The Use of Interval Methods in Signal Processing and Control for Systems Biology

The development of approaches for understanding the complex dynamics of biological systems is a growing research area in electrical engineering, particularly in the fields of signal processing and controls. The focus of our research is the exploitation of the parallels between engineering and biology through the development of optimization and identification methods. Specifically, this research consists of developing methods for the estimation of unmeasured states, the identification of parameters of kinetic models and the validation of biochemical models. This work falls under the general research topic of systems biology. We explore the use of interval analysis in developing numerical algorithms for optimization and validation of systems biology problems. A major attribute of this method is that convergence to global minima is guaranteed. This paper includes a development of an adaptive interval optimization method based on the branch-and-bound method known as smooth interval branch-and-bound. One potential impact of this research is the development of more accurate models of biological systems. This will aid in the design of drugs for cancer and disease treatment and aid in the study of how they propagate