ArticleBulletin of mathematical biology2014
Fastest time to cancer by loss of tumor suppressor genes.
Article in Bulletin of mathematical biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 7 citations in OpenAlex.
- Comprehensive Analysis of Expression, Clinicopathological Association and Potential Prognostic Significance of RABs in Pancreatic Cancer.International journal of molecular sciences · 2020Article
- Nuclear ING3 Expression Is Correlated With a Good Prognosis of Breast Cancer.Frontiers in oncology · 2020Article
- Neuregulin1 acts as a suppressor in human lung adenocarcinoma via AKT and ERK1/2 pathway.Journal of thoracic disease · 2018Article
- Optimal Proliferation and Differentiation ofStudies in applied mathematics (Cambridge, Mass.) · 2017Article
- Evolution of genetic instability in heterogeneous tumors.Journal of theoretical biology · 2016Article
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Authors and funding
2 authors at 1 institution in 1 country.
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Abstract
Genetic instability promotes cancer progression (by increasing the probability of cancerous mutations) as well as hinders it (by imposing a higher cell death rate for cells susceptible to cancerous mutation). With the loss of tumor suppressor gene function known to be responsible for a high percentage of breast and colorectal cancer (and a good fraction of lung cancer and other types as well), it is important to understand how genetic instability can be orchestrated toward carcinogenesis. In this context, this paper gives a complete characterization of the optimal (time-varying) cell mutation rate for the fastest time to a target cancerous cell population through the loss of both copies of a tumor suppressor gene. Similar to the (one-step) oncogene activation model previously analyzed, the optimal mutation rate of the present two-step model changes qualitatively with the convexity of the (mutation rate-dependent) cell death rate. However, the structure of the Hamiltonian for the new model differs significantly and intrinsically from that of the one-step model, and a completely new approach is needed for the solution of the present two-step problem. Considerable insight into the biology of optimal switching (between corner controls) is extracted from numerical results for cases with nonconvex death rates.
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