Global DNA methylation and transcriptional analyses of human ESC-derived cardiomyocytes.

With defined culture protocol, human embryonic stem cells (hESCs) are able to generate cardiomyocytes in vitro, therefore providing a great model for human heart development, and holding great potential for cardiac disease therapies. In this study, we successfully generated a highly pure population of human cardiomyocytes (hCMs) (>95% cTnT(+)) from hESC line, which enabled us to identify and characterize an hCM-specific signature, at both the gene expression and DNA methylation levels. Gene functional association network and gene-disease network analyses of these hCM-enriched genes provide new insights into the mechanisms of hCM transcriptional regulation, and stand as an informative and rich resource for investigating cardiac gene functions and disease mechanisms. Moreover, we show that cardiac-structural genes and cardiac-transcription factors have distinct epigenetic mechanisms to regulate their gene expression, providing a better understanding of how the epigenetic machinery coordinates to regulate gene expression in different cell types

Executable cancer models: successes and challenges

Making decisions on how best to treat cancer patients requires the integration of different data sets, including genomic profiles, tumour histopathology, radiological images, proteomic analysis and more. This wealth of biological information calls for novel strategies to integrate such information in a meaningful, predictive and experimentally verifiable way. In this Perspective we explain how executable computational models meet this need. Such models provide a means for comprehensive data integration, can be experimentally validated, are readily interpreted both biologically and clinically, and have the potential to predict effective therapies for different cancer types and subtypes. We explain what executable models are and how they can be used to represent the dynamic biological behaviours inherent in cancer, and demonstrate how such models, when coupled with automated reasoning, facilitate our understanding of the mechanisms by which oncogenic signalling pathways regulate tumours. We explore how executable models have impacted the field of cancer research and argue that extending them to represent a tumour in a specific patient (that is, an avatar) will pave the way for improved personalized treatments and precision medicine. Finally, we highlight some of the ongoing challenges in developing executable models and stress that effective cross-disciplinary efforts are key to forward progress in the field

Computational Integrative Models for Cellular Conversion: Application to Cellular Reprogramming and Disease Modeling

The groundbreaking identification of only four transcription factors that are able to induce pluripotency in any somatic cell upon perturbation stimulated the discovery of copious amounts of instructive factors triggering different cellular conversions. Such conversions are highly significant to regenerative medicine with its ultimate goal of replacing or regenerating damaged and lost cells. Precise directed conversion of damaged cells into healthy cells offers the tantalizing prospect of promoting regeneration in situ. In the advent of high-throughput sequencing technologies, the distinct transcriptional and accessible chromatin landscapes of several cell types have been characterized. This characterization provided clear evidences for the existence of cell type specific gene regulatory networks determined by their distinct epigenetic landscapes that control cellular phenotypes. Further, these networks are known to dynamically change during the ectopic expression of genes initiating cellular conversions and stabilize again to represent the desired phenotype. Over the years, several computational approaches have been developed to leverage the large amounts of high-throughput datasets for a systematic prediction of instructive factors that can potentially induce desired cellular conversions. To date, the most promising approaches rely on the reconstruction of gene regulatory networks for a panel of well-studied cell types relying predominantly on transcriptional data alone. Though useful, these methods are not designed for newly identified cell types as their frameworks are restricted only to the panel of cell types originally incorporated. More importantly, these approaches rely majorly on gene expression data and cannot account for the cell type specific regulations modulated by the interplay of the transcriptional and epigenetic landscape. In this thesis, a computational method for reconstructing cell type specific gene regulatory networks is proposed that aims at addressing the aforementioned limitations of current approaches. This method integrates transcriptomics, chromatin accessibility assays and available prior knowledge about gene regulatory interactions for predicting instructive factors that can potentially induce desired cellular conversions. Its application to the prioritization of drugs for reverting pathologic phenotypes and the identification of instructive factors for inducing the cellular conversion of adipocytes into osteoblasts underlines the potential to assist in the discovery of novel therapeutic interventions

Computational Integrative Models for Cellular Conversion: Application to Cellular Reprogramming and Disease Modeling

The groundbreaking identification of only four transcription factors that are able to induce pluripotency in any somatic cell upon perturbation stimulated the discovery of copious amounts of instructive factors triggering different cellular conversions. Such conversions are highly significant to regenerative medicine with its ultimate goal of replacing or regenerating damaged and lost cells. Precise directed conversion of damaged cells into healthy cells offers the tantalizing prospect of promoting regeneration in situ. In the advent of high-throughput sequencing technologies, the distinct transcriptional and accessible chromatin landscapes of several cell types have been characterized. This characterization provided clear evidences for the existence of cell type specific gene regulatory networks determined by their distinct epigenetic landscapes that control cellular phenotypes. Further, these networks are known to dynamically change during the ectopic expression of genes initiating cellular conversions and stabilize again to represent the desired phenotype. Over the years, several computational approaches have been developed to leverage the large amounts of high-throughput datasets for a systematic prediction of instructive factors that can potentially induce desired cellular conversions. To date, the most promising approaches rely on the reconstruction of gene regulatory networks for a panel of well-studied cell types relying predominantly on transcriptional data alone. Though useful, these methods are not designed for newly identified cell types as their frameworks are restricted only to the panel of cell types originally incorporated. More importantly, these approaches rely majorly on gene expression data and cannot account for the cell type specific regulations modulated by the interplay of the transcriptional and epigenetic landscape. In this thesis, a computational method for reconstructing cell type specific gene regulatory networks is proposed that aims at addressing the aforementioned limitations of current approaches. This method integrates transcriptomics, chromatin accessibility assays and available prior knowledge about gene regulatory interactions for predicting instructive factors that can potentially induce desired cellular conversions. Its application to the prioritization of drugs for reverting pathologic phenotypes and the identification of instructive factors for inducing the cellular conversion of adipocytes into osteoblasts underlines the potential to assist in the discovery of novel therapeutic interventions

Current advances in systems and integrative biology

Systems biology has gained a tremendous amount of interest in the last few years. This is partly due to the realization that traditional approaches focusing only on a few molecules at a time cannot describe the impact of aberrant or modulated molecular environments across a whole system. Furthermore, a hypothesis-driven study aims to prove or disprove its postulations, whereas a hypothesis-free systems approach can yield an unbiased and novel testable hypothesis as an end-result. This latter approach foregoes assumptions which predict how a biological system should react to an altered microenvironment within a cellular context, across a tissue or impacting on distant organs. Additionally, re-use of existing data by systematic data mining and re-stratification, one of the cornerstones of integrative systems biology, is also gaining attention. While tremendous efforts using a systems methodology have already yielded excellent results, it is apparent that a lack of suitable analytic tools and purpose-built databases poses a major bottleneck in applying a systematic workflow. This review addresses the current approaches used in systems analysis and obstacles often encountered in large-scale data analysis and integration which tend to go unnoticed, but have a direct impact on the final outcome of a systems approach. Its wide applicability, ranging from basic research, disease descriptors, pharmacological studies, to personalized medicine, makes this emerging approach well suited to address biological and medical questions where conventional methods are not ideal

Boolean Networks as Predictive Models of Emergent Biological Behaviors

Interacting biological systems at all organizational levels display emergent behavior. Modeling these systems is made challenging by the number and variety of biological components and interactions (from molecules in gene regulatory networks to species in ecological networks) and the often-incomplete state of system knowledge (e.g., the unknown values of kinetic parameters for biochemical reactions). Boolean networks have emerged as a powerful tool for modeling these systems. We provide a methodological overview of Boolean network models of biological systems. After a brief introduction, we describe the process of building, analyzing, and validating a Boolean model. We then present the use of the model to make predictions about the system's response to perturbations and about how to control (or at least influence) its behavior. We emphasize the interplay between structural and dynamical properties of Boolean networks and illustrate them in three case studies from disparate levels of biological organization.Comment: Review, to appear in the Cambridge Elements serie