UC Berkeley

Biological networks are comprised of thousands of interacting components, and these networks have complicated patterns of feedback and feed-forward motifs. It is practically impossible to use intuition to determine whether simultaneously modifying multiple pharmaceutical targets has a good therapeutic response. Even when a drug is discovered which is safe in humans and highly-effective against its target, the medical effect on the disease may be underwhelming. This provides a strong impetus for developing a systems theory for pharmaceutical drug discovery. This thesis discusses system theoretic tools which are useful for doing drug discovery. The first class of tools discussed is system identification tools, and case studies of parametric modeling are given. A new statistical system identification procedure which exploits the geometric and hierarchical structure of many biological (and engineering) systems is presented, and this new procedure is applied to engineering and biological systems. The second class of tools discussed is a new set of target selection tools. Given mathematical models of biological networks, these tools select a set of targets for pharmaceutical drugs. The targets are selected to achieve good medical outcomes for patients by reducing the effect of diseases on pathways and ensuring that the targets do not too adversely affect healthy cells. The ultimate goal of the work presented in this thesis is to create a framework which can be used to rationally select new drug targets and also be able to create personalized medicine treatments which are tailored to the particular phenotypic behavior of an individual's disease.