ProCell: Investigating cell proliferation with Swarm Intelligence

Nobile, Marco S.; Vlachou, Thalia; Spolaor, Simone; Cazzaniga, Paolo; Mauri, Giancarlo; Pelicci, Pier Giuseppe; Besozzi, Daniela

doi:10.1109/cibcb.2019.8791468

Cited by 3 publications

(5 citation statements)

References 17 publications

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“…Hence, we can conclude that the two models are equivalent as fitting, but model SFQ represents a simpler explanation for the observed data. An interesting difference with respect to our previous investigations on BM [8], [9] is that, in the case of the SPL, the PQ model fits better than SF, highlighting the fundamental role of quiescent cells for the correct reproduction of the target fluorescence histogram (Fig. 3).…”

Section: A Results On Spleen Datacontrasting

confidence: 84%

“…ProCell is a framework for the modeling and stochastic simulation of proliferating cell systems [8], [9], consisting of various sub-populations characterized by different proportions and specific distributions of cell division rate. ProCell was designed to simulate the dynamic variations in cell fluorescence that can be acquired, e.g., by flow cytometry analyses.…”

Section: B the Procell Frameworkmentioning

confidence: 99%

“…In order to further simplify the creation, editing, simulation, calibration, and maintenance of models, we completely restructured the Graphical User Interface (GUI) around ProCell's core algorithms [9], achieving a user-friendly and intuitive tool ( Fig. 1).…”

Section: B the Procell Frameworkmentioning

confidence: 99%

“…The quality of such parameterization can be assessed by means of a fitness function that associates a particle with a real value encoding the "goodness" of the fit. In this work, the core of the fitness function is the Hellinger distance [19] between simulated and the experimental fluorescence distributions (see, [8], [9] for additional information). The Hellinger distance is used in statistics to assess the difference between two distributions; thanks to the standardization and normalization steps that we perform [8], the smaller the Hellinger distance the higher the similarity between the simulation and the expected distribution, making it appealing to be used as fitness function in Computational Intelligence methods, and as a statistical measure to directly compare competing models.…”

Section: Fuzzy Self-tuning Psomentioning

confidence: 99%

“…Models in ProCell are easily interpretable, characterized by a few parameters, and expressed in simple terms of cell proportions and division intervals. Moreover, thanks to its userfriendly Graphical User Interface that has been redesigned and improved with respect to the previous version [9], it is possible to automatically calibrate the parameters of cell population models by means of FST-PSO [10], a settings-free version of Particle Swarm Optimization [11].…”

Section: Introductionmentioning

confidence: 99%

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cuProCell: GPU-Accelerated Analysis of Cell Proliferation With Flow Cytometry Data

Nobile

Nisoli

Vlachou

et al. 2020

IEEE J. Biomed. Health Inform.

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The investigation of cell proliferation can provide useful insights for the comprehension of cancer progression, resistance to chemotherapy and relapse. To this aim, computational methods and experimental measurements based on in vivo label-retaining assays can be coupled to explore the dynamic behavior of tumoral cells. Pro-Cell is a software that exploits flow cytometry data to model and simulate the kinetics of fluorescence loss that is due to stochastic events of cell division. Since the rate of cell division is not known, ProCell embeds a calibration process that might require thousands of stochastic simulations to properly infer the parameterization of cell proliferation models. To mitigate the high computational costs, in this paper we introduce a parallel implementation of ProCell's simulation algorithm, named cuProCell, which leverages Graphics Processing Units (GPUs). Dynamic Parallelism was used to Manuscript

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Section: A Results On Spleen Datacontrasting

confidence: 84%

Section: B the Procell Frameworkmentioning

confidence: 99%

Section: B the Procell Frameworkmentioning

confidence: 99%

Section: Fuzzy Self-tuning Psomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

cuProCell: GPU-Accelerated Analysis of Cell Proliferation With Flow Cytometry Data

Nobile

Nisoli

Vlachou

et al. 2020

IEEE J. Biomed. Health Inform.

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An Experimental and Computational Protocol to Study Cell Proliferation in Human Acute Myeloid Leukemia Xenografts

Vlachou

Nobile²,

Ronchini

et al. 2020

Leukemia Stem Cells

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Regulating Smart Robots and Artificial Intelligence in the European Union

Gallese

2022

JDTL

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Objective: In recent years, the need for regulation of robots and Artificial Intelligence has become apparent in Europe. European Union needs a standardized regulation that will ensure a high level of security in robotics systems to prevent potential breaches. Therefore a new regulation should make clear that it is the responsibility of producers to identify the blind spots in these systems, exposing their flaws, or, when a vulnerability is discovered in a later stage, to update the system even if that model is not on the market anymore. This article aims at suggesting some possible revisions of the existing legal provisions in the EU.Methods: The author employed the Kestemont legal methodology, analyzing legal text, comparing them, and connecting them with technical elements regarding smart robots, resulting in the highlighting of the critical provisions to be updated.Results: This article suggests some revisions to the existing regulatory proposals: according to the author, although the AI Act and the Cyberresilience Act represent a first step towards this direction, their general principles are not sufficiently detailed to guide programmers on how to implement them in practice, and policymakers should carefully assess in what cases lifelong learning models should be allowed to the market. The author suggests that the current proposal regarding mandatory updates should be expanded, as five years are a short time frame that would not cover the risks associated with long-lasting products, such as vehicles.Scientiﬁc novelty: The author has examined the existing regulatory framework regarding AI systems and devices with digital elements, highlighted the risks of the current legal framework, and suggested possible amendments to the existing regulatory proposals.Practical signiﬁcance: The article can be employed to update the existing proposals for the AI Act and the Cyber-resilience Act.

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ProCell: Investigating cell proliferation with Swarm Intelligence

Cited by 3 publications

References 17 publications

cuProCell: GPU-Accelerated Analysis of Cell Proliferation With Flow Cytometry Data

cuProCell: GPU-Accelerated Analysis of Cell Proliferation With Flow Cytometry Data

An Experimental and Computational Protocol to Study Cell Proliferation in Human Acute Myeloid Leukemia Xenografts

Regulating Smart Robots and Artificial Intelligence in the European Union

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