A stochastic model in tumor growth

A stochastic model of solid tumor growth based on deterministic Gompertz law is presented. Tumor cells evolution is described by a one-dimensional diffusion process limited by two absorbing boundaries representing healing threshold and patient death (carrying capacity), respectively. Via a numerical approach the first exit time problem is analysed for the process inside the region restricted by the boundaries. The proposed model is also implemented to simulate the effects of a time-dependent therapy. Finally, some numerical results are obtained for the specific case of a parathyroid tumor.     

A stochastic model of animal growth

The plasticity of growth of animals in time, due to the resilience of their response to the ways they can be fed, suggests the difficulty of describing growth by a stochastic model in the time domain. A model is presented which avoids this difficulty by describing growth as a Markov process in the food-consumed domain, assuming that, at conception, (1) the maximum mature weight as a number α of biomass units of mass μ, and (2) the probability B of production of a biomass unit per unit of food consumed, are specified. Constancy of α, μ and B, as the animal feeds, is the basis of the proposed Markov process. The mean growth from infancy to maturity in the food-consumed domain is then the old law of diminishing returns empirically formulated first by Spillman (1924) for cattle and swine, and confirmed by Titus, Jull &; Hendricks (1934) for fowl, and by Parks (1972) across species from mice to steers. The solution also leads to the possibility that the distribution of weights in a population of growing animals of the same species, is related to the distribution of mature weights among the individuals. An experiment by Lister &; McCance (1967) with well-fed and severely undernourished pigs, shows the stability of growth in the foodconsumed domain compared to the plasticity in the time domain. Other implications of the model are discussed.     

A simple mathematical model based on the cancer stem cell hypothesis suggests kinetic commonalities in solid tumor growth

Background

The Cancer Stem Cell (CSC) hypothesis has gained credibility within the cancer research community. According to this hypothesis, a small subpopulation of cells within cancerous tissues exhibits stem-cell-like characteristics and is responsible for the maintenance and proliferation of cancer.

Methodologies/Principal Findings

We present a simple compartmental pseudo-chemical mathematical model for tumor growth, based on the CSC hypothesis, and derived using a “chemical reaction” approach. We defined three cell subpopulations: CSCs, transit progenitor cells, and differentiated cells. Each event related to cell division, differentiation, or death is then modeled as a chemical reaction. The resulting set of ordinary differential equations was numerically integrated to describe the time evolution of each cell subpopulation and the overall tumor growth. The parameter space was explored to identify combinations of parameter values that produce biologically feasible and consistent scenarios.

Conclusions/Significance

Certain kinetic relationships apparently must be satisfied to sustain solid tumor growth and to maintain an approximate constant fraction of CSCs in the tumor lower than 0.01 (as experimentally observed): (a) the rate of symmetrical and asymmetrical CSC renewal must be in the same order of magnitude; (b) the intrinsic rate of renewal and differentiation of progenitor cells must be half an order of magnitude higher than the corresponding intrinsic rates for cancer stem cells; (c) the rates of apoptosis of the CSC, transit amplifying progenitor (P) cells, and terminally differentiated (D) cells must be progressively higher by approximately one order of magnitude. Simulation results were consistent with reports that have suggested that encouraging CSC differentiation could be an effective therapeutic strategy for fighting cancer in addition to selective killing or inhibition of symmetric division of CSCs.     

A model of tumor growth based on cell cycle kinetics

This work describes a mathematical model of growth based on the kinetics of the cell cycle. A traditional model of the cell cycle has been used, with the addition of a resting (G₀) state from which cells could reenter the reproductive cycle. The model assumes that a growth regulatory substance regulates the transition of cells to and from the resting state. Other transitions between the phases of the cycle were modeled as a first order process. Cell loss is an important feature of growth kinetics, and has been represented by a general but tractable mathematical form. The resulting model forms a system of ordinary nonlinear differential equations. Analytic methods are employed first in the study of this system. Simplifying assumptions regarding cell loss give rise to special cases for which equilibrium solutions can be found. One special case, which assumes first order loss from all cell cycle phases at equal rates, is presented here. For small time values, approximations corresponding to exponential growth were developed. The equations describing an intrinsic growth rate were derived. Simulation methods were used to further characterize the behavior of this model. Parameter values were chosen based on animal tumor cell cycle kinetic data, resulting in a set of 45 model simulations. Several tumor treatment protocols were simulated which illustrated the importance of the intrinsic growth rate and cell loss concepts. Although the qualitative behavior regarding absolute and relative growth is reasonable, this model awaits data for model fitting, parameter estimation, or revision of the equations.     

A stochastic model to analyze clonal data on multi-type cell populations

This article presents a stochastic model designed to analyze experimental data on the development of cell clones composed of two (or more) distinct types of cells. The proposed model is an extension of the traditional multi-type Bellman-Harris branching stochastic process allowing for nonidentical time-to-transformation distributions defined for different cell types. A simulated pseudo likelihood method has been developed for the parametric statistical inference from experimental data on cell clones under the proposed model. The method uses simulation-based approximations of the means and the variance-covariance matrices of cell counts. The proposed estimator for the vector of unknown parameters is strongly consistent and asymptotically normal under mild regularity conditions, while its variance-covariance matrix is estimated by the parametric bootstrap. A Monte Carlo Wald test is proposed for the test of hypotheses. Finite sample properties of the estimator have been studied by computer simulations. The model and associated methods of parametric inference have been applied to the analysis of proliferation and differentiation of cultured O-2A progenitor cells that play a key role in the development of the central nervous system. It follows from this analysis that the time to division of the progenitor cell and the time to its differentiation (into an oligodendrocyte) are not identically distributed. This biological finding suggests that a molecular event determining the type of cell transformation is more likely to occur at the start rather than at the end of the mitotic cycle.     

An FM3A mutant, G258, with a mutation that affects both cell growth and oligosaccharide-lipid synthesis

I have previously isolated a temperature-sensitive FM3A mutant (G258) defective in asparagine-linked glycosylation. G258 shows not only the temperature sensitivity for cell growth, but also temperature sensitivity for synthesis of oligosaccharide-lipid (Nishikawa, Y. (1984) J. Cell. Physiol. 119, 260-266). In the present study, I isolated revertants for cell growth from G258 cells. All three growth revertants also showed reversion on the synthesis of oligosaccharide-lipid. These results imply that the temperature sensitivity for oligosaccharide-lipid synthesis of G258 couples with the temperature sensitivity for cell growth of the mutant. A possible mechanism of the coupling between impaired oligosaccharide-lipid synthesis and growth arrest of G258 cells at 39 degrees C is discussed.     

A stochastic model of cell cycle desynchronization

A general branching process model is suggested to describe cell cycle desynchronization. Cell cycle phase times are modeled as random variables and a formula for the expected fraction of cells in S phase as a function of time is established. The model is compared to data from the literature and is also compared to previously suggested deterministic and stochastic models.     

A stochastic model for estimation of mutation rates in multiple-replication proliferation processes

In this paper we propose a stochastic model based on the branching process for estimation and comparison of the mutation rates in proliferation processes of cells or microbes. We assume in this model that cells or microbes (the elements of a population) are reproduced by generations and thus the model is more suitably applicable to situations in which the new elements in a population are produced by older elements from the previous generation rather than by newly created elements from the same current generation. Cells and bacteria proliferate by binary replication, whereas the RNA viruses proliferate by multiple replication. The model is in terms of multiple replications, which includes the special case of binary replication. We propose statistical procedures for estimation and comparison of the mutation rates from data of multiple cultures with divergent culture sizes. The mutation rate is defined as the probability of mutation per replication per genome and thus can be assumed constant in the entire proliferation process. We derive the number of cultures for planning experiments to achieve desired accuracy for estimation or desired statistical power for comparing the mutation rates of two strains of microbes. We establish the efficiency of the proposed method by demonstrating how the estimation of mutation rates would be affected when the culture sizes were assumed similar but actually diverge.      

Mathematical model for chemotherapeutic drug efficacy in arresting tumour growth based on the cancer stem cell hypothesis

OBJECTIVE: Cancer stem cells have been identified as the growth root for various malignant tumours and are thought to be responsible for cancer recurrence following treatment. MATERIALS AND METHODS: Here, a predictive mathematical model for the cancer stem cell hypothesis is used to understand tumour responses to chemotherapeutic drugs and judge the efficacy of treatments in arresting tumour growth. The impact of varying drug efficacies on different abnormal cell populations is investigated through the kinetics associated with their decline in response to therapy. RESULTS AND CONCLUSIONS: The model predicts the clinically established 'dandelion phenomenon' and suggests that the best response to chemotherapy occurs when a drug targets abnormal stem cells. We compare continuous and periodic drug infusion. For the latter, we examine the relative importance of the drug cell-kill rate and the mean time between successive therapies, to identify the key attributes for successful treatment.     

A stochastic model of the dynamics of host-pathogen systems with mutation

In this paper a stochastic model of the dynamics of host-pathogen systems with mutation is constructed. In previous works deterministic models of host-pathogen systems with no mutation were considered. The evolution of the pathogen population in any generation of the host is formulated as a multidimensional birth and death process, while the evolution of genotypic frequencies in successive generations of the host is described by a solution of a nonlinear vector difference equation. A general solution of the differential equations of the multidimensional birth and death process is presented and expressions for the stationary distribution, whenever it exists, and the mean time to extinction, when absorbing states are present, are derived. Some answers to questions raised in the discussion of a previous paper (Mode, 1962) are also contained in this paper. The research reported in this paper was supported by the United States Atomic Energy Comission, Division of Biology and Medicine Project AT(45-1)-1729.     

A stochastic model to simulate the growth of anchorage dependent cells on flat surfaces

A stochastic model is proposed to simulate the growth of anchorage dependent cells on a flat surface. The model, based on representing the cell shapes on the surface as external irregular polygons with the nuclei distributed as a set of Poisson points (producing a modified Voronoi tessellation of 2 space) and incorporating a distribution function to describe cell division of the perimeter cells of the colony, provides data not only on population dynamics but also on the patterns produced by clusters of cells in the colony. These patterns produced by the model are qualitatively similar to observations reported for some cell cultures. The periods of induction, rapid growth, and decreasing growth asymptoting to zero as confluence is reached are predicted by the model. Quantitative comparison with published experimental data for this is good. The specific growth rate computed for the period of rapid growth predicted by the model is dependent on the distribution function describing the cell division time. As the standard deviation of this increases, the specific growth rate decreases as with a consequent increase in time to achieve confluence. The removal of cells from the colony by shear forces or death is considered in the model. As the probability for removal increases, the cell density at confluence and specific growth rate decrease. The clusters of cells, patterns, in the colony are very sensitive to cell removal. By analyzing these patterns in experiments, an estimate of cell removal can be made. The areas covered by cells on a substrate are fractal patterns. The fractal dimension is always greater than 1 and is a function of the removal probability.     

A stochastic model of cytotoxic T cell responses

We have constructed a stochastic stage-structured model of the cytotoxic T lymphocyte (CTL) response to antigen and the maintenance of immunological memory. The model follows the dynamics of a viral infection and the stimulation, proliferation, and differentiation of na?ve CD8(+) T cells into effector CTL, which can eliminate virally infected cells. The model is capable of following the dynamics of multiple T cell clones, each with a T cell receptor represented by a digit string. MHC-viral peptide complexes are also represented by strings and a string match rule is used to compute the affinity of a T cell receptor for a viral epitope. The avidities of interactions are also computed by taking into consideration the density of MHC-viral peptides on the surface of an infected cell. Lastly, the model allows the probability of T cell stimulation to depend on avidity but also incorporates the notion of an antigen-independent programmed proliferative response. We compare the model to experimental data on the cytotoxic T cell response to lymphocytic choriomeningitis virus infections.     

A stochastic Markov chain model to describe lung cancer growth and metastasis

A stochastic Markov chain model for metastatic progression is developed for primary lung cancer based on a network construction of metastatic sites with dynamics modeled as an ensemble of random walkers on the network. We calculate a transition matrix, with entries (transition probabilities) interpreted as random variables, and use it to construct a circular bi-directional network of primary and metastatic locations based on postmortem tissue analysis of 3827 autopsies on untreated patients documenting all primary tumor locations and metastatic sites from this population. The resulting 50 potential metastatic sites are connected by directed edges with distributed weightings, where the site connections and weightings are obtained by calculating the entries of an ensemble of transition matrices so that the steady-state distribution obtained from the long-time limit of the Markov chain dynamical system corresponds to the ensemble metastatic distribution obtained from the autopsy data set. We condition our search for a transition matrix on an initial distribution of metastatic tumors obtained from the data set. Through an iterative numerical search procedure, we adjust the entries of a sequence of approximations until a transition matrix with the correct steady-state is found (up to a numerical threshold). Since this constrained linear optimization problem is underdetermined, we characterize the statistical variance of the ensemble of transition matrices calculated using the means and variances of their singular value distributions as a diagnostic tool. We interpret the ensemble averaged transition probabilities as (approximately) normally distributed random variables. The model allows us to simulate and quantify disease progression pathways and timescales of progression from the lung position to other sites and we highlight several key findings based on the model.     

A test of the stochastic theory of stem cell differentiation.

Stochastic theories of stem cell renewal are shown to predict turnover of intestinal crypts. While I found ample evidence of production of new crypts from direct in vivo studies in adult mice, I failed to find evidence of crypt loss. Thus, it would appear that the simple stochastic models may not provide an adequate theory of control of intestinal stem cell function.     

A stochastic model of brain cell differentiation in tissue culture

 The timing of cell differentiation can be controlled both by cell-intrinsic mechanisms and by cell-extrinsic signals. Oligodendrocyte type-2 astrocyte progenitor cells are known to be the precursor cells that give rise to oligodendrocytes. When stimulated to divide by purifed cortical astrocytes or by platelet-derived growth factor, these progenitor cells generate oligodendrocytes in vitro with a timing like that observed in vivo. The most widely accepted model of this process assumes a cell-intrinsic biological clock that resides in the progenitor cell. The intrinsic clock model originally proposed in 1986 remains as the dominant theoretical concept for the analysis of timed differentiation in this cell lineage. However, the results of a recent experimental study (Ibarrola et al., Developmental Biology, vol. 180, 1–21, 1996) are most consistent with the hypothesis that the propensity of a clone of dividing O-2A progenitor cells initially to generate at least one oligodendrocyte may be regulated by cell-intrinsic mechanisms, but that environmental signals regulate the extent of further oligodendrocyte generation. We propose a stochastic model of cell differentiation in culture to accommodate the most recent experimental findings. Our model is an age-dependent branching stochastic process with two types of cells. The model makes it possible to derive analytical expressions for the expected number of progenitor cells and of oligodendrocytes as functions of time. The model parameters were estimated by fitting these functions through data on the average (sample mean) number of both types of cells per colony at different time intervals from start of experiment. Using this method we provide a biologically meaningful interpretation of the observed pattern of oligodendrocyte generation in vitro and its modification in the presence of thyroid hormone. Received: 18 April 1997 / Revised version: 30 November 1997