Mathematical Modeling Support for Lung Cancer Therapy-A Short Review

Abstract

The paper presents a review of models that can be used to describe dynamics of lung cancer growth and its response to treatment at both cell population and intracellular processes levels. To address the latter, models of signaling pathways associated with cellular responses to treatment are overviewed. First, treatment options for lung cancer are discussed, and main signaling pathways and regulatory networks are briefly reviewed. Then, approaches used to model specific therapies are discussed. Following that, models of intracellular processes that are crucial in responses to therapies are presented. The paper is concluded with a discussion of the applicability of the presented approaches in the context of lung cancer.

Keywords: anticancer therapies; lung cancer; mathematical modeling; metastasis.

Figure 1

Main concept of ABM. The upper and lower panels represent the agents and the supplementary grids, respectively. (a) The agent represented by the red element changes its state according to the set of rules that define interactions with neighboring agents (light arrows) and the impact of elements whose concentrations define the state in the supplementary grid (black arrows). Once the new state for each agent is defined, then (b) concentrations in the supplementary grid are updated, according to the rules defining the impact of agents in the neighborhood (gray arrows) and concentrations in the neighborhood (black arrows).