Systems biology involves gathering comprehensive sets of data that define and quantify the elements of a particular biological system and computationally analyzing these data sets to establish functional and dynamic connections. The premise is that no molecule in biology acts alone. There are thousands of cause-and-effect interactions that occur. Understanding of those interactions and their effect on the biological system is enabled through massive integration of data–experimental and theoretical. Systems biology in a clinical context is called systems medicine. Representative research areas of interest in systems biology and medicine are standards for sharing data across different hardware, software, operating systems, etc; tools that will aid in data collection at a volume and quality that is consistent with the use of statistical methods; machine learning techniques for knowledge discovery; protein-protein interaction network analysis; novel algorithms for next generation DNA sequencing; evaluation and testing ontologies for describing and organizing biological and medical information; and languages for capturing bio-informatics analyses and computational processes

 

key words

Systems biology; Machine learning; Statistical methods; Ontologies; Testing; Next generation DNA sequencing; Protein-protein interaction networks; Standards;

Citizenship:  Open to U.S. citizens

Level:  Open to Postdoctoral applicants