Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2016. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 139-156). / Microbes are tiny metabolic engines with large-scale effects on industry, the environment, and human health. Understanding how the micron-scale actions (and interactions) of individual microbes give rise to macro-scale consequences remains a major challenge in microbial ecology. However, for the most part, studies employ coarsegrained sampling schemes, which average over the heterogeneous microscopic structure of microbial communities. This has limited our ability to establish mechanistic links between dynamics occurring across these disparate spatial scales. However, such links are critical for (a) making sense of the tremendous extant microbial diversity on Earth, and (b) predicting how perturbations (e.g., global climate change) may influence microbial diversity and function. In this thesis, I characterize the structure and dynamics of wild bacterial populations in the ocean at spatial scales of tens of microns. I then employ a simple, two-strain laboratory model system to link (cooperative) inter-species interactions at local scales to emergent properties at larger scales, focusing on spatially connected meta-communities undergoing range expansions into new territory. This work encompasses diverse environments (ranging from well-mixed communities in the laboratory to individual crustaceans) and approaches (including mathematical modeling, highthroughput sequencing, and traditional microbiological experiments). Altogether, we find that the microscale environment inhabited by a microbe - that is, "what the neighborhood is like" and "who lives next to whom" - shapes the structure and dynamics of wild microbial populations at local scales. Moreover, these local interactions can drive patterns of biodiversity and function, even at spatial scales much larger than the length of an individual cell. Thus, our work represents a small step toward developing mechanistic theories for how microbes shape our planet's ecosystems. / by Manoshi Sen Datta. / Ph. D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Computational and Systems Biology Program, 2013. / Cataloged from PDF version of thesis. "September 2013." Page 126 blank. / Includes bibliographical references. / The cholinergic innervation of the neocortex by afferent fibers originating in the nucleus basalis (NB) of the basal forebrain is implicated in modulating diverse neocortical functions including information processing, cortical plasticity, arousal and attention. To understand the physiological basis of these brain functions during cholinergic modulation, it is critical to identify the cortical circuit elements involved and define how their interactions contribute to cortical network dynamics. In this thesis, I present evidence showing how specific neuronal and glial cell types can be differentially modulated by acetylcholine (Ach), resulting in dynamic functional interactions during ACh-modulated information processing and cortical plasticity. Chapter 2 identifies somatostatin-expressing neurons as a dominant player in driving decorrelation and information processing through its intimate interactions with parvalbumin-expressing and pyramidal neurons. Chapter 3 highlights astrocytes and their interactions with pyramidal neurons as important drives for stimulus-specific cortical plasticity during cholinergic modulation. This is one of the earliest works that has mapped the functional role of distinct cell-types and their interactions to specific brain functions modulated by ACh, thereby setting the foundation for future studies to manipulate these specific functional interactions in both normal and diseased brains. / by Naiyan Chen. / Ph.D.

Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 153-164). / Protein-protein interactions (PPIs) play a major role in cellular function, mediating signal processing and regulating enzymatic activity. Understanding how proteins interact is essential for predicting new binding partners and engineering new functions. Mutational analysis is one way to study the determinants of protein interaction. Traditionally, the biophysical study of protein interactions has been limited by the number of mutants that could be made and analyzed, but advances in high-throughput sequencing have enabled rapid assessment of thousands of variants. The Keating lab has developed an experimental protocol that can rank peptides based on their binding affinity for a designated receptor. This technique, called SORTCERY, takes advantage of cell sorting and deep-sequencing technologies to provide more binding data at a higher resolution than has previously been achievable. New computational methods are needed to process and analyze the high-throughput datasets. In this thesis, I show how experimental data from SORTCERY experiments can be processed, modeled, and used to design novel peptides with select specificity characteristics. I describe the computational pipeline that I developed to curate the data and regression models that I constructed from the data to relate protein sequence to binding. I applied models trained on experimental data sets to study the peptide-binding specificity landscape of the Bc1-xL, Mc1-1, and Bf1-1 anti-apoptotic proteins, and I designed novel peptides that selectively bind tightly to only one of these receptors, or to a pre-specified combination of receptors. My thesis illustrates how data-driven models combined with high-throughput binding assays provide new opportunities for rational design. / by Vincent Xue. / Ph. D.

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No description available.

Understanding how neurons in the primary visual cortex (V1) of primates respond to visual patterns has been a major focus of research in neuroscience for many decades. Numerous different experimental techniques have been used to provide data about how the spatiotemporal patterns of light projected from the visual environment onto the retina relate to the spatiotemporal patterns of neural activity evoked in the visual cortex, across disparate spatial and temporal scales. However, despite the variety of data sources available (or perhaps because of it), there is still no unified explanation for how the circuitry in the eye, the subcortical visual pathways, and the visual cortex responds to these patterns. This thesis outlines a research project to build computational models of V1 that incorporate observations and constraints from an unprecedented range of experimental data sources, reconciling each data source with the others into a consistent proposal for the underlying circuitry and computational mechanisms. The final mechanistic model is the first one shown to be compatible with measurements of: (1) temporal firing-rate patterns in single neurons over tens of milliseconds obtained using single-unit electrophysiology, (2) spatiotemporal patterns in membrane voltages in cortical tissues spanning several square millimeters over similar time scales, obtained using voltage-sensitive-dye imaging, and (3) spatial patterns in neural activity over several square millimeters of cortex, measured over the course of weeks of early development using optical imaging of intrinsic signals. Reconciling this data was not trivial, in part because single-unit studies suggested short, transient neural responses, while population measurements suggested gradual, sustained responses. The fundamental principles of the resulting models are (a) that the spatial and temporal patterns of neural responses are determined not only by the particular properties of a visual stimulus and the internal response properties of individual neurons, but by the collective dynamics of an entire network of interconnected neurons, (b) that these dynamics account both for the fast time course of neural responses to individual stimuli, and the gradual emergence of structure in this network via activity-dependent Hebbian modifications of synaptic connections over days, and (c) the differences between single-unit and population measurements are primarily due to extensive and wide-ranging forms of diversity in neural responses, which become crucial when trying to estimate population responses out of a series of individual measurements. The final model is the first to include all the types of diversity necessary to show how realistic single-unit responses can add up to the very different population-level evoked responses measured using voltage-sensitive-dye imaging over large cortical areas. Additional contributions from this thesis include (1) a comprehensive solution for doing exploratory yet reproducible computational research, implemented as a set of open-source tools, (2) a general-purpose metric for evaluating the biological realism of model orientation maps, and (3) a demonstration that the previous developmental model that formed the basis of the models in this thesis is the only developmental model so far that produces realistic orientation maps. These analytical results, computational models, and research tools together provide a systematic approach for understanding neural responses to visual stimuli across time scales from milliseconds to weeks and spatial scales from microns to centimeters.

Mak, Yan Kei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 59-63). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.6 / Chapter 1.1 --- Historical background --- p.6 / Chapter 1.2 --- Why study communication complexity? --- p.7 / Chapter 1.3 --- Main ideas and results --- p.8 / Chapter 1.4 --- Recent development --- p.12 / Chapter 1.5 --- Structure of the thesis --- p.12 / Chapter 2 --- Deterministic Communication Complexity --- p.13 / Chapter 2.1 --- Definitions --- p.13 / Chapter 2.2 --- Tiling lower bound --- p.16 / Chapter 2.3 --- Fooling set lower bound --- p.21 / Chapter 2.4 --- Rank lower bound --- p.24 / Chapter 2.5 --- Comparison of the bounds --- p.27 / Chapter 3 --- Nondeterministic Communication Complexity --- p.29 / Chapter 3.1 --- Definitions --- p.29 / Chapter 3.2 --- "Gaps between N0(f), N1(f) and D(f)" --- p.31 / Chapter 3.3 --- Aho-Ullman-Yannakakis Theorem --- p.33 / Chapter 4 --- Randomized Communication Complexity --- p.38 / Chapter 4.1 --- Preliminaries --- p.38 / Chapter 4.2 --- Definitions --- p.39 / Chapter 4.3 --- Error reduction --- p.41 / Chapter 4.4 --- Exponential gap with D(f) --- p.42 / Chapter 4.5 --- The public coin model --- p.44 / Chapter 4.6 --- Distributional complexity --- p.46 / Chapter 5 --- Communication Complexity Classes --- p.51 / Chapter 5.1 --- Basic classes --- p.51 / Chapter 5.2 --- Polynomial-time hierarchy --- p.52 / Chapter 5.3 --- Reducibility and completeness --- p.53 / Chapter 6 --- Further topics --- p.56 / Chapter 6.1 --- Quantum communication complexity --- p.56 / Chapter 6.2 --- More techniques for bounds --- p.57 / Chapter 6.3 --- Complexity of communication complexity --- p.57 / Bibliography --- p.59

Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, February 2016. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 99-110). / The vast community of microbes that inhabit the human body, the human microbiota, is important to human health and disease. These microbes contribute to human metabolism, the development of the immune system and pathogen resistance, while imbalances among them have been associated with several diseases. In this work, I develop computational methods to gain key insights into the ecological principles that shape these communities. In the first chapter, I develop an evolutionary rate heuristic that leads to the discovery of a massive network of recently exchanged genes, connecting diverse bacteria throughout the human microbiota. Using this network, I examine the roles of phylogenetic distance, geographic proximity and ecological overlap in shaping rates of horizontal gene transfer. Of these factors, ecological similarity is the principal force shaping gene exchange. In the second chapter, I focus on the microbial communities within a person, identifying the factors that affect the stability of the human microbiota. Alpha-diversity is strongly correlated with stability, but the direction of this correlation changes depending on the body site or subject being examined. In contrast, beta-diversity is consistently negatively correlated to stability. I show that a simple equilibrium model explains these results and accurately predicts the correlation between diversity and stability in every body site, thus reconciling these seemingly contradictory relationships into a single model. In the final chapter, I explore the use of fecal microbiota transplantation (FMT) to treat recurrent Clostridium difficile infection. I develop a new method to infer the genotypes and frequencies of bacterial strains in metagenomics samples. I apply this method to a dataset covering twenty patients before and after FMT, uncovering key ecological rules that govern the colonization and growth of bacteria in human subjects after FMT. / by Christopher Scott Smillie. / Ph. D.

Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 210-226). / DNA methylation is an epigenetic mark that is highly conserved and important in diverse cellular processes, ranging from transposon silencing to genomic imprinting. In plants, DNA methylation is both mitotically and meiotically heritable, and changes in DNA methylation can be generationally stable and have long-lasting consequences. This thesis aims to improve understanding of DNA methylation dynamics in plants, particularly across generations and during reproduction. In the first project, I present an analysis of the generational dynamics of gene body methylation using recombinant inbred lines derived from differentially methylated parents. I show that while gene body methylation is highly generationally stable, changes in methylation state occur nonrandomly and are enriched in regions of intermediate methylation. / Important DNA methylation changes also occur during seed development in flowering plants, and these changes underlie genomic imprinting, the phenomenon of parent-of-origin specific gene expression. In plants, imprinting occurs in the endosperm, a seed tissue that functions analogously to the mammalian placenta. Imprinted expression is linked to DNA methylation patterns that serve to differentiate the maternally- and paternally-inherited alleles, but the mechanisms used to achieve imprinted expression are often unknown. I next explore imprinted expression and DNA methylation in Arabidopsis lyrata, a close relative of the model plant Arabidopsis thaliana. I find that the majority of imprinted genes in A. lyrata endosperm are also imprinted in A. thaliana, suggesting that imprinted expression is generally conserved. Surprisingly, a subset of A. lyrata imprinted genes are associated with a novel DNA methylation pattern and may be regulated by a different mechanism than their A. / thaliana counterparts. I then explore the genetics of paternal suppression of the seed abortion phenotype caused by mutation of a maternally expressed imprinted gene. Finally, I present the first large single-nuclei RNA-seq dataset generated in plants, reporting data from 1,093 individual nuclei obtained from developing seeds. I find evidence of previously uncharacterized cell states in endosperm, and examine imprinted expression at the single-cell level. Together, these projects contribute to our understanding of DNA methylation and imprinting dynamics during plant development, and highlight the strong generational stability of certain DNA methylation patterns. / by Colette Lafontaine Picard. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Computational and Systems Biology Program

The inverse geothermal problem consist of estimating the temperature distribution below the earth’s surface using temperature and heat-flux measurements on the earth’s surface. The problem is important since temperature governs a variety of the geological processes including formation of magmas, minerals, fosil fuels and also deformation of rocks. Mathematical this problem is formulated as a Cauchy problem for an non-linear elliptic equation and since the thermal properties of the rocks depend strongly on the temperature, the problem is non-linear. This problem is ill-posed in the sense that it does not satisfy atleast one of Hadamard’s definition of well-posedness. We formulated the problem as an ill-posed non-linear operator equation which is defined in terms of solving a well-posed boundary problem. We demonstrate existence of a unique solution to this well-posed problem and give stability estimates in appropriate function spaces. We show that the operator equation is well-defined in appropriate function spaces. Since the problem is ill-posed, regularization is needed to stabilize computations. We demostrate that Tikhonov regularization can be implemented efficiently for solving the operator equation. The algorithm is based on having a code for solving a well- posed problem related to the operator equation. In this study we demostrate that the algorithm works efficiently for 2D calculations but can also be modified to work for 3D calculations.