Data Integration for Regulatory Gene Module Discovery

Data Integration for Regulatory Gene Module Discovery

Alok Mishra (Imperial College London, UK)
Copyright: © 2009 |Pages: 14
DOI: 10.4018/978-1-60566-076-9.ch030
OnDemand PDF Download:
$37.50

Abstract

This chapter introduces the techniques that have been used to identify the genetic regulatory modules by integrating data from various sources. Data relating to the functioning of individual genes can be drawn from many different and diverse experimental techniques. Each piece of data provides information on a specific aspect of the cell regulation process. The chapter argues that integration of these diverse types of data is essential in order to identify biologically relevant regulatory modules. A concise review of the different integration techniques is presented, together with a critical discussion of their pros and cons. A very large number of research papers have been published on this topic, and the authors hope that this chapter will present the reader with a high-level view of the area, elucidating the research issues and underlining the importance of data integration in modern bioinformatics.
Chapter Preview
Top

Introduction

A network of transcription factors regulating transcription factors or other proteins is called a transcriptional regulatory network or gene regulatory network. The understanding and reconstruction of this regulation process at a global level is one of the major challenges for the nascent field of bio-informatics (Schlkopf et al., 2004).

Considerable work has been done by molecular biologists over the last few years in identifying the functions of specific genes. In an ideal world it would be desirable to apply these results in order to build detailed models of regulation where the precise action of each gene is understood. However, large number of genes and the complexity of the regulation process means that this approach has not been feasible. Research into discovering causal models based on the actions of individual genes has encountered a major difficulty in estimating a large number of parameters from a paucity of experimental data. Fortunately however, biological organisation opens up the possibility of modelling at a less detailed level. In nature, complex functions of living cells are carried out through the concerted activities of many genes and gene products which are organized into co-regulated sets also known as regulatory modules (Segal et al., 2003). Understanding the organization of these sets of genes will provide insights into the cellular response mechanism under various conditions. Recently a considerable volume of data on gene activity, measured using several diverse techniques, has become widely available. By fusing this data using an integrative approach, we can try to unravel the regulation process at a more global level. Although an integrated model could never be as precise as one built from a small number of genes in controlled conditions, such global modelling can provide insights into higher processes where many genes are working together to achieve a task. Various techniques from statistics, machine learning and computer science have been employed by researchers for the analysis and combination of the different types of data in an attempt to identify and understand the function of regulatory modules.

There are two underlying problems resulting from the nature of the available data. Firstly, each of the different data types (microarray, dna-binding, protein-protein interaction and sequence data) provides a partial and noisy picture of the whole process. They need to be integrated in order to obtain an improved and reliable picture of the whole underlying process. Secondly, the amount of data that is available from each of these techniques is severely limited. To learn good models we need lots of data, yet data is only available for few experiments of each type. To alleviate this problem many researchers have taken the path of merging all available datasets before carrying out an analysis. Thus there can be some confusion regarding the term integrative because it has been used to describe both of these two very different approaches to data integration: one among datasets of the same type, for example microarrays, but from different experiments, and the other among different types of data, for example microarray and DNA binding data.

In the rest of the chapter we will describe various techniques proposed to carry out both of these types of integration and will discuss their pros and cons. We will review some of the prominent research following the former approach by Ihmels et al. (2002) and Segal et al. (2005), and work following the latter approach by Bar-Jospeh et al.(2003), Tanay at al. (2004, 2005) and Lemmens et al. (2006).

Key Terms in this Chapter

Protein-Protein Interaction: describes the interaction between different protein molecules which are of central importance for virtually every process in a living cell. Since proteins are gene products, these interactions when studied along with gene expression data, provide a better understanding of the underlying processes.

K-Means Clustering: is an algorithm to group (cluster) objects based on certain attributes into a pre-determined number (K) of groups or clusters. The grouping is done by minimizing the sum of squares of distances between individual data and the corresponding cluster centre which is calculated by averaging all the data within the cluster. It is an iterative procedure that refines the groupings in multiple steps each improving the cluster quality.

Chromatin Immunoprecipitation: also popularly known as ChIP, is an experimental method to determine whether proteins (e.g. transcription factors) bind to certain regions of cells. When used with microarrays, the technique is known as ChIP-chip, and is used to identify the binding of proteins on the entire genome simultaneously.

Clustering: is the process of organizing objects into groupings (clusters) where members of one group are similar to each other but dissimilar to the objects belonging to other groups. In the field of machine learning it is assigned under the category of unsupervised learning as we have to find structure in unlabelled data.

Bayesian Network: or belief network is a probabilistic graphical model that represents a set of variables and their probabilistic dependencies. For example, a Bayesian network can be used to calculate the probability of a disease given the expression levels of certain genes. Expert knowledge is required in order to specify the structure and probabilistic dependencies among variables (genes and disease).

Gene Ontology: also commonly referred to as GO, provides a controlled vocabulary (ontology) to describe gene and gene product attributes in various organisms. It has three sub parts that describe gene products in terms of their associated biological processes, cellular components and molecular functions in a species-independent manner. It was developed to address the need for consistent descriptions of gene products in different databases (from different or the same organisms).

Microarray: also known as a gene chip, DNA chip, or gene array is glass slide on which there is a grid pattern of small spots each of which will react with single individual genes. They are commonly used for measuring expression levels of thousands of genes simultaneously, a technique called expression profiling. For example, microarrays can be used to identify disease genes by comparing gene expression in diseased and normal cells.

Complete Chapter List

Search this Book:
Reset
Editorial Advisory Board
Table of Contents
Foreword
Ralf Herwig
Preface
Andriani Daskalaki
Acknowledgment
Andriani Daskalaki
Chapter 1
Peter Ghazal
An increasing number of biological experiments and more recently clinical based studies are being conducted using large-scale genomic, proteomic and... Sample PDF
Pathway Biology Approach to Medicine
$37.50
Chapter 2
Peter Wellstead, Sree Sreenath, Kwang-Hyun Cho
In this chapter the authors describe systems and control theory concepts for systems biology and the corresponding implications for medicine. The... Sample PDF
Systems and Control Theory for Medical Systems Biology
$37.50
Chapter 3
S. Nikolov
In this chapter we investigate how the inclusion of time delay alters the dynamic properties of (a) delayed protein cross talk model, (b) time delay... Sample PDF
Mathematical Description of Time Delays in Pathways Cross Talk
$37.50
Chapter 4
Elisabeth Maschke-Dutz
In this chapter basic mathematical methods for the deterministic kinetic modeling of biochemical systems are described. Mathematical analysis... Sample PDF
Deterministic Modeling in Medicine
$37.50
Chapter 5
Andrew Kuznetsov
Biologists have used a reductionist approach to investigate the essence of life. In the last years, scientific disciplines have merged with the aim... Sample PDF
Synthetic Biology as a Proof of Systems Biology
$37.50
Chapter 6
Tuan D. Pham
Computational models have been playing a significant role for the computer-based analysis of biological and biomedical data. Given the recent... Sample PDF
Computational Models for the Analysis of Modern Biological Data
$37.50
Chapter 7
Vanathi Gopalakrishnan
This chapter provides a perspective on 3 important collaborative areas in systems biology research. These areas represent biological problems of... Sample PDF
Computer Aided Knowledge Discovery in Biomedicine
$37.50
Chapter 8
Thomas Meinel
The function of proteins is a main subject of research in systems biology. Inference of function is now, more than ever, required by the upcoming of... Sample PDF
Function and Homology of Proteins Similar in Sequence: Phylogenetic Profiling
$37.50
Chapter 9
Nikolaos G. Sgourakis, Pantelis G. Bagos, Stavros J. Hamodrakas
GPCRs comprise a wide and diverse class of eukaryotic transmembrane proteins with well-established pharmacological significance. As a consequence of... Sample PDF
Computational Methods for the Prediction of GPCRs Coupling Selectivity
$37.50
Chapter 10
Pantelis G. Bagos, Stavros J. Hamodrakas
ß-barrel outer membrane proteins constitute the second and less well-studied class of transmembrane proteins. They are present exclusively in the... Sample PDF
Bacterial ß-Barrel Outer Membrane Proteins: A Common Structural Theme Implicated in a Wide Variety of Functional Roles
$37.50
Chapter 11
L.K. Flack
Clustering methods are used to place items in natural patterns or convenient groups. They can be used to place genes into clusters to have similar... Sample PDF
Clustering Methods for Gene-Expression Data
$37.50
Chapter 12
George Sakellaropoulos, Antonis Daskalakis, George Nikiforidis, Christos Argyropoulos
The presentation and interpretation of microarray-based genome-wide gene expression profiles as complex biological entities are considered to be... Sample PDF
Uncovering Fine Structure in Gene Expression Profile by Maximum Entropy Modeling of cDNA Microarray Images and Kernel Density Methods
$37.50
Chapter 13
Wasco Wruck
This chapter describes the application of the BeadArrayTM technology for gene expression profiling. It introduces the BeadArrayTM technology, shows... Sample PDF
Gene Expression Profiling with the BeadArrayTM Platform
$37.50
Chapter 14
Djork-Arné Clevert, Axel Rasche
Readers shall find a quick introduction with recommendations into the preprocessing of Affymetrix GeneChip® microarrays. In the rapidly growing... Sample PDF
The Affymetrix GeneChip® Microarray Platform
$37.50
Chapter 15
Jacek Majewski
Eukaryotic genes have the ability to produce several distinct products from a single genomic locus. Recent developments in microarray technology... Sample PDF
Alternative Isoform Detection Using Exon Arrays
$37.50
Chapter 16
Prerak Desai
The use of systems biology to study complex biological questions is gaining ground due to the ever-increasing amount of genetic tools and genome... Sample PDF
Gene Expression in Microbial Systems for Growth and Metabolism
$37.50
Chapter 17
Heike Stier
Alternative splicing is an important part of the regular process of gene expression. It controls time and tissue dependent expression of specific... Sample PDF
Alternative Splicing and Disease
$37.50
Chapter 18
Axel Kowald
Aging is a complex biological phenomenon that practically affects all multicellular eukaryotes. It is manifested by an ever increasing mortality... Sample PDF
Mathematical Modeling of the Aging Process
$37.50
Chapter 19
Evgenia Makrantonaki
This chapter introduces an in vitro model as a means of studying human hormonal aging. For this purpose, human sebaceous gland cells were maintained... Sample PDF
The Sebaceous Gland: A Model of Hormonal Aging
$37.50
Chapter 20
R. Seigneuric, N.A.W. van Riel, M.H.W. Starmans, A. van Erk
Complex diseases such as cancer have multiple origins and are therefore difficult to understand and cure. Highly parallel technologies such as DNA... Sample PDF
Systems Biology Applied to Cancer Research
$37.50
Chapter 21
Matej Orešic, Antonio Vidal-Puig
In this chapter the authors report on their experience with the analysis and modeling of data obtained from studies of animal models related to... Sample PDF
Systems Biology Strategies in Studies of Energy Homeostasis In Vivo
$37.50
Chapter 22
Axel Rasche
We acquired new computational and experimental prospects to seek insight and cure for millions of afflicted persons with an ancient malady. Type 2... Sample PDF
Approaching Type 2 Diabetes Mellitus by Systems Biology
$37.50
Chapter 23
Alia Benkahla, Lamia Guizani-Tabbane, Ines Abdeljaoued-Tej, Slimane Ben Miled, Koussay Dellagi
This chapter reports a variety of molecular biology informatics and mathematical methods that model the cell response to pathogens. The authors... Sample PDF
Systems Biology and Infectious Diseases
$37.50
Chapter 24
Daniela Albrecht, Reinhard Guthke
This chapter describes a holistic approach to understand the molecular biology and infection process of human-pathogenic fungi. It comprises the... Sample PDF
Systems Biology of Human-Pathogenic Fungi
$37.50
Chapter 25
Jessica Ahmed
Secretases are aspartic proteases, which specifically trim important, medically relevant targets such as the amyloid-precursor protein (APP) or the... Sample PDF
Development of Specific Gamma Secretase Inhibitors
$37.50
Chapter 26
Paul Wrede
Peptides fulfill many tasks in controlling and regulating cellular functions and are key molecules in systems biology. There is a great demand in... Sample PDF
In Machina Systems for the Rational De Novo Peptide Design
$37.50
Chapter 27
Ferda Mavituna, Raul Munoz-Hernandez, Ana Katerine de Carvalho Lima Lobato
This chapter summarizes the fundamentals of metabolic flux balancing as a computational tool of metabolic engineering and systems biology. It also... Sample PDF
Applications of Metabolic Flux Balancing in Medicine
$37.50
Chapter 28
Roberta Alfieri, Luciano Milanesi
This chapter aims to describe data integration and data mining techniques in the context of systems biology studies. It argues that the different... Sample PDF
Multi-Level Data Integration and Data Mining in Systems Biology
$37.50
Chapter 29
Hendrik Hache
In this chapter, different methods and applications for reverse engineering of gene regulatory networks that have been developed in recent years are... Sample PDF
Methods for Reverse Engineering of Gene Regulatory Networks
$37.50
Chapter 30
Alok Mishra
This chapter introduces the techniques that have been used to identify the genetic regulatory modules by integrating data from various sources. Data... Sample PDF
Data Integration for Regulatory Gene Module Discovery
$37.50
Chapter 31
Elizabeth Santiago-Cortés
Biological systems are composed of multiple interacting elements; in particular, genetic regulatory networks are formed by genes and their... Sample PDF
Discrete Networks as a Suitable Approach for the Analysis of Genetic Regulation
$37.50
Chapter 32
A. Maffezzoli
In this chapter, authors review main methods, approaches, and models for the analysis of neuronal network data. In particular, the analysis concerns... Sample PDF
Investigating the Collective Behavior of Neural Networks: A Review of Signal Processing Approaches
$37.50
Chapter 33
Paolo Vicini
This chapter describes the System for Population Kinetics (SPK), a novel Web service for performing population kinetic analysis. Population kinetic... Sample PDF
The System for Population Kinetics: Open Source Software for Population Analysis
$37.50
Chapter 34
Julia Adolphs
This chapter introduces the theory of optical spectra and excitation energy transfer of light harvesting complexes in photosynthesis. The light... Sample PDF
Photosynthesis: How Proteins Control Excitation Energy Transfer
$37.50
Chapter 35
Michael R. Hamblin
Photodynamic therapy (PDT) is a rapidly advancing treatment for multiple diseases. PDT involves the administration of a nontoxic drug or dye known... Sample PDF
Photodynamic Therapy: A Systems Biology Approach
$37.50
Chapter 36
Andriani Daskalaki
Photodynamic Therapy (PDT) involves administration of a photosensitizer (PS) either systemically or locally, followed by illumination of the lesion... Sample PDF
Modeling of Porphyrin Metabolism with PyBioS
$37.50
Chapter 37
Alexey R. Brazhe, Nadezda A. Brazhe, Alexey N. Pavlov, Georgy V. Maksimov
This chapter describes the application of interference microscopy and double-wavelet analysis to noninvasive study of cell structure and function.... Sample PDF
Interference Microscopy for Cellular Studies
$37.50
Chapter 38
Cathrin Dressler, Olaf Minet, Urszula Zabarylo, Jürgen Beuthan
This chapter deals with the mitochondrias’ stress response to heat, which is the central agent of thermotherapy. Thermotherapies function by... Sample PDF
Fluorescence Imaging of Mitochondrial Long-Term Depolarization in Cancer Cells Exposed to Heat-Stress
$37.50
Chapter 39
Athina Theodosiou, Charalampos Moschopoulos, Marc Baumann, Sophia Kossida
In previous years, scientists have begun understanding the significance of proteins and protein interactions. The direct connection of those with... Sample PDF
Protein Interactions and Diseases
$37.50
Chapter 40
Bernard de Bono
From a genetic perspective, disease can be interpreted in terms of a variation in molecular sequence or expression (dose) that impairs normal... Sample PDF
The Breadth and Depth of BioMedical Molecular Networks: The Reactome Perspective
$37.50
Chapter 41
Jorge Numata
Thermodynamics is one of the best established notions in science. Some recent work in biomolecular modeling has sacrificed its rigor in favor of... Sample PDF
Entropy and Thermodynamics in Biomolecular Simulation
$37.50
Chapter 42
Isabel Reinecke, Peter Deuflhard
In this chapter some model development concepts can be used for the mathematical modeling in physiology as well as a graph theoretical approach for... Sample PDF
Model Development and Decomposition in Physiology
$37.50
Chapter 43
Mohamed Derouich
Throughout the world, seasonal outbreaks of influenza affect millions of people, killing about 500,000 individuals every year. Human influenza... Sample PDF
A Pandemic Avian Influenza Mathematical Model
$37.50
Chapter 44
Mohamed Derouich
Dengue fever is a re-emergent disease affecting more than 100 countries. Its incidence rate has increased fourfold since 1970 with nearly half the... Sample PDF
Dengue Fever: A Mathematical Model with Immunization Program
$37.50
Chapter 45
Ross Foley
The field of histopathology has encountered a key transition point with the progressive move towards use of digital slides and automated image... Sample PDF
Automated Image Analysis Approaches in Histopathology
$37.50
About the Contributors