Comparing Community Structure to Characteristics in Online Collegiate Social Networks

We study the structure of social networks of students by examining the graphs of Facebook "friendships" at five American universities at a single point in time. We investigate each single-institution network's community structure and employ graphical and quantitative tools, including standardized pair-counting methods, to measure the correlations between the network communities and a set of self-identified user characteristics (residence, class year, major, and high school). We review the basic properties and statistics of the pair-counting indices employed and recall, in simplified notation, a useful analytical formula for the z-score of the Rand coefficient. Our study illustrates how to examine different instances of social networks constructed in similar environments, emphasizes the array of social forces that combine to form "communities," and leads to comparative observations about online social lives that can be used to infer comparisons about offline social structures. In our illustration of this methodology, we calculate the relative contributions of different characteristics to the community structure of individual universities and subsequently compare these relative contributions at different universities, measuring for example the importance of common high school affiliation to large state universities and the varying degrees of influence common major can have on the social structure at different universities. The heterogeneity of communities that we observe indicates that these networks typically have multiple organizing factors rather than a single dominant one.Comment: Version 3 (17 pages, 5 multi-part figures), accepted in SIAM Revie

Spatiotemporal microbial evolution on antibiotic landscapes

This is the author accepted manuscript. The final version is available from the American Association for the Advancement of Science via the DOI in thisA key aspect of bacterial survival is the ability to evolve while migrating across spatially varying environmental challenges. Laboratory experiments, however, often study evolution in well-mixed systems. Here, we introduce an experimental device, the microbial evolution and growth arena (MEGA)-plate, in which bacteria spread and evolved on a large antibiotic landscape (120 × 60 centimeters) that allowed visual observation of mutation and selection in a migrating bacterial front.While resistance increased consistently, multiple coexisting lineages diversified both phenotypically and genotypically. Analyzing mutants at and behind the propagating front,we found that evolution is not always led by the most resistant mutants; highly resistant mutants may be trapped behindmore sensitive lineages.TheMEGA-plate provides a versatile platformfor studying microbial adaption and directly visualizing evolutionary dynamics.National Defense Science and Engineering Graduate fellowshipNIHEuropean Union FP

Nutrient levels and trade-offs control diversity in a serial dilution ecosystem

Microbial communities feature an immense diversity of species and this diversity is linked with outcomes ranging from ecosystem stability to medical prognoses. Yet the mechanisms underlying microbial diversity are under debate. While simple resource-competition models don't allow for coexistence of a large number of species, it was recently shown that metabolic trade-offs can allow unlimited diversity. Does this diversity persist with more realistic, intermittent nutrient supply? Here, we demonstrate theoretically that in serial dilution culture, metabolic trade-offs allow for high diversity. When a small amount of nutrient is supplied to each batch, the serial dilution dynamics mimic a chemostat-like steady state. If more nutrient is supplied, diversity depends on the amount of nutrient supplied due to an "early-bird" effect. The interplay of this effect with different environmental factors and diversity-supporting mechanisms leads to a variety of relationships between nutrient supply and diversity, suggesting that real ecosystems may not obey a universal nutrient-diversity relationship.Comment: Appendix follows main tex

Operon mRNAs are organized into ORF-centric structures that predict translation efficiency

Bacterial mRNAs are organized into operons consisting of discrete open reading frames (ORFs) in a single polycistronic mRNA. Individual ORFs on the mRNA are differentially translated, with rates varying as much as 100-fold. The signals controlling differential translation are poorly understood. Our genome-wide mRNA secondary structure analysis indicated that operonic mRNAs are comprised of ORF-wide units of secondary structure that vary across ORF boundaries such that adjacent ORFs on the same mRNA molecule are structurally distinct. ORF translation rate is strongly correlated with its mRNA structure in vivo, and correlation persists, albeit in a reduced form, with its structure when translation is inhibited and with that of in vitro refolded mRNA. These data suggest that intrinsic ORF mRNA structure encodes a rough blueprint for translation efficiency. This structure is then amplified by translation, in a self-reinforcing loop, to provide the structure that ultimately specifies the translation of each ORF

Deep diversification of an AAV capsid protein by machine learning.

Modern experimental technologies can assay large numbers of biological sequences, but engineered protein libraries rarely exceed the sequence diversity of natural protein families. Machine learning (ML) models trained directly on experimental data without biophysical modeling provide one route to accessing the full potential diversity of engineered proteins. Here we apply deep learning to design highly diverse adeno-associated virus 2 (AAV2) capsid protein variants that remain viable for packaging of a DNA payload. Focusing on a 28-amino acid segment, we generated 201,426 variants of the AAV2 wild-type (WT) sequence yielding 110,689 viable engineered capsids, 57,348 of which surpass the average diversity of natural AAV serotype sequences, with 12-29 mutations across this region. Even when trained on limited data, deep neural network models accurately predict capsid viability across diverse variants. This approach unlocks vast areas of functional but previously unreachable sequence space, with many potential applications for the generation of improved viral vectors and protein therapeutics

Diversity, stability, and evolvability in models of early evolution

Based on the RNA world hypothesis, we outline a possible evolutionary route from infrabiological systems to early protocells. To assess the scientific merits of the different models of prebiotic evolution and to suggest directions for future research, we investigate the diversity-maintaining ability, evolutionary/ecological stability, and evolvability criteria of existing RNA world model systems for the origin of life. We conclude that neither of the studied systems satisfies all of the aforementioned criteria, although some of them are more convincing than the others. Furthermore, we found that the most conspicuous features of the proposed prebiotic evolutionary scenarios are their increasing spatial inhomogeneity along with increasing plasticity, evolvability, and functional diversity. All of these characteristics change abruptly with the emergence of the protocells

Directed evolution of multiple genomic loci allows the prediction of antibiotic resistance

Antibiotic development is frequently plagued by the rapid emergence of drug resistance. However, assessing the risk of resistance development in the preclinical stage is difficult. Standard laboratory evolution approaches explore only a small fraction of the sequence space and fail to identify exceedingly rare resistance mutations and combinations thereof. Therefore, new rapid and exhaustive methods are needed to accurately assess the potential of resistance evolution and uncover the underlying mutational mechanisms. Here, we introduce directed evolution with random genomic mutations (DIvERGE), a method that allows an up to million-fold increase in mutation rate along the full lengths of multiple predefined loci in a range of bacterial species. In a single day, DIvERGE generated specific mutation combinations, yielding clinically significant resistance against trimethoprim and ciprofloxacin. Many of these mutations have remained previously undetected or provide resistance in a species-specific manner. These results indicate pathogen-specific resistance mechanisms and the necessity of future narrow-spectrum antibacterial treatments. In contrast to prior claims, we detected the rapid emergence of resistance against gepotidacin, a novel antibiotic currently in clinical trials. Based on these properties, DIvERGE could be applicable to identify less resistance-prone antibiotics at an early stage of drug development. Finally, we discuss potential future applications of DIvERGE in synthetic and evolutionary biology

Systematic approaches to deciphering genes and ecosystems

In this thesis I investigate how the individual components of biological systems interact, and how the form of these interactions determines overall system behavior. The interactions I study occur at widely different scales: from whole ecosystems to individual genes. Despite these differences, the approaches I use share similarities: in both realms I apply systematic experimental perturbations, then utilize mathematical and computational tools to identify novel properties of these interactions and to interpret their importance. In the first study, I examine how 3-way species interactions affect ecosystems dynamics. Ecological models typically assume that interactions occur among pairs of species. While higher-order interactions among greater numbers of species are thought to occur in natural ecosystems, the strength and overall importance of these interactions for ecosystem behavior has been unclear. My study focuses on species interactions mediated by antibiotic toxins, which either inhibit or kill antibiotic sensitive species. Here I develop a quantitative 3-way interaction assay to measure how the inhibition of a sensitive species by an antibiotic producer is affected by the presence of a third “modulator” species. Systematically testing combinations of species and antibiotics, I find that antibiotic degradation by the modulator species frequently attenuates the inhibition of the sensitive species. I then use simulations and mathematical models to show that such 3-way interactions can dramatically alter ecosystem dynamics. Ecosystems of antibiotic producing, sensitive and resistant species are thought to coexist only when they are spatially separated in the environment, but these conclusions are based on models that assume pairwise species interactions. Surprisingly, I find that the 3-way interactions created by the counteraction of antibiotic production and degradation enable coexistence even in well-mixed environments. These findings are robust to choices of parameters and modeling assumptions, and shed light on the role of antibiotic production and degradation in maintaining the diversity of natural microbial communities. In the second study, I shift to the molecular scale and develop strategies for deciphering multiple protein and mRNA selective pressures that affect gene function. Recent technological advances in “Mutagenomics”, i.e. large-scale mutagenesis and phenotyping, have enabled the systematic mapping of fitness landscapes. Current challenges include the difficulty of applying such methods to essential genes, which control many core biological processes, and also the problem of interpreting high-dimensional fitness landscapes in terms of sensible biochemical properties. Here I present advances on both fronts: first by developing MAGE-seq, a high-throughput method that combines genome engineering with a DNA sequencing-based assay to enable rapid measurement of fitness landscapes anywhere on the Escherichia coli genome. Second, I describe methods of analysis to identify key properties that determine gene fitness through a case study of infA, an essential translation initiation factor (IF1). At the protein level, I find that selection is determined primarily by amino acid properties like hydrophobicity, flexibility, size and charge, and I relate these properties to protein folding and function; at the mRNA level, I show that selection is strongest in the early regions of the gene, where codon preferences are determined by the formation of RNA hairpins containing the start codon and by the avoidance of deleterious motifs such as out-of-frame start codons. Disruption of this optimal RNA structure determines codon preferences in later regions of the gene, suggesting that gene evolution is constrained by both mRNA and protein properties. Together these experimental and analytical methods make it possible to systematically identify and engineer the key properties of biological function genome-wide.Systems Biolog