Systems of Bioinformatics is a relatively new approach, which lies in the intersection of systems biology and classical bioinformatics. It focuses on integrating information across different levels using a bottom-up approach as in systems biology with a data-driven top-down approach as in bioinformatics. The advent of omics technologies has provided the stepping-stone for the emergence of Systems Bioinformatics. These technologies provide a spectrum of information ranging from genomics, transcriptomics and proteomics to epigenomics, pharmacogenomics, metagenomics and metabolomics. Systems Bioinformatics is the framework in which systems approaches are applied to such data, setting the level of resolution as well as the boundary of the system of interest and studying the emerging properties of the system as a whole rather than the sum of the properties derived from the system’s individual components. A key approach in Systems Bioinformatics is the construction of multiple networks showing each level of the omics spectrum and their integration in a layered network that exchanges information within and between layers.
Bioinformatics and computational biology have made significant breakthroughs towards the analysis and interpretation of the data obtained from the above-mentioned omics technologies. The sheer size of data generated by these high-throughput methodologies, coupled with the need to analyze, integrate and concurrently interpret this avalanche of information in a systemic way, has paved the way to the upcoming field of Systems Bioinformatics.
Systems approaches
Biological data have tremendously expanded both in size and complexity. Systems Bioinformatics focuses on the investigation of such vast and complex biological systems and their within interactions using a ‘holistic’ rather than a ‘reductionist’ approach, much like the systems biology field. The reductionism’s approach in biology is epitomized by molecular biology, which in the past two decades has led to the generation of a plethora of omics data. These data provide information on the building blocks of the entire organism at different scales and for different types of cells, tissues and organs. Data on DNA fragments, genes, RNA fragments, peptides, proteins and metabolites measured in short time and space intervals provide a spatiotemporal distribution of these building blocks under various states of the organism.
Biological network basics
Casting biological systems as networks and analyzing their topology can be useful in understanding how such systems are organized. A network is a collection of nodes or vertices connected by edges, arcs or lines. Basic network measures can be used to analyze the components of a network, both locally and globally, and facilitate the analysis and extraction of useful information from a biological network. The most elementary characteristic of a node is its degree, i.e. the number of edges connecting one node to its neighbours.
Biological network construction methods
Biological networks can be split into two broad categories that best characterize their underlying nature:
evidence-based molecular networks that rely on experimental evidence for specific molecular interactions such as PPI networks, metabolic networks and regulatory networks
statistically inferred networks, which are based on statistical inference that rely on interactions between components established by means of statistical analysis.
Module-based approaches and network signatures
To extract biologically meaningful information from the networks and establish links to disease, methods have been developed for scanning and parsing these networks. These methods allow for significant sub-networks to be highlighted in the sea of nodes and edges, often representing important ‘modules’ that are associated with a specific disease. This type of network ‘traversing’ can be performed between networks obtained from data from different phenotypes from the same disease (staging, subtyping) or from similar diseases (disease hierarchy).
Module identification can be performed using Systems Bioinformatics approaches and constitutes a powerful tool for delineating the systematic molecular basis of disease.
Network controllability
Network controllability is defined as the potential to steer a network from a given initial state to a final desired state within a finite time and with appropriate inputs/modifications. Such modifications are also known as ‘network attacks’. Another Systems Bioinformatics’ emerging research direction related to the dynamic properties of complex networks is the way in which these properties are spreading and/or reforming during network attacks.
Network integration
A key approach in Systems Bioinformatics is the construction of multiple networks representing each level of the omics spectrum and their integration in a layered network that exchanges information within and between layers. Such an integration can be achieved via various ways.
Moreover, we provide examples of success stories and case studies that utilize such methods and tools to significantly advance research in the fields of systems biology and systems medicine.
