Modeling Cell-Cell Interactions in Regulating Multiple Myeloma Initiating Cell Fate

• Published in: IEEE journal of biomedical and health informatics Vol. 18; no. 2; pp. 484 - 491
• Main Authors: Peng, Tao, Peng, Huiming, Choi, Dong Soon, Su, Jing, Chang, Chung-Che Jeff, Zhou, Xiaobo
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-03-2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Biological system modeling; Cancer; Cancer initiating cell; Cell Communication - physiology; Cell culture; Cell Line, Tumor; Computer Simulation; Feedback, Physiological; Humans; lineage model; Mathematical model; mathematical modeling; Mathematical models; Microwave integrated circuits; Models, Biological; multiple myeloma (MM); Multiple Myeloma - physiopathology; Ordinary differential equations; parameter estimation; Sociology; Statistics; Stem cells
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2013.2281774

Cancer initiating cells have been documented in multiple myeloma and believed to be a key factor that initiates and drives tumor growth, differentiation, metastasis, and recurrence of the diseases. Although myeloma initiating cells (MICs) are likely to share many properties of normal stem cells, the underlying mechanisms regulating the fate of MICs are largely unknown. Studies designed to explore such communication are urgently needed to enhance our ability to predict the fate decisions of MICs (self-renewal, differentiation, and proliferation). In this study, we developed a novel system to understand the intercellular communication between MICs and their niche by seamlessly integrating experimental data and mathematical model. We first designed dynamic cell culture experiments and collected three types of cells (side population cells, progenitor cells, and mature myeloma cells) under various cultural conditions with flow cytometry. Then we developed a lineage model with ordinary differential equations by considering secreted factors, self-renewal, differentiation, and other biological functions of those cells, to model the cell-cell interactions among the three cell types. Particle swarm optimization was employed to estimate the model parameters by fitting the experimental data to the lineage model. The theoretical results show that the correlation coefficient analysis can reflect the feedback loops among the three cell types, the intercellular feedback signaling can regulate cell population dynamics, and the culture strategies can decide cell growth. This study provides a basic framework of studying cell-cell interactions in regulating MICs fate.