Abstract
Bioengineering has emerged as a major interdisciplinary research area that links such classical fields as biology, chemistry, and engineering. Each of these fields brings a unique set of tools to solve the problems in this emergent discipline. In this talk, the quantitative tools from process systems theory will be presented as an enabling methodology for solving bioengineering problems.

Much of our work is motivated by the observation that the human body is a complex chemical factory, composed of many highly interactive multivariable subsystems. It has also been demonstrated that natural "controllers" achieve tight regulation of these systems under a variety of conditions in order to meet stringent performance requirements - thus achieving robust performance. Systems engineering tools, including model identification, sensitivity analyses, and dynamic programming, find unique roles in characterizing the rich behavior exhibited by such systems. The systems perspective is also valuable in analyzing the integrative behavior of such complex multiscale stochastic systems, as opposed to traditional reduction techniques.

Three examples will be used to motivate these ideas: (i) a local neuronal reflex that influences central cardiac control in the rat, (ii) robustness analysis of a gene network that regulates circadian rhythm in Drosopholia, and (iii) identification of gene network models from expression data and regulatory activity data.