Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology

Algorithms and Software Tools

Hamid R Arabnia, Quoc Nam Tran

• Publisher: Morgan Kaufmann
• Year: 2015
• Print ISBN: 9780128025086
• eBook ISBN: 9780128026465
• Topic: Business
• Subtopic: Business Intelligence
• Index: Business

Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology discusses the latest developments in all aspects of computational biology, bioinformatics, and systems biology and the application of data-analytics and algorithms, mathematical modeling, and simu- lation techniques. • Discusses the development and application of data-analytical and theoretical methods, mathematical modeling, and computational simulation techniques to the study of biological and behavioral systems, including applications in cancer research, computational intelligence and drug design, high-performance computing, and biology, as well as cloud and grid computing for the storage and access of big data sets. • Presents a systematic approach for storing, retrieving, organizing, and analyzing biological data using software tools with applications to general principles of DNA/RNA structure, bioinformatics and applications, genomes, protein structure, and modeling and classification, as well as microarray analysis. • Provides a systems biology perspective, including general guidelines and techniques for obtaining, integrating, and analyzing complex data sets from multiple experimental sources using computational tools and software. Topics covered include phenomics, genomics, epigenomics/epigenetics, metabolomics, cell cycle and checkpoint control, and systems biology and vaccination research. • Explains how to effectively harness the power of Big Data tools when data sets are so large and complex that it is difficult to process them using conventional database management systems or traditional data processing applications. - Discusses the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological and behavioral systems. - Presents a systematic approach for storing, retrieving, organizing and analyzing biological data using software tools with applications. - Provides a systems biology perspective including general guidelines and techniques for obtaining, integrating and analyzing complex data sets from multiple experimental sources using computational tools and software.

Chapter 1

Supervised Learning with the Artificial Neural Networks Algorithm for Modeling Immune Cell Differentiation

Pinyi Lu^1,2; Vida Abedi^1,2; Yongguo Mei^1,2; Raquel Hontecillas^1,2; Casandra Philipson^1,2; Stefan Hoops^1,2; Adria Carbo³; Josep Bassaganya-Riera^{1,2 1} The Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA, USA
² Nutritional Immunology and Molecular Medicine Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA, USA
³ BioTherapeutics Inc., Blacksburg, VA, USA

Abstract

Computational modeling of the immune system requires practical and efficient data analytical approaches. The immune system is composed of heterogeneous cell populations and hundreds of cell types. Each cell type is highly diverse and can be further differentiated into subsets with unique and overlapping functions. Computational systems biology approaches can be used to represent and study molecular mechanisms of cell differentiation. However, such systematic modeling efforts require the building of complex intracellular signaling models with a large number of equations to accurately represent intracellular pathways and biochemical reactions. It also requires the integration of complex processes that occur at different scales of spatiotemporal magnitude. Application of supervised learning methods, such as artificial neural network (ANN), can reduce the complexity of ordinary differential equation (ODE)–based models of intracellular networks by focusing on the input and output cytokines. In addition, this modeling framework can be efficiently integrated into multiscale tissue-level models of the immune system.

Keywords

Supervised learning

artificial neural network (ANN)

CD4 + T cell differentiation

multiscale modeling (MSM)

Acknowledgments

This work was supported in part by NIAID Contract No. HHSN272201000056C to JBR and funds from the Nutritional Immunology and Molecular Medicine Laboratory (URL: www.nimml.org).

1 Introduction

1. A Immune cell differentiation and modeling

The process of immune cell differentiation plays a central role in orchestrating immune responses. It is based on the differentiation of naïve immune cells that, upon activation of their transcriptional machinery through a variety of signaling cascades, become phenotypically and functionally different entities capable of responding to a wide range of viruses, bacteria, parasites, or cancer cells. Functionally, immune cells have been classified into either regulatory or effector cell subsets. The cell differentiation process involves a series of sequential and complex biochemical reactions within the intracellular compartment of each cell. The Systems Biology Markup Language (SBML) is an Extensible Markup Language (XML)–based format widely used to represent as well as store models of biological processes. SBML allows the encoding of biological process including their dynamics. This information can be unambiguously converted into a system of ordinary differential equations (ODEs). Of note, ODE models are extensively used to model biological processes such as cell differentiation, immune responses toward specific pathogens, autoimmune processes, or intracellular activation of specific cellular pathways (Carbo et al., 2013, 2014a, b). Several equations are usually required to adequately represent these complex immunological processes, being either at the level of the whole organism, tissue, cells, or molecules.

Carbo et al. (2014b) published the first comprehensive ODE model of CD4 + T cell differentiation, which encompassed both effector T helper (Th1, Th2, Th17) and regulatory Treg cell phenotypes. CD4 + T cells play an important role in regulating adaptive immune functions as well as orchestrating other subsets to maintain homeostasis (Zhu and Paul, 2010). They interact with other immune cells by releasing cytokines that could further promote, suppress, or regulate immune responses. CD4 + T cells are essential in B-cell antibody class switching, in the activation and growth of CD8 + cytotoxic T cells, and in maximizing bactericidal activity of phagocytes such as macrophages. Mature T helper cells express the surface protein CD4, for which this subset is referred to as CD4 + T cells. Upon antigen presentation, naïve CD4 + T cells become activated and undergo a differentiation process controlled by the cytokine milieu in the tissue environment. The cytokine environmental composition, therefore, represents a critical factor in CD4 + T cell differentiation. As an example, a naïve CD4 + T cell in an environment rich in IFNγ or IL-12 will differentiate into Th1. In contrast, an environment rich in IL-4 will induce a Th2 phenotype. Some other phenoptypes are also balanced by each other: Th17 cells, induced by IL-6, IL-1β, and TGF-β, are closely balanced by regulatory T cells (induced by TGFβ only) (Eisenstein and Williams, 2009). Furthermore, competition for cytokines by competing clones of CD4 + T cells within an expanding cell population (proliferation), cell death, and expression of other selective activation factors such as the T c...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Contributors
• Preface
• Acknowledgments
• Introduction
• Chapter 1: Supervised Learning with the Artificial Neural Networks Algorithm for Modeling Immune Cell Differentiation
• Chapter 2: Accelerating Techniques for Particle Filter Implementations on FPGA
• Chapter 3: Biological Study on Pulsatile Flow of Herschel-Bulkley Fluid in Tapered Blood Vessels
• Chapter 4: Hierarchical k-Means: A Hybrid Clustering Algorithm and Its Application to Study Gene Expression in Lung Adenocarcinoma
• Chapter 5: Molecular Classification of N-Aryloxazolidinone-5-carboxamides as Human Immunodeficiency Virus Protease Inhibitors
• Chapter 6: Review of Recent Protein-Protein Interaction Techniques
• Chapter 7: Genetic Regulatory Networks: Focus on Attractors of Their Dynamics
• Chapter 8: Biomechanical Evaluation for Bone Allograft in Treating the Femoral Head Necrosis: Thorough Debridement or not?
• Chapter 9: Diels-Alderase Catalyzing the Cyclization Step in the Biosynthesis of Spinosyn A: Reality or Fantasy?
• Chapter 10: CLAST: Clustering Biological Sequences
• Chapter 11: Computational Platform for Integration and Analysis of MicroRNA Annotation
• Chapter 12: Feature Selection and Analysis of Gene Expression Data Using Low-Dimensional Linear Programming
• Chapter 13: The Big ORF Theory: Algorithmic, Computational, and Approximation Approaches to Open Reading Frames in Short- and Medium-Length dsDNA Sequences
• Chapter 14: Intentionally Linked Entities: A Detailed Look at a Database System for Health Care Informatics
• Chapter 15: Region Growing in Nonpictorial Data for Organ-Specific Toxicity Prediction
• Chapter 16: Contribution of Noise Reduction Algorithms: Perception Versus Localization Simulation in the Case of Binaural Cochlear Implant (BCI) Coding
• Chapter 17: Lowering the Fall Rate of the Elderly from Wheelchairs
• Chapter 18: Occipital and Left Temporal EEG Correlates of Phenomenal Consciousness
• Chapter 19: Chaotic Dynamical States in the Izhikevich Neuron Model
• Chapter 20: Analogy, Mind, and Life
• Chapter 21: Copy Number Networks to Guide Combinatorial Therapy of Cancer and Proliferative Disorders
• Chapter 22: DNA Double-Strand Break–Based Nonmonotonic Logic
• Chapter 23: An Updated Covariance Model for Rapid Annotation of Noncoding RNA
• Chapter 24: SMIR: A Web Server to Predict Residues Involved in the Protein Folding Core
• Chapter 25: Predicting Extinction of Biological Systems with Competition
• Chapter 26: Methodologies for the Diagnosis of the Main Behavioral Syndromes for Parkinson’s Disease with Bayesian Belief Networks
• Chapter 27: Practical Considerations in Virtual Screening and Molecular Docking
• Chapter 28: Knowledge Discovery in Proteomic Mass Spectrometry Data
• Chapter 29: A Comparative Analysis of Read Mapping and Indel Calling Pipelines for Next-Generation Sequencing Data
• Chapter 30: Two-Stage Evolutionary Quantification of In Vivo MRS Metabolites
• Chapter 31: Keratoconus Disease and Three-Dimensional Simulation of the Cornea throughout the Process of Cross-Linking Treatment
• Chapter 32: Emerging Business Intelligence Framework for a Clinical Laboratory Through Big Data Analytics
• Chapter 33: A Codon Frequency Obfuscation Heuristic for Raw Genomic Data Privacy
• Index