Systems biology is a new interdisciplinary science that derives from biology, mathematics, computer science, physics, engineering, and other disciplines. Most biological systems are too complex for even the most powerful computational models to capture all the system properties. A useful mode, however, should be able to accurately conceptualize the system under study and provide reliable predictive values. To accomplish this, a certain level of abstraction may be required that focuses on the system behaviors of interest while neglecting some of the other details. Systems biology does not investigate individual genes or proteins one at a time, as has been the highly successful mode of biology for the past 30 years. Rather, it investigates the behavior and relationships of all the elements in a particular biological system while it is functioning. Systems biomedicine can be described as an emerging approach to biomedical science that seeks to integratively infer, annotate, and quantify multi-variate complexity of the molecular and cellular processes of living systems, with ultimate aim of constructing formal algorithmic models for prediction of process outcomes from component input. Systems approached are characterized by several key attributes:

  1. A pursuit of quantitative and precise data
  2. The comprehensiveness and completeness of the datasets used
  3. A focus on interconnectivity and networks of the component parts
  4. A willingness to define, measure and manipulate biological complexity
  5. An interest to computationally (and therefore quantitatively) predict outcomes

Network analyses have been conducted primarily where the system is cell-based, such as immunology or cancer, or where the tissue is homogeneous such as the heart or liver. In our laboratory we are interested in identifying microRNA-gene networks that are essential in human inflammatory diseases and cancer, through integration of genomic and proteomic data using novel bioinformatic algorithms.

Network Model