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Cells, the fundamental unit of life, exist in an ever-changing environment. The ability to sense the environment and decide on a suitable strategy is therefore one of the most important cellular functions. In this dissertation, I will describe two examples of signal transmission and processing that utilize ions. In Chapter 2, I will demonstrate how Bacillus subtilis cells self-organize to optimize longe-range signal transmission, in a striking example of statistical physics informing multilevel cellular behavior. In Chapter 3, I will show how B. subtilis spores use stored ions to integrate external signals to decide whether to revive from dormancy. Taken together, these examples show the integral role of ions in biological signaling: weaving communication wires that span thousands of cells, and working to reignite life from a complete stop.

Proper morphogenesis requires the regulated interplay between cellular behaviors and physical constraints. Studies of physiology and morphogenesis in protist and animal systems with respect to this interplay have led to important mechanistic insights. Comparatively little work, however, has studied how morphogenetic processes evolve in the context of physical constraints. Choanoflagellates, the closest living relatives of animals, can form multicellular colonies of various shapes and sizes. This diversity and the simplicity of multicellular forms in conjunction with their important phylogenetic position makes choanoflagellates an ideal system for studying the evolution of morphogenesis. Because most work has focused on genetics and genomics, little is known about the cellular and biophysical mechanisms underlying the regulation of multicellular form in choanoflagellatesHere, I quantify the biophysical processes underlying the morphogenesis of rosette colonies in the choanoflagellate Salpingoeca rosetta. I find that rosettes reproducibly transition from an early stage of 2D growth to a later stage of 3D growth, despite the underlying stochasticity of the cell lineages. I postulate that the extracellular matrix (ECM) exerts a physical constraint on the packing of proliferating cells, thereby sculpting rosette morphogenesis. Perturbative experiments coupled with biophysical simulations demonstrate the fundamental importance of a basally-secreted ECM for rosette morphogenesis. In addition, this yields a morphospace for the shapes of multicellular colonies, consistent with observations across a range of choanoflagellates. Overall, this biophysical perspective on rosette development complements previous genetic perspectives and thus helps illuminate the interplay between cell biology and physics in regulating morphogenesis.I also present the previously undescribed species Choanoeca flexa, a splash pool choanoflagellate that forms cup-shaped colonies. The colonies rapidly invert their curvature in response to changing light levels, which they detect through a rhodopsin-cGMP pathway. Inversion is mediated by cell shape changes requiring actomyosin-mediated apical contractility and allows alternation between feeding and swimming behavior. C. flexa thus rapidly converts sensory inputs directly into multicellular contractions. In this respect, it may inform reconstructions of hypothesized animal ancestors that existed before the evolution of specialized sensory and contractile cells.

Each cell must achieve the dual tasks of replicating the genetic material and dividing its components into its daughters. This thesis is concerned with how the cell coordinates those fundamental requirements. It approaches this question from the perspective of bacterial physiology, looking at the global relationship between these processes and growth at large. The thesis aims to uncover and test quantitative principles which govern this physiological orchestration. We first review the early quantitative principles, or growth laws, and their modern expansions. This includes cell size control, its connection with the cell cycle, and homeostatic control of each. We then explore these principles in the context of the model organisms E. coli and B. subtilis. Despite their evolutionary distance, we find the two species to be fundamentally similar in their physiological control. Finally, we test the limits of replication and division coordination using single-cell measurements and nutrient-shift experiments. This leads us to reevaluate the canonical Helmstetter-Cooper cell cycle model. Instead of a strict dependency of division on replication initiation, we instead support the view that both processes are controlled by independent threshold mechanisms.

Proteins are complex macromolecules whose structure informs their function and regulation in difficult to predict ways. Understanding their shape, dynamics, and regulation all pose major challenges in terms of collecting, analyzing, and interpreting data. In my dissertation I describe two contributions to data analysis for determining the structure and dynamics of proteins using novel approaches, as well as experimental work querying the function of an enzyme with a particularly recalcitrant substrate. In the first chapter of this dissertation, I develop a tool, EMRinger, for the emerging field of high resolution electron microscopy that takes advantage of prior physical information about model geometry to more effectively determine if the model is built correctly into the map. This work adapted the tool Ringer, which had been previously developed in the Alber lab, in order to identify the dihedral angle for side chains with the greatest density, and confirm that the distribution of those peak positions does not violate the constraints of side chain dihedral angles to rotameric positions. This approach allows for orthogonal validation of backbone position in density (using the side chain density as a “lever”), which generally improves with refinement and is among the most sensitive model-in-map validation tools available for high resolution electron microscopy.In the second chapter, I present progress on applying temperature jumps to folded proteins to quantify kinetics of the intrinsic motions in proteins that impact their function and regulation. Using a pulsed infrared laser, we raise the temperature of a protein solution in nanoseconds, and follow the progression of the structure of the protein using solution x-ray scattering. As the protein changes temperature, the conformational equilibrium of the protein shifts as higher energy states become more accessible. By following this progress over the nanoseconds, microseconds and milliseconds following the heating pulse, we are able to reconstruct the relaxation of the protein into its new comformational equilibrium. With this information, we can gain kinetic information about the conformational landscapes of our sample, and using mutations we correlate the rates we observe with existing structural models of dynamics that have been characterized by x-ray crystallography. In the third chapter, I investigate the mechanics of Acidic Mammalian Chitinase, which has the role of breaking down the recalcitrant polysaccharide chitin in the stomach and lungs of mammals. Mutations to acidic mammalian chitinase have previously been identified that lead to either protection against allergic asthma, and previous work has determined that these mutations lead to an increase in the activity of the enzyme. In order to better determine how these mutations affect activity, I developed new methods to assay chitinase activity. I use these methods to characterize the effects of the asthma-associated mutations, as well as investigating the role of the individual domains of acidic mammalian chitinase in degrading crystalline chitin and the differences in behavior of acidic mammalian chitinase and the other chitinase expressed in mammals, chitotriosidase. Additionally, I attempt to engineer hyperactive chitinases, and show that direct evolution based on screening with traditional fluorogenic oligomer substrates does not necessarily lead effectively to enzymes which are hyperactive against complex substrate, emphasizing the need for sensitive and high throughput methods to quantify degradation of crystalline chitin.

The actomyosin cytoskeleton is a crucial driver of morphogenesis. Yet how the behavior of large-scale cytoskeletal patterns in deforming tissues emerges from the interplay of geometry, genetics, and mechanics remains incompletely understood. Convergent extension in Drosophila melanogaster embryos provides the opportunity to establish a quantitative understanding of the dynamics of anisotropic non-muscle myosin II. Cell-scale analysis of protein localization in fixed embryos suggests that gene expression patterns govern myosin anisotropy via complex rules. However, technical limitations have impeded quantitative and dynamic studies of this process at the whole embryo level, leaving the role of geometry open. Here, we combine in toto live imaging with quantitative analysis of molecular dynamics to characterize the distribution of myosin anisotropy and the corresponding genetic patterning. We found pair rule gene expression continuously deformed, flowing with the tissue frame. In contrast, myosin anisotropy orientation remained approximately static and was only weakly deflected from the stationary dorsal-ventral axis of the embryo. We propose that myosin is recruited by a geometrically defined static source, potentially related to the embryo-scale epithelial tension, and account for transient deflections by cytoskeletal turnover and junction reorientation by flow. With only one parameter, this model quantitatively accounts for the time course of myosin anisotropy orientation in wild-type, twist, and even-skipped embryos, as well as embryos with perturbed egg geometry. Geometric patterning of the cytoskeleton suggests a simple physical strategy to ensure a robust flow and formation of shape.

Wound healing begins as soon as the wound occurs and can continue for days, weeks, or even months. Although decades of research have established the multiple-steps required to heal a wound, little is known about the cellular response immediately following the wound. Furthermore, because the location of the wound is relevant to the healing process, healthy cells responding to a wound require positional information, or knowing where they are in relation to the wound. Thus, there are two important questions not fully understood in wound healing: 1. How do cells know where they are? and 2. How do the initial signaling mechanisms influence downstream genetic expression and overall healing? Here, using recent technological advancements in single-cell fluorescent microscopy and fluorescent biomarkers, we determine that ATP, an initial signaling molecule released from wounded cells, creates spatial patterns immediately following an epithelial wound using a simple Release and Diffusion mechanism. Then we determine that epithelial cells use paracrine signaling to spatially average the initial wound response signals over a specific distance to maximize the signal-to-noise ratio. Finally, we establish a method titled SpaSeq (Spatial Sequencing) that utilizes FACS and RNA-Seq in addition to fluorescent proteins to measure the spatio-temporal gene expression patterns following a wound.

Homolog transcription factors have emerged from genome duplication events in evolutionary history, resulting in the co-existence of many similar proteomic pairs in eukaryotic cells. Although a large number of homologs have diversified over time to obtain unique roles from their partners, others have remained relatively conserved, limiting their downstream interactions to identical target genes. The functional advantages arising from the retention of such redundancy in the proteome have remained elusive in the context of biological design. Here, using quantitative single-cell imaging and microfluidics, we find that the yeast general stress response transcription factor Msn2 and its seemingly redundant homolog Msn4 play distinct roles in mediating the gene expression in response to environmental stressors, which is contrary to previous belief. We observe at the single cell level, that although Msn4 exhibits identical translocation dynamics to Msn2, the level of its nuclear translocation during stress is significantly more heterogeneous than that of Msn2. As a result, while target genes with fast kinetics are expressed in most of the cells, slower target genes are induced only in the fraction of cells with high Msn4 activity. We further show that this Msn4-dependent diversification in the gene expression program in single cells is important for the adaptive response to environmental stresses.

Microfluidic technologies have allowed modern microbiologists the opportunity to explore biological phenomenon in ground-breaking new ways. The rapid prototyping capability of standard polydimethyl-siloxane (PDMS) microfluidic devices has put the design and optimization of these innovative new tools directly into the hands of the microbiologists who are using them. This has caused a dramatic surge in the utilization of these devices by biological researchers in the past several years. In this dissertation I present original research in which microfluidic technologies were used to advance quantitative microbiology. To provide context, in chapter 1 I highlight several key areas of research and seminal previous works where microfluidic technologies have expanded the horizon for microbial exploration. In the chapters that follow I present 3 separate bodies of original research. In chapter 2 I present a study where a novel synthetic gene oscillator is entrained to environmental oscillations through an engineered fitness advantage. In this particular study, microfluidic devices allowed for the monitoring and quantitative analysis of individual genetically engineered E. coli. In chapter 3 I present a microfluidic platform for quantitatively studying motility phenotypes in arrayed chambers of microorganisms. In chapter 4 I present a microfluidic platform for improved bacterial antibiotic susceptibility testing.

Nanoparticles have had a large impact and driven a growing commercial industry of nano-enabled products. Nanoparticles’ small size causes large changes in physicochemical properties compared to their “bulk” states, and this has been utilized for a wide variety of applications, ranging from biomedical to electronic, to cosmetic use. However, these novel properties also carry with them unknown effects on our environment. Numerous laboratory studies have found deleterious effects of nanoparticles on aquatic organisms, but we are missing key knowledge on how these toxic effects amplify in ecological systems. The aim of my dissertation is to investigate the effects of nanoparticles on freshwater systems through a series of experiments on phyto- and zooplankton and the development of quantitative models to explain these empirical results and extrapolate effects to other systems. My work has identified the importance of ecological feedbacks in nanotoxicology, specifically two novel feedbacks: algae produce dissolved organic carbon (DOC) that mitigates the toxicity of nanomaterials (silver and iron nanoparticles) to the cells themselves and a concentration of silver nanoparticles (AgNPs) that is toxic to individual Daphnia has no effect on populations of zooplankton due to population-level feedbacks. The development of quantitative models of both of these feedbacks has enabled the estimation of the strength of both nanomaterial toxicity and of the mitigating feedback. Through the development of models of algal growth, DOC production, and nanoparticle toxicity, I estimated the strength of both the inactivation of toxicity by DOC as well as the toxic strength of the various contributors to nanotoxicity (nano versus ionic, different transformations of nanoparticles). Estimating the relative contributions of different forms or products of nanoparticles to their overall toxic effect can be useful in ecological risk assessment, as it could identify toxic factors that are already regulated (such as ionic silver) along with those that are currently unregulated (nanosilver) and allow for direct comparison of their toxicity. The development of models of daphnid growth and reproduction, parameterized with individual-level data of AgNP exposure of Daphnia at multiple food rations, allowed me to identify the feedback that seemingly disrupts extrapolation between levels of biological organization. A concentration of AgNPs that is toxic to individual Daphnia has no effect on small populations of Daphnia due to population-level feedbacks in which the zooplankton population equilibrates at a lower consumer and higher resource biomass. This increase in the amount of food per individual allows the zooplankters to survive AgNP exposure. Overall, my dissertation work highlights the importance of ecological and environmental complexity when estimating the impacts of nanoparticles on freshwater systems.

Protein mass is a major constituent of bacterial cell dry weight. An exponentially growing cell diverts most of its nutrient uptake to protein synthesis. The optimal allocation of a cell's total proteins, or proteome, to various cellular functions is a major concern for the cell growing in different growth conditions. Obligatory relations between ribosomal proteome fraction and growth rate impose further constraints on proteome resources allocation. In this dissertation, I am interested in understanding the global strategy of proteome resources allocation and exploring its physiological consequences in E. coli. To understand the global strategy of proteome allocation, we apply a series of metabolic challenges to probe the responses of the proteome of exponentially growing E. coli using quantitative mass spectrometry. Despite the enormous complexity in the details of the underlying regulatory network, the proteome partitions into several coarse-grained sectors whose total abundances exhibit linear relations with the growth rate. These growth-rate dependent proteome fractions comprise about half of the proteome by mass, and their mutual dependencies can be characterized quantitatively by a simple proteome-based flux model involving only two effective parameters. The success and apparent generality of this simple model is due to the tight coordination between proteome partition and metabolism, suggesting a principle for resource-allocation in proteome economy. This general strategy of global gene regulation should serve as a basis for future studies on gene expression and constructing synthetic biological circuits. We also show that proteome resources allocation has important consequences in cell physiology. An important yet mysterious phenomenon in bacterial physiology is the overflow metabolism, where the cell excretes acetate in the case of E. coli. We observed a set linear relations between acetate excretion rate and growth rate under different modes of growth limitation. By including both the carbon and proteome resources in a simple resource allocation model, we can quantitatively describe the observed linear relations. Key model parameters of protein costs were determined directly using flux analysis and protein mass spectrometry measurements. These results suggest optimal allocation of carbon and proteome resources as a possible driving force for the occurrence of overflow metabolism.

Understanding gene regulation is of central importance to biology because controlling when, where, and how genes are expressed accounts for a large proportion of the complexity and organization of living systems. While for many years RNA was largely thought of as the middleman for converting genetic information into biochemically active proteins, it has become increasingly clear that RNAs are also major players gene regulation. This thesis examines the functional properties of regulatory RNAs in the context of bacterial RNA networks. More specifically, the three studies presented here examine signaling dynamics and crosstalk within the CsrA and Hfq-dependent small RNA regulatory systems; each of these systems (i) contain non-coding RNAs which regulate the translation of multiple target messenger RNAs, (ii) have central roles in coordinating their cells' adaptation to environmental change, and (iii) are evolutionarily conserved across a wide range of bacterial species. In this work, a combination of synthetic biology and mathematical modeling are brought to bear to uncover the operational behavior and potential of the these two model RNA networks.

Copyright ©2015 by Annual Reviews. All rights reserved. Single-cell techniques have a long history of unveiling fundamental paradigms in biology. Recent improvements in the throughput, resolution, and availability of microfluidics, computational power, and genetically encoded fluorescence have led to a modern renaissance in microbial physiology. This resurgence in research activity has offered new perspectives on physiological processes such as growth, cell cycle, and cell size of model organisms such as Escherichia coli. We expect these single-cell techniques, coupled with the molecular revolution of biology's recent half-century, to continue illuminating unforeseen processes and patterns in microorganisms, the bedrock of biological science. In this article we review major open questions in single-cell physiology, provide a brief introduction to the techniques for scientists of diverse backgrounds, and highlight some pervasive issues and their solutions. ©

Recent work has focused attention on statistical inference for the population distribution of the number of sexual partners based on survey data. The characteristics of these distributions are of interest as components of mathematical models for the transmission dynamics of sexually-transmitted diseases (STDs). Such information can be used both to calibrate theoretical models, to make predictions for real populations, and as a tool for guiding public health policy. Our previous work on this subject has developed likelihood-based statistical methods for inference that allow for low-dimensional, semi-parametric models. Inference has been based on several proposed stochastic process models for the formation of sexual partnership networks. We have also developed model selection criteria to choose between competing models, and assessed the fit of different models to three populations: Uganda, Sweden, and the USA. Throughout this work, we have emphasized the correct assessment of the uncertainty of the estimates based on the data analyzed. We have also widened the question of interest to the limitations of inferences from such data, and the utility of degree-based epidemiological models more generally. In this paper we address further statistical issues that are important in this area, and a number of confusions that have arisen in interpreting our work. In particular, we consider the use of cumulative lifetime partner distributions, heaping and other issues raised by Liljeros et al. in a recent working paper., Comment: 22 pages, 5 figures in linked working paper

The fractal properties of the total potential energy V as a function of time t are studied for a number of systems, including realistic models of proteins (PPT, BPTI and myoglobin). The fractal dimension of V(t), characterized by the exponent \gamma, is almost independent of temperature and increases with time, more slowly the larger the protein. Perhaps the most striking observation of this study is the apparent universality of the fractal dimension, which depends only weakly on the type of molecular system. We explain this behavior by assuming that fractality is caused by a self-generated dynamical noise, a consequence of intermode coupling due to anharmonicity. Global topological features of the potential energy landscape are found to have little effect on the observed fractal behavior., Comment: 17 pages, single spaced, including 12 figures