Download or read book Modeling the Gene Regulatory Dynamics in Neural Differentiation with Single Cell Data Using a Machine Learning Approach written by Yixing Hu and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Cellular differentiation is an important process where progenitor cells progressively develop into mature cells with specialized functions. Understanding the molecular characteristics and underlying regulatory mechanisms of cell fate is a central goal in biological research. Advances in single-cell sequencing technology enable the exploration of cellular differentiation at unprecedented resolution. In this thesis, I focus on characterizing and modeling of cellular differentiation using machine learning approaches. First, I present a random forest approach to identify the most discriminant genes for different cell populations in the developing brain. This method was able to identify key gene markers that revealed dorsal-ventral patterning in a heterogeneous class of progenitors present in a mouse developmental time-series dataset. Next, as cellular differentiation is marked by continuous changes in gene expression and is not well described by static cell populations, I present a framework to model the dynamics of cell fate decisions based on ordinary differential equations (ODE). I train this model on previously reported trajectory data for neural differentiation, and show that the model is able to interpolate and predict the gene expression dynamics across unobserved regions in this trajectory and extend the system dynamics for neural differentiation data. Finally, by training the model on datasets that contain rate of change information for each gene (RNA velocity), I demonstrate that the model has the capacity to predict the effects of gene deletions to the cell's overall gene expression profile with a prediction accuracy of 90%. In summary, the Neural ODE method has the ability to learn the gene regulatory dynamics from single cell data and predict the dynamics of individual genes as well as perturbation response"--

Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

Download or read book Gene Expression Data Analysis written by Pankaj Barah and published by CRC Press. This book was released on 2021-11-08 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Development of high-throughput technologies in molecular biology during the last two decades has contributed to the production of tremendous amounts of data. Microarray and RNA sequencing are two such widely used high-throughput technologies for simultaneously monitoring the expression patterns of thousands of genes. Data produced from such experiments are voluminous (both in dimensionality and numbers of instances) and evolving in nature. Analysis of huge amounts of data toward the identification of interesting patterns that are relevant for a given biological question requires high-performance computational infrastructure as well as efficient machine learning algorithms. Cross-communication of ideas between biologists and computer scientists remains a big challenge. Gene Expression Data Analysis: A Statistical and Machine Learning Perspective has been written with a multidisciplinary audience in mind. The book discusses gene expression data analysis from molecular biology, machine learning, and statistical perspectives. Readers will be able to acquire both theoretical and practical knowledge of methods for identifying novel patterns of high biological significance. To measure the effectiveness of such algorithms, we discuss statistical and biological performance metrics that can be used in real life or in a simulated environment. This book discusses a large number of benchmark algorithms, tools, systems, and repositories that are commonly used in analyzing gene expression data and validating results. This book will benefit students, researchers, and practitioners in biology, medicine, and computer science by enabling them to acquire in-depth knowledge in statistical and machine-learning-based methods for analyzing gene expression data. Key Features: An introduction to the Central Dogma of molecular biology and information flow in biological systems A systematic overview of the methods for generating gene expression data Background knowledge on statistical modeling and machine learning techniques Detailed methodology of analyzing gene expression data with an example case study Clustering methods for finding co-expression patterns from microarray, bulkRNA, and scRNA data A large number of practical tools, systems, and repositories that are useful for computational biologists to create, analyze, and validate biologically relevant gene expression patterns Suitable for multidisciplinary researchers and practitioners in computer science and the biological sciences

Download or read book Genes Signals written by Mark Ptashne and published by CSHL Press. This book was released on 2002 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: P. 103.

Download or read book Interpretable Machine Learning Methods for Regulatory and Disease Genomics written by Peyton Greis Greenside and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: It is an incredible feat of nature that the same genome contains the code to every cell in each living organism. From this same genome, each unique cell type gains a different program of gene expression that enables the development and function of an organism throughout its lifespan. The non-coding genome - the ~98 of the genome that does not code directly for proteins - serves an important role in generating the diverse programs of gene expression turned on in each unique cell state. A complex network of proteins bind specific regulatory elements in the non-coding genome to regulate the expression of nearby genes. While basic principles of gene regulation are understood, the regulatory code of which factors bind together at which genomic elements to turn on which genes remains to be revealed. Further, we do not understand how disruptions in gene regulation, such as from mutations that fall in non-coding regions, ultimately lead to disease or other changes in cell state. In this work we present several methods developed and applied to learn the regulatory code or the rules that govern non-coding regions of the genome and how they regulate nearby genes. We first formulate the problem as one of learning pairs of sequence motifs and expressed regulator proteins that jointly predict the state of the cell, such as the cell type specific gene expression or chromatin accessibility. Using pre-engineered sequence features and known expression, we use a paired-feature boosting approach to build an interpretable model of how the non-coding genome contributes to cell state. We also demonstrate a novel improvement to this method that takes into account similarities between closely related cell types by using a hierarchy imposed on all of the predicted cell states. We apply this method to discover validated regulators of tadpole tail regeneration and to predict protein-ligand binding interactions. Recognizing the need for improved sequence features and stronger predictive performance, we then move to a deep learning modeling framework to predict epigenomic phenotypes such as chromatin accessibility from just underlying DNA sequence. We use deep learning models, specifically multi-task convolutional neural networks, to learn a featurization of sequences over several kilobases long and their mapping to a functional phenotype. We develop novel architectures that encode principles of genomics in models typically designed for computer vision, such as incorporating reverse complementation and the 3D structure of the genome. We also develop methods to interpret traditionally ``black box" neural networks by 1) assigning importance scores to each input sequence to the model, 2) summarizing non-redundant patterns learned by the model that are predictive in each cell type, and 3) discovering interactions learned by the model that provide indications as to how different non-coding sequence features depend on each other. We apply these methods in the system of hematopoiesis to interpret chromatin dynamics across differentiation of blood cell types, to understand immune stimulation, and to interpret immune disease-associated variants that fall in non-coding regions. We demonstrate strong performance of our boosting and deep learning models and demonstrate improved performance of these machine learning frameworks when taking into account existing knowledge about the biological system being modeled. We benchmark our interpretation methods using gold standard systems and existing experimental data where available. We confirm existing knowledge surrounding essential factors in hematopoiesis, and also generate novel hypotheses surrounding how factors interact to regulate differentiation. Ultimately our work provides a set of tools for researchers to probe and understand the non-coding genome and its role in controlling gene expression as well as a set of novel insights surrounding how hematopoiesis is controlled on many scales from global quantification of regulatory sequence to interpretation of individual variants.

Download or read book Machine Learning in Single Cell RNA seq Data Analysis written by Khalid Raza and published by Springer Nature. This book was released on with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Computational Methods for Studying Cellular Differentiation Using Single cell RNA sequencing written by Hui Ting Grace Yeo and published by . This book was released on 2020 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: Single-cell RNA-sequencing (scRNA-seq) enables transcriptome-wide measurements of single cells at scale. As scRNA-seq datasets grow in complexity and size, more complex computational methods are required to distill raw data into biological insight. In this thesis, we introduce computational methods that enable analysis of novel scRNA-seq perturbational assays. We also develop computational models that seek to move beyond simple observations of cell states toward more complex models of underlying biological processes. In particular, we focus on cellular differentiation, which is the process by which cells acquire some specific form or function. First, we introduce barcodelet scRNA-seq (barRNA-seq), an assay which tags individual cells with RNA ‘barcodelets’ to identify them based on the treatments they receive. We apply barRNA-seq to study the effects of the combinatorial modulation of signaling pathways during early mESC differentiation toward germ layer and mesodermal fates. Using a data-driven analysis framework, we identify combinatorial signaling perturbations that drive cells toward specific fates. Second, we describe poly-adenine CRISPR gRNA-based scRNA-seq (pAC-seq), a method that enables the direct observation of guide RNAs (gRNAs) in scRNA-seq. We apply it to assess the phenotypic consequences of CRISPR/Cas9-based alterations of gene cis-regulatory regions. We find that power to detect transcriptomic effects depend on factors such as rate of mono/biallelic loss, baseline gene expression, and the number of cells per target gRNA. Third, we propose a generative model for analyzing scRNA-seq containing unwanted sources of variation. Using only weak supervision from a control population, we show that the model enables removal of nuisance effects from the learned representation without prior knowledge of the confounding factors. Finally, we develop a generative modeling framework that learns an underlying differentiation landscape from population-level time-series data. We validate the modeling framework on an experimental lineage tracing dataset, and show that it is able to recover the expected effects of known modulators of cell fate in hematopoiesis.

Download or read book Application of Machine Learning to Mapping and Simulating Gene Regulatory Networks written by Hien-haw Liow and published by . This book was released on 2015 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation explores, proposes, and examines methods of applying modernmachine learning and Bayesian statistics in the quantitative and qualitative modeling of gene regulatory networks using high-throughput gene expression data. A semi-parametric Bayesian model based on random forest is developed to infer quantitative aspects of gene regulation relations; a parametric model is developed to predict geneexpression levels solely from genotype information. Simulation of network behavior is shown to complement regression analysis greatly in capturing the dynamics of gene regulatory networks. Finally, as an application and extension of novel approaches in gene expression analysis, new methods of discovering topological structure of gene regulatory networks are developed and shown to provide improvement over existing methods.

Download or read book Computational Study of Gene Transcription Initialization and Regulation written by Hansi Zheng and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression and play an essential role in phenotype development. The regulation mechanism behind miRNA reveals insight into gene expression and gene regulation. Transcription Start Site(TSS) is the key to studying gene expression. However, the TSSs of miRNAs can be thousands of nucleotides away from the precursor miRNAs, which makes it hard to be detected by conventional RNA-Seq experiments. Some previous methods tried to take advantage of sequencing data using sequence features or integrated epigenetic markers, but resulted in either not condition-specific or low-resolution prediction. Furthermore, the availability of a large amount of Single-Cell RNA-Seq(scRNA-Seq) data provides remarkable opportunities for studying gene regulatory mechanisms at single-cell resolution. Incorporating the gene regulatory mechanisms can assist with cell type identification and state discovery from scRNA-Seq data. In this dissertation, we studied computational modeling of gene transcription initialization and expression, including two novel approaches to identify TSSs with various type of conditions and one case study at the single-cell level. Firstly, we studied how TSS can be identified based on Cap Analysis Gene Expression (CAGE) experiments data using the thriving Deep Learning Neural Network. We used a control model to study the Deepbind binding score features that the protein binding motif model can improve overall prediction performance. Furthermore, comparing data from unseen cell lines showed better performance than existing tools. Secondly, to better predict the TSSs of miRNA in a condition-specific manner, we built D-miRT, a two-steam convolutional neural network based on integrated low-resolution epigenetic features and high-resolution sequence features. D-miRT outperformed all baseline models and demonstrated high accuracy for miRNA TSS prediction tasks. Compared with the most recent approaches on cell-specific miRNA TSS identification using cell lines that were unseen to the model training processes, D-miRT also showed superior performance. Thirdly, to study gene transcription initialization and regulation from single-cell perspective, we developed INSISTC, an unsupervised machine learning-based approach that incorporated network structure information for single-cell type classification. In contrast to other clustering algorithms, we showed that INSISTC with the SC3 algorithm provides cluster number estimation. Future studies on gene expression and regulation will benefit from INSISTC's adaptability with regard to the kinds of biological networks that can be used.

Download or read book Algorithms for Modeling Gene Regulation and Determining Cell Type Using Single cell Molecular Profiles written by Hannah Andersen Pliner and published by . This book was released on 2019 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: Single-cell genomic technologies are helping us answer key biological questions that have long remained elusive. How do a single cell and a single genome generate such complex multicellular organisms as humans? More specifically, how do these cells orchestrate specific transcriptional programs depending on their cell type? New technologies like single-cell RNA-seq and single-cell ATAC-seq allow us to examine the transcription and regulation of individual cells as they develop; however, these methods have important limitations. A primary limitation with all single-cell data is data sparsity, which must be overcome computationally to extract useful information from these experiments. In this dissertation, I present two algorithms designed to overcome the sparsity of single-cell data and allow biological discovery. I first introduce Cicero for single-cell chromatin accessibility data, which is both an algorithm that calculates co-accessibility scores to assign distal regulatory elements to genes, and a software system that adapts existing single-cell RNA-seq analysis techniques for use with single-cell chromatin accessibility data. In Chapter 2, I apply Cicero to an in vitro myoblast differentiation assay and find evidence for the use of ”chromatin hubs” during myogenesis. In Chapter 3, I apply Cicero to single-cell ATAC-seq data from mouse bone marrow and recapitulate known patterns of hematopoiesis and known cis-regulation of the b-globin locus. In Chapter 4, I introduce a second algorithm, Garnett, which uses single-cell expression data to train and apply automated cell type classifiers. The accuracy of this technology is demonstrated with data from various single-cell RNA-seq methods and tissue sources. In a final chapter, I reflect on the development of software for biological applications and future directions for this work.

Download or read book Biologically Interpretable Machine Learning Methods to Understand Gene Regulation for Disease Phenotypes written by Ting Jin and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gene expression and regulation is a key molecular mechanism driving the development of human diseases, particularly at the cell type level, but it remains elusive. For example in many brain diseases, such as Alzheimer's disease (AD), understanding how cell-type gene expression and regulation change across multiple stages of AD progression is still challenging. Moreover, interindividual variability of gene expression and regulation is a known characteristic of the human brain and brain diseases. However, it is still unclear how interindividual variability affects personalized gene regulation in brain diseases including AD, thereby contributing to their heterogeneity. Recent technological advances have enabled the detection of gene regulation activities through multi-omics (i.e., genomics, transcriptomics, epigenomics, proteomics). In particular, emerging single-cell sequencing technologies (e.g., scRNA-seq, scATAC-seq) allow us to study functional genomics and gene regulation at the cell-type level. Moreover, these multi-omics data of populations (e.g., human individuals) provide a unique opportunity to study the underlying regulatory mechanisms occurring in brain disease progression and clinical phenotypes. For instance, PsychAD is a large project generating single-cell multi-omics data including many neuronal and glial cell types, aiming to understand the molecular mechanisms of neuropsychiatric symptoms of multiple brain diseases (e.g., AD, SCZ, ASD, Bipolar) from over 1,000 individuals. However, analyzing and integrating large-scale multi-omics data at the population level, as well as understanding the mechanisms of gene regulation, also remains a challenge. Machine learning is a powerful and emerging tool to decode the unique complexities and heterogeneity of human diseases. For instance, Beebe-Wang, Nicosia, et al. developed MD-AD, a multi-task neural network model to predict various disease phenotypes in AD patients using RNA-seq. Additionally, with advancements in graph neural networks, which possess enhanced capabilities to represent sophisticated gene network structures like gene regulation networks that control gene expression. Efforts have also been made to capture the gene regulation heterogeneity of brain diseases. For instance, Kim SY has applied graph convolutional networks to offer personalized diagnostic insights through population graphs that correspond with disease progression. However, many existing machine learning methods are often limited to constructing accurate models for disease phenotype prediction and frequently lack biological interpretability or personalized insights, especially in gene regulation. Therefore, to address these challenges, my Ph.D. works have developed three machine-learning methods designed to decode the gene regulation mechanisms of human diseases. First, in this dissertation, I will present scGRNom, a computational pipeline that integrates multi-omic data to construct cell-type gene regulatory networks (GRNs) linking non-coding regulatory elements. Next, I will introduce i-BrainMap an interpretable knowledge-guided graph neural network model to prioritize personalized cell type disease genes, regulatory linkages, and modules. Thirdly, I introduce ECMaker, a semi-restricted Boltzmann machine (semi-RBM) method for identifying gene networks to predict diseases and clinical phenotypes. Overall, all our interpretable machine learning models improve phenotype prediction, prioritize key genes and networks associated with disease phenotypes, and are further aimed at enhancing our understanding of gene regulatory mechanisms driving disease progression and clinical phenotypes.

Download or read book Elucidation of Time varying Gene Regulatory Networks Controlled by REST During Neural Differentiation of HiPSCs written by Vivek Parthasarathy and published by . This book was released on 2016 with total page 63 pages. Available in PDF, EPUB and Kindle. Book excerpt: RE1-Silencing Transcription Factor (REST), a member of the Kruppel-type zinc finger transcription factor family, is believed to act as a master negative regulator of neurogenesis. During neurogenesis, the decreasing expression of REST leads to increased expression of neuronal genes and the emergence of neuronal processes over time. The temporal patterns of REST-controlled processes and transcriptional regulatory events underlying neural induction and early neural development have not been studied utilizing a systems-level approach on high-throughput time course data. Human-derived induced Pluripotent Stem Cells (iPSCs) combined with well-established neural differentiation protocols allow for the in-vitro elucidation of gene expression patterns characteristic of human neurodevelopment during this crucial early in-vivo developmental phase. Using time series data capturing genome-wide transcriptome information from human iPSCs differentiating into i) Cortical and ii) Hypothalamic neurons, REST-controlled gene regulatory networks (GRNs) were generated. These GRNs captured transcription factor-target regulatory interactions across the time series and were investigated in order to elucidate early neurodevelopment towards the two neuronal phenotypes. Functional enrichment analysis of gene sets obtained from these GRNs was used to determine where along the time series different REST-controlled neuronal processes emerged. The systems-level approach allowed for temporal resolution of genome-wide trans-regulatory interactions over the time course, the dynamics of which underlie neural differentiation and development. The outcome of the research is a novel qualitative kinetic model consisting of the time-varying GRNs under the control of REST that gives insight into the i) temporal sequence of emergent neuronal processes accompanying neurodifferentiation and ii) the temporal sequence of key transcriptional regulatory events underlying the early neural differentiation of hiPSCs.

Download or read book Computational Methods for Analyzing and Modeling Gene Regulation Dynamics written by Jason Ernst and published by . This book was released on 2008 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "Gene regulation is a central biological process whose disruption can lead to many diseases. This process is largely controlled by a dynamic network of transcription factors interacting with specific genes to control their expression. Time series microarray gene expression experiments have become a widely used technique to study the dynamics of this process. This thesis introduces new computational methods designed to better utilize data from these experiments and to integrate this data with static transcription factor-gene interaction data to analyze and model the dynamics of gene regulation. The first method, STEM (Short Time-series Expression Miner), is a clustering algorithm and software specifically designed for short time series expression experiments, which represent the substantial majority of experiments in this domain. The second method, DREM (Dynamic Regulatory Events Miner), integrates transcription factor-gene interactions with time series expression data to model regulatory networks while taking into account their dynamic nature. The method uses an Input-Output Hidden Markov Model to identify bifurcation points in the time series expression data. While the method can be readily applied to some species, the coverage of experimentally determined transcription factor-gene interactions in most species is limited. To address this we introduce two methods to improve the computational predictions of these interactions. The first of these methods, SEREND (SEmi-supervised REgulatory Network Discoverer), motivated by the species E. coli is a semi-supervised learning method that uses verified transcription factor-gene interactions, DNA sequence binding motifs, and gene expression data to predict new interactions. We also present a method motivated by human genomic data, that combines motif information with a probabilistic prior on transcription factor binding at each location in the organism's genome, which it infers based on a diverse set of genomic properties. We applied these methods to yeast, E. coli, and human cells. Our methods successfully predicted interactions and pathways, many of which have been experimentally validated. Our results indicate that by explicitly addressing the temporal nature of regulatory networks we can obtain accurate models of dynamic interaction networks in the cell."

Download or read book Integrative Modeling for Genome wide Regulation of Gene Expression written by Zhengqing Ouyang and published by Stanford University. This book was released on 2010 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-throughput genomics has been increasingly generating the massive amount of genome-wide data. With proper modeling methodologies, we can expect to archive a more comprehensive understanding of the regulatory mechanisms of biological systems. This work presents integrative approaches for the modeling and analysis of gene regulatory systems. In mammals, gene expression regulation is combinatorial in nature, with diverse roles of regulators on target genes. Microarrays (such as Exon Arrays) and RNA-Seq can be used to quantify the whole spectrum of RNA transcripts. ChIP-Seq is being used for the identification of transcription factor (TF) binding sites and histone modification marks. RNA interference (RNAi), coupled with gene expression profiles, allow perturbations of gene regulatory systems. Our approaches extract useful information from those genome-wide measurements for effectively modeling the logic of gene expression regulation. We present a predictive model for the prediction of gene expression from ChIP-Seq signals, based on quantitative modeling of regulator-gene association strength, principal component analysis, and regression-based model selection. We demonstrate the combinatorial regulation of TFs, and their power for explaining genome-wide gene expression variation. We also illustrate the roles of covalent histone modification marks on predicting gene expression and their regulation by TFs. We present a dynamical model of gene expression profiling, and derive the perturbed behaviors of the ordinary differential equation (ODE) system. Based on that, we present a regularized multivariate regression method for inferring the gene regulatory network of a stable cell type. We model the sparsity and stability of the network by a regularization approach. We applied the approaches to both a simulation data set and the RNAi perturbation data in mouse embryonic stem cells.

Download or read book A Single cell Analysis Approach to Understanding Molecular Organization and Plasticity in the Brain written by James Hyun-Woo Park and published by . This book was released on 2017 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: Single-cell transcriptional heterogeneity pervades the fully differentiated brain. This heterogeneity is particularly prevalent in brain nuclei involved in the autonomic regulation of physiological functions such as cardiovascular homeostasis. Because neuronal function largely depends on its transcriptome, such heterogeneity confounds our understanding of how heterogeneous neurons contribute to their broader phenotypic function. In addition to the transcriptome, functional connectivity and in vivo anatomical environment are additional factors central to defining a neuron’s functional state. Given their importance, these factors may provide the added context necessary to understand how a distribution of heterogeneous neurons contributes to phenotypic function. Consequently, the overall goal of this work is to establish an organizational framework that characterizes single-neuron heterogeneity within a brain nucleus and elucidates its functional relevance. ☐ Towards this goal, we have taken a combined experimental and computational approach to determine the organizing principles driving complex interaction networks within and among transcriptionally diverse neurons within a brain nucleus. First, we generated a large-scale gene expression dataset from several hundred neurons, selected on the basis of their synaptic input types, taken from the nucleus tractus solitarius (NTS), a brainstem nucleus involved in the central regulation of blood pressure. Our analysis of these neurons revealed an organizational structure in which transcriptional variability aligns with synaptic input type along a continuum of graded gene expression. This continuum is populated by distinct neuronal subtypes characterized by gene groups exhibiting correlated expression. ☐ In order to identify the molecular mechanisms driving this correlated behavior, we next developed a fuzzy logic modeling-based methodology to model quantitatively causal gene interaction networks from single-cell transcriptomic data. Our modeling results suggest that distinct input stimuli operating on distinct network structures corresponding to these subtypes can drive neurons through various transcriptional states. These results suggest that transcriptional heterogeneity represents a neuron’s adaptive response to various inputs. Based on these results, we propose that neuronal adaptation may be a mechanism through which the NTS robustly regulates blood pressure and cardiovascular homeostasis. ☐ To test this proposal, we examined what impact adaptation to neuronal subtypes in the NTS and brainstem would have on the short-term autonomic regulation of cardiovascular homeostasis under the simulated disease state of systolic heart failure via mathematical modeling. We developed a closed-loop control model characterizing neuronal regulation of the cardiovascular system by integrating previous quantitative models that simulated various aspects of the cardiovascular system. Because the goal of this study was to investigate the effects of neuronal subtype adaptation, we incorporated brainstem neuronal subtypes, such as those identified in our analysis of the NTS. Modeling simulation results suggest that adaptation of these neuronal components can compensate for an impaired cardiovascular state due to systolic heart failure by decreasing neuronal inhibition (i.e. parasympathetic tone) of cardiac contractility. ☐ Finally, we tested the utility of a single-cell analysis approach to interpret single-cell heterogeneity throughout the brain by identifying a cellular network organization in a distinct brain nucleus – the suprachiasmatic nucleus (SCN), which regulates circadian rhythms in mammals. Similar to our analysis of the NTS, we generated and analyzed a high-dimensional gene expression dataset consisting of hundreds of transcriptionally heterogeneous SCN neurons. Our multivariate analysis of these neurons revealed both known and previously undescribed SCN neuron-types, which organize into a neuronal interaction network via known paracrine signaling mechanisms underlying the synchronizing functions of the SCN. ☐ Based on the analysis of heterogeneous single neurons, we have identified an organizational framework with which we can now interpret single-cell heterogeneity; a heterogeneous neuronal population comprises a mixture of distinct neuronal subtypes whose adaptive response to inputs is driven by distinct regulatory networks. Such adaptation provides a mechanism in which the brain is able to regulate robustly physiological functions by providing compensatory effects under perturbed or challenged states.

Download or read book Computational Approaches to Understand Cell Type Specific Gene Regulation written by Shilu Zhang and published by . This book was released on 2021 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transcriptional regulatory networks are networks of regulatory proteins such as transcription factors, signaling protein level and chromatin modifications that together determine the transcriptional status of genes in different contexts such as cell types, diseases, and environmental conditions. Changes in regulatory networks can significantly alter the type or function of a cell. Therefore, identifying regulatory networks and determining how they transform over diverse cell types is key to understanding mammalian development and disease. In this dissertation, we have developed several computational methods to integrate regulatory genomic datasets such as chromatin marks, transcription factors and long-range regulatory interactions from multiple cell types to predict regulatory network connections and their dynamics.Our first contribution is HiC-Reg to predict long-range interactions in new cell types using one-dimensional regulatory genomic datasets such as chromatin marks, architectural and transcription factor proteins, and accessibility. Our second contribution is Cell type Varying Networks (CVN), a method to capture the interactions between chromatin marks, TFs and expression levels in each cell type on a lineage. Finally, we developed single-cell Multi-Task learning Network Inference (scMTNI), for inference of cell type-specific gene regulatory networks that leverages scRNA-seq and scATAC-seq measurements and captures the dynamic changes of networks across cell lineages. We applied these methods to simulated and real data, interpreted the results using existing literature, and provided biological insights for cell type-specific gene regulation. In particular, we identified network components that are common and differentially wired across the cellular stages that provide novel insight into network dynamics during reprogramming and hematopoietic differentiation. Taken together, we provide a powerful set of computational tools that integrate different omic datasets to infer cell type-specific regulatory networks which are applicable to different biological questions.

Download or read book Computational Methods for Analyzing and Modeling Gene Regulation and 3D Genome Organization written by Anastasiya Belyaeva and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Biological processes from differentiation to disease progression are governed by gene regulatory mechanisms. Currently large-scale omics and imaging data sets are being collected to characterize gene regulation at every level. Such data sets present new opportunities and challenges for extracting biological insights and elucidating the gene regulatory logic of cells. In this thesis, I present computational methods for the analysis and integration of various data types used for cell profiling. Specifically, I focus on analyzing and linking gene expression with the 3D organization of the genome. First, I describe methodologies for elucidating gene regulatory mechanisms by considering multiple data modalities. I design a computational framework for identifying colocalized and coregulated chromosome regions by integrating gene expression and epigenetic marks with 3D interactions using network analysis. Then, I provide a general framework for data integration using autoencoders and apply it for the integration and translation between gene expression and chromatin images of naive T-cells. Second, I describe methods for analyzing single modalities such as contact frequency data, which measures the spatial organization of the genome, and gene expression data. Given the important role of the 3D genome organization in gene regulation, I present a methodology for reconstructing the 3D diploid conformation of the genome from contact frequency data. Given the ubiquity of gene expression data and the recent advances in single-cell RNA-sequencing technologies as well as the need for causal modeling of gene regulatory mechanisms, I then describe an algorithm as well as a software tool, difference causal inference (DCI), for learning causal gene regulatory networks from gene expression data. DCI addresses the problem of directly learning differences between causal gene regulatory networks given gene expression data from two related conditions. Finally, I shift my focus from basic biology to drug discovery. Given the current COVID19 pandemic, I present a computational drug repurposing platform that enables the identification of FDA approved compounds for drug repurposing and investigation of potential causal drug mechanisms. This framework relies on identifying drugs that reverse the signature of the infection in the space learned by an autoencoder and then uses causal inference to identify putative drug mechanisms.