Multiobjective Optimization Based-Approach for Discovering Novel Cancer Therapies
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 therap...
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| Veröffentlicht in: | IEEE/ACM transactions on computational biology and bioinformatics 2012-01, Vol.9 (1), p.169-184 |
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| creator | Mahoney, A. W. Podgorski, G. J. Flann, N. S. |
| description | 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. |
| doi_str_mv | 10.1109/TCBB.2010.39 |
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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. 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J.</creatorcontrib><creatorcontrib>Flann, N. S.</creatorcontrib><title>Multiobjective Optimization Based-Approach for Discovering Novel Cancer Therapies</title><title>IEEE/ACM transactions on computational biology and bioinformatics</title><addtitle>TCBB</addtitle><addtitle>IEEE/ACM Trans Comput Biol Bioinform</addtitle><description>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. 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S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c408t-d5137a380f17cef42677e8dd43333c45799a5abb9f527555aec8c2a3ab09818c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Algorithms</topic><topic>Angiogenesis</topic><topic>Angiogenesis Inhibitors</topic><topic>Biology computing</topic><topic>Biomedical Research</topic><topic>Blood vessels</topic><topic>Cancer</topic><topic>Cancer therapy</topic><topic>cellular Potts model</topic><topic>Computational Biology - methods</topic><topic>computational discovery</topic><topic>Computational modeling</topic><topic>Computer Simulation</topic><topic>CPM</topic><topic>Drug Discovery - methods</topic><topic>Drugs</topic><topic>GGH</topic><topic>Glazier-Graner-Hogeweg model</topic><topic>High performance computing</topic><topic>Humans</topic><topic>Medical treatment</topic><topic>Models, Biological</topic><topic>Monte Carlo Method</topic><topic>multiobjective optimization</topic><topic>Neoplasms</topic><topic>Neoplasms - drug therapy</topic><topic>Neovascularization, Pathologic - drug therapy</topic><topic>parallel search</topic><topic>Pipelines</topic><topic>Recruitment</topic><topic>Vascular Endothelial Growth Factor A - antagonists & inhibitors</topic><topic>VegF</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mahoney, A. 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| subjects | Algorithms Angiogenesis Angiogenesis Inhibitors Biology computing Biomedical Research Blood vessels Cancer Cancer therapy cellular Potts model Computational Biology - methods computational discovery Computational modeling Computer Simulation CPM Drug Discovery - methods Drugs GGH Glazier-Graner-Hogeweg model High performance computing Humans Medical treatment Models, Biological Monte Carlo Method multiobjective optimization Neoplasms Neoplasms - drug therapy Neovascularization, Pathologic - drug therapy parallel search Pipelines Recruitment Vascular Endothelial Growth Factor A - antagonists & inhibitors VegF |
| title | Multiobjective Optimization Based-Approach for Discovering Novel Cancer Therapies |
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