1986 — 1997 |
Foster, David M [⬀] |
P41Activity Code Description: Undocumented code - click on the grant title for more information. |
Resource Facility For Kinetic Analysis @ University of Washington
This proposal is for the 5th year through the 9th year of the Resource Facility for Kinetic Analysis (RFKA) at the University of Washington. Kinetic analysis and integrated systems modeling have contributed substantially to our understanding of physiology and pathophysiology of metabolic systems in humans and animals. In recent years, many experimental biologists have become aware of the usefulness of these techniques in their research. With this has come the recognition that the discipline of kinetic modeling requires its own expertise. RFKA provides that expertise as a part of fulfilling its goals which are: 1) the development and application of modeling technology to biomedical problems, 2) the enhancement and maintenance of computer based methodologies for kinetic modeling with major emphasis on the SAAM (Simulation, Analysis and Modeling) programs, 3) the provision of service to the biomedical community via consultation in the use of modeling in the analysis of kinetic data, 4) the education and training of individuals in the use of modeling technology in biomedical research, and 5) the dissemination of our technology, expertise and accomplishments. Modeling and experimentation are both integral parts of the testing of hypotheses. Modeling is needed in the planning as well as in the analysis of experiments, and provides more focused and efficient experimental designs. By enhancing the contribution to modeling, RFKA supports this concept of an integrated experimental design. RFKA is distributed over three sites at three major universities; the Administrative Core of RFKA is located at the University of Washington. All sites take part in the functions that define a Resource, but each contributes a unique expertise in at least one area. The geographical distribution and diversity of interest in each has, over the first 2 1/2 years, facilitated a growth in collaborative and service activities.
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1996 — 1997 |
Foster, David M [⬀] |
P41Activity Code Description: Undocumented code - click on the grant title for more information. |
Service in Rfka @ University of Washington
technology /technique development; model design /development; endocrine gland /system; education; computers; cardiovascular system; biomedical resource; bioengineering /biomedical engineering;
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1997 — 1999 |
Foster, David M [⬀] |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
General Purpose Population Analysis Module For Saam Ii @ University of Washington
DESCRIPTION: Population analysis is the methodology used to quantify intersubject variability in kinetic studies. In this revised research project, the applicants propose to develop a general population analysis package and to interface this package with the SAAM-II software system. This system will typically be used for the population modeling of pharmacokinetic/pharmacodynamic systems and metabolic studies using tracer kinetics. Population analysis is widely used on pharmacokinetic studies since it is the key to understanding how drugs behave in human and animals. It provides the foundation for the intelligent design of dosage regiments to treat disease processes. In metabolic studies, it is used to identify which parameters in a model change when a population of normal subjects is compared to a population of subjects with a known pathological condition. There are three significant obstacles in such modeling efforts: (i) there is no software package that includes both parameteric and nonparameteric methods, (ii) certain methods currently in use have not had the rigorous statistical and numerical analyses that one could desire, and (iii) current software is either limited in modeling capabilities or is not user friendly. This project brings together a unique group of researchers to overcome these obstacles by developing and incorporating into the SAAM-II a general purpose population analysis module. More specifically, the following three aims are proposed: 1) Develop convergent numerical algorithms for parametric and nonparameteric methods. 2) Prove consistency of algorithm in Aim-1. Investigate efficiency and robustness of these methods via simulation studies and compare with other existing methods. 3) Interface the algorithms in Aim-1 with the general model building/graphical user interface of SAAM-II. The result will be a population analysis program with general model building capabilities and a graphical user interface that is powerful, flexible and easy to use. Such programs do not currently exists. Such a package will improve the analysis of clinical trials, and resulting drug therapy and patient care.
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1997 |
Foster, David M [⬀] |
P41Activity Code Description: Undocumented code - click on the grant title for more information. |
Specification &Design of Saam Ii @ University of Washington
technology /technique development; model design /development; statistics /biometry; computers; biomedical resource; bioengineering /biomedical engineering;
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1998 — 2007 |
Foster, David M [⬀] |
P41Activity Code Description: Undocumented code - click on the grant title for more information. |
Resource Facility For Population Kinetics @ University of Washington
DESCRIPTION (provided by applicant): This application requests continuation of funds for years 06-10 for the Resource on Population Kinetics (RFPK-also referred to as the Resource). RFPK's major goal is the development, application and dissemination of modeling methodology and software tools for biomedical uses in defining, understanding and managing health and disease. This goal is achieved by promoting the application of integrated systems modeling in biomedical research. This is done by providing consultation in model development and experimental design, by offering educational programs, and by developing new methods for incorporation in state-of-the-art software tools. Modern methods of scientific computing, in conjunction with inexpensive and powerful hardware, are driving a dynamic paradigm shift in how state-of-the-art scientific, engineering and business research are performed. RFPK is at the forefront of this shift in the biomedical community in its ability to communicate and collaborate with diverse disciplines in order to conduct its research. This multidisciplinary group is comprised of faculty, staff, students and collaborators in various fields of medicine, mathematics, statistics, software engineering, and the pharmaceutical industry. This research model unifies concept development, experimentation, simulation and implementation. Examples of this new approach in action are the human genome project, the design of the Boeing 777, and the design of financial derivatives. Factors driving this approach are the need to understand and manage complex dynamical systems, the need for accurate descriptions of physical phenomena, and the need for efficiency in the use of funds for research and development. The full impact of this paradigm shift has not reached the biomedical research community. The problem is rooted in the absence of software tools and educational programs targeted at the development of modeling skills for this community. A program that integrates both software development and education must be designed to answer this need. RFPK addresses this need directly in its specific aims, which are to: (1) develop new modeling methodologies for biological systems; (2) specify, design, develop, test, validate and maintain new software tools incorporating cutting-edge mathematics, statistics and computer science; (3) provide service to the research community through collaboration and consultation; (4) provide educational programs for the research community in systems modeling applications; and (5) disseminate RFPK technology and expertise via diverse educational programs that integrate medicine, statistics, computer science and bioengineering in a comprehensive problem-solving approach. Making these new tools and methods available to a broad audience is a complex problem that is best accomplished on a university campus. RFPK has assembled a multidisciplinary team consisting of scientific experts in model development and testing who collaborate with mathematicians, statisticians, and software engineers. RFPK involves four major universities, all of which contribute unique expertise to the individual projects. Its Administrative Core is located at the University of Washington.
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1999 |
Foster, David M [⬀] |
P41Activity Code Description: Undocumented code - click on the grant title for more information. |
Effect of Ethanol On Lipoprotein Kinetics Assessed by Population Analysis @ University of Washington
The specific aims of this collaborative projecxt are to apply the new methods described in this aplication to obtain more accurate estimated of the population parameters of both minimal models and thus of both glucose disposal and beta-cell function in norma, obese and mominsulin-dependetn (NIDDM) subjects. Besides providing the population parameters and covariance matrix, these analyses will also permit us to indlude the covariates. For example, obesity is importnat in understanding the relation between glucose disposal and insulin secretion parameters and anthropometric variables, e.g. visceral adiposity (measured by TAC). In NIDDM, a major issue is to understand the integrated relation between glucose disposal and insulin secretion parameters in the light of other crucial variables such as blood pressure, triglyceride and FFA levels of visceral. These covariates will be included in the population analyses, and will help in our understanding of macrovascular/microvascular complita tions.
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1999 |
Foster, David M [⬀] |
P41Activity Code Description: Undocumented code - click on the grant title for more information. |
Algorithm Development &Software Design For Population Kinetic Analysis @ University of Washington
The specific are: 1. Develop convergent numerical algorithms for the parametric and nonparametric estimators in population kinetic analysis to be implemented in Project 2. 2. Analyze the statistical properties of the estimators in Specific Aim 1: consistency, asymptotic normality, asymptotic confidence regions and hypothesis testing. 3. Investigate efficiency and robustness of the estimators in Specific Aim I via simulation studies. For the parametric case, we will develop four algorithms: a "true" maximum likelihood (ML) algorithm, a Global Two Stage (GTS) algorithm, a NONMEM type algorithm, and a Lindstrom-Bates type algorithm. The ML algorithm is based on Monte Carlo integration for evaluating the objective function. The GTS, NONMEM, and Lindstrom-Bates type algorithms are all based on the extended least squares (ELS) method. For the nonparametric case, we will develop a Mallet type algorithm for mixed effects models. For the parametric case, consistency is a difficult issue. Consistency means that the estimated values converge to the true values as the number of subjects gets arbitrarily large. It is important to note that the original estimation procedures ot'NONMEM and Lindstrom-Bates are not consistent for 2eneral nonlinear models. The only algorithm that is consistent relative to the true parameter values is the true maximum likelihood algorithm. For the class of ELS algorithms we develop, there is a generalized notion of consistency, which means that the estimated values converge to the values that best approximate the model. We will investigate the required theory for the generalized consistency and asymptotic normality of these algorithms. The formulas for the asymptotic confidence intervals and hypothesis testing will follow from the same theory. For the nonparametric case, the consistency of the method, relative to the true values of the model, has already been established. What remains then is the determination of the asymptotic confidence intervals for estimated parameters such as means, medians, trimmed means, etc. At present these results have not been derived for the nonparametric case. We will use the theory of maximum likelihood estimation in infinite dimensional spaces for this purpose. Efficiency of an (unbiased) estimator is measured by the generalized variance of estimated values, with the Cramer-Rao lower bound being optimal. Relative efficiency of two estimators compares the corresponding generalized variances. Robustness measures how an algorithm performs when there are violations in the model and/or probability distribution assumptions. By utilizing Monte Carlo simulation studies, these properties can be investigated without requiring 1 asymptotic conditions.
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2008 |
Foster, David M [⬀] |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Identifiability of Nonlinear Biological Models @ University of Washington
DESCRIPTION (provided by applicant): Mathematical models of biological systems are essential to the quantitative understanding of physiological and pathophysiological mechanisms in humans and animals. Physiologically plausible models, the structure of which reflect available knowledge and assumptions about the systems, are usually nonlinear and characterized by a large number of unknown parameters. Examples of such models are enzyme kinetics and pharmacokinetic-pharmacodynamic models. Before performing an experiment to estimate these unknown parameters from the data, the following question arises: will the data we are about to collect (usually at a substantial expense) contain enough information to precisely and unequivocally estimate (for example, via least squares or maximum likelihood) all the unknown parameters of the postulated model? This question, set in the (theoretical) context of an error-free model structure and noise-free data, is usually referred to as the a priori global identifiability problem. Despite its theoretical nature, it is an essential, but often overlooked, prerequisite for model parameter estimation from real data. The solution of the identifiability problem is however in general very difficult, since one needs to solve a system of nonlinear algebraic equations which is increasing in number of terms and nonlinearity degree with the model order. The specific aims of this application focus on the development of an algorithm and a software tool to test a priori global identifiability of nonlinear compartmental models, a very inclusive class of ordinary nonlinear differential equation models based on conservation of mass. These models are widely used to study the kinetics of endogenous (e.g. substrates, hormones, enzymes) and exogenous (e.g., drugs, radiotracers) substances in living systems. The problem has been solved for a very limited set of models, but no solution exists in the general case. We will develop an algorithm based on computer algebra which allows to decrease the system complexity, thus providing the number of solutions for each parameter of the model. The software we propose to develop will be based on the client-server architecture paradigm, and will be open source, user-friendly and platform-independent. Such a tool would be very useful in experiment design. The software will also help in defining minimal input-output experimental configurations to assure a priori global identifiability: this is particularly important in clinical studies where severe constraints exist on experiment design, i.e. the number of inputs and outputs is limited for ethical and practical reasons.
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