Computational methods represent an effective mean for the analysis of complex biological processes, such as cell proliferation, especially when combined to well established experimental protocols. In particular, mathematical modeling coupled with computational intelligence algorithms can be successfully exploited to investigate different aspects of cell population dynamics in the context of tumor growth. To this aim, we defined ProCell, a modeling and simulation framework specifically designed for the investigation of cell proliferation, which makes use of Fuzzy Self-Tuning Particle Swarm Optimization to estimate the unknown parameters of cell population models. ProCell is here applied to the analysis of cell proliferation in acute myeloid leukemia, a hematological malignancy characterized by an inherent intra-tumoral heterogeneity that plays an important role in disease recurrence and resistance to chemotherapy. ProCell allowed to provide new insights on the intricate organization of cells with highly heterogeneous proliferative potential, and to highlight the important role of different cell types in the progression and evolution of the disease. ProCell is available under the GPL 2.0 license on GitHub at https://github.com/aresio/ProCell.
Nobile, M., Vlachou, T., Spolaor, S., Cazzaniga, P., Mauri, G., Pelicci, P., et al. (2019). ProCell: Investigating cell proliferation with Swarm Intelligence. In 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2019 (pp.1-8). Institute of Electrical and Electronics Engineers Inc. [10.1109/CIBCB.2019.8791468].
ProCell: Investigating cell proliferation with Swarm Intelligence
Nobile, MS;Spolaor, S;Cazzaniga, P;Mauri, G;Besozzi, D
2019
Abstract
Computational methods represent an effective mean for the analysis of complex biological processes, such as cell proliferation, especially when combined to well established experimental protocols. In particular, mathematical modeling coupled with computational intelligence algorithms can be successfully exploited to investigate different aspects of cell population dynamics in the context of tumor growth. To this aim, we defined ProCell, a modeling and simulation framework specifically designed for the investigation of cell proliferation, which makes use of Fuzzy Self-Tuning Particle Swarm Optimization to estimate the unknown parameters of cell population models. ProCell is here applied to the analysis of cell proliferation in acute myeloid leukemia, a hematological malignancy characterized by an inherent intra-tumoral heterogeneity that plays an important role in disease recurrence and resistance to chemotherapy. ProCell allowed to provide new insights on the intricate organization of cells with highly heterogeneous proliferative potential, and to highlight the important role of different cell types in the progression and evolution of the disease. ProCell is available under the GPL 2.0 license on GitHub at https://github.com/aresio/ProCell.
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