4 edition of **Mathematical modeling of cell proliferation** found in the catalog.

- 147 Want to read
- 40 Currently reading

Published
**1985**
by CRC Press in Boca Raton, Fla
.

Written in English

- Hematopoiesis -- Mathematical models,
- Hematopoietic stem cells,
- Cell proliferation -- Mathematical models

**Edition Notes**

Includes bibliographies and index.

Statement | editors, H.-Erich Wichmann, Markus Loeffler. |

Contributions | Wichmann, H.-Erich 1946-, Loeffler, Markus, 1954- |

Classifications | |
---|---|

LC Classifications | QP91 .M32 1985 |

The Physical Object | |

Pagination | 2 v. : |

ID Numbers | |

Open Library | OL2858781M |

ISBN 10 | 0849355036, 0849355044 |

LC Control Number | 84021463 |

The mathematical models prevalently used to represent stem cell proliferation do not have the level of accuracy that might be desired. The hyperbolastic growth models promise a greater degree of precision in representing data of stem cell by: Cell Proliferation is an open access journal devoted to studies into all aspects of cell proliferation and differentiation in normal and abnormal states; control systems and mechanisms operating at inter- and intracellular, molecular and genetic levels; modification by and interactions with chemical and physical agents; mathematical modelling; and the development of new techniques.

The CARRGO model may be combined with other mathematical models which estimate cancer cell growth and proliferation rates non-invasively with MRI data [9,11,46] to produce a fine-tuned and benchmarked suite of mathematical models, which may aide in optimization of dosing and scheduling of CAR T-cells for greater individualized and personalized. Abstract: We present a novel mathematical model of heterogeneous cell proliferation where the total population consists of a subpopulation of slow-proliferating cells and a subpopulation of fast-proliferating cells. The model incorporates two cellular processes, asymmetric cell division and induced switching between proliferative states, which are important determinants for the heterogeneity.

Measures of goodness-of-fit for the competing mathematical models demonstrated that two distinct proliferation rates are more likely than a single proliferation rate, providing quantitative evidence of a population of “label-retaining” tumor cells. The mathematical modeling also allowed us to estimate the proliferation rate of this label. This model is a new study in the filed of analysis of cell kinetics and cell division using mathematical modeling and optimized by Genetic Algorithm. We use our mathematical analysis in conjunction with experimental data from the division analysis of primitive cells to characterize the maturation/proliferation .

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This volume introduces some basic mathematical models for cell cycle, proliferation, cancer, and cancer therapy. Chapter 1 gives an overview of the modeling of the cell division cycle. Chapter 2 describes how tumor secretes growth factors to form new blood vessels in its vicinity, which provide it with nutrients it needs in order to grow.

VolumeIssue 2, JunePages Mathematical modeling of T-cell proliferation. Author links open overlay panel I.A. Sidorov A.A. RomanyukhaCited by: Mathematical Modeling of T-Cell Proliferation I. SIDOROV Institute of Biochemistry and Physiology of Microorganisms, Puschino, Russia AND A.

ROMANYUKHA Institute of Numerical Mathematics, Moscow, Russia Received 4 June ; revised 24 August ABSTRACT A mathematical model of the T-lymphocyte proliferation process (in vivo and in vitro) is by: The mathematical models prevalently used to represent stem cell proliferation do not have the level of accuracy that might be desired.

The hyperbolastic growth models promise Mathematical modeling of cell proliferation book greater degree of. Enrico Gavagnin, Christian A. Yates, in Handbook of Statistics, Abstract.

Mathematical models are vital interpretive and predictive tools used to assist in the understanding of cell migration. There are typically two approaches to modeling cell migration: either microscale, discrete or.

Central to proliferation modeling is the incorporation of available biological data and validation with experimental data. Areas covered: We present an overview of past and current mathematical strategies directed at understanding tumor cell proliferation. We identify areas for mathematical development as motivated by available experimental and.

Author summary We assess the impact of genetics and aging on immune system dynamics by investigating the dynamics of proliferation of T lymphocytes across their differentiation through thymus and spleen in mice.

Understanding cell proliferation dynamics requires specific experimental methods and mathematical modelling. Our investigation is based upon single-cell. MultiCellular Tumor Spheroids are 3D cell cultures that can accurately reproduce the behavior of solid tumors.

It has been experimentally observed that large spheroids exhibit a decreasing gradient of proliferation from the periphery to the center of these multicellular 3D models: the proportion of proliferating cells is higher in the periphery while the non-proliferating quiescent cells.

Molecular mechanisms and mathematical models of circadian clock–cell cycle coupling. Early seminal studies demonstrated the existence of coupling between the canonical TTFL components of the circadian network and the cell cycle, revealing regulation of c-Myc transcription by PER2 [] and regulation of the G2/M inhibitor Wee1 by BMAL1–CLOCK [] (Figure 1a).

Kim presents a mathematical model based on microRNAs that balance cell proliferation and migration in different microenvironmental conditions in glioblastoma, suggesting a post-surgery injection of chemoattractants and glucose to counteract the diffusive spread of residual cells.

Request PDF | Mathematical models of tumor cell proliferation: A review of the literature | Introduction: A defining hallmark of cancer is aberrant cell proliferation.

Efforts to understand the. This paper examines how adult stem cells maintain their ability to carry out a complex set of tasks, including tissue regeneration and replacement of defective cells. To do so, stem cell populations must coordinate differentiation, proliferation, and cell death (apoptosis) to maintain an appropriate distribution of epigenetic states.

Using the tools of applied mathematics, and borrowing. The importance of these techniques has led to a recent and intensive effort to develop and understand mathematical models for use in analysis of cell proliferation data [6, 12].

Among these are partial differential equation (PDE) structured population models which have been shown to accurately fit histogram data obtained from CFSE flow.

Tissue engineering involves growing cells within supporting scaffolds to obtain structures for in vivo implantation with adequate functionality. Obtaining a proper oxygen supply, high cell density, and a uniform cell distribution in a three-dimensional (3D) growth support are important challenges.

Both experiments and quantitative mathematical models are needed to better understand the. mathematical models of cell proliferation are often restricted to details that are most pertinent to the experimental situation under consideration.

The main requiremen t is that the model must. We apply a mathematical model for receptor‐mediated cell uptake and processing of epidermal growth factor (EGF) to analyze and predict proliferation responses to fibroblastic cells transfected with various forms of the EGF receptor (EGFR) to EGF.

An Der Heiden and C. Kaltschmidt, Mathematical model for NF‐κB–driven proliferation of adult neural stem cells, Cell Proliferation, 39, 6, (), (). Wiley Online Library. Central to proliferation modeling is the incorporation of available biological data and validation with experimental data.

Areas covered: We present an overview of past and current mathematical strategies directed at understanding tumor cell proliferation.

In the model, migratory cells are stored in a matrix where rows of the matrix represent individual cells and columns contain parameters related to that cell. These parameters include number of days since the last proliferation event T P, number of days since the last migration event T M, and cell location in spherical coordinates.

Lei J, Levin SA, Nie Q () Mathematical model of adult stem cell regeneration with cross-talk between genetic and epigenetic regulation.

Proc Natl Acad Sci U S A (10):E–E doi: /pnas PubMed PubMedCentral CrossRef Google Scholar. In this work we use a mathematical model of predator-prey dynamics to explore the kinetics of CAR T-cell killing in glioma: the Chimeric Antigen Receptor t-cell treatment Response in GliOma (CARRGO) model.

The model includes rates of cancer cell proliferation, CAR T-cell killing, CAR T-cell proliferation and exhaustion, and CAR T-cell persistence.Mathematical Modeling of Stem Cells Related to Cancer 5 cell lines to a mathematical model of the stem cell population ([49]--[51], [35]).

The model has four distinct compartments representing hematopoietic stem cells and circulating leukocytes, platelets and erythrocytes.

The stem cells are pluripotential and self-renewing. In systems biology experimental approaches are combined with mathematical modeling to understand complex behavior of cells and organisms. Experimental approaches and mathematical models are connected through a cyclic workflow [].Experimental data is used as input for mathematical models that, in turn, generate biological predictions.