1
|
Ma C, Gurkan-Cavusoglu E. A comprehensive review of computational cell cycle models in guiding cancer treatment strategies. NPJ Syst Biol Appl 2024; 10:71. [PMID: 38969664 PMCID: PMC11226463 DOI: 10.1038/s41540-024-00397-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 06/24/2024] [Indexed: 07/07/2024] Open
Abstract
This article reviews the current knowledge and recent advancements in computational modeling of the cell cycle. It offers a comparative analysis of various modeling paradigms, highlighting their unique strengths, limitations, and applications. Specifically, the article compares deterministic and stochastic models, single-cell versus population models, and mechanistic versus abstract models. This detailed analysis helps determine the most suitable modeling framework for various research needs. Additionally, the discussion extends to the utilization of these computational models to illuminate cell cycle dynamics, with a particular focus on cell cycle viability, crosstalk with signaling pathways, tumor microenvironment, DNA replication, and repair mechanisms, underscoring their critical roles in tumor progression and the optimization of cancer therapies. By applying these models to crucial aspects of cancer therapy planning for better outcomes, including drug efficacy quantification, drug discovery, drug resistance analysis, and dose optimization, the review highlights the significant potential of computational insights in enhancing the precision and effectiveness of cancer treatments. This emphasis on the intricate relationship between computational modeling and therapeutic strategy development underscores the pivotal role of advanced modeling techniques in navigating the complexities of cell cycle dynamics and their implications for cancer therapy.
Collapse
Affiliation(s)
- Chenhui Ma
- Department of Electrical, Computer and Systems Engineering, Case Western Reserve University, Cleveland, OH, USA.
| | - Evren Gurkan-Cavusoglu
- Department of Electrical, Computer and Systems Engineering, Case Western Reserve University, Cleveland, OH, USA
| |
Collapse
|
2
|
Celora GL, Bader SB, Hammond EM, Maini PK, Pitt-Francis JM, Byrne HM. DNA-structured mathematical model of cell-cycle progression in cyclic hypoxia. J Theor Biol 2022; 545:111104. [PMID: 35337794 DOI: 10.1016/j.jtbi.2022.111104] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 03/15/2022] [Accepted: 03/18/2022] [Indexed: 01/22/2023]
Abstract
New experimental data have shown how the periodic exposure of cells to low oxygen levels (i.e., cyclic hypoxia) impacts their progress through the cell-cycle. Cyclic hypoxia has been detected in tumours and linked to poor prognosis and treatment failure. While fluctuating oxygen environments can be reproduced in vitro, the range of oxygen cycles that can be tested is limited. By contrast, mathematical models can be used to predict the response to a wide range of cyclic dynamics. Accordingly, in this paper we develop a mechanistic model of the cell-cycle that can be combined with in vitro experiments, to better understand the link between cyclic hypoxia and cell-cycle dysregulation. A distinguishing feature of our model is the inclusion of impaired DNA synthesis and cell-cycle arrest due to periodic exposure to severely low oxygen levels. Our model decomposes the cell population into five compartments and a time-dependent delay accounts for the variability in the duration of the S phase which increases in severe hypoxia due to reduced rates of DNA synthesis. We calibrate our model against experimental data and show that it recapitulates the observed cell-cycle dynamics. We use the calibrated model to investigate the response of cells to oxygen cycles not yet tested experimentally. When the re-oxygenation phase is sufficiently long, our model predicts that cyclic hypoxia simply slows cell proliferation since cells spend more time in the S phase. On the contrary, cycles with short periods of re-oxygenation are predicted to lead to inhibition of proliferation, with cells arresting from the cell-cycle in the G2 phase. While model predictions on short time scales (about a day) are fairly accurate (i.e, confidence intervals are small), the predictions become more uncertain over longer periods. Hence, we use our model to inform experimental design that can lead to improved model parameter estimates and validate model predictions.
Collapse
Affiliation(s)
| | - Samuel B Bader
- Oxford Institute for Radiation Oncology, Department of Oncology, University of Oxford, Oxford, UK
| | - Ester M Hammond
- Oxford Institute for Radiation Oncology, Department of Oncology, University of Oxford, Oxford, UK
| | - Philip K Maini
- Mathematical Institute, University of Oxford, Oxford, UK
| | | | - Helen M Byrne
- Mathematical Institute, University of Oxford, Oxford, UK
| |
Collapse
|
3
|
Bernard D, Mondesert O, Gomes A, Duthen Y, Lobjois V, Cussat-Blanc S, Ducommun B. A checkpoint-oriented cell cycle simulation model. Cell Cycle 2019; 18:795-808. [PMID: 30870080 DOI: 10.1080/15384101.2019.1591125] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Modeling and in silico simulations are of major conceptual and applicative interest in studying the cell cycle and proliferation in eukaryotic cells. In this paper, we present a cell cycle checkpoint-oriented simulator that uses agent-based simulation modeling to reproduce the dynamics of a cancer cell population in exponential growth. Our in silico simulations were successfully validated by experimental in vitro supporting data obtained with HCT116 colon cancer cells. We demonstrated that this model can simulate cell confluence and the associated elongation of the G1 phase. Using nocodazole to synchronize cancer cells at mitosis, we confirmed the model predictivity and provided evidence of an additional and unexpected effect of nocodazole on the overall cell cycle progression. We anticipate that this cell cycle simulator will be a potential source of new insights and research perspectives.
Collapse
Affiliation(s)
- David Bernard
- a IRIT, CNRS, UT1 , Université de Toulouse , Toulouse , France.,b ITAV, CNRS, UT3 , Université de Toulouse , Toulouse , France
| | - Odile Mondesert
- b ITAV, CNRS, UT3 , Université de Toulouse , Toulouse , France
| | - Aurélie Gomes
- b ITAV, CNRS, UT3 , Université de Toulouse , Toulouse , France
| | - Yves Duthen
- a IRIT, CNRS, UT1 , Université de Toulouse , Toulouse , France.,b ITAV, CNRS, UT3 , Université de Toulouse , Toulouse , France
| | - Valérie Lobjois
- b ITAV, CNRS, UT3 , Université de Toulouse , Toulouse , France
| | - Sylvain Cussat-Blanc
- a IRIT, CNRS, UT1 , Université de Toulouse , Toulouse , France.,b ITAV, CNRS, UT3 , Université de Toulouse , Toulouse , France
| | - Bernard Ducommun
- b ITAV, CNRS, UT3 , Université de Toulouse , Toulouse , France.,c CHU de Toulouse , Toulouse , France
| |
Collapse
|
4
|
Weber TS, Jaehnert I, Schichor C, Or-Guil M, Carneiro J. Quantifying the length and variance of the eukaryotic cell cycle phases by a stochastic model and dual nucleoside pulse labelling. PLoS Comput Biol 2014; 10:e1003616. [PMID: 25058870 PMCID: PMC4109856 DOI: 10.1371/journal.pcbi.1003616] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2013] [Accepted: 03/27/2014] [Indexed: 11/18/2022] Open
Abstract
A fundamental property of cell populations is their growth rate as well as the time needed for cell division and its variance. The eukaryotic cell cycle progresses in an ordered sequence through the phases and and is regulated by environmental cues and by intracellular checkpoints. Reflecting this regulatory complexity, the length of each phase varies considerably in different kinds of cells but also among genetically and morphologically indistinguishable cells. This article addresses the question of how to describe and quantify the mean and variance of the cell cycle phase lengths. A phase-resolved cell cycle model is introduced assuming that phase completion times are distributed as delayed exponential functions, capturing the observations that each realization of a cycle phase is variable in length and requires a minimal time. In this model, the total cell cycle length is distributed as a delayed hypoexponential function that closely reproduces empirical distributions. Analytic solutions are derived for the proportions of cells in each cycle phase in a population growing under balanced growth and under specific non-stationary conditions. These solutions are then adapted to describe conventional cell cycle kinetic assays based on pulse labelling with nucleoside analogs. The model fits well to data obtained with two distinct proliferating cell lines labelled with a single bromodeoxiuridine pulse. However, whereas mean lengths are precisely estimated for all phases, the respective variances remain uncertain. To overcome this limitation, a redesigned experimental protocol is derived and validated in silico. The novelty is the timing of two consecutive pulses with distinct nucleosides that enables accurate and precise estimation of both the mean and the variance of the length of all phases. The proposed methodology to quantify the phase length distributions gives results potentially equivalent to those obtained with modern phase-specific biosensor-based fluorescent imaging. Among the important characteristics of dividing cell populations is the time necessary for cells to complete each of the cell cycle phases, that is, to increase the cell's mass, to duplicate and repair its genome, to properly segregate its chromosomes, and to make decisions whether to continue dividing or enter a quiescent state. The cycle phase times also determine the maximal rate at which a dividing cell population can grow in size. Cell cycle phase completion times largely differ between cell types, cellular environments as well as metabolic stages, and can thus be considered as part of the phenotype of a given cell. Our article advances the methods to quantitatively characterize this phenotype. We introduce a novel phase-resolved cell cycle progression model and use it to estimate the mean and variance of the cycle phase completion times based on nucleoside analog pulse labelling experiments. This classic workhorse of cell cycle kinetic studies is revamped by our approach to potentially rival in accuracy and precision with modern phase-specific biosensor-based fluorescent imaging, while superseding the latter in its application scope.
Collapse
Affiliation(s)
- Tom Serge Weber
- Instituto Gulbenkian de Ciência, Oeiras, Portugal
- Department of Biology, Humboldt-Universität zu Berlin, Berlin Germany and Research Center ImmunoSciences, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Irene Jaehnert
- Tumorbiological Laboratory, Neurosurgical Department, Ludwig-Maximilians-University Munich, Klinikum Grosshadern, Munich, Germany
| | - Christian Schichor
- Tumorbiological Laboratory, Neurosurgical Department, Ludwig-Maximilians-University Munich, Klinikum Grosshadern, Munich, Germany
| | - Michal Or-Guil
- Department of Biology, Humboldt-Universität zu Berlin, Berlin Germany and Research Center ImmunoSciences, Charité - Universitätsmedizin Berlin, Berlin, Germany
- * E-mail: (MOG); (JC)
| | - Jorge Carneiro
- Instituto Gulbenkian de Ciência, Oeiras, Portugal
- * E-mail: (MOG); (JC)
| |
Collapse
|
5
|
Abstract
Population balance modeling is undergoing phenomenal growth in its applications, and this growth is accompanied by multifarious reviews. This review aims to fortify the model's fundamental base, as well as point to a variety of new applications, including modeling of crystal morphology, cell growth and differentiation, gene regulatory processes, and transfer of drug resistance. This is accomplished by presenting the many faces of population balance equations that arise in the foregoing applications.
Collapse
Affiliation(s)
| | - Meenesh R. Singh
- School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47907
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, California 94704
- Joint Center for Artificial Photosynthesis, Lawrence Berkeley National Laboratory, Berkeley, California 94720
| |
Collapse
|
6
|
Bley T. From single cells to microbial population dynamics: modelling in biotechnology based on measurements of individual cells. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2014; 124:211-27. [PMID: 21072703 DOI: 10.1007/10_2010_79] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
The development of dynamic modelling of microbial populations in bioprocesses is reviewed. In the 1960s Arnold Fredrickson established the theoretical basis of such models, and other researchers have subsequently advanced them. This review explores the relationships that describe cell proliferation and evaluates the importance of the application of flow cytometry to the fundamental parameterisation of the models for their use in bioprocess engineering. The section "Individual-Based Modelling" discusses recent theoretical developments. Delay-differential equations are demonstrated to describe accurately temporal variation of the cell proliferation cycle and specialised approaches and related iconography are applied to stochastic and deterministic modelling of stages of cellular development. Synchronised cultures of the bacterium Cupriavidus necator were prepared and monitored using a flow cytometer. The data obtained demonstrate that cell proliferation could be simulated quantitatively using the developed model.
Collapse
Affiliation(s)
- Thomas Bley
- Institute of Food Technology and Bioprocess Engineering, Dresden University of Technology, 01062, Dresden, Germany,
| |
Collapse
|
7
|
Wu J, Tzanakakis ES. Deconstructing stem cell population heterogeneity: single-cell analysis and modeling approaches. Biotechnol Adv 2013; 31:1047-62. [PMID: 24035899 DOI: 10.1016/j.biotechadv.2013.09.001] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2013] [Revised: 09/02/2013] [Accepted: 09/03/2013] [Indexed: 12/26/2022]
Abstract
Isogenic stem cell populations display cell-to-cell variations in a multitude of attributes including gene or protein expression, epigenetic state, morphology, proliferation and proclivity for differentiation. The origins of the observed heterogeneity and its roles in the maintenance of pluripotency and the lineage specification of stem cells remain unclear. Addressing pertinent questions will require the employment of single-cell analysis methods as traditional cell biochemical and biomolecular assays yield mostly population-average data. In addition to time-lapse microscopy and flow cytometry, recent advances in single-cell genomic, transcriptomic and proteomic profiling are reviewed. The application of multiple displacement amplification, next generation sequencing, mass cytometry and spectrometry to stem cell systems is expected to provide a wealth of information affording unprecedented levels of multiparametric characterization of cell ensembles under defined conditions promoting pluripotency or commitment. Establishing connections between single-cell analysis information and the observed phenotypes will also require suitable mathematical models. Stem cell self-renewal and differentiation are orchestrated by the coordinated regulation of subcellular, intercellular and niche-wide processes spanning multiple time scales. Here, we discuss different modeling approaches and challenges arising from their application to stem cell populations. Integrating single-cell analysis with computational methods will fill gaps in our knowledge about the functions of heterogeneity in stem cell physiology. This combination will also aid the rational design of efficient differentiation and reprogramming strategies as well as bioprocesses for the production of clinically valuable stem cell derivatives.
Collapse
Affiliation(s)
- Jincheng Wu
- Department of Chemical and Biological Engineering, State University of New York at Buffalo, Buffalo, NY 14260, USA.
| | | |
Collapse
|
8
|
Müller T, Dürr R, Isken B, Schulze-Horsel J, Reichl U, Kienle A. Distributed modeling of human influenza a virus-host cell interactions during vaccine production. Biotechnol Bioeng 2013; 110:2252-66. [DOI: 10.1002/bit.24878] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2012] [Revised: 12/20/2012] [Accepted: 02/14/2013] [Indexed: 11/06/2022]
|
9
|
Hirt BV, Wattis JA, Preston SP, Laughton CA. The effects of a telomere destabilizing agent on cancer cell-cycle dynamics—Integrated modelling and experiments. J Theor Biol 2012; 295:9-22. [DOI: 10.1016/j.jtbi.2011.10.038] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2011] [Revised: 10/30/2011] [Accepted: 10/31/2011] [Indexed: 01/12/2023]
|
10
|
Fadda S, Cincotti A, Cao G. A novel population balance model to investigate the kinetics of in vitro cell proliferation: Part I. model development. Biotechnol Bioeng 2011; 109:772-81. [DOI: 10.1002/bit.24351] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2011] [Revised: 09/13/2011] [Accepted: 10/10/2011] [Indexed: 11/06/2022]
|
11
|
Experimental methods and modeling techniques for description of cell population heterogeneity. Biotechnol Adv 2011; 29:575-99. [DOI: 10.1016/j.biotechadv.2011.03.007] [Citation(s) in RCA: 91] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2010] [Revised: 02/04/2011] [Accepted: 03/31/2011] [Indexed: 11/24/2022]
|
12
|
Clairambault J. Optimizing cancer pharmacotherapeutics using mathematical modeling and a systems biology approach. Per Med 2011; 8:271-286. [PMID: 29783516 DOI: 10.2217/pme.11.20] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Research in mathematics and in mathematical biology on cancer and its treatments has been soaring in the past 10 years at an unprecedented speed. Such thriving is likely due as much to new findings in fundamental biology as to an emerging general interest from mathematicians and engineers towards applications in biology and medicine and to their subsequently designed representations and predictions of tumor processes that are now allowed by modern means of computation and simulation. This article, which does not claim the status of an extended review paper on mathematical models of cancer and its treatment, is focused on modeling in a systems biology perspective. I will list here the most necessary mathematical methods, in my opinion, which, while enforcing already existing methods, should be further developed towards designing and applying optimized individualized treatments of cancer in the clinic.
Collapse
Affiliation(s)
- Jean Clairambault
- INRIA Paris-Rocquencourt, Domaine de Voluceau, F78153 Rocquencourt, France.
| |
Collapse
|
13
|
Mancuso L, Scanu M, Pisu M, Concas A, Cao G. Experimental analysis and modelling of in vitro HUVECs proliferation in the presence of various types of drugs. Cell Prolif 2010; 43:617-28. [PMID: 21040000 PMCID: PMC6496246 DOI: 10.1111/j.1365-2184.2010.00711.x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2010] [Accepted: 06/17/2010] [Indexed: 01/13/2023] Open
Abstract
OBJECTIVES This study focuses on experimental analysis and corresponding mathematical simulation of in vitro HUVECs (human umbilical vein endothelial cells) proliferation in the presence of various types of drugs. MATERIALS AND METHODS HUVECs, once seeded in Petri dishes, were expanded to confluence. Temporal profiles of total count obtained by classic haemocytometry and cell size distribution measured using an electronic Coulter counter, are quantitatively simulated by a suitable model based on the population balance approach. Influence of drugs on cell proliferation is also properly simulated by accounting for suitable kinetic equations. RESULTS AND DISCUSSION The models' parameters have been determined by comparison with experimental data related to cell population expansion and cell size distribution in the absence of drugs. Inhibition constant for each type of drug has been estimated by comparing the experimental data with model results concerning temporal profiles of total cell count. The reliability of the model and its predictive capability have been tested by simulating cell size distribution for experiments performed in the presence of drugs. The proposed model will be useful in interpreting effects of selected drugs on expansion of readily available human cells.
Collapse
Affiliation(s)
- L. Mancuso
- Department of Chemical Engineering and Materials, University of Cagliari, Research Unit of Consorzio Interuniversitario Nazionale “La Chimica per l’Ambiente”, Piazza d’Armi, Cagliari, Italy
| | - M. Scanu
- Department of Chemical Engineering and Materials, University of Cagliari, Research Unit of Consorzio Interuniversitario Nazionale “La Chimica per l’Ambiente”, Piazza d’Armi, Cagliari, Italy
| | - M. Pisu
- CRS4 (Center for Advanced Studies, Research and Development in Sardinia), Piscinamanna Site, Building 1, Pula, Cagliari, Italy
| | - A. Concas
- CRS4 (Center for Advanced Studies, Research and Development in Sardinia), Piscinamanna Site, Building 1, Pula, Cagliari, Italy
| | - G. Cao
- Department of Chemical Engineering and Materials, University of Cagliari, Research Unit of Consorzio Interuniversitario Nazionale “La Chimica per l’Ambiente”, Piazza d’Armi, Cagliari, Italy
- CRS4 (Center for Advanced Studies, Research and Development in Sardinia), Piscinamanna Site, Building 1, Pula, Cagliari, Italy
| |
Collapse
|
14
|
Kehoe DE, Jing D, Lock LT, Tzanakakis ES. Scalable stirred-suspension bioreactor culture of human pluripotent stem cells. Tissue Eng Part A 2010; 16:405-21. [PMID: 19739936 DOI: 10.1089/ten.tea.2009.0454] [Citation(s) in RCA: 173] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Advances in stem cell biology have afforded promising results for the generation of various cell types for therapies against devastating diseases. However, a prerequisite for realizing the therapeutic potential of stem cells is the development of bioprocesses for the production of stem cell progeny in quantities that satisfy clinical demands. Recent reports on the expansion and directed differentiation of human embryonic stem cells (hESCs) in scalable stirred-suspension bioreactors (SSBs) demonstrated that large-scale production of therapeutically useful hESC progeny is feasible with current state-of-the-art culture technologies. Stem cells have been cultured in SSBs as aggregates, in microcarrier suspension and after encapsulation. The various modes in which SSBs can be employed for the cultivation of hESCs and human induced pluripotent stem cells (hiPSCs) are described. To that end, this is the first account of hiPSC cultivation in a microcarrier stirred-suspension system. Given that cultured stem cells and their differentiated progeny are the actual products used in tissue engineering and cell therapies, the impact of bioreactor's operating conditions on stem cell self-renewal and commitment should be considered. The effects of variables specific to SSB operation on stem cell physiology are discussed. Finally, major challenges are presented which remain to be addressed before the mainstream use of SSBs for the large-scale culture of hESCs and hiPSCs.
Collapse
Affiliation(s)
- Daniel E Kehoe
- Department of Chemical and Biological Engineering, State University of New York at Buffalo, Buffalo, New York 14260, USA
| | | | | | | |
Collapse
|
15
|
Mancuso L, Liuzzo MI, Fadda S, Pisu M, Cincotti A, Arras M, La Nasa G, Concas A, Cao G. In vitro ovine articular chondrocyte proliferation: experiments and modelling. Cell Prolif 2010; 43:310-20. [PMID: 20412130 DOI: 10.1111/j.1365-2184.2010.00676.x] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
This study focuses on analysis of in vitro cultures of chondrocytes from ovine articular cartilage. Isolated cells were seeded in Petri dishes, then expanded to confluence and phenotypically characterized by flow cytometry. The sigmoidal temporal profile of total counts was obtained by classic haemocytometry and corresponding cell size distributions were measured electronically using a Coulter Counter. A mathematical model recently proposed (1) was adopted for quantitative interpretation of these experimental data. The model is based on a 1-D (that is, mass-structured), single-staged population balance approach capable of taking into account contact inhibition at confluence. The model's parameters were determined by fitting measured total cell counts and size distributions. Model reliability was verified by predicting cell proliferation counts and corresponding size distributions at culture times longer than those used when tuning the model's parameters. It was found that adoption of cell mass as the intrinsic characteristic of a growing chondrocyte population enables sigmoidal temporal profiles of total counts in the Petri dish, as well as cell size distributions at 'balanced growth', to be adequately predicted.
Collapse
Affiliation(s)
- L Mancuso
- Dipartimento di Ingegneria Chimica e Materiali, Università degli Studi di Cagliari, Cagliari, Italy
| | | | | | | | | | | | | | | | | |
Collapse
|
16
|
Exponentially modified Gaussian (EMG) relevance to distributions related to cell proliferation and differentiation. J Theor Biol 2010; 262:257-66. [DOI: 10.1016/j.jtbi.2009.10.005] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2009] [Revised: 09/25/2009] [Accepted: 10/06/2009] [Indexed: 11/22/2022]
|
17
|
Mancuso L, Ilaria Liuzzo M, Fadda S, Cincotti A, Pisu M, Concas A, Cao G. Experimental analysis and modeling of bone marrow mesenchymal stem cells proliferation. Chem Eng Sci 2010. [DOI: 10.1016/j.ces.2009.06.034] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
18
|
Mancuso L, Liuzzo MI, Fadda S, Pisu M, Cincotti A, Arras M, Desogus E, Piras F, Piga G, La Nasa G, Concas A, Cao G. Experimental analysis and modelling of in vitro proliferation of mesenchymal stem cells. Cell Prolif 2009; 42:602-16. [PMID: 19614674 DOI: 10.1111/j.1365-2184.2009.00626.x] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
OBJECTIVES Stem cell therapies based on differentiation of adult or embryonic stem cells into specialized ones appear to be effective for treating several human diseases. This work addresses the mathematical simulation of proliferation kinetics of stem cells. MATERIALS AND METHODS Sheep bone marrow mesenchymal stem cells (phenotype characterized by flow cytometry analysis) seeded at different initial concentrations in Petri dishes were expanded to confluence. Sigmoid temporal profiles of total counts obtained through classic haemocytometry were quantitatively interpreted by both a phenomenological logistic equation and a novel model based on a one-dimensional, single-staged population balance approach capable of taking into account contact inhibition at confluence. The models' parameters were determined by comparison with experimental data on population expansion starting from single seeding concentration. Reliability of the models was tested by predicting cell proliferation carried out starting from different seeding concentrations. RESULTS AND DISCUSSION It was found that the proposed population balance modelling approach was successful in predicting the experimental data over the whole range of initial cell numbers investigated, while prediction capability of phenomenological logistic equation was more limited.
Collapse
Affiliation(s)
- L Mancuso
- Department of Chemical Engineering and Materials Science, University of Cagliari, Cagliari, Italy
| | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
19
|
Sherer E, Hannemann RE, Rundell AE, Ramkrishna D. Application of stochastic equations of population balances to sterilization processes. Chem Eng Sci 2009. [DOI: 10.1016/j.ces.2008.05.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
20
|
Affiliation(s)
- Eric Sherer
- School of Chemical Engineering Purdue University West Lafayette, Indiana 47907
| | | |
Collapse
|