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Systems Biomedicine: It’S Your Turn —Recent Progress in Systems Biomedicine
Quantitative Biology QB DOI 10.1007/s40484-013-0009-z REVIEW Systems biomedicine: It’s your turn —Recent progress in systems biomedicine Zhuqin Zhang, Zhiguo Zhao, Bing Liu, Dongguo Li, Dandan Zhang, Houzao Chen and Depei Liu* State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, China * Correspondence: [email protected] Received December 25, 2012; Revised April 9, 2013; Accepted April 19, 2013 The concept of “systems biology” is raised by Hood in 1999. It means studying all components with a systematic view. Systems biomedicine is the application of systems biology in medicine. It studies all components in a whole system and aims to reveal the patho-physiologic mechanisms of disease. In recent years, with the development of both theory and technology, systems biomedicine has become feasible and popular. In this review, we will talk about applications of some methods of omics in systems biomedicine, including genomics, metabolomics (proteomics, lipidomics, glycomics), and epigenomics. We will particularly talk about microbiomics and omics for common diseases, two fields which are developed rapidly recently. We also give some bioinformatics related methods and databases which are used in the field of systems biomedicine. At last, some examples that illustrate the whole biological system will be given, and development for systems biomedicine in China and the prospect for systems biomedicine will be talked about. INTRODUCTION plus cooperation between different fields, including biology, mathematics and computer science, systems The concept of “systems biology” is raised by Hood in biomedicine has received renewed interest. -
The Human Proteome Co-Regulation Map Reveals Functional Relationships Between Proteins
bioRxiv preprint doi: https://doi.org/10.1101/582247; this version posted March 19, 2019. The copyright holder for this preprint (which was 19/03/2019not certified by peer review) is the author/funder, who hasNBT_manuscript_revised granted bioRxiv a license - toGoogle display Docs the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. 1 The human proteome co-regulation map reveals functional relationships 2 between proteins 3 Georg Kustatscher1 *, Piotr Grabowski2 *, Tina A. Schrader3 , Josiah B. Passmore3 , Michael 4 Schrader3 , Juri Rappsilber1,2# 5 1 Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh EH9 3BF, UK 6 2 Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, 13355 Berlin, 7 Germany 8 3 Biosciences, University of Exeter, Exeter EX4 4QD, UK 9 * Equal contribution 10 # Communicating author: [email protected] 11 Submission as Resource article. 12 The annotation of protein function is a longstanding challenge of cell biology that 13 suffers from the sheer magnitude of the task. Here we present ProteomeHD, which 14 documents the response of 10,323 human proteins to 294 biological perturbations, 15 measured by isotope-labelling mass spectrometry. Using this data matrix and robust 16 machine learning we create a co-regulation map of the cell that reflects functional 17 associations between human proteins. The map identifies a functional context for 18 many uncharacterized proteins, including microproteins that are difficult to study with 19 traditional methods. Co-regulation also captures relationships between proteins 20 which do not physically interact or co-localize. For example, co-regulation of the 21 peroxisomal membrane protein PEX11β with mitochondrial respiration factors led us 22 to discover a novel organelle interface between peroxisomes and mitochondria in 23 mammalian cells. -
Csbc Research and Highlights (2016-2020)
Released March 2021 CSBC RESEARCH AND HIGHLIGHTS (2016-2020) Shannon Hughes, Ph.D. Hannah Dueck, Ph.D. Dan Gallahan, Ph.D. NCI Division of Cancer Biology June 15, 2020 1 Released March 2021 CSBC Research and Highlights (2016-2020) The major goal of the NCI Cancer Systems Biology Consortium initiative is to advance the mechanistic understanding of cancer using systems biology approaches that build and test predictive models of disease initiation, progression, and response to treatment. While a translational research component is not required for CSBC-supported Centers and Projects the ultimate goal of CSBC research is to make a positive impact on the lives of cancer patients. Although not explicitly solicited, five major research themes (U54s) and questions (U01s) have emerged across the CSBC portfolio: (a) the role of tumor heterogeneity and evolution in cancer; (b) biological mechanisms of therapeutic sensitivity and resistance; (c) tumor-immune interactions in cancer progression and treatment; (d) cell-cell interactions and complexities of the tumor microenvironment; and (e) systems analysis of metastatic disease. This document provides CSBC research highlights related to the five broad areas above as compiled by NCI Program Staff. The overview is not meant to be an exhaustive literature review within these areas of cancer biology but reflect contributions from CSBC investigators. Therefore, all references are purposefully limited to CSBC-supported manuscripts to highlight the breadth of research and impact across the CSBC portfolio. In some cases, links to papers currently under review are provided. Only a subset of the over 430 consortium publications (as of May 15, 2020) are highlighted here, a more complete collection of publications and tools associated with CSBC research is available through the Cancer Complexity Knowledge Portal. -
Epigenetics and Systems Biology 1St Edition Ebook
EPIGENETICS AND SYSTEMS BIOLOGY 1ST EDITION PDF, EPUB, EBOOK Leonie Ringrose | 9780128030769 | | | | | Epigenetics and Systems Biology 1st edition PDF Book Regulation of lipogenic gene expression by lysine-specific histone demethylase-1 LSD1. BEDTools: a flexible suite of utilities for comparing genomic features. New technologies are also needed to study higher order chromatin organization and function. For example, acetylation of the K14 and K9 lysines of the tail of histone H3 by histone acetyltransferase enzymes HATs is generally related to transcriptional competence. The idea that multiple dynamic modifications regulate gene transcription in a systematic and reproducible way is called the histone code , although the idea that histone state can be read linearly as a digital information carrier has been largely debunked. Namespaces Article Talk. It seems existing structures act as templates for new structures. As part of its efforts, the society launched a journal, Epigenetics , in January with the goal of covering a full spectrum of epigenetic considerations—medical, nutritional, psychological, behavioral—in any organism. Sooner or later, with the advancements in biomedical tools, the detection of such biomarkers as prognostic and diagnostic tools in patients could possibly emerge out as alternative approaches. Genotype—phenotype distinction Reaction norm Gene—environment interaction Gene—environment correlation Operon Heritability Quantitative genetics Heterochrony Neoteny Heterotopy. The lysine demethylase, KDM4B, is a key molecule in androgen receptor signalling and turnover. Khan, A. Hidden categories: Webarchive template wayback links All articles lacking reliable references Articles lacking reliable references from September Wikipedia articles needing clarification from January All articles with unsourced statements Articles with unsourced statements from June This mechanism enables differentiated cells in a multicellular organism to express only the genes that are necessary for their own activity. -
Back-Of-The-Envelope Guide (A Tutorial) to 10 Intracellular Landscapes
MOJ Proteomics & Bioinformatics Review Article Open Access Back-of-the-envelope guide (a tutorial) to 10 intracellular landscapes Abstract Volume 7 Issue 1 - 2018 Landscape is a metaphor for conceptualizing and visualizing a score across one or Maurice HT Ling1,2 more biological entities or concepts. This review provides a cursory overview of 1Colossus Technologies LLP, Republic of Singapore 10 landscapes (in alphabetical order, copy number, fluxome, genome, molecular, 2HOHY Pte. Ltd., Republic of Singapore metabolome, mutation, phenome, proteome, regulome, and transcriptome) in intracellular biology without going into extensive depth; hence, this article can act as Correspondence: Maurice HT Ling, Colossus Technologies a first tutorial into intracellular landscapes. The value ahead is to be able to compare LLP, 8 Burns Road, Trivex, Singapore 369977, Republic of and interrogate across multiple landscapes at different resolutions. Singapore, Tel +6596669233, Email [email protected] Received: January 29, 2018 | Published: February 06, 2018 Definition of a “landscape” Since Wright,1 the concept of landscape had been used to reference a metaphoric representation of an overview of one or more biological 1 The concept of landscape was first introduced by Sewell Wright, entities. This article reviews 10 landscapes (in alphabetical order, as a metaphor to visual a scale or score across a biological entity, copy number, fluxome, genome, molecular, metabolome, mutation, such as a chromosome (Figure 1). In a linear biological entity, such as phenome, proteome, regulome, and transcriptome) in intracellular a chromosome; the biological entity is represented on the horizontal biology with the aim of presenting a cursory, back of the envelope axis and the score, such as GC content, is represented on the vertical overview without going into extensive depth into each landscape. -
Bioinformatics Strategies for Identification of Cancer Biomarkers and Targets in Pathogens Associated with Cancer
FEDERAL UNIVERSITY OF MINAS GERAIS INSTITUTE OF BIOLOGICAL SCIENCE DEPARTMENT OF GENERAL BIOLOGY GRADUATE PROGRAM OF BIOINFORMATICS BIOINFORMATICS STRATEGIES FOR IDENTIFICATION OF CANCER BIOMARKERS AND TARGETS IN PATHOGENS ASSOCIATED WITH CANCER PhD STUDENT: Debmalya Barh SUPERVISOR: Prof. Dr. Vasco Ariston de Carvalho Azevedo Page 1 of 237 DEBMALYA BARH Bioinformatics strategies for identification of cancer biomarkers and targets in pathogens associated with cancer PhD STUDENT: Debmalya Barh SUPERVISOR: Prof. Dr. Vasco Ariston de Carvalho Azevedo FEDERAL UNIVERSITY OF MINAS GERAIS INSTITUTE OF BIOLOGICAL SCIENCES BELO HORIZONTE - MG February – 2017 Page 2 of 237 043 Barh, Debmalya. Bioinformatics strategies for identification of cancer biomarkers and targets in pathogens associated with cancer [manuscrito] / Debmalya Barh. – 2017. 237 f. : il. ; 29,5 cm. Supervisor: Vasco Ariston de Carvalho Azevedo. Tese (doutorado) – Federal University of Minas Gerais, Institute of Biological Sciences. 1. Bioinformática - Teses. 2. Marcadores biológicos. 3. Doenças transmissíveis - Teses. 4. Transcriptômica - Teses. 5. MicroRNAs - Teses. I. Azevedo , Vasco Ariston de Carvalho. II. Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. III. Título. CDU: 573:004 TABLE OF CONTENTS TABLE OF CONTENTS……………………………………………………………………………………i DEDICATION………………………………………………………………………………………..……iii ACKNOWLEDGEMENTS…………………………………………………………………………..……iv LIST OF ABBREVIATIONS…………………………………………………………………….……….. v ABSTRACT………………………………………………………………………………………...……..vii -
Mrna Structure Dynamics Identifies the Embryonic RNA Regulome
bioRxiv preprint doi: https://doi.org/10.1101/274290; this version posted March 1, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. mRNA structure dynamics identifies the embryonic RNA regulome Jean-Denis Beaudoin1,*,‡, Eva Maria Novoa2,3,4,5,*, Charles E Vejnar1, Valeria Yartseva1, Carter Takacs1, Manolis Kellis2,3 and Antonio J Giraldez1,6,‡ RNA folding plays a crucial role in RNA function. However, our knowledge of the global structure of the transcriptome is limited to steady-state conditions, hindering our understanding of how RNA structure dynamics influences gene function. Here, we have characterized mRNA structure dynamics during zebrafish development. We observe that on a global level, translation guides structure rather than structure guiding translation. We detect a decrease in structure in translated regions, and we identify the ribosome as a major remodeler of RNA structure in vivo. In contrast, we find that 3’-UTRs form highly folded structures in vivo, which can affect gene expression by modulating miRNA activity. Furthermore, we find that dynamic 3’-UTR structures encode RNA decay elements, including regulatory elements in nanog and cyclin A1, key maternal factors orchestrating the maternal-to-zygotic transition. These results reveal a central role of RNA structure dynamics in gene regulatory programs. Introduction Previous studies have focused on the global analysis of RNA structures in cells at RNA carries out a broad range of functions,1 and steady-state. However, in an organism, critical RNA structure has emerged as a fundamental developmental and metabolic decisions are often regulatory mechanism, modulating various post- made when cells transition between cellular transcriptional events such as splicing2, states. -
Organizational Properties of a Functional Mammalian Cis-Regulome
bioRxiv preprint doi: https://doi.org/10.1101/550897; this version posted February 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Organizational properties of a functional mammalian cis-regulome Virendra K. Chaudhri, Krista Dienger-Stambaugh, Zhiguo Wu, Mahesh Shrestha and Harinder Singh*# Division of Immunobiology and Center for Systems Immunology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, 45220, USA #Current address: Center for Systems Immunology, Departments of Immunology and Computational and Systems Biology, The University of Pittsburgh, Pittsburgh, PA 15213 *Corresponding author Email: [email protected] bioRxiv preprint doi: https://doi.org/10.1101/550897; this version posted February 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Abstract Mammalian genomic states are distinguished by their chromatin and transcription profiles. Most genomic analyses rely on chromatin profiling to infer cis-regulomes controlling distinctive cellular states. By coupling FAIRE-seq with STARR-seq and integrating Hi-C we assemble a functional cis-regulome for activated murine B- cells. Within 55,130 accessible chromatin regions we delineate 9,989 active enhancers communicating with 7,530 promoters. The cis-regulome is dominated by long range enhancer-promoter interactions (>100kb) and complex combinatorics, implying rapid evolvability. Genes with multiple enhancers display higher rates of transcription and multi-genic enhancers manifest graded levels of H3K4me1 and H3K27ac in poised and activated states, respectively. Motif analysis of pathway-specific enhancers reveals diverse transcription factor (TF) codes controlling discrete processes. -
Genome Sequencing: Then & Now
“Aint you got an ‘ome to go to?” Genomics Transcriptomics Proteomics Metabolomics Physiomics toxicogenomics pharmacogenomics ecotoxicopharmacogenomics phosphatome, glycome, secretome metronome, mobilome, gardenome, etc… http://omics.org/index.php/Alphabetically_ordered_list_of_omes_and_omics --A-- Alignmentome: conceived before 2003. The whole set of Bacteriome: an organelle of bacteria. (Bacteriome.org). Also Carbome: BiO center. 2003. The whole set of carbone multiple sequence and structure alignments in the totality of baterial genes and proteins. based living organisms in the universe. (Carbome.org) bioinformatics. Alignments are the most important Bacterome: The totality of (Bacterome.org) Carbomics: BiO center. 2003. (Carbomics.org) representation in bioinformatics especially for homology and Behaviorome: The totality of (Behaviorome.org) Cardiogenomics: The omics approach research of evolution study. (Alignmentome.org) Behavioromics: The omics approach research of Cardiogenome (Cardiogenomics.org) in biology Alignmentomics: conceived before 2003. The study of Behavioromics (Behavioromics.org) in biology Cellome: concept is derived from the understanding that aligning strings and sequences especially in bioinformatics. Behaviourome: The totality of (Behaviourome.org) cells as a whole can be used for therapeutic purposes. As an (Alignmentomics.org) Behaviouromics: The omics approach research of important bioresource, cells are kept for biotechnology. As Alignome: 2003 . The whole set of string alignment Behaviouromics (Behaviouromics.org) in biology a distinct group concept, cellome refers to such cells and algorithms such as FASTA, BLAST and HMMER. Bibliome: 1999. EBI (European Bioinformatics Institute). their genetic materials. (Cellome.org) (Alignome.org) The whole of biological science and technology literature. Cellomics: The study of Cellome. Cellomics is also a Alignomics: The omics approach research of Alignomics Within bibliome, each word and context is interconnected company name Cellomics, Inc. -
Identification of a Primitive Intestinal Transcription Factor Network Shared Between Esophageal Adenocarcinoma and Its Precancerous Precursor State
Downloaded from genome.cshlp.org on September 28, 2021 - Published by Cold Spring Harbor Laboratory Press Research Identification of a primitive intestinal transcription factor network shared between esophageal adenocarcinoma and its precancerous precursor state Connor Rogerson,1 Edward Britton,1 Sarah Withey,2 Neil Hanley,2,3 Yeng S. Ang,2,4 and Andrew D. Sharrocks1 1School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, United Kingdom; 2School of Medical Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Sciences Centre, University of Manchester, Manchester M13 9PT, United Kingdom; 3Endocrinology Department, Central Manchester University Hospitals NHS Foundation Trust, Manchester M13 9WU, United Kingdom; 4GI Science Centre, Salford Royal NHS FT, University of Manchester, Salford M6 8HD, United Kingdom Esophageal adenocarcinoma (EAC) is one of the most frequent causes of cancer death, and yet compared to other common cancers, we know relatively little about the molecular composition of this tumor type. To further our understanding of this cancer, we have used open chromatin profiling to decipher the transcriptional regulatory networks that are operational in EAC. We have uncovered a transcription factor network that is usually found in primitive intestinal cells during embryonic development, centered on HNF4A and GATA6. These transcription factors work together to control the EAC transcrip- tome. We show that this network is activated in Barrett’s esophagus, the putative precursor state to EAC, thereby providing novel molecular evidence in support of stepwise malignant transition. Furthermore, we show that HNF4A alone is sufficient to drive chromatin opening and activation of a Barrett’s-like chromatin signature when expressed in normal human epithe- lial cells. -
Systems Biology and Experimental Model Systems of Cancer
Journal of Personalized Medicine Review Systems Biology and Experimental Model Systems of Cancer Gizem Damla Yalcin y , Nurseda Danisik y, Rana Can Baygin y and Ahmet Acar * Department of Biological Sciences, Middle East Technical University, Universiteler Mah. Dumlupınar Bulvarı 1, Çankaya, Ankara 06800, Turkey; [email protected] (G.D.Y.); [email protected] (N.D.); [email protected] (R.C.B.) * Correspondence: [email protected] These authors contributed equally. y Received: 7 September 2020; Accepted: 16 October 2020; Published: 19 October 2020 Abstract: Over the past decade, we have witnessed an increasing number of large-scale studies that have provided multi-omics data by high-throughput sequencing approaches. This has particularly helped with identifying key (epi)genetic alterations in cancers. Importantly, aberrations that lead to the activation of signaling networks through the disruption of normal cellular homeostasis is seen both in cancer cells and also in the neighboring tumor microenvironment. Cancer systems biology approaches have enabled the efficient integration of experimental data with computational algorithms and the implementation of actionable targeted therapies, as the exceptions, for the treatment of cancer. Comprehensive multi-omics data obtained through the sequencing of tumor samples and experimental model systems will be important in implementing novel cancer systems biology approaches and increasing their efficacy for tailoring novel personalized treatment modalities in cancer. In this review, we discuss emerging cancer systems biology approaches based on multi-omics data derived from bulk and single-cell genomics studies in addition to existing experimental model systems that play a critical role in understanding (epi)genetic heterogeneity and therapy resistance in cancer. -
Interactome Networks and Human Disease
Leading Edge Review Interactome Networks and Human Disease Marc Vidal,1,2,* Michael E. Cusick,1,2 and Albert-La´ szlo´ Baraba´ si1,3,4,* 1Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA 2Department of Genetics, Harvard Medical School, Boston, MA 02115, USA 3Center for Complex Network Research (CCNR) and Departments of Physics, Biology and Computer Science, Northeastern University, Boston, MA 02115, USA 4Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA *Correspondence: [email protected] (M.V.), [email protected] (A.-L.B.) DOI 10.1016/j.cell.2011.02.016 Complex biological systems and cellular networks may underlie most genotype to phenotype relationships. Here, we review basic concepts in network biology, discussing different types of interactome networks and the insights that can come from analyzing them. We elaborate on why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease. Introduction phenotypic associations, there would still be major problems Since the advent of molecular biology, considerable progress to fully understand and model human genetic variations and their has been made in the quest to understand the mechanisms impact on diseases. that underlie human disease, particularly for genetically inherited To understand why, consider the ‘‘one-gene/one-enzyme/ disorders. Genotype-phenotype relationships, as summarized in one-function’’ concept originally framed by Beadle and Tatum the Online Mendelian Inheritance in Man (OMIM) database (Am- (Beadle and Tatum, 1941), which holds that simple, linear berger et al., 2009), include mutations in more than 3000 human connections are expected between the genotype of an organism genes known to be associated with one or more of over 2000 and its phenotype.