Department of Electrical and Computer Engineering, Texas Tech University
Current one-size-fits-all methods of cancer treatment select drugs that target the whole population, or some large segment of the population. The outlined research is based on the vision that considering a patient?s individual genetic make-up will help in selecting drugs that yields the best personalized prognosis. New generation of cancer drugs designed to interfere with specific molecular targets have been developed in the last decade. The success of these targeted drugs have been limited since the selection of the drugs and the time and sequence of drug administration are based on empirical principles without mathematical models to design and estimate the efficiency of intervention strategies. The goal of this proposal is to provide a theoretical and computational framework to estimate the uncertainties in genetic regulatory network modeling and generate robust therapeutic strategies for genetic diseases such as cancer.
One of the objectives of genetic regulatory network modeling is to design and analyze therapeutic intervention strategies aimed at moving the network out of undesirable states, such as those associated with disease, and into desirable ones. However, limited experimental data prevent accurate inference of the mathematical model of the genetic regulatory network. For the success of a mathematically designed intervention strategy for genetic diseases, it is critical to (a) study the effect of modeling errors on the predictive power of the inferred network model and on the intervention outcome and (b) design control strategies that posses some degree of robustness to the modeling uncertainties. The project is timely and appropriate as increasing the effectiveness of cancer therapies in the clinical setting will require intervention strategies that possess some degree of robustness to the uncertainties in the modeling process. The proposed research is expected to provide bounds on the performance of mathematically designed intervention strategies. Algorithms to design robust control strategies with the objectives of avoiding extremely undesirable results (minimax design) or improving the expected chances of success (Bayesian approach) will be developed. The developed strategies will be applied to the problem of intervention in human cancer cell lines and targeted therapy in mice models, in collaboration with the PI?s medical collaborators at Translational Genomics Research Institute (TGen) and University of Texas Health Sciences Center at San Antonio (UTHSCSA).
The interdisciplinary nature of this proposal promises to foster cross-fertilization of ideas between electrical engineering and systems biology through research and education. Some of the salient features of the education and outreach plan are to (i) develop an interdisciplinary graduate course on Genetic Regulatory Network Modeling and Control and an undergraduate course on Engineering Applications in Biology, (ii) establish a research laboratory at Texas Tech University to prepare students with skills in Genomic Signal Processing (GSP) research, (iii) involve undergraduates in GSP research through Project Laboratory Courses and the TTU Howard Hughes Medical Institute Undergraduate Science Education Program, (iv) increase awareness of GSP among K-12 students through the Clark Scholars program and presentations at events involving high school students, and (v) further interactions with the Medical Research Community. The research results will be disseminated to the broad audience via peer reviewed publications, conference presentations and seminars.
