacterial succession, each defined by dominant taxa with predicted gene content associated with metabolic pathways relevant to the leaf characteristics and course of decomposition. "Home" leaves contained bacterial communities with distinct functional capacities to degrade aromatic compounds. Given known spatial variation of alder aromatics, this finding helps explain locally accelerated decomposition. Bacterial decomposer communities adjust to intraspecific variation in leaves at spatial scales of less than a kilometer, providing a mechanism for rapid response to changes in resources such as range shifts among plant genotypes. Such rapid responses among bacterial communities in turn may maintain high rates of carbon and nutrient cycling through aquatic ecosystems.IMPORTANCE Community ecologists have traditionally treated individuals within a species as uniform, with individual-level biodiversity rarely considered as a regulator of community and ecosystem function. In our study system, we have documented clear evidence of within-species variation causing local ecosystem adaptation to fluxes across ecosystem boundaries. In this striking pattern of a "home-field advantage," leaves from individual trees tend to decompose most rapidly when immediately adjacent to their parent tree. Here, we merge community ecology experiments with microbiome approaches to describe how bacterial communities adjust to within-species variation in leaves over spatial scales of less than a kilometer. The results show that bacterial community compositional changes facilitate rapid ecosystem responses to environmental change, effectively maintaining high rates of carbon and nutrient cycling through ecosystems

Highlighting the Need for Systems-Level Experimental Characterization of Plant Metabolic Enzymes

The biology of living organisms is determined by the action and interaction of a large number of individual gene products, each with specific functions. Discovering and annotating the function of gene products is key to our understanding of these organisms. Controlled experiments and bioinformatic predictions both contribute to functional gene annotation. For most species it is difficult to gain an overview of what portion of gene annotations are based on experiments and what portion represent predictions. Here, I survey the current state of experimental knowledge of enzymes and metabolism in Arabidopsis thaliana as well as eleven economically important crops and forestry trees – with a particular focus on reactions involving organic acids in central metabolism. I illustrate the limited availability of experimental data for functional annotation of enzymes in most of these species. Many enzymes involved in metabolism of citrate, malate, fumarate, lactate, and glycolate in crops and forestry trees have not been characterized. Furthermore, enzymes involved in key biosynthetic pathways which shape important traits in crops and forestry trees have not been characterized. I argue for the development of novel high-throughput platforms with which limited functional characterization of gene products can be performed quickly and relatively cheaply. I refer to this approach as systems-level experimental characterization. The data collected from such platforms would form a layer intermediate between bioinformatic gene function predictions and in-depth experimental studies of these functions. Such a data layer would greatly aid in the pursuit of understanding a multiplicity of biological processes in living organisms

Effects of memory on the shapes of simple outbreak trees

Genomic tools, including phylogenetic trees derived from sequence data, are increasingly used to understand outbreaks of infectious diseases. One challenge is to link phylogenetic trees to patterns of transmission. Particularly in bacteria that cause chronic infections, this inference is affected by variable infectious periods and infectivity over time. It is known that non-exponential infectious periods can have substantial effects on pathogens’ transmission dynamics. Here we ask how this non-Markovian nature of an outbreak process affects the branching trees describing that process, with particular focus on tree shapes. We simulate Crump-Mode-Jagers branching processes and compare different patterns of infectivity over time. We find that memory (non-Markovian-ness) in the process can have a pronounced effect on the shapes of the outbreak’s branching pattern. However, memory also has a pronounced effect on the sizes of the trees, even when the duration of the simulation is fixed. When the sizes of the trees are constrained to a constant value, memory in our processes has little direct effect on tree shapes, but can bias inference of the birth rate from trees. We compare simulated branching trees to phylogenetic trees from an outbreak of tuberculosis in Canada, and discuss the relevance of memory to this dataset