Translational Bioinformatics and Systems Biology Methods for Personalized Medicine

Qing Yan

• Publisher: Academic Press
• Year: 2017
• ISBN:
  9780128043882
• Topic: Computer Science
• Subtopic: Bioinformatics
• Index: Computer Science

Translational Bioinformatics and Systems Biology Methods for Personalized Medicine introduces integrative approaches in translational bioinformatics and systems biology to support the practice of personalized, precision, predictive, preventive, and participatory medicine. Through the description of important cutting-edge technologies in bioinformatics and systems biology, readers may gain an essential understanding of state-of-the-art methodologies.

The book discusses topics such as the challenges and tasks in translational bioinformatics; pharmacogenomics, systems biology, and personalized medicine; and the applicability of translational bioinformatics for biomarker discovery, epigenomics, and molecular dynamics. It also discusses data integration and mining, immunoinformatics, and neuroinformatics. With broad coverage of both basic scientific and clinical applications, this book is suitable for a wide range of readers who may not be scientists but who are also interested in the practice of personalized medicine.

• Introduces integrative approaches in translational bioinformatics and systems biology to support the practice of personalized, precision, predictive, preventive, and participatory medicine
• Presents a problem-solving oriented methodology to deal with practical problems in various applications
• Covers both basic scientific and clinical applications in order to enhance the collaboration between researchers and clinicians
• Brings integrative and multidisciplinary approaches to bridge the gaps among various knowledge domains in the field

Part I

Concepts and Basic Tools

Outline

Chapter One. Introduction: Translational Bioinformatics and Personalized Medicine

Chapter Two. Systems and Dynamical Medicine: The Roles of Translational Bioinformatics

Chapter Three. Translational Bioinformatics Support for “Omics” Studies: Methods and Resources

Chapter Four. Data Integration, Data Mining, and Decision Support in Biomedical Informatics

Chapter One

Introduction

Translational Bioinformatics and Personalized Medicine

Abstract

The challenges that healthcare and pharmaceutical industries are facing demand improvements in various aspects, from scientific research to clinical practice. To solve these problems and improve the quality of care, it is urgent to translate the scientific findings from biomedicine into better clinical procedures and results. Because information and knowledge are the major contents in such translational process, novel bioinformatics methodologies such as data integration and knowledge discovery across various domains become critical. As an interdisciplinary field itself, translational bioinformatics provides a special opportunity for overcoming the barriers and obstacles among knowledge domains and clinical branches, and between basic science and clinical bedside practices. The novel translational bioinformatics methodologies would allow for the identification of improved drug targets, drug development pipelines, and reduced adverse reactions, with better quality of care. An important task of translational bioinformatics for supporting personalized medicine is to construct predictive models for disease progression and treatment responses.

Keywords

Data; Diseases; Information; Integration; Knowledge; Personalized medicine; Pharmacogenomics; Systems biology; Systems medicine; Translational bioinformatics

1.1. Current Challenges in Biomedicine

The tremendous challenges that healthcare and the pharmaceutical industries are facing demand improvements in various aspects, from scientific research to clinical practice. A few examples of these challenges are the rapidly rising costs of clinical care and the growing expenses in drug research and development.

On the other hand, fewer new drugs are being approved by the US Food and Drug Administration, with an increasing rate of high-profile drug withdrawals (Caskey, 2007). In the meantime, the high incidence of adverse drug reactions (ADRs) has become so severe that ADRs are one of the leading causes of morbidity and mortality although many of them are preventable (Ross et al., 2007; Yan, 2011).

Improvements in both scientific and technical aspects are needed to overcome the obstacles and meet the challenges. Considering the scientific aspect, the reductionist drug discovery methods featuring “one-size-fits-all” and single target have been found to contribute to various ADRs (Yan, 2011). These conventional approaches ignore differences between individuals and the interrelationships among drugs, humans, and the environment at various system levels.

In the technological aspect, the gaps in multidisciplinary communications and collaborations have made it difficult to translate the scientific discoveries into more efficient and effective clinical outcomes. In addition, the inadequacies of standardization in the physician ordering systems have led to numerous clinical mistakes and adverse events (Yan, 2010). Another computational challenge related to systems medicine is the integration and analysis of voluminous datasets for identifying patient and disease subtypes (Saqi et al., 2016).

In the scientific aspect, an important factor behind the challenges and obstacles is the conventional healthcare model that is reductionism based and disease centered (Ray, 2004). Such models originating from the late 19th century emphasize the linear bonds between clinical symptoms and pathological detections regarding diseases, diagnosis, and therapeutic approaches (Loscalzo and Barabasi, 2011). On the basis of the reductionist philosophies rather than the complex and nonlinear systems in reality, these simple models are no longer applicable with the novel discoveries in functional genomics and systems biology.

Specifically, approaches such as high-throughput (HTP) technologies and understandings in proteomics, metabolomics, epigenomics, and interactomics have revealed the interrelationships among the components at different system levels (see Chapter 3). Such novel findings request revolutionary improvements in healthcare practice. The novel direction in response to these demands should be heading toward the integrative paradigm that is human centered and individual based (Yan, 2008a).

This change of gear is not possible without scientific and technological support. However, the current situation is that many of the scientific discoveries just stay in the scientific laboratories but cannot benefic clinical practice (Yan, 2010). Although there have been significant scientific advancements, thorough understandings, accurate diagnosis, and effective therapies are still needed for most of the complex diseases.

To solve these problems and improve the quality of care, it is urgent not only to improve but also to translate the scientific findings in biomedicine into better clinical procedures and results (Yan, 2011). The term “translation” here emphasizes the bidirectional flow of information and knowledge between the “bench” side of the basic scientific research and the “bedside” of clinical performance.

Because information and knowledge are the major contents in such translational process, novel bioinformatics methodologies such as data management and knowledge discovery across various domains become critical (see Chapter 4). These approaches would also enable better strategies for drug discovery, development, and administration with lower costs and higher efficiencies.

By addressing the challenges in personalized medicine, translational bioinformatics provides the opportunities and detailed strategies not only for the management and analyses of biomedical data but also for the promotion of proactive and participatory health (Overby and Tarczy-Hornoch, 2013). Translational bioinformatics can serve as the pivotal “vehicle” to integrate various emerging disciplines including pharmacogenomics and systems biology toward the advancement of personalized, preventive, predictive, and participatory (P4) medicine (Hood and Flores, 2012; also see Chapter 2). This chapter will provide an introduction and extensive discussion of this “vehicle.”

1.2. Translational Bioinformatics as the “Vehicle” Toward Personalized Medicine

1.2.1. The Demand

The advancements in the emerging fields of pharmacogenomics and systems biology may contribute to the development of persona...