Using Omics Data and Systems Biology Approaches Based on Network Analysis to Understand Biological Systems

Dear Colleagues,

During the 20th century, biological research was characterized by Reductionism, assuming that “the whole is no more than the sum of its parts” and that every biological theory could be deduced by studying the simplest components of a biological system. This way of thinking has allowed for a listing of those molecules that were mostly present in a cell and, eventually, has revealed the complexity of biological systems, as well as the limitations of Reductionism itself. Starting from the 21st century, scientists began to investigate biological systems from a holistic point of view, by assuming they consist of integrated molecular networks that communicate at multiple levels.

To address the complexity of biological systems, in recent decades, systems biology-oriented approaches have been developed. These novel approaches encompass many scientific disciplines, such as biology, mathematics, and computer science. In this scenario, -omics technologies are playing a fundamental role by providing massive amounts of data in an increasingly fast, efficient, and affordable manner. At the same time, more and more mathematical models have been developed to better integrate -omics data at a multiscale level. These models were, and currently are, developed as computational tools and algorithms that assist researchers in data handling, processing, and modeling with the goal of extracting relevant information in an unbiased way.

Although these approaches are increasingly adopted to address all kinds of biological questions, they still remain largely unexplored, and their full potential has yet to be reached. On the other hand, biologists and physicians are demonstrating their interest, and a growing number of studies are addressed through the system biology perspective. The landscape of possible applications is dominated by the use of transcriptomic data, usually visualized and analyzed by means of gene co-expression networks. A smaller number of works have relied on the combination of proteomic and metabolomic data with protein–protein interaction (PPI) network models. However, the improvement of high-throughput mass spectrometry-based technologies is increasing that number of studies, as well as the application of multi-omics integrative strategies.

Based on these premises, the aim of this Special Issue concerns those studies that take advantage of -omics technologies coupled to systems biology approaches, based on but not limited to PPI and co-expression networks. All research fields of application, ranging from medicine to plant science, are eligible. Special attention will also be given to the potential that may derive from the use of topological analysis and network models. In the same way, studies concerning computational tools and algorithms supporting biologists in data processing are welcome, too.

Dr. Dario Di Silvestre
Dr. Scardoni Giovanni
Dr. Gabriele Tosadori
Dr. Thanh-Phuong Nguyen
Prof. Dr. Jonathan W. Boyd
Guest Editors

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• omics data
• systems biology
• data integration
• computational biology
• network analysis
• network topology
• machine learning
• biomedicine
• pharmacotherapy personalized medicine
• microbiology
• infection
• plant biology