Systems Biology in Heterogenous Tissues: Integrating Multiple *Omics Datasets to Understand Hematopoietic Differentiation Motivation: Systems biology integrates expression, methylation, transcription factor binding and histone modification profiles with other physiological characteristics of a specific organ. Repositories that provide the required data, like ENCODE, generally work on a high level and do not take the heterogeneity of cell types within an organ into consideration. The hematopoietic system allows the characterization and study of each cell type involved in the generation of blood cells from bone marrow stem cells and thus provides a good foundation for systems biology studies. Here we compare RNA expression, DNA methylation, chromatin accessibility, DNA binding proteins and histone modification profiles in seven different hematopoietic populations using a Bayesian nonparametric hierarchical latentclass mixedeffect model known as IDEAS to characterize epigenetic changes associated with hematopoietic differentiation. Unlike other existing approaches IDEAS considers various cell types of a biological systems in concert instead of disjointly. Results: Using the VISION database and the IDEAS toolkit we provide insights into the transcriptional, epigenetic and regulatory programs governing the hematopoietic differentiation process. The characterization of the different hematopoietic components and their interactions provide the foundations for a systems biology model of hematopoiesis. Previous hematopoietic epigenome segmentation studies have focused on histone modifications, chromatin accessibility and DNA binding protein profiles. DNA methylation has been shown to vary markedly in hematopoietic populations. Inclusion of DNA methylation in these segmentation studies increased the original 36state model of regulatory interactions to 41 states. These new DNA methylationrelated states were associated with repressive marks, active RNA transcription, and a novel state regulated by DNA methylation alone. Imputing epigenetic models on inputs systematically perturbed for hematopoietic populations resulted in models of varying degrees of overlap, which were quantified and set in context with underlying biological processes. Conclusion: Our data show that methylation has a strong impact on functional genomic modeling and can be used to discern cell type specific epigenetic regulatory behavior by leveraging imputation for missing cell type data.

Using Convolutional Neural Network to Study the Regulatory Relationship Between DNA Methylation and Gene Expression In this work, we propose a novel framework based on convolutional neural networks to integrate information of DNA methylation and DNA sequences for studying their relationship with gene expression. Using the data from 8 cell types, we show that the trained models provide higher explained variation of gene expression from DNA methylation (up to 30%) compared to methods that only consider one genomic data type or from one region (up to 12%). In addition, the extracted sequence motifs from the filters of the first convolutional layers reveal the involvement of regulatory modulators including DNA Methyltransferase 1 (DNMT1) and other transcription factors, shedding light on the complex regulatory mechanisms of DNA methylation. We found that more than half of the 20 most enriched motifs correspond to regulators that are well supported by previous publications and ChIPSeq transcription factor binding datasets. Our work has demonstrated the usefulness of convolutional neural networks in revealing complex relationships from omics data.