Systems Biology in Integration of Clinical Data
Systems biology is an integrative research strategy that combines experimental and computational biology to identify the molecular mechanisms underlying the components of a complex biological system and to obtain a quantitative description. This rapidly developing field integrates mathematical models with publicly available experimentally observed high throughput data, such as genomes, transcriptomes, proteomes, metabolomes and metagenomes, and reconstructs biochemical networks for Systematic analysis of complex systems using interdisciplinary computational tools. In the context of systems biology, the computational approach focuses on the dynamics and interplay between biological systems such as cells, tissues and organs using a holistic approach rather than reductionism that focuses on individual components and typically excludes information regarding time, space and context
In systems biology, predictive mathematical models are employed for the analysis of given experimental data in a quantitative fashion, for gaining new biological knowledge or for performing predictive simulations. There are two approaches to systems biology: the top-down and the bottom-up approach. The top-down approach is a data-driven process
where high-throughput experimental data are analysed with the objective of finding patterns or the function of biological subsystems (or pathways) in the system being studied. By contrast, the bottom up approach is typically hypothesis driven where detailed knowledge of subsystems is reconstructed into a mathematical model that can be used to describe the whole system.