Browsing by Author "Hartemink, Alexander J"

Vocal learning is the ability modify vocal output based on auditory input and is the basis of human speech acquisition. It is shared by few distantly related bird and mammal orders, and is thus very likely to be an example of convergent evolution, having evolved independently in multiple lineages. This complex behavior is presumed to require networks of regulated genes to develop the necessary neural circuits for learning and maintaining vocalizations. Deciphering these networks has been limited by the lack of high throughput genomic tools in vocal learning avian species and the lack of a solid computational framework to understand the relationship between gene expression and behavior. This dissertation provides new insights into the evolution and mechanisms of vocal learning by taking a top-down, systems biology approach to understanding gene expression regulation across avian and mammalian species. First, I worked with colleagues to develop a zebra finch Agilent oligonucleotide microarray, including developing programs for more accurate annotation of oligonucleotides and genes. I then used these arrays and tools in multiple collaborative, but related projects, to measure transcriptome expression data in vocal learning and non-learning avian species, under a number of behavioral paradigms, with a focus on song production. To make sense of the avian microarray data, I compiled microarray data from other sources, including expression analyses across over 900 human brain regions generated by Allen Brain Institute. To compare these data sets, I developed and performed a variety of computational analyses including clustering, linear models, gene set enrichment analysis, motif discovery, and phylogenetic inference, providing a novel framework to study the gene regulatory networks associated with a complex behavior. Using the developed framework, we are able to better understand vocal learning at different levels: how the brain regions for vocal learning evolved and how those brain regions function during the production of learned vocalizations. At the evolutionary level, we identified genes with unique expression patterns in the brains of vocal learning birds and humans. Interesting candidates include genes related to formation of neural connections, in particular the SLIT/ROBO axon guidance pathway. This algorithm also allowed us to identify the analogous regions that are a part of vocal learning circuit across species, providing the first quantitative evidence relating the human vocal learning circuit to the avian vocal learning circuit. With the avian song system verified as a model for human speech at the molecular level, we conducted an experiment to better understand what is happening in those brain regions during singing by profiling gene expression in a time course as birds are producing song. Surprisingly, an overwhelming majority of the gene expression identified was strongly enriched in a particular region. We also found a tight coupling between the behavioral function of a particular region and the gene expression pattern. To gain insight into the mechanisms of this gene regulation, we conducted a genomic scan of transcription factor binding sites in zebra finch. Many transcription factor binding sites were enriched in the promoters of genes with a particular temporal patterns, several of which had already been hypothesized to play a role in the neural system. Using this data set of gene expression profiles and transcription factor binding sites along with separate experiments conducted in mouse, we were able uncover evidence that the transcription factor CARF plays a role in neuron homeostasis. These results have broadened our understanding of the molecular basis of vocal learning at multiple levels. Overall, this dissertation outlines a novel way of approaching the study of the relationship between genes and behavior.

Transcriptional regulation has been studied intensively in recent decades. One important aspect of this regulation is the interaction between regulatory proteins, such as transcription factors (TF) and nucleosomes, and the genome. Different high-throughput techniques have been invented to map these interactions genome-wide, including ChIP-based methods (ChIP-chip, ChIP-seq, etc.), nuclease digestion methods (DNase-seq, MNase-seq, etc.), and others. However, a single experimental technique often only provides partial and noisy information about the whole picture of protein-DNA interactions. Therefore, the overarching goal of this dissertation is to provide computational developments for jointly modeling different experimental datasets to achieve a holistic inference on the protein-DNA interaction landscape.

We first present a computational framework that can incorporate the protein binding information in MNase-seq data into a thermodynamic model of protein-DNA interaction. We use a correlation-based objective function to model the MNase-seq data and a Markov chain Monte Carlo method to maximize the function. Our results show that the inferred protein-DNA interaction landscape is concordant with the MNase-seq data and provides a mechanistic explanation for the experimentally collected MNase-seq fragments. Our framework is flexible and can easily incorporate other data sources. To demonstrate this flexibility, we use prior distributions to integrate experimentally measured protein concentrations.

We also study the ability of DNase-seq data to position nucleosomes. Traditionally, DNase-seq has only been widely used to identify DNase hypersensitive sites, which tend to be open chromatin regulatory regions devoid of nucleosomes. We reveal for the first time that DNase-seq datasets also contain substantial information about nucleosome translational positioning, and that existing DNase-seq data can be used to infer nucleosome positions with high accuracy. We develop a Bayes-factor-based nucleosome scoring method to position nucleosomes using DNase-seq data. Our approach utilizes several effective strategies to extract nucleosome positioning signals from the noisy DNase-seq data, including jointly modeling data points across the nucleosome body and explicitly modeling the quadratic and oscillatory DNase I digestion pattern on nucleosomes. We show that our DNase-seq-based nucleosome map is highly consistent with previous high-resolution maps. We also show that the oscillatory DNase I digestion pattern is useful in revealing the nucleosome rotational context around TF binding sites.

Finally, we present a state-space model (SSM) for jointly modeling different kinds of genomic data to provide an accurate view of the protein-DNA interaction landscape. We also provide an efficient expectation-maximization algorithm to learn model parameters from data. We first show in simulation studies that the SSM can effectively recover underlying true protein binding configurations. We then apply the SSM to model real genomic data (both DNase-seq and MNase-seq data). Through incrementally increasing the types of genomic data in the SSM, we show that different data types can contribute complementary information for the inference of protein binding landscape and that the most accurate inference comes from modeling all available datasets.

This dissertation provides a foundation for future research by taking a step toward the genome-wide inference of protein-DNA interaction landscape through data integration.

Cell division and growth are complex processes fundamental to all living organisms. In the budding yeast, Saccharomyces cerevisiae, these two processes are known to be coordinated with one another as a cell's mass must roughly double before division. Moreover, cell-cycle progression is dependent on cell size with smaller cells at birth generally taking more time in the cell cycle. This dependence is a signature of size control. Systems biology is an emerging field that emphasizes connections or dependencies between biological entities and processes over the characteristics of individual entities. Statistical models provide a quantitative framework for describing and analyzing these dependencies. In this dissertation, I take a statistical systems biology approach to study cell division and growth and the dependencies within and between these two processes, drawing on observations from richly informative microscope images and time-lapse movies. I review the current state of knowledge on these processes, highlighting key results and open questions from the biological literature. I then discuss my development of machine learning and statistical approaches to extract cell-cycle information from microscope images and to better characterize the cell-cycle progression of populations of cells. In addition, I analyze single cells to uncover correlation in cell-cycle progression, evaluate potential models of dependence between growth and division, and revisit classical assertions about budding yeast size control. This dissertation presents a unique perspective and approach towards comprehensive characterization of the coordination between growth and division.

The genomic DNA is bound by a myriad of proteins to form the chromatin inside the nucleus of the cell. The proteins can bind to the genome in different combinations leading to a combinatorial explosion in the number of possible chromatin configurations. The differences in the chromatin configurations for the same genome sequence give rise to distinct cell types. Likewise, cells of the same type also undergo changes in chromatin configurations under different environmental conditions. Key changes to the occupancy profile of the chromatin may dictate changes in gene regulation or vice versa. Therefore, it is important to decipher the chromatin occupancy profiles of the genome and understand how these configurations are related to the transcription of genes.

In this dissertation, we analyze chromatin using chromatin accessibility data sets, particularly MNase-seq and ATAC-seq, that describe the protein-bound and unbound regions of the genome. We first describe a state-space model that uses chromatin accessibility data to jointly infer the occupancy profile of hundreds of proteins binding to the genome. We apply our model to the yeast genome to study the occupancy profile of transcription factors and nucleosomes. We further extend our model to study chromatin dynamics of yeast cells subjected to cadmium stress. In doing so, we identify genomic regions exhibiting changes in the occupancy profile of transcription factors and nucleosomes. Upon comparing with available gene expression data we find that key changes in chromatin configuration occur around gene bodies that are differentially regulated during cadmium stress. Our analyses highlight how specific changes to the occupancy profiles relate to gene expression.

Building upon the interrelatedness of chromatin and transcription, we describe a regression-based approach that predicts transcription from chromatin accessibility data sets. We find that the chromatin accessibility in specific parts of the genome is highly correlated to gene expression. These genomic regions are potential regulatory regions that can lie far away from the gene body and interact with the genes due to the looping of the DNA. Our model identifies these regulatory regions in a gene-specific manner that helps us further understand the connections between chromatin and transcription.

The cell cycle is one of the fundamental processes in all living organisms, and all cells arise from the division of existing cells. To better understand the regulation of the cell cycle, synchrony experiments are widely used to monitor cellular dynamics during this process. In such experiments, a large population of cells is generally arrested or selected at one stage of the cycle, and then released to progress through subsequent division stages. Measurements are then taken in this population at a variety of time points after release to provide insight into the dynamics of the cell cycle. However, due to cell-to-cell variability and asymmetric cell division, cells in a synchronized population lose synchrony over time. As a result, the time-series measurements from the synchronized cell populations do not accurately reflect the underlying dynamics of cell-cycle processes.

In this thesis, we introduce a deconvolution algorithm that learns a more accurate view of cell-cycle dynamics, free from the convolution effects associated with imperfect cell synchronization. Through wavelet-basis regularization, our method sharpens signal without sharpening noise, and can remarkably increase both the dynamic range and the temporal resolution of time-series data. Though it can be applied to any such data, we demonstrate the utility of our method by applying it to a recent cell-cycle transcription time course in the eukaryote Saccharomyces cerevisiae. We show that our method more sensitively detects cell-cycle-regulated transcription, and reveals subtle timing differences that are masked in the original population measurements. Our algorithm also explicitly learns distinct transcription programs for both mother and daughter cells, enabling us to identify 82 genes transcribed almost entirely in the early G1 in a daughter-specific manner.

In addition to the cell-cycle deconvolution algorithm, we introduce DOMAIN, a protein-protein interaction (PPI) network alignment method, which employs a novel direct-edge-alignment paradigm to detect conserved functional modules (e.g., protein complexes, molecular pathways) from pairwise PPI networks. By applying our approach to detect protein complexes conserved in yeast-fly and yeast-worm PPI networks, we show that our approach outperforms two widely used approaches in most alignment performance metrics. We also show that our approach enables us to identify conserved cell-cycle-related functional modules across yeast-fly PPI networks.

Hundreds of different factors adorn the eukaryotic genome, binding to it in large number. These DNA binding factors (DBFs) include nucleosomes, transcription factors (TFs), and other proteins and protein complexes, such as the origin recognition complex (ORC). DBFs compete with one another for binding along the genome, yet many current models of genome binding do not consider different types of DBFs together simultaneously. Additionally, binding is a stochastic process that results in a continuum of binding probabilities at any position along the genome, but many current models tend to consider positions as being either binding sites or not.

Here, we present a model that allows a multitude of DBFs, each at different concentrations, to compete with one another for binding sites along the genome. The result is an 'occupancy profile', a probabilistic description of the DNA occupancy of each factor at each position. We implement our model efficiently as the software package COMPETE. We demonstrate genome-wide and at specific loci how modeling nucleosome binding alters TF binding, and vice versa, and illustrate how factor concentration influences binding occupancy. Binding cooperativity between nearby TFs arises implicitly via mutual competition with nucleosomes. Our method applies not only to TFs, but also recapitulates known occupancy profiles of a well-studied replication origin with and without ORC binding.

We then develop a statistical framework for tuning our model concentrations to further improve its predictions. Importantly, this tuning optimizes with respect to actual biological data. We take steps to ensure that our tuned parameters are biologically plausible.

Finally, we discuss novel extensions and applications of our model, suggesting next steps in its development and deployment.

Identifying and deciphering the complex regulatory information embedded in the genome is critical to our understanding of biology and the etiology of complex diseases. The regulation of gene expression is governed largely by the occupancy of transcription factors (TFs) at various cognate binding sites. Characterizing TF binding is particularly challenging since TF occupancy is not just complex but also dynamic. Current genome-wide surveys of TF binding sites typically use chromatin immunoprecipitation (ChIP), which is limited to measuring one TF at a time, thus less scalable in profiling the dynamics of TF occupancy across cell types or conditions. This dissertation develops novel computational frameworks to model sequencing data from DNase and/or MNase nuclease digestion assays that allows multiple TFs to be surveyed in a single experiment, in both human and yeast. We predicted occupancy landscapes and constructed a cell-type specificity map for many TFs across human cell types, revealed novel relationships between TF occupancy and TF expression, and monitored the occupancy dynamics of various TFs in response to androgen and estrogen hormone simulations. The TF/cell type occupancy matrix generated from our model expands the total output of the ENCODE ChIP-seq efforts by a factor of nearly 200 times. These computational frameworks serve as an innovative and cost effective strategy which enables efficient profiling of TF occupancy landscapes across different cell types or dynamic conditions in a high-throughput manner.

Many biological processes are effectively modeled as networks, but a frequent assumption is that these networks do not change during data collection. However, that assumption does not hold for many phenomena, such as neural growth during learning or changes in genetic regulation during cell differentiation. Approaches are needed that explicitly model networks as they change in time and that characterize the nature of those changes.

In this work, we develop a new class of graphical models in which the conditional dependence structure of the underlying data-generation process is permitted to change over time. We first present the model, explain how to derive it from Bayesian networks, and develop an efficient MCMC sampling algorithm that easily generalizes under varying levels of uncertainty about the data generation process. We then characterize the nature of evolving networks in several biological datasets.

We initially focus on learning how neural information flow networks change in songbirds with implanted electrodes. We characterize how they change in response to different sound stimuli and during the process of habituation. We continue to explore the neurobiology of songbirds by identifying changes in neural information flow in another habituation experiment using fMRI data. Finally, we briefly examine evolving genetic regulatory networks involved in Drosophila muscle differentiation during development.

We conclude by suggesting new experimental directions and statistical extensions to the model for predicting novel neural flow results.

In this paper, we propose a new learning algorithm for non-stationary Dynamic Bayesian Networks is proposed. Although a number of effective learning algorithms for non-stationary DBNs have previously been proposed and applied in Signal Pro- cessing and Computational Biology, those algorithms are based on batch learning algorithms that cannot be applied to online time-series data. Therefore, we propose a learning algorithm based on a Particle Filtering approach so that we can apply that algorithm to online time-series data. To evaluate our algorithm, we apply it to the simulated data set and the real-world financial data set. The result on the simulated data set shows that our algorithm performs accurately makes estimation and detects change. The result applying our algorithm to the real-world financial data set shows several features, which are suggested in previous research that also implies the effectiveness of our algorithm.