The goal of this review is to place the exciting advances that have occurred in our understanding of the molecular biology of the types 1, 2, and 3 (D1, D2, and D3, respectively) iodothyronine deiodinases into a biochemical and physiological context. We review new data regarding the mechanism of selenoprotein synthesis, the molecular and cellular biological properties of the individual deiodinases, including gene structure, mRNA and protein characteristics, tissue distribution, subcellular localization and topology, enzymatic properties, structure-activity relationships, and regulation of synthesis, inactivation, and degradation. These provide the background for a discussion of their role in thyroid physiology in humans and other vertebrates, including evidence that D2 plays a significant role in human plasma T3 production. We discuss the pathological role of D3 overexpression causing “consumptive hypothyroidism” as well as our current understanding of the pathophysiology of iodothyronine deiodination during illness and amiodarone therapy. Finally, we review the new insights from analysis of mice with targeted disruption of the Dio2 gene and overexpression of D2 in the myocardium. (Endocrine Reviews 23: 38–89, 2002)

Biochemistry, cellular and molecular biology, and physiological roles of the iodothyronine selenodeiodinases.

Mathematical models are utilized to approximate various highly complex engineering, physical, environmental, social, and economic phenomena. Model parameters exerting the most influence on model results are identified through a ‘sensitivity analysis’. A comprehensive review is presented of more than a dozen sensitivity analysis methods. This review is intended for those not intimately familiar with statistics or the techniques utilized for sensitivity analysis of computer models. The most fundamental of sensitivity techniques utilizes partial differentiation whereas the simplest approach requires varying parameter values one-at-a-time. Correlation analysis is used to determine relationships between independent and dependent variables. Regression analysis provides the most comprehensive sensitivity measure and is commonly utilized to build response surfaces that approximate complex models.

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A review of techniques for parameter sensitivity analysis of environmental models

Motivation: Mathematical description of biological reaction networks by differential equations leads to large models whose parameters are calibrated in order to optimally explain experimental data. Often only parts of the model can be observed directly. Given a model that sufficiently describes the measured data, it is important to infer how well model parameters are determined by the amount and quality of experimental data. This knowledge is essential for further investigation of model predictions. For this reason a major topic in modeling is identifiability analysis.

Results: We suggest an approach that exploits the profile likelihood. It enables to detect structural non-identifiabilities, which manifest in functionally related model parameters. Furthermore, practical non-identifiabilities are detected, that might arise due to limited amount and quality of experimental data. Last but not least confidence intervals can be derived. The results are easy to interpret and can be used for experimental planning and for model reduction.

Availability: An implementation is freely available for MATLAB and the PottersWheel modeling toolbox at http://web.me.com/andreas.raue/profile/software.html.

Contact: andreas.raue@me.com

Supplementary information:Supplementary data are available at Bioinformatics online.

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Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood

Background: A number of recent advances in our understanding of thyroid physiology may shed light on why some patients feel unwell while taking levothyroxine monotherapy. The purpose of this task force was to review the goals of levothyroxine therapy, the optimal prescription of conventional levothyroxine therapy, the sources of dissatisfaction with levothyroxine therapy, the evidence on treatment alternatives, and the relevant knowledge gaps. We wished to determine whether there are sufficient new data generated by well-designed studies to provide reason to pursue such therapies and change the current standard of care. This document is intended to inform clinical decision-making on thyroid hormone replacement therapy; it is not a replacement for individualized clinical judgment. Methods: Task force members identified 24 questions relevant to the treatment of hypothyroidism. The clinical literature relating to each question was then reviewed. Clinical reviews were supplemented, when relevant, with related...

Guidelines for the Treatment of Hypothyroidism: Prepared by the American Thyroid Association Task Force on Thyroid Hormone Replacement

This paper reviews the role of uncertainty in the identification of mathematical models of water quality and in the application of these models to problems of prediction. More specifically, four problem areas are examined in detail: uncertainty about model structure, uncertainty in the estimated model parameter values, the propagation of prediction errors, and the design of experiments in order to reduce the critical uncertainties associated with a model. The review is rather lengthy, and it has therefore been prepared in effect as two papers. There is a shorter, largely nontechnical version, which gives a quick impression of the current and future issues in the analysis of uncertainty in water quality modeling. Enclosed by this shorter discussion is the main body of the review dealing in turn with (1) identifiability and experimental design, (2) the generation of preliminary model hypotheses under conditions of sparse, grossly uncertain field data, (3) the selection and evaluation of model structure, (4) parameter estimation (model calibration), (5) checks and balances on the identified model, i.e., model “verification” and model discrimination, and (6) prediction error propagation. Much time is spent in discussing the algorithms of system identification, in particular, the methods of recursive estimation, and in relating these algorithms and the subject of identification to the problems of prediction uncertainty and first-order error analysis. There are two obvious omissions from the review. It is not concerned primarily with either the development and solution of stochastic differential equations or the issue of decision making under uncertainty, although clearly some reference must be made to these topics. In brief, the review concludes (not surprisingly) that much work has been done on the analysis of uncertainty in the development of mathematical models of water quality, and much remains to be done. A lack of model identifiability has been an outstanding difficulty in the interpretation and explanation of past observed system behavior, and there is ample evidence to show that the “larger,” more “comprehensive” models are easily capable of generating highly uncertain predictions of future behavior. For the future of the subject, it is speculated that there is the possibility of progress in the development of novel algorithms for model structure identification, a need for new questions to be posed in the problem of prediction, and a distinct challenge to the conventional views of this review in the new forms of knowledge representation and manipulation now emerging from the field of artificial intelligence.

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Water quality modeling: A review of the analysis of uncertainty

The notion of identifiability addresses the question of whether it is at all possible to obtain unique solutions for unknown parameters of interest in a mathematical model, from data collected in well-defined stimulus-response experiments performed on a dynamic system represented by the model. This critical aspect of the modeling and data reduction problem is reviewed, analyzed, and unified, with emphasis on applications in biology. Several physiological system models are examined in detail. They illustrate the importance of identifiability analysis prior to performing a parameter estimation experiment, the algebraic difficulties, possible pitfalls, and not-so-apparent ambiguities that may be encountered--even for simple models--and the utility of the concept in experiment design. Methods for testing for identifiability also are reviewed and compared, with emphasis on applicability and limitations. The usefulness of a given model for predicting the time course of system variables (not parameters) of interest normally inaccessible to direct measurement (e.g., tissue concentrations), a common use for quantitative models, is shown to depend intimately on the identifiability properties of the model, in a manner not easy but essential to assess: state variable predictions made in this manner can be inherently ambiguous.

Parameter and structural identifiability concepts and ambiguities: a critical review and analysis.

Identifiability of Model Parameter

1. Introduction 2. Control Systems Terminology 3. Differential Equations, Difference Equations, and Linear Systems 4. The LaPlace Transform 5. The Z-Transform 6. Stability 7. Transfer Functions 8. Block Diagram Algebra and Transfer Functions of Systems 9. Signal Flow Graphs 10. System Sensitivity Measures and Classification of Feedback Systems 11. Analysis and Design of Feedback Control Systems: Objectives and Methods 12. Nyquist Analysis 13. Nyquist Design 14. Root-Locus Design 15. Bode Analysis 16. Bode Design 17. Nichols Chart Analysis 18. Nichols Chart Design 19. Introduction to Nonlinear Control Systems 20. Introduction to Advanced Systems 21. Topics in Control Systems 22. Analysis and Design

Feedback and Control Systems

We constructed a novel physiologically-based pharmacokinetic (PBPK) model for predicting interactions between the neonatal Fc receptor (FcRn) and anti-carcinoembryonic antigen (CEA) monoclonal antibodies (mAbs) with varying affinity for FcRn. Our new model, an integration and extension of several previously published models, includes aspects of mAb-FcRn dynamics within intracellular compartments not represented in previous PBPK models. We added mechanistic structure that details internalization of class G immunoglobulins by endothelial cells, subsequent FcRn binding, recycling into plasma of FcRn-bound IgG and degradation of free endosomal IgG. Degradation in liver is explicitly represented along with the FcRn submodel in skin and muscle. A variable tumor mass submodel is also included, used to estimate the growth of an avascular, necrotic tumor core, providing a more realistic picture of mAb uptake by tumor. We fitted the new multiscale model to published anti-CEA mAb biodistribution data, i.e. concentration-time profiles in tumor and various healthy tissues in mice, providing new estimates of mAb-FcRn related kinetic parameters. The model was further validated by successful prediction of F(ab')2 mAb fragment biodistribution, providing additional evidence of its potential value in optimizing intact mAb and mAb fragment dosing for clinical imaging and immunotherapy applications.

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A Predictive Model of Therapeutic Monoclonal Antibody Dynamics and Regulation by the Neonatal Fc Receptor (FcRn)

Dynamic Systems Biology Modeling and Simuation consolidates and unifies classical and contemporary multiscale methodologies for mathematical modeling and computer simulation of dynamic biological systems - from molecular/cellular, organ-system, on up to population levels. The book pedagogy is developed as a well-annotated, systematic tutorial - with clearly spelled-out and unified nomenclature - derived from the author's own modeling efforts, publications and teaching over half a century. Ambiguities in some concepts and tools are clarified and others are rendered more accessible and practical. The latter include novel qualitative theory and methodologies for recognizing dynamical signatures in data using structural (multicompartmental and network) models and graph theory; and analyzing structural and measurement (data) models for quantification feasibility. The level is basic-to-intermediate, with much emphasis on biomodeling from real biodata, for use in real applications. * Introductory coverage of core mathematical concepts such as linear and nonlinear differential and difference equations, Laplace transforms, linear algebra, probability, statistics and stochastics topics; PLUS ...* The pertinent biology, biochemistry, biophysics or pharmacology for modeling are provided, to support understanding the amalgam of "math modeling with life sciences.* Strong emphasis on quantifying as well as building and analyzing biomodels: includes methodology and computational tools for parameter identifiability and sensitivity analysis; parameter estimation from real data; model distinguishability and simplification; and practical bioexperiment design and optimization.* Companion website provides solutions and program code for examples and exercises using Matlab, Simulink, VisSim, SimBiology, SAAMII, AMIGO, Copasi and SBML-coded models.* A full set of PowerPoint slides are available from the author for teaching from his textbook. He uses them to teach a 10 week quarter upper division course at UCLA, which meets twice a week, so there are 20 lectures. They can easily be augmented or stretched for a 15 week semester course. Importantly, the slides are editable, so they can be readily adapted to a lecturer's personal style and course content needs. The lectures are based on excerpts from 12 of the first 13 chapters of DSBMS. They are designed to highlight the key course material, as a study guide and structure for students following the full text content. The complete PowerPoint slide package (~25 MB) can be obtained by instructors (or prospective instructors) by emailing the author directly, at: joed@cs.ucla.edu

Joseph J. DiStefano

Papers

Parameter and structural identifiability concepts and ambiguities: a critical review and analysis.

Identifiability of Model Parameter

Feedback and Control Systems

A Predictive Model of Therapeutic Monoclonal Antibody Dynamics and Regulation by the Neonatal Fc Receptor (FcRn)

Dynamic Systems Biology Modeling and Simulation