Cells migration as well as their growth are due to several interactions, e.g., with either other (close) cells or the micro-environment. The analysis of both such phenomena plays a key role especially during the tissue formation/regeneration and in tumorigenesis and, in many cases, can also give a significant support to cancer pharmacogenomic studies. We focus attention on the analysis of both growth and migration of the MDAMB-231 breast cancer cells, since the breast cancer is actually registering the highest mortality of any cancer in women worldwide. More specifically, we aim at analyzing the changes of the same breast tissue for increasing hour intervals. For this purpose, we propose optimization-based approaches to support such an analysis by studying the images of in vitro human breast cells produced in different time instants by a microscope with 25x magnification. Firstly, each image is converted into a proper cell-graph. Then, in order to evaluate the similarity among two cell-graphs, each related to an image of the same tissue in a different instant of time, a Maximum Common Edge Subgraph Problem (MCESP) is solved. We model the MCESP through Integer Linear Programming (ILP) and, in order to efficiently address realistic instances, we also design a Tabu Search meta-heuristic. Moreover, a hybrid solution approach combining the Tabu Search with ILP is also proposed. Preliminary numerical results, carried out on both a set of benchmark instances and a set of realistic case studies, show the promising performances of our approaches compared to those of the literature.
A Graph-Based Analysis on the Growth and Migration of MDA-MB-231 Breast Cancer Cells / Bracci, Massimo; Marinelli, Fabrizio; Pisacane, Ornella; Pizzuti, Andrea. - ELETTRONICO. - (2019), pp. 202-202. (Intervento presentato al convegno International Conference on Optimization and Decision Science (ODS2019) tenutosi a Dept. of Economics and Business Studies (Università di Genova) nel 4-7 September 2019).