Encyclopedia of Systems Biology
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Systems Biology Lecture Outline (Systems Biology) Michael K
Spring 2019 – Epigenetics and Systems Biology Lecture Outline (Systems Biology) Michael K. Skinner – Biol 476/576 CUE 418, 10:35-11:50 am, Tuesdays & Thursdays January 22 & 29, 2019 Weeks 3 and 4 Systems Biology (Components & Technology) Components (DNA, Expression, Cellular, Organ, Physiology, Organism, Differentiation, Development, Phenotype, Evolution) Technology (Genomics, Transcriptomes, Proteomics) (Interaction, Signaling, Metabolism) Omics (Data Processing and Resources) Required Reading ENCODE (2012) ENCODE Explained. Nature 489:52-55. Tavassoly I, Goldfarb J, Iyengar R. (2018) Essays Biochem. 62(4):487-500. Literature Sundaram V, Wang T. Transposable Element Mediated Innovation in Gene Regulatory Landscapes of Cells: Re-Visiting the "Gene-Battery" Model. Bioessays. 2018 40(1). doi: 10.1002/bies.201700155. Abil Z, Ellefson JW, Gollihar JD, Watkins E, Ellington AD. Compartmentalized partnered replication for the directed evolution of genetic parts and circuits. Nat Protoc. 2017 Dec;12(12):2493- 2512. Filipp FV. Crosstalk between epigenetics and metabolism-Yin and Yang of histone demethylases and methyltransferases in cancer. Brief Funct Genomics. 2017 Nov 1;16(6):320-325. Chen Z, Li S, Subramaniam S, Shyy JY, Chien S. Epigenetic Regulation: A New Frontier for Biomedical Engineers. Annu Rev Biomed Eng. 2017 Jun 21;19:195-219. Hollick JB. Paramutation and related phenomena in diverse species. Nat Rev Genet. 2017 Jan;18(1):5-23. Macovei A, Pagano A, Leonetti P, Carbonera D, Balestrazzi A, Araújo SS. Systems biology and genome-wide approaches to unveil the molecular players involved in the pre-germinative metabolism: implications on seed technology traits. Plant Cell Rep. 2017 May;36(5):669-688. Bagchi DN, Iyer VR. -
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. -
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. -
Handbook of Systems Biology Concepts and Insights
Chapter 3 Interactome Networks Anne-Ruxandra Carvunis1,2, Frederick P. Roth1,3, Michael A. Calderwood1,2, Michael E. Cusick1,2, Giulio Superti-Furga4 and Marc Vidal1,2 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, 3Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, Ontario M5S-3E1, Canada, & Samuel Lunenfeld Research Institute, Mt. Sinai Hospital, Toronto, Ontario M5G-1X5, Canada, 4Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria Chapter Outline Introduction 45 Predicting Gene Functions, Phenotypes and Disease Life Requires Systems 45 Associations 53 Cells as Interactome Networks 46 Assigning Functions to Individual Interactions, Protein Interactome Networks and GenotypeePhenotype Complexes and Network Motifs 54 Relationships 47 Towards Dynamic Interactomes 55 Mapping and Modeling Interactome Networks 47 Towards Cell-Type and Condition-Specific Towards a Reference ProteineProtein Interactome Map 48 Interactomes 55 Strategies for Large-Scale ProteineProtein Interactome Evolutionary Dynamics of ProteineProtein Interactome Mapping 48 Networks 56 Large-Scale Binary Interactome Mapping 49 Concluding Remarks 57 Large-Scale Co-Complex Interactome Mapping 50 Acknowledgements 58 Drawing Inferences from Interactome Networks 51 References 58 Refining and Extending Interactome Network Models 51 INTRODUCTION generations of scientists and attempt to design new thera- Life Requires Systems peutic strategies. The next question then should be: even if we fully What is Life? The answer to the question posed by understood each of these four basic requirements of Schro¨dinger in a short but incisive book published in 1944 biology, would we be anywhere near a complete under- remains elusive more than seven decades later. -
A Platform for Parameter Unlimited Molecular Geometry Imaging Obviously Enabling Life Saving Measures in ALS
Advances in Pure Mathematics, 2018, 8, 321-334 http://www.scirp.org/journal/apm ISSN Online: 2160-0384 ISSN Print: 2160-0368 A Platform for Parameter Unlimited Molecular Geometry Imaging Obviously Enabling Life Saving Measures in ALS Walter Schubert1,2 1Molecular Pattern Recognition Research Group, Medical Faculty, Otto von Guericke University, Magdeburg, Germany 2Human Toponome Project, TNL, Munich, Germany How to cite this paper: Schubert, W. Abstract (2018) A Platform for Parameter Unlimited Molecular Geometry Imaging Obviously This article deals with a molecular geometry imaging platform capable of Enabling Life Saving Measures in ALS. mapping the spatial protein-colocalisation and anti-colocalisation code of Advances in Pure Mathematics, 8, 321-334. large molecular systems at a time. The platform called molecular unlimited https://doi.org/10.4236/apm.2018.83017 systems imaging cycler (MUSIC) robotics was applied to amyotrophic lateral Received: February 15, 2018 sclerosis (ALS). The detection of ALS specific cells with a corresponding mul- Accepted: March 24, 2018 timolecular geometry in the blood led to therapeutic depletion of these cells Published: March 27, 2018 and to recovery of the treated patient, obviously because this therapy inter- feres with pathogenic invasion of these cells into the central nervous system, Copyright © 2018 by author and Scientific Research Publishing Inc. where they axotomize motor axons. Large scale geometry MUSIC robotics This work is licensed under the Creative imaging of up to 4.5 × 10481 power of combinatorial molecular resolution is Commons Attribution International key to detect these cells and to control depletion therapy for clinical success. License (CC BY 4.0). -
IBEX: an Open and Extensible Method for High Content Multiplex Imaging of Diverse Tissues Andrea J
IBEX: An open and extensible method for high content multiplex imaging of diverse tissues Andrea J. Radtke1,13,*, Colin J. Chu2,3,13, Ziv Yaniv4, Li Yao5, James Marr6, Rebecca T. Beuschel1, Hiroshi Ichise2, Anita Gola2,7, Juraj Kabat8, Bradley Lowekamp4, Emily Speranza2,9, Joshua Croteau10, Nishant Thakur2, Danny Jonigk11, Jeremy Davis12, Jonathan M. Hernandez12, and Ronald N. Germain1,2,* 1Center for Advanced Tissue Imaging, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA 2Lymphocyte Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA 3Translational Health Sciences, Bristol Medical School, University of Bristol, United Kingdom 4Bioinformatics and Computational Bioscience Branch, NIAID, NIH, Bethesda, MD, USA 5Howard Hughes Medical Institute, Chevy Chase, MD, USA 6Leica Microsystems Inc., Wetzlar, Germany 7Howard Hughes Medical Institute, Robin Neustein Laboratory of Mammalian Cell Biology and Development, The Rockefeller University, New York, NY, USA 8Biological Imaging Section, Research Technologies Branch, NIAID, NIH, Bethesda, MD, USA 9Innate Immunity and Pathogenesis Section, Laboratory of Virology, NIAID, NIH, Hamilton, MT, USA 10Department of Business Development, BioLegend, Inc., San Diego, CA, USA 11Institute of Pathology, Hannover Medical School, Hannover, Germany, Member of the German Center for Lung Research (DZL), Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH) 12Surgical Oncology Program, Metastasis Biology Section, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, USA 13Co-first authors *Co-corresponding authors: Andrea J. Radtke ([email protected]); Ronald N. Germain, ([email protected]) Andrea J. Radtke: 0000-0003-4379-8967, Colin J. Chu: 0000-0003-2088-8310, Ziv Yaniv: 0000-0003-0315-7727, Li Yao: 0000-0003-2390-083X, James Marr: 0000-0001-8353-1429, Rebecca T. -
Systems Biology: Perspectives on Multiscale Modeling in Research on Endocrine-Related Cancers
26 6 Endocrine-Related R Clarke et al. Multiscale modeling in cancer 26:6 R345–R368 Cancer systems biology REVIEW Systems biology: perspectives on multiscale modeling in research on endocrine-related cancers Robert Clarke1, John J Tyson2, Ming Tan3, William T Baumann4, Lu Jin1, Jianhua Xuan5 and Yue Wang5 1Department of Oncology, Georgetown University Medical Center, Washington, District of Columbia, USA 2Department of Biological Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA 3Department of Biostatistics, Bioinformatics & Biomathematics, Georgetown University Medical Center, Washington, District of Columbia, USA 4Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA 5Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, Virginia, USA Correspondence should be addressed to R Clarke: [email protected] Abstract Key Words Drawing on concepts from experimental biology, computer science, informatics, f systems biology mathematics and statistics, systems biologists integrate data across diverse platforms f mathematical biology and scales of time and space to create computational and mathematical models of the f computational biology integrative, holistic functions of living systems. Endocrine-related cancers are well suited f predictive modeling to study from a systems perspective because of the signaling complexities arising from the roles of growth factors, hormones and their receptors as critical regulators of cancer cell biology and from the interactions among cancer cells, normal cells and signaling molecules in the tumor microenvironment. Moreover, growth factors, hormones and their receptors are often effective targets for therapeutic intervention, such as estrogen biosynthesis, estrogen receptors or HER2 in breast cancer and androgen receptors in prostate cancer. -
Released March 2021
Released March 2021 EVALUATION REPORT FOR THE NATIONAL CANCER INSTITUTE CANCER SYSTEMS BIOLOGY CONSORTIUM Hannah Dueck, Ph.D. Shannon Hughes, Ph.D. Dan Gallahan, Ph.D. NCI Division of Cancer Biology June 15, 2020 (Executive summary and Appendix 5 – Additional Data added August 2020) 1 Released March 2021 Table of Contents Executive Summary ........................................................................................................................... 3 Goal 1: Advance understanding of mechanisms that underlie fundamental processes in cancer .................................... 3 Goal 2: Support the broad application of systems biology approaches in cancer research .............................................. 3 Goal 3: Support the growth of a strong and stable research community in cancer systems biology ................................ 4 Introduction ...................................................................................................................................... 5 Evaluation Purpose and Charge to the External Panel .................................................................................5 Introduction to the CSBC ............................................................................................................................6 Goal 1: Advance the understanding of mechanisms that underlie fundamental processes in cancer ... 7 CSBC scientific advances .............................................................................................................................8 Dissemination -
Systems Biology of Reproduction Discussion Outline (Systems Biology) Michael K
Spring 2020 – Systems Biology of Reproduction Discussion Outline (Systems Biology) Michael K. Skinner – Biol 475/575 Weeks 1 and 2 (January 23, 2020) Systems Biology Primary Papers 1. Westerhoff & Palsson (2004) Nat Biotech 22:1249-1252 2. Joyner (2011) J Appl Physiol 111:335-342 3. Clarke, et al. (2019) Endocr Relat Cancer. 26(6):R345-368 Discussion Student 1 - Ref #1 above -How does this support evolutionary systems biology? -What was the convergence discussed? -Give an example that supports this perspective. Student 2 - Ref #2 above -What is the problem with reductionism? -What is the void? -What is the solution? Student 3 - Ref #3 above -What is multiscale modeling? -What is the role of mathematical modeling? -What network analysis insights into endocrine cancers are described? HISTORICAL PERSPECTIVE The evolution of molecular biology into systems biology Hans V Westerhoff1 & Bernhard O Palsson2 Systems analysis has historically been performed in many high-throughput technologies—more emphasis is placed on the sec- areas of biology, including ecology, developmental biology ond root, which sprung from nonequilibrium thermodynamics theory and immunology. More recently, the genomics revolution in the 1940s, the elucidation of biochemical pathways and feedback has catapulted molecular biology into the realm of systems controls in unicellular organisms and the emerging recognition of net- biology. In unicellular organisms and well-defined cell lines works in biology. We conclude by discussing how these two lines of of higher organisms, systems approaches are making definitive work are now merging in contemporary systems biology. strides toward scientific understanding and biotechnological applications. We argue here that two distinct lines of inquiry Scaling-up molecular biology in molecular biology have converged to form contemporary In the decades following its foundational discoveries of the structure systems biology. -
What and How Do Cancer Systems Biologists Explain?
What and How Do Cancer Systems Biologists Explain? Anya Plutynski and Marta Bertolaso*y In this article, we argue, first, that there are very different research projects that fall under the heading of “systems biology of cancer.” While they share some general features, they differ in their aims and theoretical commitments. Second, we argue that some explana- tions in systems biology of cancer are concerned with properties of signaling networks (such as robustness or fragility) and how they may play an important causal role in pat- terns of vulnerability to cancer. Further, some systems biological explanations are com- pelling illustrations of how “top-down” and “bottom-up” approaches to the same phe- nomena may be integrated. 1. Introduction. “Systems biology” has become an extremely popular field of study—one that has attracted the attention of funding agencies and insti- tutions around the globe. This is especially so in the context of medicine. Cancer systems biology, for example, is presented as a more “systematic” approach to such a complex disease. However, those who adopt this label are not concerned with the same family of questions and do not necessarily endorse one common set of theoretical commitments or methods. Our project in this article is to examine what—if anything—is distinctive about explanations offered in the domain of cancer systems biology. In ser- vice of this aim, we first briefly discuss the historical origins of the field. We follow a twofold classification of systems biology approaches, drawing on O’Malley and Dupré (2005): a “pragmatic” and a “systems theoretical” ap- proach.