Phase transitions in fluids and biological systems

In this thesis, I consider systems from two seemingly different fields: fluid dynamics and microbial ecology. In these systems, the unifying features are the existences of global non-equilibrium steady states. I consider generic and statistical models for transitions between these global states, and I relate the model results with experimental data. A theme of this thesis is that these rather simple, minimal models are able to capture a lot of functional detail about complex dynamical systems. In Part I, I consider the transition between laminar and turbulent flow. I find that quantitative and qualitative features of pipe flow experiments, the superexponential lifetime and the splitting of turbulent puffs, and the growth rate of turbulent slugs, can all be explained by a coarse-grained, phenomenological model in the directed percolation universality class. To relate this critical phenomena approach closer to the fluid dynamics, I consider the transition to turbulence in the Burgers equation, a simplified model for Navier-Stokes equations. Via a transformation to a model of directed polymers in a random medium, I find that the transition to Burgers turbulence may also be in the directed percolation universality class. This evidence implies that the turbulent-to-laminar transition is statistical in nature and does not depend on details of the Navier-Stokes equations describing the fluid flow. In Part II, I consider the disparate subject of microbial ecology where the complex interactions within microbial ecosystems produce observable patterns in microbe abundance, diversity and genotype. In order to be able to study these patterns, I develop a bioinformatics pipeline to multiply align and quickly cluster large microbial metagenomics datasets. I also develop a novel metric that quantifies the degree of interactions underlying the assembly of a microbial ecosystem, particularly the transition between neutral (random) and niche (deterministic) assembly. I apply this metric to 16S rRNA metagenomic studies of 6 vertebrate gastrointestinal microbiomes and find that they assembled through a highly non-neutral process. I then consider a phase transition that may occur in nutrient-poor environments such as ocean surface waters. In these systems, I find that the experimentally observed genome streamlining, specialization and opportunism may well be generic statistical phenomena

The Use of Statistics in Experimental Physics

Most mathematicians are aware of the importance of statistics in biological sciences, business, and economics, but are less aware that statistics is used every day in experimental physics. This paper gives three interesting examples of how statistics plays a vital role in physics. These examples use the basic statistical tools of residuals analysis and goodness of fit

Characterization of the Fecal Microbiome from Non-Human Wild Primates Reveals Species Specific Microbial Communities

BACKGROUND: Host-associated microbes comprise an integral part of animal digestive systems and these interactions have a long evolutionary history. It has been hypothesized that the gastrointestinal microbiome of humans and other non-human primates may have played significant roles in host evolution by facilitating a range of dietary adaptations. We have undertaken a comparative sequencing survey of the gastrointestinal microbiomes of several non-human primate species, with the goal of better understanding how these microbiomes relate to the evolution of non-human primate diversity. Here we present a comparative analysis of gastrointestinal microbial communities from three different species of Old World wild monkeys. METHODOLOGY/PRINCIPAL FINDINGS: We analyzed fecal samples from three different wild non-human primate species (black-and-white colobus [Colubus guereza], red colobus [Piliocolobus tephrosceles], and red-tailed guenon [Cercopithecus ascanius]). Three samples from each species were subjected to small subunit rRNA tag pyrosequencing. Firmicutes comprised the vast majority of the phyla in each sample. Other phyla represented were Bacterioidetes, Proteobacteria, Spirochaetes, Actinobacteria, Verrucomicrobia, Lentisphaerae, Tenericutes, Planctomycetes, Fibrobacateres, and TM7. Bray-Curtis similarity analysis of these microbiomes indicated that microbial community composition within the same primate species are more similar to each other than to those of different primate species. Comparison of fecal microbiota from non-human primates with microbiota of human stool samples obtained in previous studies revealed that the gut microbiota of these primates are distinct and reflect host phylogeny. CONCLUSION/SIGNIFICANCE: Our analysis provides evidence that the fecal microbiomes of wild primates co-vary with their hosts, and that this is manifested in higher intraspecies similarity among wild primate species, perhaps reflecting species specificity of the microbiome in addition to dietary influences. These results contribute to the limited body of primate microbiome studies and provide a framework for comparative microbiome analysis between human and non-human primates as well as a comparative evolutionary understanding of the human microbiome

Robust computational analysis of rRNA hypervariable tag datasets

Next-generation DNA sequencing is increasingly being utilized to probe microbial communities, such as gastrointestinal microbiomes, where it is important to be able to quantify measures of abundance and diversity. The fragmented nature of the 16S rRNA datasets obtained, coupled with their unprecedented size, has led to the recognition that the results of such analyses are potentially contaminated by a variety of artifacts, both experimental and computational. Here we quantify how multiple alignment and clustering errors contribute to overestimates of abundance and diversity, reflected by incorrect OTU assignment, corrupted phylogenies, inaccurate species diversity estimators, and rank abundance distribution functions. We show that straightforward procedural optimizations, combining preexisting tools, are effective in handling large (10(5)-10(6)) 16S rRNA datasets, and we describe metrics to measure the effectiveness and quality of the estimators obtained. We introduce two metrics to ascertain the quality of clustering of pyrosequenced rRNA data, and show that complete linkage clustering greatly outperforms other widely used methods

Quantification of the relative roles of niche and neutral processes in structuring gastrointestinal microbiomes

The theoretical description of the forces that shape ecological communities focuses around two classes of models. In niche theory, deterministic interactions between species, individuals, and the environment are considered the dominant factor, whereas in neutral theory, stochastic forces, such as demographic noise, speciation, and immigration, are dominant. Species abundance distributions predicted by the two classes of theory are difficult to distinguish empirically, making it problematic to deduce ecological dynamics from typical measures of diversity and community structure. Here, we show that the fusion of species abundance data with genome-derived measures of evolutionary distance can provide a clear indication of ecological dynamics, capable of quantifying the relative roles played by niche and neutral forces. We apply this technique to six gastrointestinal microbiomes drawn from three different domesticated vertebrates, using high-resolution surveys of microbial species abundance obtained from carefully curated deep 16S rRNA hypervariable tag sequencing data. Although the species abundance patterns are seemingly well fit by the neutral theory of metacommunity assembly, we show that this theory cannot account for the evolutionary patterns in the genomic data; moreover, our analyses strongly suggest that these microbiomes have, in fact, been assembled through processes that involve a significant nonneutral (niche) contribution. Our results demonstrate that high-resolution genomics can remove the ambiguities of process inference inherent in classic ecological measures and permits quantification of the forces shaping complex microbial communities