{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:40:39Z","timestamp":1790037639776,"version":"4.0.1"},"reference-count":56,"publisher":"Society for Industrial & Applied Mathematics (SIAM)","issue":"4","funder":[{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["U54CA143906"],"award-info":[{"award-number":["U54CA143906"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["SIAM Rev."],"published-print":{"date-parts":[[2016,1]]},"abstract":"<jats:p>We describe a cell-molecular-based evolutionary mathematical model of tumor development driven by a stochastic Moran birth-death process. The cells in the tumor carry molecular information in the form of a numerical genome which we represent as a four-digit binary string used to differentiate cells into 16 molecular types. The binary string is able to undergo stochastic point mutations that are passed to a daughter cell after each birth event. The value of the binary string determines the cell fitness, with lower fit cells (e.g., 0000) defined as healthy phenotypes, and higher fit cells (e.g., 1111) defined as malignant phenotypes. At each step of the birth-death process, the two phenotypic subpopulations compete in a prisoner's dilemma evolutionary game with the healthy cells playing the role of cooperators, and the cancer cells playing the role of defectors. Fitness, birth-death rates of the cell populations, and overall tumor fitness are defined via the prisoner's dilemma payoff matrix. Mutation parameters include passenger mutations (mutations conferring no fitness advantage) and driver mutations (mutations which increase cell fitness). The model is used to explore key emergent features associated with tumor development, including tumor growth rates as it relates to intratumor molecular heterogeneity. The tumor growth equation states that the growth rate is proportional to the logarithm of cellular diversity\/heterogeneity. The Shannon entropy from information theory is used as a quantitative measure of heterogeneity and tumor complexity based on the distribution of the four-digit binary sequences produced by the cell population. To track the development of heterogeneity from an initial population of healthy cells (0000), we use dynamic phylogenetic trees which show clonal and subclonal expansions of cancer cell subpopulations from an initial malignant cell. We show that tumor growth rates are not constant throughout tumor development and are generally much higher in the subclinical range than in later stages of development, which leads to a Gompertzian growth curve. We explain the early exponential growth of the tumor and the later saturation associated with the Gompertzian curve which results from our evolutionary simulations using simple statistical mechanics principles related to the degree of functional coupling of the cell states. We then compare dosing strategies at early stage development, midstage (clinical stage), and late-stage development of the tumor. If used early during tumor development in the subclinical stage, well before the cancer cell population is selected for growth, therapy is most effective at disrupting key emergent features of tumor development.<\/jats:p>","DOI":"10.1137\/15m1044825","type":"journal-article","created":{"date-parts":[[2016,11,3]],"date-time":"2016-11-03T12:13:40Z","timestamp":1478175220000},"page":"716-736","source":"Crossref","is-referenced-by-count":44,"title":["An Evolutionary Model of Tumor Cell Kinetics and the Emergence of Molecular Heterogeneity Driving Gompertzian Growth"],"prefix":"10.1137","volume":"58","author":[{"given":"Jeffrey","family":"West","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zaki","family":"Hasnain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul","family":"Macklin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul K.","family":"Newton","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"351","published-online":{"date-parts":[[2016,11,3]]},"reference":[{"key":"atypb1","doi-asserted-by":"publisher","DOI":"10.1038\/nrc2013"},{"key":"atypb2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1749-6632.2009.04880.x"},{"key":"atypb3","unstructured":"S. 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H.","year":"1984","journal-title":"Cancer Res."},{"key":"atypb24","doi-asserted-by":"publisher","DOI":"10.1016\/j.ccr.2007.01.013"},{"key":"atypb25","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1113205"},{"key":"atypb26","doi-asserted-by":"publisher","DOI":"10.1158\/0008-5472.CAN-12-2217"},{"key":"atypb27","doi-asserted-by":"publisher","DOI":"10.1007\/BF00046903"},{"key":"atypb28","unstructured":"J. Maynard Smith,\n                      Evolution and the Theory of Games\n                      , Cambridge University Press, Cambridge, UK, 1982."},{"key":"atypb29","doi-asserted-by":"crossref","unstructured":"M. A. 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