ABSTRACT
ABSTRACT With the sequencing of whole genomes and the development of a variety of analysis methods and technologies (omics) to measure many of the cellular components, we now have the opportunity to understand the complete descriptions of complex biological systems at a molecular, cellular, tissue, and possibly organismal levels, and to ultimately develop predictive models of human diseases. Systems biology is a biology-based interdisciplinary eld applied to biological research using a variety of genome-scale datasets from different omics technologies that focuses on complex interactions
CONTENTS
2.1 Introduction .................................................................................................. 24 2.2 Omics Technologies ..................................................................................... 26 2.3 Systems Science ............................................................................................ 27 2.4 Systems Biology............................................................................................28 2.5 Systems Biology for Understanding the Molecular Pathways and
Regulatory Networks Underlying the Specic Biological Processes.... 30 2.6 Systems Biology Approach for the Discovery of Therapeutic
Drug Targets ................................................................................................. 32 2.7 Systems Biology Approach for the Discovery of Biomarkers ................34 2.8 Multiomics Data Integration and Analysis ..............................................36 2.9 Bioinformatics and Computational Approaches for the
Integration of Multiomics Data ..................................................................38 2.10 Integration of Different Types of Datasets ............................................... 39 2.11 Data Interpretation and Presentation .......................................................40 2.12 Computational Modeling-From Pathways to Disease .........................42 2.13 Challenges in Validating the Candidate Targets and Biomarkers........43 2.14 Conclusions ...................................................................................................45 Acknowledgments ................................................................................................46 References ............................................................................................................... 47
within biological systems, using a holistic approach (as opposed to a reductionist approach). This approach analyzes the biology as an informational science, probing biological systems as individual entities as well as a whole and their dynamic interactions with the environment. Systems biology aims to model and discover emergent properties at the cellular and molecular level functioning as a system, which typically involves metabolic and signaling networks. This approach has a signicant power in searching for informative novel therapeutic targets and new diagnostic disease biomarkers since it focuses on the underlying mechanistic and molecular network perturbations of a disease. The information gained from network dynamics through experimentation and computational modeling enables us to assess the state of the networks and thus to identify molecular interactions, and derive new hypotheses to better understand the pathogenesis of the disease or prevent its progression by manipulating the network states. This approach, which includes diagnostics and therapeutics, is becoming widely used in clinical and pharmaceutical research. However, understanding the factors that contribute to the disease state is
rather challenging compared to collecting information on the different components involved. By using the newly emerging omics technologies, we can have a deeper understanding of the mechanisms playing a role in the pathogenesis of a specic disease. Thus, systems biology is generating a paradigm shift in our approaches to modeling and experimental levels. Here, we discuss systems biology as a holistic approach to obtain, inte-
grate, and analyze complex datasets from multiple experimental sources using interdisciplinary tools and review examples of modeling strategies to build cellular models on the basis of detailed multiparametric datasets.
