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Multiobjective Optimization Based-Approach for Discovering Novel Cancer Therapies
January/February 2012 (vol. 9 no. 1)
pp. 169-184
Arthur W. Mahoney, Utah State University, Logan
Gregory J. Podgorski, Utah State University, Logan
Nicholas S. Flann, Utah State University, Logan
Solid tumors must recruit new blood vessels for growth and maintenance. Discovering drugs that block tumor-induced development of new blood vessels (angiogenesis) is an important approach in cancer treatment. The complexity of angiogenesis presents both challenges and opportunities for cancer therapies. Intuitive approaches, such as blocking VegF activity, have yielded important therapies. But there maybe opportunities to alter nonintuitive targets either alone or in combination. This paper describes the development of a high-fidelity simulation of angiogenesis and uses this as the basis for a parallel search-based approach for the discovery of novel potential cancer treatments that inhibit blood vessel growth. Discovering new therapies is viewed as a multiobjective combinatorial optimization over two competing objectives: minimizing the estimated cost of practically developing the intervention while minimizing the simulated oxygen provided to the tumor by angiogenesis. Results show the effectiveness of the search process by finding interventions that are currently in use, and more interestingly, discovering potential new approaches that are nonintuitive yet effective.

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Index Terms:
Cancer therapy, cellular Potts model, CPM, Glazier-Graner-Hogeweg model, GGH, multiobjective optimization, parallel search, computational discovery, angiogenesis, VegF.
Arthur W. Mahoney, Gregory J. Podgorski, Nicholas S. Flann, "Multiobjective Optimization Based-Approach for Discovering Novel Cancer Therapies," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 1, pp. 169-184, Jan.-Feb. 2012, doi:10.1109/TCBB.2010.39
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