Computational Models for the Analysis of Modern Biological Data

Computational Models for the Analysis of Modern Biological Data

Tuan D. Pham (James Cook University, Australia)
Copyright: © 2009 |Pages: 9
DOI: 10.4018/978-1-60566-076-9.ch006
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Abstract

Computational models have been playing a significant role for the computer-based analysis of biological and biomedical data. Given the recent availability of genomic sequences and microarray gene expression, and proteomic data, there is an increasing demand for developing and applying advanced computational techniques for exploring these types of data such as: functional interpretation of gene expression data, deciphering of how genes, and proteins work together in pathways and networks, extracting and analysing phenotypic features of mitotic cells for high throughput screening of novel anti-mitotic drugs. Successful applications of advanced computational algorithms to solving modern life-science problems will make significant impacts on several important and promising issues related to genomic medicine, molecular imaging, and the scientific knowledge of the genetic basis of diseases. This chapter reviews the fusion of engineering, computer science, and information sciences with biology and medicine to address some latest technical developments in the computational analyses of modern biological data: microarray gene expression data, mass spectrometry data, and bioimaging.
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Microarray Gene Expression Data

Microarrays are a relatively new biotechnology that provides novel insights into gene expression and gene regulation (Brazma and Vilo, 2000; Whitchurch, 2002; Zhang at al, 2002; Pham et al, 2006a). Microarray technology has been applied in diverse areas ranging from genetics and drug discovery to disciplines such as virology, microbiology, immunology, endocrinology, and neurobiology. Microarray-based methods are the most widely used technology for large-scale analysis of gene expression because they allow simultaneous study of mRNA abundance for thousands of genes in a single experiment (Kellum and Liu, 2003). The generation of DNA microarray image spots involves the hybridization of two probes labelled with a fluorescent red dye or a fluorescent green dye. The relative image intensity values of the red dye and the green dye on a particular spot of the arrays indicate the expression ratio for the corresponding gene of the two samples from which the mRNAs have been extracted. Thus, robust image processing of microarray spots plays an important role in microarray technology (Nagarajan, 2003; Liew at al, 2003; Lukac et al, 2004).

DNA microarray data consists of a large number of genes and a relatively small number of experimental samples. The number of genes on an array is in the order of thousands, and because this far exceeds the number of samples, dimension reduction is needed to allow efficient analysis of data classification techniques. Many statistical and machine-learning techniques based different computational methodologies have been applied for cancer classification in microarray experiments. These techniques include linear discriminant analysis, k-nearest neighbor algorithms, Bayes classifiers, decision trees, neural networks, and support vector machines (Dudoit and Fridlyand, 2003; Golub et al, 1999; Guyon et al, 2002). Nevertheless, common tasks of most classifiers are to perform feature selection and decision logic.

Based on the motivation that conventional statistical methods for pattern classification break down when there are more variables (genes) than there are samples, Nguyen and Rocke (2002) proposed a partial least-squares method for classifying human tumor samples using microarray gene expression data. Zhou et al. (2004) proposed a Bayesian approach for selecting the strongest genes based on microarray gene expression data and the logistic regression model for classifying and predicting cancer genes. Yeung et al. (2005) reported that conventional methods for gene selection and classification do not take into account model uncertainty and use a single set of selected genes for prediction, and introduced a Bayesian model averaging method, which considers the uncertainty by averaging over multiple sets of overlapping relevant genes. Furey et al. (2000) applied support vector machines for the classification of cancer tissue samples or cell types using microarrays. Lee et al. (2003) proposed a Bayesian model for gene selection for cancer classification using microarray data. Statnikov et al. (2005) carried out a comprehensive evaluation of classification methods for cancer diagnosis based on microarray gene expression data.

Recently Pham et al. (2006b) carried out cancer classification by transforming microarray data into spectral vectors. The same authors used the spectral difference or spectral distortion between the pair of spectra for pattern comparison, which appears to be a potential approach for the cancer classification using microarray gene expression data.

Key Terms in this Chapter

Time-Lapse Microcopy Imaging: Microscopy imaging that captures images of dynamic events at predetermined time intervals.

Naive Bayes Classifier: A c lassification technique that is based on the so-called Bayesian theorem.

Feature Extraction: Extraction of representative properties of an object for the purpose of classification.

Support Vector Machines: machine learning algorithms that map input vectors to a higher dimensional space where a maximal separating hyperplane is constructed.

Wavelet Transform: The representation of a signal in terms of scaled and translated copies of a finite length or fast decaying oscillating waveform.

Linear Predictive Coding: An encoding method that allows the prediction of the value of the signal at each sample as a linear combination of the past values of the signal.

Proteomics: The study of the structure and function of proteins,

Geostatistics: Applied statistics of spatially correlated data

High Content Screening: A high throughput platform for understanding the functions of genes, RNA, proteins, and other cellular constituents at the level of the living cell.

k-Nearest Neighbor Algorithms: Methods for classifying objects based on closest training samples in the feature space.

Genetic Algorithms: Biologically inspired optimization methods.

Spectral Distortion: A measure of mismatch between two signals based on their spectral properties.

Artificial Neural Networks: Machine learning methods consisting of interconnecting artificial neurons that simulate the properties of biological neural networks.

Feature Reduction: Compression of the feature space of an object.

Cluster Analysis: Methods for grouping objects of similar kind into respective categories.

Microarray Gene Expression Data: Modern biotechnological data generated for studying the interaction of large numbers of genes and how a cell’s regulatory networks control genes simultaneously.

Biomarker Discovery: Discovery of molecular parameters associated with the presence and severity of specific disease states.

Decision Trees: Predictive models that map the observations about an event to infer about its target value.

Mass Spectrometry Data: A dataset that consists of relative intensities a chromatographic retention time and the ratios of molecular mass over charge. The mass spectrum for a sample is a function of the molecules and used to test for presence or absence of one or more molecules which may relate to a diseased state or a cell type.

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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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About the Contributors