Title: Multiscale agent-based modelling of ovarian cancer progression under the stimulation of the STAT 3 pathway

Authors: Le Zhang; Yao Xue; Beini Jiang; Costas Strouthos; Zhenfeng Duan; Yukun Wu; Jing Su; Xiaobo Zhou

Addresses: College of Computer and Information Science, Southwest University, Chongqing 400715, China ' Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI 49931, USA ' Department of Computational Science and Engineering, Michigan Technological University, Houghton, MI 49931, USA ' Computation-based Science and Technology Research Center, The Cyprus Institute, 1645 Nicosia, Cyprus ' Sarcoma Biology Laboratory, Center for Sarcoma and Connective Tissue Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, USA ' Center for Vaccine Development, University of Maryland School of Medicine, Baltimore, MD 21201, Maryland, USA ' Department of Pathology, The Methodist Hospital, Research Institute and Weill Cornell Medical College, 6565 Fannin St, Houston, Texas, USA ' Department of Pathology, The Methodist Hospital, Research Institute and Weill Cornell Medical College, 6565 Fannin St, Houston, Texas, USA

Abstract: This research is developed to simulate ovarian cancer progression with signal transducers and activators of the transcription 3 (STAT 3) pathway. The main focus is on studying how the STAT 3 pathway affects the cancer cells' biomechanical phenotype under the stimulation of the interleukin-6 (IL-6) cytokine and various well-known microscopic factors. The simulated results agreed with recent experimental evidence that ovarian cancer cells with a stimulated STAT 3 pathway have high survival rates and drug resistance. And we discussed how the IL6 and these well-known microscopic factors impacted the cancer progression.

Keywords: agent-based modelling; STAT 3 pathway; multiscale modelling; drug resistance; computational biology; IL-6; interleukin-6; ovarian cancer progression; mulit-agent systems; MAS; agent-based systems; cancer cells; biomechanical phenotype; simulation; bioinformatics.

DOI: 10.1504/IJDMB.2014.060050

International Journal of Data Mining and Bioinformatics, 2014 Vol.9 No.3, pp.235 - 253

Received: 11 Oct 2011
Accepted: 29 Dec 2011
Published online: 21 Oct 2014 *

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