Modeling small cell lung cancer (SCLC) biology through deterministic and stochastic mathematical models

Ravi Salgia _, Isa Mambetsariev, Blake Hewelt, Srisairam Achuthan, Haiqing Li, Valeriy Poroyko, Yingyu Wang and Martin Sattler

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Oncotarget. 2018; 9:26226-26242. https://doi.org/10.18632/oncotarget.25360

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Ravi Salgia1, Isa Mambetsariev1, Blake Hewelt1, Srisairam Achuthan2, Haiqing Li2, Valeriy Poroyko1, Yingyu Wang2 and Martin Sattler3,4

1City of Hope, Department of Medical Oncology and Therapeutics Research, Duarte 91010, CA, USA

2City of Hope, Center for Informatics, Duarte 91010, CA, USA

3Dana-Farber Cancer Institute, Department of Medical Oncology, Boston 02215, MA, USA

4Harvard Medical School, Department of Medicine, Boston 02115, MA, USA

Correspondence to:

Ravi Salgia, email: [email protected]

Keywords: small cell lung cancer; computational modeling; discrete model; continuous model; systems biology

Received: March 07, 2018     Accepted: April 24, 2018     Published: May 25, 2018


Mathematical cancer models are immensely powerful tools that are based in part on the fractal nature of biological structures, such as the geometry of the lung. Cancers of the lung provide an opportune model to develop and apply algorithms that capture changes and disease phenotypes. We reviewed mathematical models that have been developed for biological sciences and applied them in the context of small cell lung cancer (SCLC) growth, mutational heterogeneity, and mechanisms of metastasis. The ultimate goal is to develop the stochastic and deterministic nature of this disease, to link this comprehensive set of tools back to its fractalness and to provide a platform for accurate biomarker development. These techniques may be particularly useful in the context of drug development research, such as combination with existing omics approaches. The integration of these tools will be important to further understand the biology of SCLC and ultimately develop novel therapeutics.

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