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Mathematical modeling of phosphorus losses from land application of hog and cattle manure.

Mathematical models may provide a means to estimate phosphorus (P) losses from land application of manure. Phosphorus losses typically occur during brief episodes of runoff and erosion. Models must be able to simulate P losses during these episodes by representing the basic chemical, physical, and biological processes by which these losses occur. The mathematical model ecosys combines dynamic distributed flow of solutes and nonsolutes through runoff and erosion with convective-dispersive transport of solutes, and both biologically and thermodynamically driven transformations between solutes and nonsolutes. This model was tested against P lost in runoff, erosion, and leachate measured during 90 min of controlled rainfall at 65 mm h(-1) on soils from six sites at which different rates of manure had been applied over the previous 3 to 6 yr. Transport and transformation kinetics in the model enabled it to simulate changes of dissolved inorganic phosphorus (DIP) in runoff from >1.0 to <0.05 mg L(-1) and changes of total phosphorus (TP) in sediment from 15 to 3 mg L(-1) measured during controlled rainfall on soils with diverse P contents. Results from 60-yr model runs using these kinetics with different application rates of cattle manure indicated that (i) a positive interaction exists between annual rainfall and application rate on P losses and (ii) rates greater than 30 Mg ha(-1) yr(-1) would cause TP concentrations in water leaving the site to rise above acceptable limits. The interaction between rainfall and rate suggests that P losses from manure application at any site should be assessed under the upper range of likely rainfall intensities.

Agriculture↗

A comparison of mathematical models for estimating right ventricular volumes in animals and man.

Volume of 19 right ventricular canine casts and 11 right ventricular human casts were obtained by water displacement and compared to three different mathematical models for estimating right ventricular volumes by biplane cineangiography. In the canine studies, significant linear correlation coefficients were obtained using the longest measured length method (r = 0.92), the triangular modification of Simpson's rule (r = 0.93), and the elliptical modification of Simpson's rule (r = 0.93). The human studies resulted in similar significant correlation coefficients of 0.96, 0.97, and 0.97, respectively. Although the highest correlation with the lowest standard error of estimate was obtained using the triangular model, all three mathematical models produced volume estimations that feel within acceptabe biological limits of accuracy.

Animals↗

Modeling of immunosensors under nonequilibrium conditions. I. Mathematic modeling of performance characteristics.

Immunosensors for the detection of small analytes that use analyte-enzyme conjugates as signal generators require special attention if operated under nonequilibrium conditions. If the size of the analyte and the analyte-enzyme conjugate differ substantially, the two antigens do not diffuse at the same rate. This can cause time-dependent shifts in the sensitivity of competitive immunoassays. Therefore, immunosensors operating at short incubation times require precise timing that meets closely the specifications for which the sensors were calibrated. As an example, we have analyzed kinetic binding curves for the quantitative determination of progesterone with an immobilized monoclonal antibody and a conjugate between horseradish peroxidase and progesterone as signal generator. Mathematical paradigms have been developed to simulate the diffusion, antigen-antibody complex formation, and competitive binding processes in this analytical system. Dose-response curves obtained under nonequilibrium conditions can vary substantially from those obtained at equilibrium of antigen-antibody interaction. The degree of this variation depends on the performance characteristics of the major components of the immunosensor. The developed mathematical solutions reflect experimental results and can be used to model optimal conditions for immunosensors operating under nonequilibrium conditions. In this paper (Part I), we report on the mathematical modeling of the interaction between analyte, analyte-enzyme conjugate, and an immobilized antibody. In Part II (W. Schramm and S.-H. Paek (1991) Anal. Biochem. 196), we present experimental results and compare them with the theoretical models.

Antibodies↗

[Mathematical modeling of cyclic kinetics of hematopoiesis].

Mathematical models of the time course of formation of platelets, erythrocytes, granulocytes and lymph cells of mammals have been developed. They are systems of nonlinear differential equations where concentrations of mature blood cells and their bone marrow precursors are the variables. The models represent the main stages in the development of the various types of blood cells and allow for specific formation of red and white blood cells. Verification with the aid of oscilation theory methods and computer-aided numerical calculations have shown that the models reproduce all dynamic variations of the hemopoietic system, including stable fluctuations of concentrations of the various types of blood cells and their precursors (limiting cycles). Calculated parameters of stable fluctuations are in good agreement with experimental data. Within the framework of the models the origination of limiting cycles is described and their interpretation is given. These models can be used to simulate monthly biologic rhythms inherent in the various types of hemopoiesis as well as to analyze flight biomedical data and to discriminate space flight effects on hemopoiesis.

Blood Cells↗

Mathematical modeling of mitochondrial energy transduction.

A mathematical model of mitochondrial energy transduction is presented. The model contains rate equations for the main steps of oxidative phosphorylation. It was used to simulate the relations of respiration and ATP formation to extra- and intramitochondrial ATP/ADP ratios under various steady-state conditions. Furthermore, the model equations allowed to compute control coefficients, which quantify the control exerted by different steps on respiration. The distribution of control within mitochondria is demonstrated to depend on the metabolic state of mitochondria and also on the properties of extramitochondrial enzymes involved in ATP turnover. The simulated steady-state data as well as computed control coefficients were found in close agreement with experimental data.

Adenosine Diphosphate↗

The quick machine--a mathematical model for the extrinsic activation of coagulation.

The present paper describes a mathematical model of the kinetics of the extrinsic coagulation cascade in vitro. The coagulation factors FI, FII, FV, FVII, FX, heparin and antithrombin III (ATIII) as well as soluble fibrin polymers are considered. The effect of single-factor deficiencies of the factors II, V, VII and X, diseases like hypo- and dysfibrinogenaemia, hepatic insufficiency, inhibited polymerisation by degradation products, heparin therapy with and without ATIII deficiency and coumarin therapy on prothrombin time can be portrayed. Physiology of coagulation is represented in a dynamic mathematical model as a differential equation system. The model is based on three reaction types: enzymatic cleavage, complex formation and polymerisation. The model was implemented in a continuous simulation program on a personal computer using the Pascal programming language. Unknown rate constants were estimated by chi 2 fit. Prothrombin time calculated by the model was compared to the training set of 20 plasma samples. In most but not all cases the model harmonized quite well with the coagulometric data.

Blood Coagulation↗

A mathematical model for the branched chain amino acid biosynthetic pathways of Escherichia coli K12.

As a first step toward the elucidation of the systems biology of the model organism Escherichia coli, it was our goal to mathematically model a metabolic system of intermediate complexity, namely the well studied end product-regulated pathways for the biosynthesis of the branched chain amino acids L-isoleucine, L-valine, and L-leucine. This has been accomplished with the use of kMech (Yang, C.-R., Shapiro, B. E., Mjolsness, E. D., and Hatfield, G. W. (2005) Bioinformatics 21, in press), a Cellerator (Shapiro, B. E., Levchenko, A., Meyerowitz, E. M., Wold, B. J., and Mjolsness, E. D. (2003) Bioinformatics 19, 677-678) language extension that describes a suite of enzyme reaction mechanisms. Each enzyme mechanism is parsed by kMech into a set of fundamental association-dissociation reactions that are translated by Cellerator into ordinary differential equations. These ordinary differential equations are numerically solved by Mathematica. Any metabolic pathway can be simulated by stringing together appropriate kMech models and providing the physical and kinetic parameters for each enzyme in the pathway. Writing differential equations is not required. The mathematical model of branched chain amino acid biosynthesis in E. coli K12 presented here incorporates all of the forward and reverse enzyme reactions and regulatory circuits of the branched chain amino acid biosynthetic pathways, including single and multiple substrate (Ping Pong and Bi Bi) enzyme kinetic reactions, feedback inhibition (allosteric, competitive, and non-competitive) mechanisms, the channeling of metabolic flow through isozymes, the channeling of metabolic flow via transamination reactions, and active transport mechanisms. This model simulates the results of experimental measurements.

Acetolactate Synthase↗

Mathematical modeling of bioerodible, polymeric drug delivery systems.

The aim of this article is to give an introduction into mathematical modeling approaches of bioerodible controlled drug delivery systems and to present the most important erosion theories reported in the literature. First, important parameters such as degradation and erosion are defined and physicochemical methods for their investigation are briefly presented. Then, phenomenological empirical models as well as models based on diffusion and chemical reaction theory are discussed. Due to the significant chemical and physicochemical differences among individual bioerodible polymers used for controlled drug delivery systems, various mathematical models have been developed to describe the chemical reactions and physical mass transport processes involved in erosion-controlled drug release. Various examples of practical applications of these models to experimental drug release data are given. For those involved in the design and development of biodegradable drug delivery systems this will help to choose the appropriate mathematical model for a specific drug release problem. Important selection criteria such as the desired predictive power and precision, but also the effort required to apply a model to a particular system will be discussed. Furthermore, before models can be used for drug release predictions certain parameters such as drug dissolution or polymer degradation rate constants, have to be known. The number of parameters to be determined significantly differs between the models. The practical benefit of carefully choosing the right model is that effects of composition and device geometry on the drug release kinetics can be predicted which can reduce laborious formulation studies to a minimum.

Drug Delivery Systems↗

A mathematical model for analysis of pharmacologically induced changes in the kinetics of cardiac muscle.

A mathematical model of the isometric contraction of cardiac muscle is developed and utilized to characterize the inotropic and lusitropic effects of cardioactive compounds in isolated guinea pig left atria. In contrast to metrics that are based on minima and maxima of an isometric twitch and its derivative function, the entire time course of the twitch is used to quantify the kinetics of the contraction-relaxation cycle. The model relates observed tension to a time-dependent activation function that describes generation of internal force and a coupling function that determines mechanical response to the activation function. The model is structured so that it is suitable for nonlinear curve fitting to observed data. Results obtained using the model for fitting experimental data from tissues treated with different classes of cardioactive compounds agree with more qualitative results presented by other authors. Experiments using the model to fit data over an extended (90 min) time course revealed differences in the kinetic profiles of milrinone and forskolin. Computer simulations that demonstrate the effect of each model parameter on twitch kinetics are presented, and the relationships between the model and other theoretical and empirical models of cardiac muscle are discussed. The mathematical model is useful to enable a more quantitative understanding of the kinetics of cardiac muscle contraction and relaxation and identify compounds that may be selective for inotropic or lusitropic effects.

Animals↗

[Development of a mathematical model for simulation of artificial ventilation].

This article describes a mathematical model for the simulation of artificial ventilation employing only the figures for air pressure and air flow between respirator and lungs. A discussion of methodological aspects of the problem shows that the model of the respiratory system can be reduced to an equivalent circuit diagram comprising only resistances and capacitances. The structure of the mathematical model is oriented to the elements of the real respiratory system, and its modularity permits ready adaptation to a variety of real-life situations. Particular emphasis was placed on the correct parametrization of the model with the aid of data originally collected from physical experiments. Finally, some selected model runs that demonstrate the range of validity and the accuracy of the model are presented.

Airway Resistance↗

Mathematical models from laws of growth to tools for biologic analysis: fifty years of "Growth".

Mathematical models of size and shape have played a prominent role in the first half century of Growth. In honor of the fiftieth anniversary of the journal, this paper reviews the development of these models. An historical perspective is taken with a focus on the changing context in which mathematical models have been studied. Early models were thought to represent principles or laws of growth. Today, models are viewed as tools for biologic analysis. We trace this contextual shift through specific models developed and used in Growth.

Growth↗

A validated mathematical model of cell-mediated immune response to tumor growth.

Mathematical models of tumor-immune interactions provide an analytic framework in which to address specific questions about tumor-immune dynamics. We present a new mathematical model that describes tumor-immune interactions, focusing on the role of natural killer (NK) and CD8+ T cells in tumor surveillance, with the goal of understanding the dynamics of immune-mediated tumor rejection. The model describes tumor-immune cell interactions using a system of differential equations. The functions describing tumor-immune growth, response, and interaction rates, as well as associated variables, are developed using a least-squares method combined with a numerical differential equations solver. Parameter estimates and model validations use data from published mouse and human studies. Specifically, CD8+ T-tumor and NK-tumor lysis data from chromium release assays as well as in vivo tumor growth data are used. A variable sensitivity analysis is done on the model. The new functional forms developed show that there is a clear distinction between the dynamics of NK and CD8+ T cells. Simulations of tumor growth using different levels of immune stimulating ligands, effector cells, and tumor challenge are able to reproduce data from the published studies. A sensitivity analysis reveals that the variable to which the model is most sensitive is patient specific, and can be measured with a chromium release assay. The variable sensitivity analysis suggests that the model can predict which patients may positively respond to treatment. Computer simulations highlight the importance of CD8+ T-cell activation in cancer therapy.

Animals↗

A simple mathematical model for diffusional sampler operation.

A simple mathematical model of the molecular basis for the function of a diffusional sampler for dilute mixtures of gaseous contaminants in supporting gases is presented. The model is based on the movement of single molecules of the contaminant between sections of a tubular diffusion path on a step-by-step basis; the length of the step and of each section of the tube are equal to the mean free path, lambda, under the specified conditions. When the model is used, the coefficient of diffusion, D, can be calculated from lambda and the average velocity, v, of the contaminant molecule. Both lambda and v were calculated independently using equations which involved the minimum number of assumptions. The value of D so estimated was of the same order as that in the literature, differing by a factor of less than 2. It should be emphasized that the model represents a statistical, thermodynamic approach to understanding diffusional samplers, and its utility is independent of the means of estimating lambda and v for specific gas pairs.

Diffusion↗

A nonmathematical view of mathematical models for cancer.

A qualitative view of mathematical models for cancer is presented. The Armitage and Doll multistage and Moolgavkar two-stage models of cancer are discussed in terms of their physical models. Time-related factors for these models as well as some of their characteristics are presented. The effect of age at first exposure, duration of exposure, time since last exposure and stage of carcinogenic effect on risk are detailed.

Age Factors↗

[Mathematical modeling of cardiovascular system in patients with hemorrhage and hypothermia].

Mathematical modeling is the most expedient method for studying the response of human cardiovascular system to a combined effect of several external factors, in particular, hemorrhage and hypothermia. A complex mathematical model integrating models of the functional systems of human body and the physiological effects of disturbing factors can be used to study the combined effect of external factors. It allows the response of human body to various combinations of external effects of different intensities to be assessed with high accuracy. In addition, the model makes it possible to prognosticate the dynamics of changes in the cardiovascular system parameters from their initial variation. This facilitates prediction of the development of patient's state.

Cardiovascular System↗

[Mathematical model of the air sterilization process].

The mathematical model of the air sterilization process is proposed. The model describes nonstationary temperature fields for a sterilizing agent and objects under sterilization in devices of the chamber type. It is useful in studying the effects of control factors on the sterilization process, in calculating optimum charges for air sterilizers, and in developing automated systems to handle the operating process of the air sterilization.

Air↗

Mathematical modeling to estimate efficacy of postmilking teat disinfection in split-udder trials of dairy cows.

A mathematical model was used to estimate the efficacy of postmilking teat disinfection from observations in split-udder trials with natural exposure. Data were studied from an outbreak of Staphylococcus aureus IMI during a split-udder trial in a commercial herd with low SCC. The efficacy of postmilking teat disinfection was similar when calculated based on incidence density rates or on transmission rates of IMI in dipped and control quarters. If, however, first and subsequent S. aureus IMI in a cow were not assumed to be independent and were therefore treated separately in the models, the efficacy of post-milking teat disinfection was calculated as being higher with the modeling procedure. The analysis using mathematical modeling, which includes the effect of the number of existing IMI on the number of new IMI, is presented and discussed. This analysis also allows estimation of the basic reproduction ratio. The impact of postmilking teat disinfection on transmission of pathogens is quantified, and proposals for additional preventive measures can be generated. We concluded that efficacy estimations from split-udder trials, assuming quarters to be independent observations, might underestimate the effect of postmilking teat disinfectants on udder pathogens.

Animals↗

Cytocidal effect and DNA damage of nedaplatin: a mathematical model and analysis of experimental data.

PURPOSE: Cell cycle non-specific anticancer agents such as cis-diamminedichloroplatinum(II) are believed to depend linearly on the value of the area under the drug concentration time curve, which is supported by a mathematical model. However, the quantitative non-linear phenomena of both the cytocidal effect and DNA crosslink formation by cisdiammine(glycolato)platinum (nedaplatin) have been shown in vitro. Therefore, we developed a new mathematical model to explain these phenomena. METHODS: We assumed that nedaplatin enters intracellular fluid from medium through simple diffusion to form DNA crosslinks that kill cells. We developed a mathematical model to represent this assumption using differential equations that we then solved using an original computer program. The calculated results were compared with the experimental data. RESULTS: The drug's simple diffusion rate constant, the DNA crosslink formation rate constant, and the crosslink-dependent cell death rate constant in the model were 1.8 x 10(-14) (l h-1), 1.6 x 10(8) (l mol-1/2 h-1), 5.45 x 10(1) (mol-1), respectively. The model fits the experimental results statistically. The model also demonstrated theoretical proof that continuous exposure at a low dose was superior to the short exposure at a high dose seen in published experimental data. CONCLUSIONS: We developed a mathematical model to describe the non-linear pharmacodynamic effect of nedaplatin in vitro. This model may provide a novel drug infusion procedure for cancer patients.

Antineoplastic Agents↗