PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “Mathematical Model”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8Linked to original sources

Oncogenes, anti-oncogenes and the immune response to cancer: a mathematical model.

We develop a mathematical model for the initial growth of a tumour after a mutation in which either an oncogene is expressed or an anti-oncogene (i.e. tumour suppressor gene) is lost. Our model incorporates mitotic control by several biochemicals, with quite different regulatory characteristics, and we consider mutations affecting the cellular response to these control mechanisms. Our mathematical representation of these mutations reflects the current understanding of the roles of oncogenes and anti-oncogenes in controlling cell proliferation. Numerical solutions of our model, for biologically relevant parameter values, show that the different types of mutations have quite different effects. Mutations affecting the cell response to chemical regulators, or resulting in autonomy from such regulators, cause an advancing wave of tumour cells and a receding wave of normal cells. By contrast, mutations affecting the production of a mitotic regulator cause a slow localized increase in the numbers of both normal and mutant cells. We extend our model to investigate the possible effects of an immune response to cancer by including a first order removal of mutant cells. When this removal rate exceeds a critical value, the immune system can suppress tumour growth; we derive an expression for this critical value as a function of the parameters characterizing the mutation. Our results suggest that the effectiveness of the immune response after an oncogenic mutation depends crucially on the way in which the mutation affects the biochemical control of cell division.

Animals↗

Mathematical modeling relevant to closed artificial ecosystems.

The mathematical modeling of ecosystems has contributed much to the understanding of the dynamics of such systems. Ecosystems can include not only the natural variety, but also artificial systems designed and controlled by humans. These can range from agricultural systems and activated sludge plants, down to mesocosms, microcosms, and aquaria, which may have practical or research applications. Some purposes may require the design of systems that are completely closed, as far as material cycling is concerned. In all cases, mathematical modeling can help not only to understand the dynamics of the system, but also to design methods of control to keep the system operating in desired ranges. This paper reviews mathematical modeling relevant to the simulation and control of closed or semi-closed artificial ecosystems designed for biological production and recycling in applications in space.

Animals↗

Formulation of a mathematical model to predict solar water disinfection.

A mathematical model was formulated that will facilitate the prediction of solar disinfection by analyzing the effect of sunlight exposure (x(1)) and the load of bacterial contamination (x(2)), as predictor variables, on the efficiency of solar disinfection (y). Aliquots of 0.1 ml containing average numbers of E. coli, ranging between 1 and 5 x 10(3)cells/ml raw water, were introduced into each of the 96 wells of polystyrene microtitre plates. Plates, with the lid on, were exposed to sunlight for varying exposures ranging between 1.04 x 10(3) and 8.40 x 10(3)kJ m(-2). Double strength nutrient broth was then added. After 48 h incubation wells containing visible contamination were considered as containing one cell or more that survived the exposure. Data showed that disinfection is dependent both on the load of bacterial contamination and sunlight exposure. This relationship is characterized by curves having shoulders followed by a steep decline and then tailing off in an asymptotic fashion. The shoulder size increased with the increase of the contamination load, however, the slope remains the same. Statistical analysis indicates a positive correlation among the variables (R(2) = 0.893); the mathematical model, y=1-(1-e(-kx(1)))(x(2)), represents the relationship, with k being the solar inactivation constant. The exposure required to produce a given decontamination level can be predicted using the equation: x(1)=-1/kln[1-(1-y)(-1/x(2))]e(-micro/rho.m/A), where micro is the linear attenuation coefficient (m(-1)), rho is the density, m is the mass and A is the area of the exposed part of the sample. The predictor variables (x(1), x(2)) strongly influence the efficiency of solar disinfection, which can be predicted using the suggested mathematical model. The present data provides a means to predict the efficiency of solar disinfection as an approach to improve the quality of drinking water mainly in developing countries with adequate sunshine all year-round.

Disinfection↗

Evaluation of a mathematical model for predicting the relationship between protein and energy intakes of low-birth-weight infants and the rate and composition of weight gain.

A model for predicting the relationship between protein and energy intakes of low-birth-weight (LBW) infants and the rate and composition of weight gain is described. It is based on linear multiple regression equations summarizing the rates of weight gain, nitrogen retention, and energy retention of 101 previously studied LBW infants fed protein intakes ranging from 2.25 to 3.9 g.kg-1.d-1 and concomitant energy intakes ranging from 115 to 147 kcal.kg-1.d-1 plus current theory concerning nutrient retention and body composition. To test the validity of the model, three combinations of protein and energy intake predicted by the model to result in specific rates and compositions of weight gain were fed to 44 LBW infants, and the observed rates of weight gain, protein accretion, and fat accretion were compared with the rates predicted by the model. Differences in these and other outcome variables between two of the groups, the intakes of which differed only in energy, also were compared to provide additional insight into the effect of concomitant energy intake on protein utilization. Across groups, actual outcomes correlated closely with predicted outcomes, supporting the validity of the model for the total population. However, outcomes of individual infants deviated as much as 30% from predicted outcomes; the magnitude of the deviation was independent of birth weight, gestational age, or size for gestational age.(ABSTRACT TRUNCATED AT 250 WORDS)

Body Composition↗

A mathematical model for the analysis of the turnover of protein mixtures. II. Modified power functions as special models.

In part 1 of this series a model for the protein turnover characterized by an inhomogeneous protein pool with a distribution of turnover constants was developed. This model is mathematically described, taking into account that the protein turnover is experimentally determined almost exclusively by tracer experiments, but a system of integro-differential equations. The analytical solution of this system of equations is elaborated for 3 special cases: tracer elimination from the protein pool after pulse labelling, tracer accumulation due to continuous labelling, and elimination after stopping this continuous incorporation of tracer. The applicability of the resulting modified power function is tested using an example for the literature (Garlic et al.: Biochem. J. 156, 657 (1976)) and computer generated data. On this background the existence of several classes of proteins computer generated data. On this background the existence of several classes of proteins with typical life times is discussed. Finally, the general applicability of power functions for tracer kinetic problems is treated from the point fo view of the results obtained in this paper.

Kinetics↗

A mathematical model of drug resistance: heterogeneous tumors.

A mathematical model is developed to describe the growth and control of a heterogeneous tumor. The main aspect of the model is that it takes into account induced drug resistance. The mathematical model is a system of two ordinary differential equations that describes the growth of the cancer along with the effects of chemotherapy. The model is analyzed to determine what some of the critical parameters are; how we determine an effective treatment; how combination chemotherapy should be delivered; and how this model may help us develop more effective cancer chemotherapeutic treatments.

Animals↗

Epidemiology, HIV and drugs: mathematical models and data.

The utility of mathematical models in understanding the dynamics of HIV transmission among injecting drug users (IDUs) and their non-IDU sex partners is discussed. We emphasize the need for collaborative relationships between modellers and drug-use researchers, and we stress that models should be based on data in both their formulation and development stages. We outline some of the possible data requirements of transmission models and we highlight the need for the collection of appropriate quantitative data, so that modellers can estimate specific parameters for their models. We believe that discussion of the needs and utility of mathematical modelling will lead to mutually beneficial collaborations between theoreticians and drug-use researchers. These collaborations may aid in the design and the evaluation of effective behavioural or medical intervention strategies.

England↗

A mathematical model for the curves of intrauterine growth.

Different mathematical models for a chart of intrauterine growth were tested attempting to employ uncomplicated curves. In this manner, a chart of foetal growth was elaborated by plotting the cube root of neonate weight against the gestational age. Two different regions can be clearly seen in this chart of growth. In the first one, until the 34th week of pregnancy, there is a linear relationship between the two variables. In the second region, for pregnancies above the 34th week, the curve has a quadratic equation, reaching the maximum between the 43rd and 44th week, decreasing posteriorly. The goodness of fit obtained by the present mathematical model is more satisfactory than that obtained by mathematical models with a single linear equation.

Anthropometry↗

Analysis of chemotactic bacterial distributions in population migration assays using a mathematical model applicable to steep or shallow attractant gradients.

The mathematical model developed by Rivero et al. (1989, Chem. Engng Sci. 44, 2881-2897) is applied to literature data measuring chemotactic bacterial population distributions in response to steep as well as shallow attractant gradients. This model is based on a fundamental picture of the sensing and response mechanisms of individual bacterial cells, and thus related individual cell properties such as swimming speed and tumbling frequency to population parameters such as the random motility coefficient and the chemotactic sensitivity coefficient. Numerical solution of the model equations generates predicted bacterial density and attractant concentration profiles for any given experimental assay. We have previously validated the mathematical model from experimental work involving a step change in the attractant gradient (Ford et al., 1991 Biotechnol. Bioengng, 37, 647-660; Ford and Lauffenburger, 1991, Biotechnol. Bioengng, 37, 661-672). Within the context of this experimental assay, effects of attractant diffusion and consumption, random motility, and chemotactic sensitivity on the shape of the profiles are explored to enhance our understanding of this complex phenomenon. We have applied this model to various other types of gradients with successful interpretation of data reported by Dalquist et al. (1972, Nature New Biol. 236, 120-123) for Salmonella typhimurium validating the mathematical model and supporting the involvement of high and low affinity receptors for serine chemotaxis by these cells.

Bacterial Physiological Phenomena↗

The use of a mathematical model in rhinomanometry.

The authors consider the mathematical model proposed by the Swedish Group (Broms et al.). This model permits the pressure gradient-flow recording as obtained from anterior or posterior rhinomanometry to be converted into a mathematical formula. The model was tested for its mathematical, statistical, and clinical utility with 32 normal test subjects. It is the conviction of the authors, although not totally without reservation, that this is the best mathematical model in existence.

Adolescent↗

On the dangers of adjusting the parameters values of mechanism-based mathematical models.

Mechanism-based mathematical models describe systems in terms of identifiable physical processes, and the parameters are assumed to have fundamental physical significance. Ideally, the parameter values are measured independent of the system being modeled, but these values are often adjusted to give the best fit of model predictions to experimental data. A systematic investigation of the effects of such parameter adjustment was conducted by developing a model system comprising a known reaction mechanism and known rate constants. Simulations of experiments were run, and then attempts were made to model the system under a variety of problematic, but realistic, conditions. (1) When one rate constant was seriously in error, adjustment of a different rate constant gave the greatest improvement in the model fit. (2) When a contaminant was present in the experiment, the effects could be hidden by the adjustment of the rate constants. (3) When an incorrect reaction mechanism was assumed, the error could be hidden by parameter adjustment if the concentrations of only one of the reacting species were considered or if an unweighted fit was used for the optimization. (4) Parameter values adjusted for one set of experimental conditions gave a poorer fit than did the unadjusted parameter values when attempting to model a new set of experimental condition (addition of an inhibitor). These results show the potential dangers of adjusting parameter values and the importance of measuring as many variables as possible in a complex system.

Animals↗

Mathematical modeling of biofilms.

A set of mathematical equations constitutes a mathematical model if it aims to represent a real system and is based on some theory of that system's operation. On this definition, mathematical models, some very simple, are everywhere in science. A complex system like a biofilm requires modeling by numerical methods and, because of inevitable uncertainties in its theoretical basis, may not be able to make precise predictions. Nevertheless, such models almost always give new insight into the mechanisms involved, and stimulate further investigation. The way in which diffusion coefficients are measured for use in a model, particularly whether they include effects of reversible reaction, is a key element in the modeling. Reasons are given for separating diffusion from reversible reaction effects and dealing with them in a separate subroutine of the model.

Bacterial Adhesion↗

[Short-term dynamics of intertidal microphytobenthos biomass. Mathematical model].

We formulate a deterministic mathematical model to describe the dynamics of the microphytobenthos of intertidal mudflats. It is 'minimal' because it only takes into account the essential processes governing the functioning of the system: the autotrophic production, the active upward and downward migrations of epipelic microalgae, the saturation of the mud surface by a biofilm of diatoms and the global net loss rates of biomass. According to the photic environment of the benthic diatoms inhabiting intertidal mudflats, and to their migration rhythm, the model is composed of two sub-systems of ordinary differential equations; they describe the simultaneous evolution of the biomass 'S' concentrated in the mud surface biofilm--the photic layer--and of the biomass 'F' diluted in the topmost centimetre of the mud--the aphotic layer. Qualitatively, the model solutions agree fairly well with the in situ observed dynamics of the S + F biomass. The study of the mathematical properties of the model, under some simplifying assumptions, shows the convergence of solutions to a stable cyclic equilibrium, whatever the frequencies of the physical synchronizers of the production. The sensitivity analysis reveals the necessity of a better knowledge of the processes of biomass losses, which so far are uncertain, and may further vary in space and time.

Biomass↗

From passive diffusion to active cellular migration in mathematical models of tumour invasion.

Mathematical models of tumour invasion appear as interesting tools for connecting the information extracted from medical imaging techniques and the large amount of data collected at the cellular and molecular levels. Most of the recent studies have used stochastic models of cell translocation for the comparison of computer simulations with histological solid tumour sections in order to discriminate and characterise expansive growth and active cell movements during host tissue invasion. This paper describes how a deterministic approach based on reaction-diffusion models and their generalisation in the mechano-chemical framework developed in the study of biological morphogenesis can be an alternative for analysing tumour morphological patterns. We support these considerations by reviewing two studies. In the first example, successful comparison of simulated brain tumour growth with a time sequence of computerised tomography (CT) scans leads to a quantification of the clinical parameters describing the invasion process and the therapy. The second example considers minimal hypotheses relating cell motility and cell traction forces. Using this model, we can simulate the bifurcation from an homogeneous distribution of cells at the tumour surface toward a nonhomogeneous density pattern which could characterise a pre-invasive stage at the tumour-host tissue interface.

Brain↗

Fetal O2 changes in response to hypoxic stress: a mathematical model.

We developed a mathematical model to compute the time course of PO2 changes in fetal blood vessels during hypoxia. The model represents the circulation and major organs as a system of paths and nodes. We calculated outflow O2 for a path from its inflow O2 content and its distribution of transit times. The O2 content at a given node equals the flow-weighted sum of O2 delivered by different paths. Placental O2 transfer and organ O2 consumption are related to their arterial PO2 levels. We simulated the effects of uterine contractions with Gaussian-shaped decreases in placental O2 transfer. Increasing the intensity and increasing the duration of hypoxic episodes have comparable effects. Liver O2 consumption decreases more than that of other organs during hypoxic episodes. At the peak of a contraction, fetal systemic PO2 values decrease only about one-fourth as much as those in end-capillary placental blood. This indicates that despite rapid circulation times, fetal O2 reserves protect it against severe, short term hypoxia.

Brain↗

The role of N-methyl-D-aspartate (NMDA) receptors in wind-up: a mathematical model.

We present a mathematical model for the phenomenon of wind-up (Mendell, 1966, Exper. Neur. 16,316-22) which occurs in many neurons. We concentrate on its occurrence in the substantia gelatinosa of the dorsal horns of the spinal cord, where it is connected with certain pathological and nonpathological pain states. The model is a development of the model by Britton & Skevington (1989, J. Theor. Biol. 137, 91-105) for Melzack & Wall's gate control theory of pain (1965, Science, New York, 150, 971-9; 1982, The Challenge of Pain, Penguin: Harmondsworth), modified to take account of more recent information. Its variables are the electric potentials of various cells in the midbrain and the spinal cord. Britton & Skevington's original model simulated many of the phenomena observed in acute pain in humans, but not the wind-up mechanism. This is not surprising, since this model did not include the N-methyl-D-aspartate (NMDA) receptors that are now recognized as being crucial to the phenomenon. Here we rectify this omission, and obtain good agreement between the model and experimental data on wind-up. The positive feedback that NMDA receptors exhibit is shown to be the essential feature in producing wind-up. As an independent test of the model we simulate a completely different experimental set-up, and obtain good qualitative agreement with data there. Finally, we present a prediction of the model that has yet to be tested experimentally.

Animals↗

Biological exposure index of styrene suggested by a physiologico-mathematical model.

We used a physiologico-mathematical model to study the biological exposure index of styrene correlated to the Threshold Limit Value (TLV) suggested by the ACGIH for 1986-87. This model allows the solvent concentrations in blood, alveolar air, fat tissue, and in other biological media to be estimated and simultaneously the kinetics of its metabolites to be followed when a specific exposure is settled. The comparison between the results obtained from the mathematical model and the numerous research projects documented in the literature suggests a reciprocal validation. Moreover, some biological parameters (particularly the alveolar ventilation) can explain the variability of results obtained from studies concerning the solvent pollution of the factories, which used biological monitoring. The ranges of styrene concentrations in blood and alveolar air and the urinary concentrations of its metabolites (mandelic and phenylglioxylic acids) are discussed in connection with the exposure at 215 mg/m3. Important differences correlated to the definition of set-levels of TLV and Biological Exposure Index (BEI) have been found: particularly the TLVs lead to different solvent uptakes according to some biological parameters; the BEI can better explain the individual solvent uptake and body burden.

Environmental Exposure↗