Computational Biology of Embryonic Stem Cells


by

Ming Zhan

DOI: 10.2174/97816080502531120101
eISBN: 978-1-60805-025-3, 2012
ISBN: 978-1-60805-434-3

  
  


Indexed in: Scopus

Computational biology in combination with large-scale biology has played a critical role in exploring the great potentials of embryoni...[view complete introduction]
PDF US $
- Single user / Non-Library usage: 34
- Multi user / Library usage: 136
Print-On-Demand (P.O.D): *106
Special Offer for Single user / Non-Library usage (PDF + P.O.D): *123

*(Excluding Mailing and Handling)
Purchase: Book Chapters
Download Flyers

Table of Contents

Foreword , Pp. i

MahendraRao
Download Free

Preface , Pp. ii-iv (3)

Ming Zhan
Download Free

List of Contributors , Pp. v-ix (5)

Ming Zhan
Download Free

A Genetic Network Identification Algorithm Combining Experiment and Computation , Pp. 3-24 (22)

Ipsita Banerjee, Keith Task and Spandan Maiti
Purchase Chapter

Causality Reasoning and Discovery for Systems Biology Investigations , Pp. 25-43 (19)

Yi Liu, Hong Yu and Jing-Dong J. Han
Purchase Chapter

Exploring Stem Cell Gene Expression Signatures using AutoSOME Cluster Analysis , Pp. 44-70 (27)

Aaron M. Newman and James B. Cooper
Purchase Chapter

Image-Enhanced Systems Biology: A Multiscale, Multidimensional Approach to Modeling and Controlling Stem Cell Function , Pp. 71-87 (17)

George Plopper, Melinda Larsen and Bülent Yener
Purchase Chapter

Computational Analysis of DNA-Methylation and Application to Human Embryonic Stem Cells , Pp. 88-108 (21)

Lukas Chavez
Purchase Chapter

Transcriptional Co-Expression Analysis of Embryonic Stem Cells , Pp. 109-132 (24)

Yu Sun and Ming Zhan
Purchase Chapter

Computational Analysis of Alternative Polyadenylation in Embryonic Stem Cells and Induced Pluripotent Cells , Pp. 133-146 (14)

Zhe Ji, Mainul Hoque and Bin Tian
Purchase Chapter

Genomics of Alternative Splicing in Stem Cells , Pp. 147-160 (14)

Stephanie C. Huelga and Gene W. Yeo
Purchase Chapter

Computational Biology of microRNA-Pluripotency Gene Networks in Embryonic Stem Cells , Pp. 161-179 (19)

Preethi H. Gunaratne and Jayantha B. Tennakoon
Purchase Chapter

Role of Translationally Regulated Genes in Embryonic Stem Cell Differentiation: Integration of Transcriptome and Translational State Profiling , Pp. 180-192 (13)

Qian Yi Lee, Winston Koh, Prabha Sampath and Vivek Tanavde
Purchase Chapter

Paired SAGE-microarray Expression Data Sets Reveal Antisense Transcripts Differentially Expressed in Embryonic Stem Cell Differentiation , Pp. 193-215 (23)

Reatha Sandie, Christopher J. Porter, Gareth A. Palidwor, Feodor Price, Paul M. Krzyzanowski, Enrique M. Muro, Sebastian Hoersch, Mandy Smith, Pearl A. Campbell, Carolina Perez-Iratxeta, Michael A. Rudnicki and Miguel A. Andrade-Navarro
Purchase Chapter

Computational Analysis of ChIP-seq Data and Its Application to Embryonic Stem Cells , Pp. 216-229 (14)

Xu Han and Lin Feng
Purchase Chapter

The FunGenES Database: A Reference and Discovery Tool for Embryonic Stem Cells and their Derivatives , Pp. 230-245 (16)

Antonis K. Hatzopoulos
Purchase Chapter

Index , Pp. 246-250 (5)

Ming Zhan
Download Free

Foreword

The stem cell field has seen several dramatic breakthroughs in the past decade. These include improvements in deriving ESC lines by a variety of methods as well as using adult cells and transforming them into pluripotent cells using subsets of transcription factors, microRNA and other regulatory proteins. These advance have resulted in an explosion in the number of pluripotent cells available.

Pluripotent stem cells have been derived from young and aged individuals, individuals of varying ethnic backgrounds, individuals carrying specific disease mutations as well as from cohorts of individuals carrying specific genetic traits.

It has quickly become clear that one needs to develop methods of comparing cell types and rapidly analyzing similarities and differences so that one can hone in on key regulatory pathways or molecules. Computational or mathematical simulation is also a powerful tool allowing quantitative description and systems exploration of pathways or networks. Moreover,methods or tools must be developed to dissect the large-scale and high-throughput “omics” data available for studiesof these important cells.

It is in response to these needs that Dr. Ming Zhan has compiled this book on Computational Biology of Embryonic Stem Cells. Leading authorities in the field describe theories and techniques to extract and explore useful information from different cell populations that have been grown in different laboratories under differing conditions. The contributors also describe various algorithms and data mining techniques to understand the roles for microRNA, antisense, methylation and transcription factor regulation of stem cell proliferation and differentiation.

It is rare to find all of these methodologies andanalyses well compiled in a useful and logically organized manner, and I hope that the readers of this book will find it as valuable as I did.

Mahendra Rao, Ph.D.
Director, Center for Regenerative Medicine
National Institute of Health, USA


Preface

Embryonic stem (ES) cells hold a great promise for regenerative medicine and treatment of illness such as neurodegenerative diseases, spinal cord injury, diabetes, and heart disease. In recent years, computational biologyhas significantly changed the landscape of the ES cell research, resulting in many significant discoveries. This book brings together reviews and reports from leading scientists to provide a comprehensive and updated introduction to the field of computational biology of ES cells.

The topics of this book is diverse, ranging from pure to applied computational biology research, and from integrated to systems biology studies on ES cells. We first introduce various bioinformatics algorithms and computational methodologies used for stem cell research. Banerjee et al. describe a method for reconstructing gene regulatory networks governing ES cell differentiation based ondiscrete temporal gene expression data.The method is formulated using an inherent feature of biological network, the sparsity of interconnection between transcription factors. Liu et al. review algorithms for structure learning of Bayesian networks and elucidating causal knowledge. The chapter answers the question about to what extent and by which means we can extract valuable biological knowledge from various experiment data. Newman et al. present AutoSOME, a novel unsupervised method for automatic clustering of large, high-dimensional data without prior knowledge of cluster number or structure. By applying this novel method on stem cell microarray data, the authors illustrate how to identify gene co-expression modules along with clusters of cellular phenotypes in a single step, and how to visualize transcriptome variation among stem cells using an intuitive network display. Inintroducing mathematical modeling studies on ES cells, Plopperet al. describe a multi-scale and multi-dimensional modeling of stem cells. The integrative modeling highlights how data gathered from one level can benefit research across multiple scales, addressing the challenge in analyzing increased amount of information of stem cells, which spans the entire breadth of biological fields, from molecular biology to population biology.

We next present focused computational analyses of the genome, transcriptome, proteome, epigenome and regulatory network of ES cells, and database for stem cell research. Chavez provides an overview of experimental techniques and computational methods for genome-wide methylation analysis, with focus on human ES cells. Sun & Zhan demonstratetranscriptional co-expression profiling of ES cells at global, pathway, and chromosome levels for exploring molecular mechanisms guiding ESC self-renewal and differentiation.Jiet al. describe computational analysis of alternative polyadenylation in ES cells and induced pluripotent stem (iPS) cells. The computational method describedallows examining regulation of 3’UTR by alternative polyadenylationusing DNA microarray data, and post-transcriptional regulation through cis-elements in 3’UTRs. Huelga & Yeo describegenome-wide detection of alternative splicing and highlight the importance of cis- and trans-factors in regulating alternative splicing in stem cells. Gunaratne & Tennakoon illustrate a microRNA-pluripotency gene network in ES Cells, and review the latest experimental technologies and computational algorithms forrevealing genomic and epigenetic changes associated with self-renewal and differentiation of ES cells. Lee et al.presentan integrated analysis of transcriptome and translation states for genome-wide identification of translationally regulated genes in ES cells. Sandie et al. demonstrate computational identification of non-coding antisense transcripts implicated in stem cell differentiation based on SAGE data and gene expression data. Han & Feng review the state-of-the-art ChIP-seq analysis tools developed for predicting ChIP-enriched genomic sites, and present a computational analysis of ChIP-seq data in ES cells. Finally, Hatzopoulos introduces the “Functional Genomics in Embryonic Stem Cells” (FunGenES) database. The database allows searching for gene expression and co-expression profiling data of mouse ES cells, as well as functional information of the relevant genes in embryonic development, adult homeostasis and disease.

The contributing authors of the book not only describe their researches and review the latestdevelopment of the field, but also discussthe future perspectives of the research. The book is a valuable reference and handbook for researchers and clinicians conducting stem cell research, and students and medical professionals interested in regenerative medicine, developmental biology, bioinformatics and computational biology.

Ming Zhan, Ph.D.
Associate Professor, Cornell University Weiss Medical College
Chief of Bioinformatics, The Methodist Hospital Research Institute

List of Contributors

Editor(s):
Ming Zhan
The Methadolist Hospital, Research Institute
USA




Contributor(s):
Miguel A. Andrade-Navarro
Ottawa Hospital Research Institute
Robert-Rössle-Strasse
Ottawa
ON
Canada


Ipsita Banerjee
Department of Chemical and Petroleum Engineering
University of Pittsburgh
Pittsburgh
PA
USA


Pearl A. Campbell
Ottawa Hospital Research Institute
Ottawa
ON
Canada


Lukas Chavez
Department of Vertebrate Genomics
Max-Planck-Institute for Molecular Genetics
Berlin
Germany


James B. Cooper
Department of Molecular
Cellular and Developmental Biology, University of California
Santa Barbara, CA
USA


Lin Feng
School of Computer Engineering,
Nanyang Technological University
Singapore


Preethi H. Gunaratne
Department of Biology & Biochemistry, Department of Pathology
University of Houston, Human Genome Sequencing Center, Baylor College of Medicine
Houston
TX
USA


Jing-Dong J. Han
Chinese Academy of Sciences Key Laboratory of Molecular Developmental Biology, Center for Molecular Systems Biology,
Institute of Genetics and Developmental Biology, Chinese Academy of Sciences
Beijing
China


Han Xu
School of Computer Engineering
Nanyang Technological University
Singapore


Antonis K. Hatzopoulos
Department of Medicine
Division of Cardiovascular Medicine, Vanderbilt University
Nashville
TN
USA


Sebastian Hoersch
Informatics and Computing Core, Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology
Cambridge
MA
USA


Mainul Hoque
Department of Biochemistry and Molecular Biology
Graduate School of Biomedical Sciences and New Jersey Medical School
Newark
New Jersey
USA


Stephanie C. Huelga
Bioinformatics Graduate Program, Stem Cell Initiative, Department of Cellular and Molecular Medicine
Institute for Genomic Medicine, University of California
San Diego
CA
USA


Zhe Ji
Department of Biochemistry and Molecular Biology, Graduate School of Biomedical Sciences and New Jersey Medical School
University of Medicine and Dentistry of New Jersey
Newark
New Jersey
USA.


Winston Koh
Bioinformatics Institute, Agency for Science Technology and Research (A*STAR)
Singapore


Paul M. Krzyzanowski
Ottawa Hospital Research Institute
Ottawa
ON
Canada


Melinda Larsen
Department of Biological Science
State University of New York
Albany
NY
USA


Qian Yi Lee
Bioinformatics Institute, Agency for Science Technology and Research (A*STAR)
Singapore



Yi Liu
Chinese Academy of Sciences Key Laboratory of Molecular Developmental Biology, Center for Molecular Systems Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences
Beijing
China


Spandan Maiti
Department of Chemical and Petroleum Engineering
University of Pittsburgh
Pittsburgh
PA
USA


Enrique M. Muro
Max Delbrück Center for Molecular Medicine, Robert-Rössle-Strasse
Berlin
Germany


Aaron M. Newman
Department of Molecular, Cellular and Developmental Biology
University of California
Santa Barbara
CA
USA


Gareth A. Palidwor
Ottawa Hospital Research Institute
Ottawa
ON
Canada


Gareth A. Palidwor
Ottawa Hospital Research Institute
Ottawa
ON
Canada


Carolina Perez-Iratxeta
Ottawa Hospital Research Institute
Ottawa
ON
Canada


Christopher J. Porter
Ottawa Hospital Research Institute
Ottawa
ON
Canada


Feodor Price
Ottawa Hospital Research Institute
Ottawa
ON
Canada


Michael A. Rudnicki
Ottawa Hospital Research Institute
Ottawa
ON
Canada


Prabha Sampath
Institute of Medical Biology, Agency for Science Technology and Research (A*STAR),
Singapore


Reatha Sandie
Ottawa Hospital Research Institute
Ottawa
ON
Canada


Mandy Smith
Ottawa Hospital Research Institute
Ottawa
ON
Canada


Yu Sun
Bioinformatics Unit, National Institute on Aging, NIH
Baltimore
MD
USA


Vivek Tanavde
Bioinformatics Institute, Agency for Science Technology and Research (A*STAR)
Singapore


Keith Task
Department of Chemical and Petroleum Engineering
Pittsburgh
PA
USA


Jayantha B. Tennakoon
Department of Biology & Biochemistry
Houston
TX
USA


Bin Tian
Department of Biochemistry and Molecular Biology, Graduate School of Biomedical Sciences and New Jersey Medical School
University of Medicine and Dentistry of New Jersey,
Newark
New Jersey
USA


Bülent Yener
Department of Computer Science
Rensselaer Polytechnic Institute
Troy
NY
USA


Gene W. Yeo
Bioinformatics Graduate Program, Stem Cell Initiative, Department of Cellular and Molecular Medicine
Institute for Genomic Medicine, University of California
San Diego
CA
USA


Hong Yu
Chinese Academy of Sciences Key Laboratory of Molecular Developmental Biology, Center for Molecular Systems Biology
Institute of Genetics and Developmental Biology, Chinese Academy of Sciences
Beijing
China


Ming Zhan
Bioinformatics Unit, National Institute on Aging, NIH. Baltimore, MD, USA(currently, The Methodist Hospital Research Institute)
Houston
TX
USA




Advertisement


Webmaster Contact: urooj@benthamscience.org Copyright © 2014 Bentham Science