Uncovering The Molecular Networks Of Metabolic Diseases Using Systems Biology

The past few decades have seen dramatic increase in the prevalence of metabolic diseases (MetDs) including obesity, type 2 diabetes (T2D) and cardiovascular disease (CVD), imposing unprecedented burden on public health worldwide. MetDs stem from a complex interplay of multiple genes and cumulative exposure to environmental risk factors, yet the exact etiology remains elusive. To address this challenge, I embarked interdisciplinary systems biology studies encompassing the development of a multi-omics integration tool, elucidation of genetically perturbed tissue networks shared by T2D and CVD, and examination of environmentally perturbed gene networks by a prevalent endocrine disrupting chemical (EDC). First, I developed a multi-omics integration pipeline named Mergeomics, which consists of independent modules that 1) leverage multi-omics association data to identify biological processes that are perturbed in disease, and 2) overlay the disease-associated processes onto molecular interaction networks to pinpoint hubs as potential key regulators. Unlike existing tools that are mostly dedicated to specific data type or settings, the Mergeomics pipeline accepts and integrates datasets across platforms, data types, and species. The performance of Mergeomics was demonstrated by both simulation and case studies that include genome-wide, epigenome-wide, and transcriptome-wide datasets of total cholesterol and fasting glucose. I then applied Mergeomics to identify the shared gene networks between CVD and T2D through a comprehensive integrative analysis driven by five multi-ethnic genome-wide association studies (GWAS) for CVD and T2D, expression quantitative trait loci (eQTLs), ENCODE, and tissue-specific gene network models from CVD and T2D relevant tissues. The shared networks captured both known and novel processes underlying CVD and T2D. I also predicted 15 key drivers for the shared gene networks and cross-validated the regulatory role of top key drivers using in vitro siRNA knockdown, in vivo gene knockout, and two Hybrid Mouse Diversity Panels each comprised of >100 strains. Lastly, I leveraged systems biology approaches to assess the target tissues, molecular pathways, and gene regulatory networks associated with a developmental exposure to the model EDC Bisphenol A (BPA). Prenatal BPA exposure was found to cause transcriptomic and methylomic alterations in the adipose, hypothalamus, and liver tissues in mouse offspring, with cross-tissue perturbations in lipid metabolism as well as tissue-specific alterations in histone subunits, glucose metabolism and extracellular matrix. Network modeling prioritized main molecular targets of BPA, including Pparg, Hnf4a, Esr1, and Fasn. Moreover, integrative analyses identified the association of BPA molecular signatures with MetDs phenotypes in mouse and human. In summary, I presented the community a flexible and robust computational pipeline for multidimensional data integration, and offered mechanistic insights into the genetic and environmental underpinnings of MetDs by exploiting the power of systems biology through both computational and experimental approaches.

Common complex metabolic diseases (MetDs) such as obesity, type 2 diabetes (T2D), coronary artery disease (CAD) and non-alcoholic fatty liver disease (NAFLD), impose an unprecedented burden on public health worldwide and demonstrate sex differences. Our general hypothesis is that genetic risk factors perturb set of genes in the form of functional gene networks, which subsequently induces the initiation and progression of MetDs. Following this hypothesis, our research focuses on dissecting the molecular networks that are perturbed by genetic risk factors of MetDs utilizing multiomics systems biology approaches. To address this challenge, I embarked interdisciplinary systems biology studies encompassing the development of an accessible multi-omics integration webserver, elucidation of genetically perturbed tissue networks in numerous MetDs, and uncovering the relative contribution of three sex factors in gene regulation in tissues relevant to MetDs. First, I contributed to the development of a user-friendly webserver for multiomics integration, network modeling, and network-based drug repositioning for complex diseases such as MetDs. Second, I investigated the genetically perturbed gene networks that underly various MetDs, namely, lipid traits, diabetes, CAD, and NAFLD. Third, I employed systems biology approaches to uncover the individual and interactive contribution of three sex factors (sex chromosomes, gonads, and sex hormones) in gene regulation in tissues relevant to MetDs. Completion of these projects offer a user-friendly bioinformatic tool, molecular insights, and drug candidates for MetDs.

Big data, genomics, and quantitative approaches to network-based analysis are combining to advance the frontiers of medicine as never before. With contributions from leading experts, Network Medicine introduces this rapidly evolving field of research, which promises to revolutionize the diagnosis and treatment of human diseases.

Many potential applications of synthetic and systems biology are relevant to the challenges associated with the detection, surveillance, and responses to emerging and re-emerging infectious diseases. On March 14 and 15, 2011, the Institute of Medicine's (IOM's) Forum on Microbial Threats convened a public workshop in Washington, DC, to explore the current state of the science of synthetic biology, including its dependency on systems biology; discussed the different approaches that scientists are taking to engineer, or reengineer, biological systems; and discussed how the tools and approaches of synthetic and systems biology were being applied to mitigate the risks associated with emerging infectious diseases. The Science and Applications of Synthetic and Systems Biology is organized into sections as a topic-by-topic distillation of the presentations and discussions that took place at the workshop. Its purpose is to present information from relevant experience, to delineate a range of pivotal issues and their respective challenges, and to offer differing perspectives on the topic as discussed and described by the workshop participants. This report also includes a collection of individually authored papers and commentary.

This volume aims to provide a timely view of the state-of-the-art in systems biology. The editors take the opportunity to define systems biology as they and the contributing authors see it, and this will lay the groundwork for future studies. The volume is well-suited to both students and researchers interested in the methods of systems biology. Although the focus is on plant systems biology, the proposed material could be suitably applied to any organism.

This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.

This book presents a range of current research topics in biological network modeling, as well as its application in studies on human hosts, pathogens, and diseases. Systems biology is a rapidly expanding field that involves the study of biological systems through the mathematical modeling and analysis of large volumes of biological data. Gathering contributions from renowned experts in the field, some of the topics discussed in depth here include networks in systems biology, the computational modeling of multidrug-resistant bacteria, and systems biology of cancer. Given its scope, the book is intended for researchers, advanced students, and practitioners of systems biology. The chapters are research-oriented, and present some of the latest findings on their respective topics.

Technologies collectively called omics enable simultaneous measurement of an enormous number of biomolecules; for example, genomics investigates thousands of DNA sequences, and proteomics examines large numbers of proteins. Scientists are using these technologies to develop innovative tests to detect disease and to predict a patient's likelihood of responding to specific drugs. Following a recent case involving premature use of omics-based tests in cancer clinical trials at Duke University, the NCI requested that the IOM establish a committee to recommend ways to strengthen omics-based test development and evaluation. This report identifies best practices to enhance development, evaluation, and translation of omics-based tests while simultaneously reinforcing steps to ensure that these tests are appropriately assessed for scientific validity before they are used to guide patient treatment in clinical trials.