Optimal Identification, Analysis, and Engineering of Bionetworks and Biomolecules

Xiao-jiang Feng, Department of Chemistry, Princeton University

Recent advances in high-throughput technology, analytical biochemistry, and synthetic biology provide the opportunity to study the holistic behavior of bionetworks and manipulate their properties in a desirable fashion. The complexity of bionetworks, however, determines that traditional methods based on experience, intuition, or simple analysis can often be ineffective for achieving these goals. This talk will introduce several newly developed computational methods that optimally integrate with the laboratory capabilities to achieve reliable and efficient identification, analysis, and engineering of bionetworks and biomolecules. These methods have been applied to gene network connectivity inference, optimal model identification, genetic and neural network engineering, and molecular discovery and evolution. The results show that employing the concepts and techniques in optimal control engineering not only enables better experimental design and data analysis, but can also provide deeper understanding of the underlying biological principles.