ProCell: investigating cell proliferation with Swarm Intelligence

Marco Nobile; Thalia Vlachou; Simone Spolaor; Paolo Cazzaniga; Giancarlo Mauri; Pier Giuseppe Pelicci; Giancarlo Mauri

doi:10.1109/CIBCB.2019.8791468

ProCell: investigating cell proliferation with Swarm Intelligence

Marco Nobile, Thalia Vlachou, Simone Spolaor, Paolo Cazzaniga, Giancarlo Mauri, Pier Giuseppe Pelicci, Giancarlo Mauri

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

2 Citations (Scopus)

1 Downloads (Pure)

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.

Original language	English
Title of host publication	2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
Editors	Giacomo Baruzzo, Sebastian Daberdaku, Barbara Di Camillo, Simone Furini, Emanuele Domenico Giordano, Giuseppe Nicosia
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Number of pages	8
ISBN (Electronic)	978-1-7281-1462-0
ISBN (Print)	978-1-7281-1463-7
DOIs	https://doi.org/10.1109/CIBCB.2019.8791468
Publication status	Published - Jul 2019
Externally published	Yes
Event	2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2019 - Certosa di Pontignano, Siena, Italy Duration: 9 Jul 2019 → 11 Jul 2019 Conference number: 16 https://cibcb2019.icas.xyz

Conference

Conference	2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2019
Abbreviated title	CIBCB 2019
Country/Territory	Italy
City	Siena
Period	9/07/19 → 11/07/19
Internet address	https://cibcb2019.icas.xyz

Keywords

Fuzzy Self-Tuning PSO
Modeling
acute myeloid leukemia
computational intelligence
parameter estimation
simulation

Access to Document

10.1109/CIBCB.2019.8791468

Cite this

Nobile, M., Vlachou, T., Spolaor, S., Cazzaniga, P., Mauri, G., Pelicci, P. G., & Mauri, G. (2019). ProCell: investigating cell proliferation with Swarm Intelligence. In G. Baruzzo, S. Daberdaku, B. Di Camillo, S. Furini, E. D. Giordano, & G. Nicosia (Eds.), 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) [8791468] Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CIBCB.2019.8791468

Nobile, Marco ; Vlachou, Thalia ; Spolaor, Simone et al. / ProCell : investigating cell proliferation with Swarm Intelligence. 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). editor / Giacomo Baruzzo ; Sebastian Daberdaku ; Barbara Di Camillo ; Simone Furini ; Emanuele Domenico Giordano ; Giuseppe Nicosia. Piscataway : Institute of Electrical and Electronics Engineers, 2019.

@inproceedings{78559bbfc51946168305eef1af5f4842,

title = "ProCell: investigating cell proliferation with Swarm Intelligence",

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.",

keywords = "Fuzzy Self-Tuning PSO, Modeling, acute myeloid leukemia, computational intelligence, parameter estimation, simulation",

author = "Marco Nobile and Thalia Vlachou and Simone Spolaor and Paolo Cazzaniga and Giancarlo Mauri and Pelicci, {Pier Giuseppe} and Giancarlo Mauri",

year = "2019",

month = jul,

doi = "10.1109/CIBCB.2019.8791468",

language = "English",

isbn = "978-1-7281-1463-7",

editor = "Giacomo Baruzzo and Sebastian Daberdaku and {Di Camillo}, Barbara and Simone Furini and Giordano, {Emanuele Domenico} and Giuseppe Nicosia",

booktitle = "2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2019, CIBCB 2019 ; Conference date: 09-07-2019 Through 11-07-2019",

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}

Nobile, M, Vlachou, T, Spolaor, S, Cazzaniga, P, Mauri, G, Pelicci, PG & Mauri, G 2019, ProCell: investigating cell proliferation with Swarm Intelligence. in G Baruzzo, S Daberdaku, B Di Camillo, S Furini, ED Giordano & G Nicosia (eds), 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)., 8791468, Institute of Electrical and Electronics Engineers, Piscataway, 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2019, Siena, Italy, 9/07/19. https://doi.org/10.1109/CIBCB.2019.8791468

ProCell : investigating cell proliferation with Swarm Intelligence. / Nobile, Marco; Vlachou, Thalia; Spolaor, Simone et al.

2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). ed. / Giacomo Baruzzo; Sebastian Daberdaku; Barbara Di Camillo; Simone Furini; Emanuele Domenico Giordano; Giuseppe Nicosia. Piscataway : Institute of Electrical and Electronics Engineers, 2019. 8791468.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - ProCell

T2 - 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2019

AU - Nobile, Marco

AU - Vlachou, Thalia

AU - Spolaor, Simone

AU - Cazzaniga, Paolo

AU - Mauri, Giancarlo

AU - Pelicci, Pier Giuseppe

AU - Mauri, Giancarlo

N1 - Conference code: 16

PY - 2019/7

Y1 - 2019/7

N2 - 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.

AB - 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.

KW - Fuzzy Self-Tuning PSO

KW - Modeling

KW - acute myeloid leukemia

KW - computational intelligence

KW - parameter estimation

KW - simulation

UR - http://www.scopus.com/inward/record.url?scp=85071445224&partnerID=8YFLogxK

U2 - 10.1109/CIBCB.2019.8791468

DO - 10.1109/CIBCB.2019.8791468

M3 - Conference contribution

SN - 978-1-7281-1463-7

BT - 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

A2 - Baruzzo, Giacomo

A2 - Daberdaku, Sebastian

A2 - Di Camillo, Barbara

A2 - Furini, Simone

A2 - Giordano, Emanuele Domenico

A2 - Nicosia, Giuseppe

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

Y2 - 9 July 2019 through 11 July 2019

ER -

Nobile M, Vlachou T, Spolaor S, Cazzaniga P, Mauri G, Pelicci PG et al. ProCell: investigating cell proliferation with Swarm Intelligence. In Baruzzo G, Daberdaku S, Di Camillo B, Furini S, Giordano ED, Nicosia G, editors, 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). Piscataway: Institute of Electrical and Electronics Engineers. 2019. 8791468 doi: 10.1109/CIBCB.2019.8791468

ProCell: investigating cell proliferation with Swarm Intelligence

Abstract

Conference

Keywords

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Best paper award at IEEE CIBCB 2019

Cite this

ProCell: investigating cell proliferation with Swarm Intelligence

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Prizes

Best paper award at IEEE CIBCB 2019

Cite this