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Modeling and Simulation Reviews

(78 References)

Theil, F. P., T. W. Guentert, et al. (2003). "Utility of physiologically based pharmacokinetic models to drug development and rational drug discovery candidate selection." Toxicol Lett 138(1-2): 29-49.

            The present paper proposes a modeling and simulation strategy for the prediction of pharmacokinetics (PK) of drug candidates by using currently available in silico and in vitro based prediction tools for absorption, distribution, metabolism and excretion (ADME). These methods can be used to estimate specific ADME parameters (such as rate and extent of absorption into portal vein, volume of distribution, metabolic clearance in the liver). They can also be part of a physiologically based pharmacokinetic (PBPK) model to simulate concentration-time profiles in tissues and plasma resulting from the overall PK after intravenous or oral administration. Since the ADME prediction tools are built only on commonly generated in silico and in vitro data, they can be applied already in early drug discovery, prior to any in vivo study. With the suggested methodology, the following advantages of the mechanistic PBPK modeling framework can now be utilized to explore potential clinical candidates already in drug discovery: (i) prediction of plasma (blood) and tissue PK of drug candidates prior to in vivo experiments, (ii) supporting a better mechanistic understanding of PK properties, as well as helping the development of more rationale PK-PD relationships from tissue kinetic data predicted, and hence facilitating a more rational decision during clinical candidate selection, and (iii) the extrapolation across species, routes of administration and dose levels.

Bhalla, U. S. (2003). "Understanding complex signaling networks through models and metaphors." Prog Biophys Mol Biol 81(1): 45-65.

            Signaling networks are complex both in terms of the chemical and biophysical events that underlie them, and in the sheer number of interactions. Computer models are powerful tools to deal with both aspects of complexity, but their utility goes beyond simply replicating signaling events in silicon. Their great advantage is as a tool to understanding. The completeness of the description demanded by computer models highlights gaps in knowledge. The quantitative description in models facilitates a mapping between different kinds of analysis methods for complex systems. Systems analysis methods can highlight stable states of signaling networks and describe the transitions between them. Modeling also reveals functional similarities between signaling network properties and other well-understood systems such as electronic devices and neural networks. These suggest various metaphors as a tool to understanding. Based on such descriptions, it is possible to regard signaling networks as systems that decode complex inputs in time, space and chemistry into combinatorial output patterns of signaling activity. This would provide a natural interface to the combinatorial input patterns required by genetic circuits. Thus, a combination of computer modeling methods to capture the complexity and details, and useful abstractions revealed by these models, is necessary to achieve both rigorous description as well as human understanding.

Zeuthen, T. and N. MacAulay (2002). "Passive water transport in biological pores." Int Rev Cytol 215: 203-30.

            Three kinds of membrane proteins have been shown to have water channels properties: the aquaporins, the cotransporters, and the uniports. A molecular-kinetic description of water transport in pores is compared to analytical models based on macroscopic parameters such as pore diameter and length. The use and limitations of irreversible thermodynamics is discussed. Experimental data on water and solute permeability in aquaporins are reviewed. No unifying transport model based on macroscopic parameters can be set up; for example, there is no correlation between solute diameter and permeability. Instead, the influence of hydrogen bonds between solute and pore, and the pH dependence of permeability, point toward a model based upon chemical interactions. The atomic model for AQP1 based on electron crystallographic data defines the dimensions and chemical nature of the aqueous pore. These structural data combined with quantum mechanical modeling and computer simulation might result in a realistic description of water transport. Data on water and solute permeability in cotransporters and uniports are reviewed. The function of these proteins as substrate transporters involves a series of conformational changes. The role of conformational equilibria on the water permeability will be discussed.

Wilderer, P. A., H. J. Bungartz, et al. (2002). "Modern scientific methods and their potential in wastewater science and technology." Water Res 36(2): 370-93.

            Application of novel analytical and investigative methods such as fluorescence in situ hybridization, confocal laser scanning microscopy (CLSM), microelectrodes and advanced numerical simulation has led to new insights into micro- and macroscopic processes in bioreactors. However, the question is still open whether or not these new findings and the subsequent gain of knowledge are of significant practical relevance and if so, where and how. To find suitable answers it is necessary for engineers to know what can be expected by applying these modern analytical tools. Similarly, scientists could benefit significantly from an intensive dialogue with engineers in order to find out about practical problems and conditions existing in wastewater treatment systems. In this paper, an attempt is made to help bridge the gap between science and engineering in biological wastewater treatment. We provide an overview of recently developed methods in microbiology and in mathematical modeling and numerical simulation. A questionnaire is presented which may help generate a platform from which further technical and scientific developments can be accomplished. Both the paper and the questionnaire are aimed at encouraging scientists and engineers to enter into an intensive, mutually beneficial dialogue.

Wiechert, W. (2002). "Modeling and simulation: tools for metabolic engineering." J Biotechnol 94(1): 37-63.

            Mathematical modeling is one of the key methodologies of metabolic engineering. Based on a given metabolic model different computational tools for the simulation, data evaluation, systems analysis, prediction, design and optimization of metabolic systems have been developed. The currently used metabolic modeling approaches can be subdivided into structural models, stoichiometric models, carbon flux models, stationary and nonstationary mechanistic models and models with gene regulation. However, the power of a model strongly depends on its basic modeling assumptions, the simplifications made and the data sources used. Model validation turns out to be particularly difficult for metabolic systems. The different modeling approaches are critically reviewed with respect to their potential and benefits for the metabolic engineering cycle. Several tools that have emerged from the different modeling approaches including structural pathway synthesis, stoichiometric pathway analysis, metabolic flux analysis, metabolic control analysis, optimization of regulatory architectures and the evaluation of rapid sampling experiments are discussed.

van de Waterbeemd, H. (2002). "High-throughput and in silico techniques in drug metabolism and pharmacokinetics." Curr Opin Drug Discov Devel 5(1): 33-43.

            The high-throughput screening (HTS) of large proprietary compound collections and combinatorial libraries has increased the pressure on gathering pharmacokinetic and drug metabolism data as early as possible. Properties related to absorption, distribution, metabolism and excretion (ADME) can be estimated by a range of in vivo and in vitro methods, most of which are now available or under development in high(er)-throughput modus. In addition, progress has been made in in silico methods using various quantitaTive structure-activity relationship (QSAR) and molecular modeling techniques that employ a range of recently introduced descriptors tailored to e-ADME. These in silico approaches are promising filters for virtual libraries to aid synthesis as well as the selection of compounds for acquisition and screening in the early stages of drug discovery.

Tsuzuki, T., T. Kawahara, et al. (2002). "[Connectionist modeling of higher-level cognitive processes]." Shinrigaku Kenkyu 72(6): 541-55.

            Connectionist modeling is one approach to understanding human intelligence using simulated networks of neuron-like processing units. In this article, we report on recent progress in connectionist models that simulate empirical data of higher-level cognitive processes, these being memory, learning, language, thinking, cognitive development, and social cognition. We also review and summarize the advantages and disadvantages of these connectionist models. The computational framework of connectionist modeling has the potential to integrate specialized psychological findings of different areas using the same architectures and local functions of units and connections, inspired from neuroscience. In particular, the problems of dealing with structured information in distributed form, and doing tasks that require variable binding in connectionist networks are discussed from several different perspectives. As one possible solution to treat systematic mental representations properly, the symbolic connectionist model, which is a hybrid approach using symbolic representations and connectionist architectures, is explained. We argue that connectionist computer simulation offers significant benefits for today's psychological researches, and that connectionist modeling is likely to have an important influence on future studies.

Trelease, R. B. (2002). "Anatomical informatics: Millennial perspectives on a newer frontier." Anat Rec 269(5): 224-35.

            One of the most ancient of sciences, anatomy has evolved over many centuries. Its methods have progressively encompassed dissection instruments, manual illustration, stains, microscopes, cameras and photography, and digital imaging systems. Like many other more modern scientific disciplines in the late 20th century, anatomy has also benefited from the revolutionary development of digital computers and their automated information management and analytical capabilities. By using newer methods of computer and information sciences, anatomists have made outstanding contributions to science, medicine, and education. In that regard, there is a strong rationale for recognizing anatomical informatics as a proper subdiscipline of anatomy. A high-level survey of the field reveals important anatomical applications of computer sciences methods in imaging, image processing and visualization, virtual reality, modeling and simulation, structural database processing, networking, and artificial intelligence. Within this framework, computational anatomy is a developing field focusing on data-driven mathematical models of bodily structures. Mastering such computer sciences and informatics methods is crucial for new anatomists, who will shape the future in research, clinical knowledge, and teaching.

Tesfatsion, L. (2002). "Agent-based computational economics: growing economies from the bottom up." Artif Life 8(1): 55-82.

            Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Thus, ACE is a specialization of economics of the basic complex adaptive systems paradigm. This study outlines the main objectives and defining characteristics of the ACE methodology and discusses similarities and distinctions between ACE and artificial life research. Eight ACE research areas are identified, and a number of publications in each area are highlighted for concrete illustration. Open questions and directions for future ACE research are also considered. The study concludes with a discussion of the potential benefits associated with ACE modeling, as well as some potential difficulties.

Sun, W. and P. Lal (2002). "Recent development on computer aided tissue engineering--a review." Comput Methods Programs Biomed 67(2): 85-103.

            The utilization of computer-aided technologies in tissue engineering has evolved in the development of a new field of computer-aided tissue engineering (CATE). This article reviews recent development and application of enabling computer technology, imaging technology, computer-aided design and computer-aided manufacturing (CAD and CAM), and rapid prototyping (RP) technology in tissue engineering, particularly, in computer-aided tissue anatomical modeling, three-dimensional (3-D) anatomy visualization and 3-D reconstruction, CAD-based anatomical modeling, computer-aided tissue classification, computer-aided tissue implantation and prototype modeling assisted surgical planning and reconstruction.

Scott, H. L. (2002). "Modeling the lipid component of membranes." Curr Opin Struct Biol 12(4): 495-502.

            During the past several years, there have been a number of advances in the computational and theoretical modeling of lipid bilayer structural and dynamical properties. Molecular dynamics (MD) simulations have increased in length and time scales by about an order of magnitude. MD simulations continue to be applied to more complex systems, including mixed bilayers and bilayer self-assembly. A critical problem is bridging the gap between the still very small MD simulations and the time and length scales of experimental observations. Several new and promising techniques, which use atomic-level correlation and response functions from simulations as input to coarse-grained modeling, are being pursued.

Sardari, S. and D. Sardari (2002). "Applications of artificial neural network in AIDS research and therapy." Curr Pharm Des 8(8): 659-70.

            In recent years considerable effort has been devoted to applying pattern recognition techniques to the complex task of data analysis in drug research. Artificial neural networks (ANN) methodology is a modeling method with great ability to adapt to a new situation, or control an unknown system, using data acquired in previous experiments. In this paper, a brief history of ANN and the basic concepts behind the computing, the mathematical and algorithmic formulation of each of the techniques, and their developmental background is presented. Based on the abilities of ANNs in pattern recognition and estimation of system outputs from the known inputs, the neural network can be considered as a tool for molecular data analysis and interpretation. Analysis by neural networks improves the classification accuracy, data quantification and reduces the number of analogues necessary for correct classification of biologically active compounds. Conformational analysis and quantifying the components in mixtures using NMR spectra, aqueous solubility prediction and structure-activity correlation are among the reported applications of ANN as a new modeling method. Ranging from drug design and discovery to structure and dosage form design, the potential pharmaceutical applications of the ANN methodology are significant. In the areas of clinical monitoring, utilization of molecular simulation and design of bioactive structures, ANN would make the study of the status of the health and disease possible and brings their predicted chemotherapeutic response closer to reality.

Saiz, L. and M. L. Klein (2002). "Computer simulation studies of model biological membranes." Acc Chem Res 35(6): 482-9.

            This Account is focused on computer simulation studies of model biological membrane systems with potential applications in biomedical research. In the past decade, classical molecular dynamics has provided novel insights into the properties of model biomembrane systems, including the nature of the DNA-lipid interactions, the effect of pore-forming transmembrane peptides on the lipid environment, and the partitioning of volatile anesthetic molecules. Such simulations, employing full atomic detail, are typically restricted to systems of dimensions less than approximately 10 nm. Simplified models of the coarse-grain type have been intended to bridge the gap between full atomistic detail and the mesoscopic (micron) regime. The use of such models is illustrated with the example of anesthetics in a phospholipid bilayer.

Phoenix, D. A., F. Harris, et al. (2002). "The prediction of amphiphilic alpha-helices." Curr Protein Pept Sci 3(2): 201-21.

            A number of sequence-based analyses have been developed to identify protein segments, which are able to form membrane interactive amphiphilic alpha-helices. Earlier techniques attempted to detect the characteristic periodicity in hydrophobic amino acid residues shown by these structure and included the Molecular Hydrophobic Potential (MHP), which represents the hydrophobicity of amino acid residues as lines of isopotential around the alpha-helix and analyses based on Fourier transforms. These latter analyses compare the periodicity of hydrophobic residues in a putative alpha-helical sequence with that of a test mathematical function to provide a measure of amphiphilicity using either the Amphipathic Index or the Hydrophobic Moment. More recently, the introduction of computational procedures based on techniques such as hydropathy analysis, homology modelling, multiple sequence alignments and neural networks has led to the prediction of transmembrane alpha-helices with accuracies of the order of 95% and transmembrane protein topology with accuracies greater than 75%. Statistical approaches to transmembrane protein modeling such as hidden Markov models have increased these prediction levels to an even higher level. Here, we review a number of these predictive techniques and consider problems associated with their use in the prediction of structure / function relationships, using alpha-helices from G-coupled protein receptors, penicillin binding proteins, apolipoproteins, peptide hormones, lytic peptides and tilted peptides as examples.

Noble, D. (2002). "Modeling the heart--from genes to cells to the whole organ." Science 295(5560): 1678-82.

            Successful physiological analysis requires an understanding of the functional interactions between the key components of cells, organs, and systems, as well as how these interactions change in disease states. This information resides neither in the genome nor even in the individual proteins that genes code for. It lies at the level of protein interactions within the context of subcellular, cellular, tissue, organ, and system structures. There is therefore no alternative to copying nature and computing these interactions to determine the logic of healthy and diseased states. The rapid growth in biological databases; models of cells, tissues, and organs; and the development of powerful computing hardware and algorithms have made it possible to explore functionality in a quantitative manner all the way from the level of genes to the physiological function of whole organs and regulatory systems. This review illustrates this development in the case of the heart. Systems physiology of the 21st century is set to become highly quantitative and, therefore, one of the most computer-intensive disciplines.

Neves, S. R. and R. Iyengar (2002). "Modeling of signaling networks." Bioessays 24(12): 1110-7.

            Biochemical networks, including those containing signaling pathways, display a wide range of regulatory properties. These include the ability to propagate information across different time scales and to function as switches and oscillators. The mechanisms underlying these complex behaviors involve many interacting components and cannot be understood by experiments alone. The development of computational models and the integration of these models with experiments provide valuable insight into these complex systems-level behaviors. Here we review current approaches to the development of computational models of biochemical networks and describe the insights gained from models that integrate experimental data, using three examples that deal with ultrasensitivity, flexible bistability and oscillatory behavior. These types of complex behavior from relatively simple networks highlight the necessity of using theoretical approaches in understanding higher order biological functions.

Muzikant, A. L. and R. C. Penland (2002). "Models for profiling the potential QT prolongation risk of drugs." Curr Opin Drug Discov Devel 5(1): 127-35.

            The appearance of QT prolongation and arrhythmic events associated with a compound undergoing clinical trials can greatly hamper drug development programs. Assessing the risk of a compound during preclinical studies to cause this cardiotoxicity is thus critically important to the pharmaceutical industry. A wide variety of preclinical approaches exist to evaluate potential QT issues, including in vitro, in vivo and in silico (i.e., computer simulation) methods. We present an evaluation of recent reports implementing these techniques, with an emphasis on the linkage between drug-induced cardiac action potential changes and QT prolongation both in vitro and in silico. We conclude with a strategy that integrates in silico modeling with in vitro and in vivo experimentation to create a compelling package for assessing potential proarrhythmic risk of a compound.

Moro, S. and K. A. Jacobson (2002). "Molecular modeling as a tool to investigate molecular recognition in P2Y receptors." Curr Pharm Des 8(26): 2401-13.

            Nucleotides are emerging as an ubiquitous family of extracellular signaling molecules. These effects are mediated through a specific class of plasma membrane receptors called P2 receptors that, according to the molecular structure, are further subdivided into two subfamilies: P2Y and P2X. Specifically, P2X-receptors are ligand-gated ion channels, whereas P2Y-receptors belong to the superfamily of G-protein-coupled receptors. In this review, we focus our attention to GPCRs molecular architecture, with the special emphasis on our work on the human P2Y(1) receptor. In fact, despite an enormous amount of research on the structure and function of these receptors, fundamental understanding of the molecular details of ligand/GPCR interactions remains very rudimentary. How agonist binding transforms a resting GPCR into its active form and the microscopic basis of binding site blockade by an antagonist are generally still unclear. In the absence of high-resolution structural knowledge of GPCRs, such questions only can be addressed by building models, which are tested through pharmacological and biochemical studies. In this review, we underline how different molecular modeling approaches can help the investigation of both receptor architecture and ligand/receptor molecular recognition.

Morgan, J. A. and D. Rhodes (2002). "Mathematical modeling of plant metabolic pathways." Metab Eng 4(1): 80-9.

            The understanding of the control of metabolic flux in plants requires integrated mathematical formulations of gene and protein expression, enzyme kinetics, and developmental biology. Plants have a large number of metabolically active compartments, and non-steady-state conditions are frequently encountered. Consequently steady-state metabolic flux balance and isotopic flux balance modeling approaches have limited utility in probing plant metabolic systems. Transient isotopic flux analysis and kinetic modeling are powerful proven techniques for the quantification of metabolic fluxes in compartmentalized, dynamic metabolic systems. These tools are now widely used to address metabolic flux responses to environmental and genetic perturbations in plant metabolism. Continued developments in isotopic and kinetic modeling, quantifying metabolite exchange between compartments, and transcriptional and posttranscriptional regulatory mechanisms governing enzyme level and activity will enable simulation of large sections of plant metabolism under non-steady-state conditions. Metabolic control analysis will continue to make substantial contributions to the understanding of quantitative distribution of control of flux. From the synergy between mathematical models and experiments, creative methods for controlling the distribution of flux by genetic or environmental means will be discovered and rationally implemented.

Meibohm, B. and H. Derendorf (2002). "Pharmacokinetic/pharmacodynamic studies in drug product development." J Pharm Sci 91(1): 18-31.

            In the quest of ways for rationalizing and accelerating drug product development, integrated pharmacokinetic/pharmacodynamic (PK/PD) concepts provide a highly promising tool. PK/PD modeling concepts can be applied in all stages of preclinical and clinical drug development, and their benefits are multifold. At the preclinical stage, potential applications might comprise the evaluation of in vivo potency and intrinsic activity, the identification of bio-/surrogate markers, as well as dosage form and regimen selection and optimization. At the clinical stage, analytical PK/PD applications include characterization of the dose-concentration-effect/toxicity relationship, evaluation of food, age and gender effects, drug/drug and drug/disease interactions, tolerance development, and inter- and intraindividual variability in response. Predictive PK/PD applications can also involve extrapolation from preclinical data, simulation of drug responses, as well as clinical trial forecasting. Rigorous implementation of the PK/PD concepts in drug product development provides a rationale, scientifically based framework for efficient decision making regarding the selection of potential drug candidates, for maximum information gain from the performed experiments and studies, and for conducting fewer, more focused clinical trials with improved efficiency and cost effectiveness. Thus, PK/PD concepts are believed to play a pivotal role in streamlining the drug development process of the future.

Kirby, S. (2002). "Natural language from artificial life." Artif Life 8(2): 185-215.

            This article aims to show that linguistics, in particular the study of the lexico-syntactic aspects of language, provides fertile ground for artificial life modeling. A survey of the models that have been developed over the last decade and a half is presented to demonstrate that ALife techniques have a lot to offer an explanatory theory of language. It is argued that this is because much of the structure of language is determined by the interaction of three complex adaptive systems: learning, culture, and biological evolution. Computational simulation, informed by theoretical linguistics, is an appropriate response to the challenge of explaining real linguistic data in terms of the processes that underpin human language.

Ethier, C. R. (2002). "Computational modeling of mass transfer and links to atherosclerosis." Ann Biomed Eng 30(4): 461-71.

            In the context of atherogenesis, mass transport refers to the movement of atherogenic molecules from flowing blood into the artery wall, or vice versa. Although LDL transport clearly plays a role in atherosclerotic plaque development, it is much less clear whether abnormalities in mass transfer patterns are in themselves atherogenic. A powerful way of addressing this question is through computational modeling, which provides detailed descriptions of local mass transport features. Here we briefly review the strategy and some of the pros and cons of such a modeling approach, and then focus on results gained from studies in a variety of arterial geometries. The general picture is that zones of hypoxia (low oxygen transport from blood to wall) and elevated LDL tend to colocalize with each other, and with areas of atherosclerotic lesion development and/or intimal thickening. The picture is complicated by the fact that such zones also tend to have "abnormal" wall shear stress patterns, which are also believed to be atherogenic. Taken together, these results suggest, but do not prove, a role for mass transport in atherogenesis.

Elcock, A. H. (2002). "Modeling supramolecular assemblages." Curr Opin Struct Biol 12(2): 154-60.

            There has been some progress (but not much) in simulating supramolecular assemblages in the past year. The two main technical advances have been, firstly, the establishment of a protocol for extracting equilibrium thermodynamic data from forced (i.e. nonequilibrium) simulations and experiments, and, secondly, the development of a method for accurately calculating the electrostatics of enormous systems. Some recent applications have demonstrated the increasing feasibility of performing meaningful simulations of very large systems.

Demarco, J. J., I. J. Chetty, et al. (2002). "A Monte Carlo tutorial and the application for radiotherapy treatment planning." Med Dosim 27(1): 43-50.

            Monte Carlo-based treatment planning algorithms are advancing rapidly and will certainly be implemented as part of conventional treatment planning systems in the near future. This paper was designed as a basic tutorial for using the Monte Carlo method as applied to radiotherapy treatment planning. The tutorial addresses the basic transport differences between photon and electron transport as well as the sampling distributions. The implementation of a virtual linac source model and the conversion from the Monte Carlo source modeling reference plane into the treatment reference plane is discussed. The implementation of a thresholding algorithm for converting CT electron density to patient specific materials is also presented. A 6-field prostate boost treatment is used to compare a conventional treatment planning algorithm (pencil beam model) with a Monte Carlo simulation algorithm. The agreement between the 2 calculation methods is good based upon the qualitative comparison of the isodose distribution and the dose-volume histograms for the prostate and the rectum. The effects of statistical uncertainty on the Monte Carlo calculation are also presented.

de Jong, H. (2002). "Modeling and simulation of genetic regulatory systems: a literature review." J Comput Biol 9(1): 67-103.

            In order to understand the functioning of organisms on the molecular level, we need to know which genes are expressed, when and where in the organism, and to which extent. The regulation of gene expression is achieved through genetic regulatory systems structured by networks of interactions between DNA, RNA, proteins, and small molecules. As most genetic regulatory networks of interest involve many components connected through interlocking positive and negative feedback loops, an intuitive understanding of their dynamics is hard to obtain. As a consequence, formal methods and computer tools for the modeling and simulation of genetic regulatory networks will be indispensable. This paper reviews formalisms that have been employed in mathematical biology and bioinformatics to describe genetic regulatory systems, in particular directed graphs, Bayesian networks, Boolean networks and their generalizations, ordinary and partial differential equations, qualitative differential equations, stochastic equations, and rule-based formalisms. In addition, the paper discusses how these formalisms have been used in the simulation of the behavior of actual regulatory systems.

Colombo, G. and G. Carrea (2002). "Modeling enzyme reactivity in organic solvents and water through computer simulations." J Biotechnol 96(1): 23-33.

            In this article, we review how molecular modeling techniques can be used to shed light on how water and organic solvents influence the reactivity of enzymes. The application of thermodynamics-based models allowed the first qualitative predictions on the selectivity of many reaction types. However, it was with the application of quantum mechanical/molecular mechanical (QM/MM) methods that quantitative models of actual reactivity patterns could be realistically formulated.

Botta, M., F. Corelli, et al. (2002). "Molecular modeling as a powerful technique for understanding small-large molecules interactions." Farmaco 57(2): 153-65.

            In the present review we summarize recent work, aimed at a better understanding of the interactions in macromolecule ligand complexes, performed by means of computational tools such as pseudoreceptor generation, molecular docking, conformational search and energy minimization. While the first approach has been applied when the three-dimensional structural properties of the biological target were unknown, the remaining protocols exploited the knowledge of the overall structure of the involved macromolecules and their active sites. Molecular modeling techniques were used in the cases reported to study and propose macromolecular binding sites and to predict their interactions with bioactive conformers of the ligands.

Azar, F. S., D. N. Metaxas, et al. (2002). "Methods for modeling and predicting mechanical deformations of the breast under external perturbations." Med Image Anal 6(1): 1-27.

            Currently, high field (1.5 T) superconducting MR imaging does not allow live guidance during needle breast procedures. The current procedure allows the physician only to calculate approximately the location and extent of a cancerous tumor in the compressed patient breast before inserting the needle. It can then become relatively uncertain that the tissue specimen removed during the biopsy actually belongs to the lesion of interest. A new method for guiding clinical breast biopsy is presented, based on a deformable finite element model of the breast. The geometry of the model is constructed from MR data, and its mechanical properties are modeled using a non-linear material model. This method allows imaging the breast without or with mild compression before the procedure, then compressing the breast and using the finite element model to predict the tumor's position during the procedure. A silicon phantom containing a stiff inclusion was imaged uncompressed then compressed. A model of the phantom was constructed and compressed using custom-written software, and also using a commercial FEM simulation package. The displacement of the inclusion's corners was recorded both in the real phantom and in the two compressed models. A patient's breast was imaged uncompressed then compressed. A deformable model of the uncompressed breast was constructed, then compressed. The displacement of a cyst and of two vitamin E pills taped to the surface of the breast were recorded both in the real and in the modeled breast. The entire procedure lasted less than a half-hour, making it clinically useful. The results show that it is possible to create a deformable model of the breast based on finite elements with non-linear material properties, capable of modeling and predicting breast deformations in a clinically useful amount of time.

Augen, J. (2002). "The evolving role of information technology in the drug discovery process." Drug Discov Today 7(5): 315-23.

            Information technologies for chemical structure prediction, heterogeneous database access, pattern discovery, and systems and molecular modeling have evolved to become core components of the modern drug discovery process. As this evolution continues, the balance between in silico modeling and 'wet' chemistry will continue to shift and it might eventually be possible to step through the discovery pipeline without the aid of traditional laboratory techniques. Rapid advances in the industrialization of gene sequencing combined with databases of protein sequence and structure have created a target-rich but lead-poor environment. During the next decade, newer information technologies that facilitate the molecular modeling of drug-target interactions are likely to shift this balance towards molecular-based personalized medicine -- the ultimate goal of the drug discovery process.

Yoganandan, N., S. Kumaresan, et al. (2001). "Biomechanics of the cervical spine Part 2. Cervical spine soft tissue responses and biomechanical modeling." Clin Biomech (Bristol, Avon) 16(1): 1-27.

            OBJECTIVE: The responses and contributions of the soft tissue structures of the human neck are described with a focus on mathematical modeling. Spinal ligaments, intervertebral discs, zygapophysial joints, and uncovertebral joints of the cervical spine are included. Finite element modeling approaches have been emphasized. Representative data relevant to the development and execution of the model are discussed. A brief description is given on the functional mechanical role of the soft tissue components. Geometrical characteristics such as length and cross-sectional areas, and material properties such as force-displacement and stress-strain responses, are described for all components. Modeling approaches are discussed for each soft tissue structure. The final discussion emphasizes the normal and abnormal (e.g., degenerative joint disease, iatrogenic alteration, trauma) behaviors of the cervical spine with a focus on all these soft tissue responses. A brief description is provided on the modeling of the developmental biomechanics of the pediatric spine with a focus on soft tissues. Relevance. Experimentally validated models based on accurate geometry, material property, boundary, and loading conditions are useful to delineate the clinical biomechanics of the spine. Both external and internal responses of the various spinal components, a data set not obtainable directly from experiments, can be determined using computational models. Since soft tissues control the complex structural response, an accurate simulation of their anatomic, functional, and biomechanical characteristics is necessary to understand the behavior of the cervical spine under normal and abnormal conditions such as facetectomy, discectomy, laminectomy, and fusion.

Weiss, J. A. and J. C. Gardiner (2001). "Computational modeling of ligament mechanics." Crit Rev Biomed Eng 29(3): 303-71.

            This article provides a critical review of past and current techniques for the computational modeling of ligaments and tendons. A brief overview of relevant concepts from the fields of continuum mechanics and finite element analysis is provided. The structure and function of ligaments and tendons are reviewed in detail, with emphasis on the relationship of microstructural tissue features to the continuum mechanical hehavior. Experimental techniques for the material characterization of biological soft tissues are discussed. Past and current efforts related to the constitutive modeling of ligaments and tendons are classified by the particular technique and dimensionality. Applications of one-dimensional and three-dimensional constitutive models in the representation of the mechanical behavior of joints are presented. Future research directions are identified.

Wang, W., O. Donini, et al. (2001). "Biomolecular simulations: recent developments in force fields, simulations of enzyme catalysis, protein-ligand, protein-protein, and protein-nucleic acid noncovalent interactions." Annu Rev Biophys Biomol Struct 30: 211-43.

            Computer modeling has been developed and widely applied in studying molecules of biological interest. The force field is the cornerstone of computer simulations, and many force fields have been developed and successfully applied in these simulations. Two interesting areas are (a) studying enzyme catalytic mechanisms using a combination of quantum mechanics and molecular mechanics, and (b) studying macromolecular dynamics and interactions using molecular dynamics (MD) and free energy (FE) calculation methods. Enzyme catalysis involves forming and breaking of covalent bonds and requires the use of quantum mechanics. Noncovalent interactions appear ubiquitously in biology, but here we confine ourselves to review only noncovalent interactions between protein and protein, protein and ligand, and protein and nucleic acids.

Trent, J. O. (2001). "Molecular modeling of drug-DNA complexes: an update." Methods Enzymol 340: 290-326.

Teutli Leon, M. M., M. T. Oropeza Guzman, et al. (2001). "Mathematical modeling of electrochemical remediation for soils under galvanostatic conditions." Environ Technol 22(1): 17-26.

            This work proposes a mathematical model for the electrochemical remediation of clayey soils based on the total volume concept for a two-phase system. The mathematical formulation was done including contributions from theories for: groundwater, membranes, porous electrodes and environmental soil chemistry. The resulting model accounts for: free and complexed species in the soil matrix and the pore solution; chemical reactions taking place on either phase and/or between phases; a dynamic soil surface charge affected by the ion content of the pore solution; and electroneutrality of the total volume. Soil surface charge was included in a modified Ohm's law (voltage gradient) and in a modified Schlog's law (convective movement). Numerical implementation was done using orthogonal collocation on finite elements for spatial derivatives, and forward finite differences for time derivatives. Visual Fortran supported by IMSL subroutines was used for computer simulation. Model predictions were successfully compared with reported experimental data. Also, an analysis of pH profiles through the soil is provided for conditions when parameters including hydrostatic head, applied current density and initial pH are modified.

Tagamets, M. A. and B. Horwitz (2001). "Interpreting PET and fMRI measures of functional neural activity: the effects of synaptic inhibition on cortical activation in human imaging studies." Brain Res Bull 54(3): 267-73.

            Human brain imaging methods such as postiron emission tomography and functional magnetic resonance imaging have recently achieved widespread use in the study of both normal cognitive processes and neurological disorders. While many of these studies have begun to yield important insights into human brain function, the relationship between these measurements and the underlying neuronal activity is still not well understood. One open question is how neuronal inhibition is reflected in these imaging results. In this paper, we describe how large-scale modeling can be used to address this question. Specifically, we identify three factors that may play a role in how inhibition affects imaging results: (1) local connectivity; (2) context; and (3) type of inhibitory connection. Simulation results are presented that show how the interaction among these three factors can explain seemingly contradictory experimental results. The modeling suggests that neuronal inhibition can raise brain imaging measures if there is either low local excitatory recurrence or if the region is not otherwise being driven by excitation. Conversely, with high recurrence or actively driven excitation, inhibition can lower observed values.

Starr, J. M. and A. Campbell (2001). "Mathematical modeling of Clostridium difficile infection." Clin Microbiol Infect 7(8): 432-7.

            Clostridium difficile diarrhea is a major cause of morbidity and mortality in hospitals. However, the number of cases in an outbreak is usually relatively small. This precludes many traditional statistical methods of modeling epidemics. Stochastic models are designed to deal with small numbers and are promising methods of understanding C. difficile epidemiology. This is illustrated by a reversible jump Markov chain Monte Carlo model based on the herd immunity hypothesis of C. difficile outbreaks.

Soman, K. V., C. H. Schein, et al. (2001). "Homology modeling and simulations of nuclease structures." Methods Mol Biol 160: 263-86.

Soffers, A. E., M. G. Boersma, et al. (2001). "Computer-modeling-based QSARs for analyzing experimental data on biotransformation and toxicity." Toxicol In Vitro 15(4-5): 539-51.

            Over the past decades the description of quantitative structure-activity relationships (QSARs) has been undertaken in order to find predictive models and/or mechanistic explanations for chemical as well as biological activities. This includes QSAR studies in toxicology. In an approach beyond the classical QSAR approaches, attempts have been made to define parameters for the QSAR studies on the basis of quantum mechanical computer calculations. The conversion of relatively small xenobiotics within the active sites of biotransformation enzymes can be expected to follow the general rules of chemistry. This makes the description of QSARs on the basis of only one parameter, chosen on the basis of insight in the mechanism, feasible. In contrast, toxicological endpoints can very often be the result of more than one physico-chemical interaction of the compound with the model system of interest. Therefore the description of quantitative structure-toxicity relationships often does not follow a one-descriptor mechanistic approach but starts from the other end, describing QSARs by multi-parameter approaches. The present paper focuses on the possibilities and restrictions of using computer-based QSAR modeling for analyzing experimental toxicological data, with emphasis on examples from the field of biotransformation and toxicity.

Rybak, I. A., J. F. Paton, et al. (2001). "Neurogenesis of the respiratory pattern: insights from computational modeling." Adv Exp Med Biol 499: 165-70.

Pandy, M. G. (2001). "Computer modeling and simulation of human movement." Annu Rev Biomed Eng 3: 245-73.

            Recent interest in using modeling and simulation to study movement is driven by the belief that this approach can provide insight into how the nervous system and muscles interact to produce coordinated motion of the body parts. With the computational resources available today, large-scale models of the body can be used to produce realistic simulations of movement that are an order of magnitude more complex than those produced just 10 years ago. This chapter reviews how the structure of the neuromusculoskeletal system is commonly represented in a multijoint model of movement, how modeling may be combined with optimization theory to simulate the dynamics of a motor task, and how model output can be analyzed to describe and explain muscle function. Some results obtained from simulations of jumping, pedaling, and walking are also reviewed to illustrate the approach.

O'Reilly, R. C. (2001). "Generalization in interactive networks: the benefits of inhibitory competition and Hebbian learning." Neural Comput 13(6): 1199-241.

            Computational models in cognitive neuroscience should ideally use biological properties and powerful computational principles to produce behavior consistent with psychological findings. Error-driven backpropagation is computationally powerful and has proven useful for modeling a range of psychological data but is not biologically plausible. Several approaches to implementing backpropagation in a biologically plausible fashion converge on the idea of using bidirectional activation propagation in interactive networks to convey error signals. This article demonstrates two main points about these error-driven interactive networks: (1) they generalize poorly due to attractor dynamics that interfere with the network's ability to produce novel combinatorial representations systematically in response to novel inputs, and (2) this generalization problem can be remedied by adding two widely used mechanistic principles, inhibitory competition and Hebbian learning, that can be independently motivated for a variety of biological, psychological, and computational reasons. Simulations using the Leabra algorithm, which combines the generalized recirculation (GeneRec), biologically plausible, error-driven learning algorithm with inhibitory competition and Hebbian learning, show that these mechanisms can result in good generalization in interactive networks. These results support the general conclusion that cognitive neuroscience models that incorporate the core mechanistic principles of interactivity, inhibitory competition, and error-driven and Hebbian learning satisfy a wider range of biological, psychological, and computational constraints than models employing a subset of these principles.

Novosel'tsev, V. N., A. Novosel'tseva Zh, et al. (2001). "[Mathematical modeling and simulation of life history and tradeoffs]." Adv Gerontol 7: 52-64.

            General trends in mathematical life history modeling are reviewed. In modern experimental biogerontology life history consists of all traits that affect fecundity and survival. Tradeoffs are of special importance in the theory of life history, studied either as an evolutionary phenomenon or as a feature of individual life history. Tradeoffs between cost of reproduction and survival are mostly studied and modeled. Special attention is given in the review to statistical modeling of life history (Monte-Carlo techniques). A number of modeling paradigms is presented and their perspectives are discussed.

Nagatomo, T., T. Ohnuki, et al. (2001). "Beta-adrenoceptors: three-dimensional structures and binding sites for ligands." Jpn J Pharmacol 87(1): 7-13.

            Recent progress in analyzing the structures and functions of G-protein coupled receptors (GPCRs) including beta-adrenoceptors (beta-ARs) has been made by pharmacological, physiological and molecular biological techniques. The three-dimensional (3D) structures, interaction sites with ligands and conformational changes of these receptor subtypes due to ligand binding are now better understood by the simulation of these receptors using computer-aided molecular modeling. Based on these techniques, numbers and conformations of amino acid sequences of each subtype (beta1-, beta2- and beta3-ARs) were defined and also interaction sites or modes of interaction between ligands and beta-ARs could be analyzed three-dimensionally. In addition, simulation of 3D structures of beta-ARs by molecular modeling could clearly determine the limited size, space or pocket for fitting with ligands. These studies will give some clues for the clarification of other GPCRs. Thus, this review summarizes current findings on chemical structures of ligands, amino acid sequences, 3D structures and important amino acids of beta-AR subtypes for interacting with ligands obtained from mutagenesis, chimeric studies and molecular modeling techniques.

Loew, L. M. and J. C. Schaff (2001). "The Virtual Cell: a software environment for computational cell biology." Trends Biotechnol 19(10): 401-6.

            The newly emerging field of computational cell biology requires software tools that address the needs of a broad community of scientists. Cell biological processes are controlled by an interacting set of biochemical and electrophysiological events that are distributed within complex cellular structures. Computational modeling is familiar to researchers in fields such as molecular structure, neurobiology and metabolic pathway engineering, and is rapidly emerging in the area of gene expression. Although some of these established modeling approaches can be adapted to address problems of interest to cell biologists, relatively few software development efforts have been directed at the field as a whole. The Virtual Cell is a computational environment designed for cell biologists as well as for mathematical biologists and bioengineers. It serves to aid the construction of cell biological models and the generation of simulations from them. The system enables the formulation of both compartmental and spatial models, the latter with either idealized or experimentally derived geometries of one, two or three dimensions.

Linthicum, D. S., S. Y. Tetin, et al. (2001). "Antibody-ligand interactions: computational modeling and correlation with biophysical measurements." Comb Chem High Throughput Screen 4(5): 439-49.

            Several new aspects of computer-assisted molecular modeling strategies and biophysical techniques, such as fluorescence spectroscopy, circular dichroism, and absorption spectroscopy, have proved useful in the analysis and description of antibody-ligand interactions. The molecular features involved in determining the specificity of antibody-ligand interactions, such as electrostatics (e.g. partial charges, salt bridges, p-cation motifs), hydrogen-bonds, polarization, hydrophobic interactions, hydration and solvation effects, entropy, and kinetics can be identified using a battery of biophysical techniques. An understanding of these parameters is essential to our use of antibodies as tools in high throughput screening of chemical libraries for the discovery of novel compounds.

Korenberg, M. J., R. David, et al. (2001). "Parallel cascade identification and its application to protein family prediction." J Biotechnol 91(1): 35-47.

            Parallel cascade identification is a method for modeling dynamic systems with possibly high order nonlinearities and lengthy memory, given only input/output data for the system gathered in an experiment. While the method was originally proposed for nonlinear system identification, two recent papers have illustrated its utility for protein family prediction. One strength of this approach is the capability of training effective parallel cascade classifiers from very little training data. Indeed, when the amount of training exemplars is limited, and when distinctions between a small number of categories suffice, parallel cascade identification can outperform some state-of-the-art techniques. Moreover, the unusual approach taken by this method enables it to be effectively combined with other techniques to significantly improve accuracy. In this paper, parallel cascade identification is first reviewed, and its use in a variety of different fields is surveyed. Then protein family prediction via this method is considered in detail, and some particularly useful applications are pointed out.

Koopman, J. S., G. Jacquez, et al. (2001). "New data and tools for integrating discrete and continuous population modeling strategies." Ann N Y Acad Sci 954: 268-94.

            Realistic population models have interactions between individuals. Such interactions cause populations to behave as systems with nonlinear dynamics. Much population data analysis is done using linear models assuming no interactions between individuals. Such analyses miss strong influences on population behavior and can lead to serious errors--especially for infectious diseases. To promote more effective population system analyses, we present a flexible and intuitive modeling framework for infection transmission systems. This framework will help population scientists gain insight into population dynamics, develop theory about population processes, better analyze and interpret population data, design more powerful and informative studies, and better inform policy decisions. Our framework uses a hierarchy of infection transmission system models. Four levels are presented here: deterministic compartmental models using ordinary differential equations (DE); stochastic compartmental (SC) models that relax assumptions about population size and include stochastic effects; individual event history models (IEH) that relax the SC compartmental structure assumptions by allowing each individual to be unique. IEH models also track each individual's history, and thus, allow the simulation of field studies. Finally, dynamic network (DNW) models relax the assumption of the previous models that contacts between individuals are instantaneous events that do not affect subsequent contacts. Eventually it should be possible to transit between these model forms at the click of a mouse. An example is presented dealing with Cryptosporidium. It illustrates how transiting model forms helps assess water contamination effects, evaluate control options, and design studies of infection transmission systems using nucleotide sequences of infectious agents.

Klonowski, W. (2001). "Non-equilibrium proteins." Comput Chem 25(4): 349-68.

            There exist no methodical studies concerning non-equilibrium systems in cellular biology. This paper is an attempt to partially fill this shortcoming. We have undertaken an extensive data-mining operation in the existing scientific literature to find scattered information about non-equilibrium subcellular systems, in particular concerning fast proteins, i.e. those with short turnover half-time. We have advanced the hypothesis that functionality in fast proteins emerges as a consequence of their intrinsic physical instability that arises due to conformational strains resulting from co-translational folding (the interdependence between chain elongation and chain folding during biosynthesis on ribosomes). Such intrinsic physical instability, a kind of conformon (Klonowski-Klonowska conformon, according to Ji, (Molecular Theories of Cell Life and Death, Rutgers University Press, New Brunswick, 1991)) is probably the most important feature determining functionality and timing in these proteins. If our hypothesis is true, the turnover half-time of fast proteins should be positively correlated with their molecular weight, and some experimental results (Ames et al., J. Neurochem. 35 (1980) 131) indeed demonstrated such a correlation. Once the native structure (and function) of a fast protein macromolecule is lost, it may not be recovered--denaturation of such proteins will always be irreversible; therefore, we searched for information on irreversible denaturation. Only simulation and modeling of protein co-translational folding may answer the questions concerning fast proteins (Ruggiero and Sacile, Med. Biol. Eng. Comp. 37 (Suppl. 1) (1999) 363). Non-equilibrium structures may also be built up of protein subunits, even if each one taken by itself is in thermodynamic equilibrium (oligomeric proteins; sub-cellular sol-gel dissipative network structures).

Gu, C., M. A. Province, et al. (2001). "Meta-analysis for model-free methods." Adv Genet 42: 255-72.

            The intricate nature of complex genetic traits dictates that novel methodologies be developed and utilized to achieve better power, better accuracy, and more favorable balance between type I and type II errors than could be achieved by the traditional methods as they are used in mapping Mendelian traits. Meta-analysis provides one such method for synthesizing information from multiple studies. This has the advantage of being able to pool relatively weak signals from individual studies into a collectively stronger evidence of genetic effects, while at the same time providing a quantitative framework for modeling variability among studies. The traditional lod score measures significance level of a linkage effect in an individual study, and its additive property make it a natural candidate for combining results across independent studies. To incorporate the within-study variation of the linkage effect into the pooled overall measure of genetic effect, the effect sizes (such as the proportion of genes shared identical-by-descent, IBD) should be pooled directly across studies. Traditional regression models and mixed effects models can be used to estimate the overall genetic effect size and its variance, and to test heterogeneity among studies. Our simulation studies show that designing studies with moderate power and pooling their results via meta-analysis may be more cost-effective than large dedicated studies. We believe that, as a newly emerging methodology, the meta-analysis approach has the potential to become an integral part of our toolbox that will expedite the search for complex human disease genes.

Furtaw, E. J., Jr. (2001). "An overview of human exposure modeling activities at the USEPA's National Exposure Research Laboratory." Toxicol Ind Health 17(5-10): 302-14.

            The computational modeling of human exposure to environmental pollutants is one of the primary activities of the US Environmental Protection Agency (USEPA)s National Exposure Research Laboratory (NERL). Assessment of human exposures is a critical part of the overall risk assessment paradigm. In exposure assessment, we analyze the source-to-dose sequence of processes, in which pollutants are released from sources into the environment, where they may move through multiple environmental media, and to human receptors via multiple pathways. Exposure occurs at the environment-human interface, where pollutants are contacted in the course of human activities. Exposure may result in a dose, by which chemicals enter the body through multiple portals of entry, primarily inhalation, ingestion, and dermal absorption. Within the body, absorbed pollutants are distributed to, metabolized within, and eliminated from various organs and tissues, where they may cause toxicologic responses or adverse health effects. The NERL's modeling efforts are directed at improving our understanding of this sequence of processes, by characterizing the various factors influencing exposures and dose, and their associated variabilities and uncertainties. Modeling at the NERL is one of three essential programmatic elements, along with measurements and methods development. These are pursued interactively to advance our understanding of exposure-related processes. Exposure models are developed and run using the best currently available measurement data to simulate and predict population exposure and dose distributions, and to identify the most important factors and their variabilities and uncertainties. This knowledge is then used to guide the development of improved methods and measurements needed to obtain better data to improve the assessment and reduce critical uncertainties. These models and measurement results are tools that can be used in risk assessments and in risk management decisions in order to reduce harmful exposures. Current areas of the NERL's exposure modeling emphasis include: Pollutant concentrations in ambient (outdoor) air using the Third Generation Air Quality Modeling System's Community Multiscale Air Quality model (Models-3/CMAQ); Air flow and pollutant concentrations at local and microenvironmental scales using computational fluid dynamics (CFD); Human inhalation exposure to airborne particulate matter, air toxics, and multipathway exposure to pesticides, using the Stochastic Human Exposure and Dose Simulation (SHEDS) model; Human and ecological exposure and risk assessments of hazardous waste sites using Framework for Risk Analysis in Multimedia Environmental Systems--Multimedia, Multipathway, Multireceptor Risk Assessment (FRAMES-3MRA), one of many software programs available from the NERL's Center for Exposure Assessment Modeling (CEAM); Physiologically based pharmacokinetic (PBPK) modeling of pesticides and volatile organic compounds (VOCs) in the Exposure-Related Dose-Estimating Model (ERDEM). A brief historical overview of the NERL's evolution of human exposure models is presented, with examples of the present state-of-the-science represented by SHEDS and FRAMES-3MRA.

Carson, J. H., H. Cui, et al. (2001). "RNA trafficking in oligodendrocytes." Results Probl Cell Differ 34: 69-81.

            A2RE and hnRNP A2 have been identified as important cis/trans determinants for MBP RNA trafficking in oligodendrocytes. Since A2RE-like sequences are found in several different transported RNAs, and since hnRNP A2 is expressed in most cell types, this may represent a general RNA trafficking pathway shared by a variety of different RNAs in different cell types. In oligodendrocytes, A2RE/hnRNP A2 determinants are involved in at least four steps in the RNA trafficking pathway: (1) export from the nucleus to the cytoplasm, (2) granule assembly in the perikaryon, (3) transport along microtubules in the processes, and (4) translation activation in the myelin compartment. The components of the cellular machinery mediating each of these steps are known. How A2RE/hnRNP A2 determinants interact with these components to mediate RNA trafficking is being investigated by a combination of: biochemistry to analyze molecular interactions in vitro, imaging to visualize molecular interactions in living cells, and computational modeling to simulate molecular interactions in the Virtual Cell.

Bornholdt, S. (2001). "Modeling genetic networks and their evolution: a complex dynamical systems perspective." Biol Chem 382(9): 1289-99.

            After finishing the sequence of the human genome, a functional understanding of genome dynamics is the next major step on the agenda of the biosciences. New approaches, such as microarray techniques, and new methods of bioinformatics provide powerful tools aiming in this direction. In the last few years, important parts of genome organization and dynamics in a number of model organisms have been determined. However, an integrated view of gene regulation on a genomic scale is still lacking. Here, genome function is discussed from a complex dynamical systems perspective: which dynamical properties can a large genomic system exhibit in principle, given the local mechanisms governing the small subsystems that we know today? Models of artificial genetic networks are used to explore dynamical principles and possible emergent dynamical phenomena in networks of genetic switches. One observes evolution of robustness and dynamical self-organization in large networks of artificial regulators that are based on the dynamic mechanism of transcriptional regulators as observed in biological gene regulation. Possible biological observables and ways of experimental testing of global phenomena in genome function and dynamics are discussed. Models of artificial genetic networks provide a tool to address questions in genome dynamics and their evolution and allow simulation studies in evolutionary genomics.

Young, A. J., C. O'Brien, et al. (2000). "Physiological problems associated with wearing NBC protective clothing during cold weather." Aviat Space Environ Med 71(2): 184-9.

            This report considers how thermal balance of soldiers wearing nuclear, biological and chemical (NBC) protective clothing in combination with the Extreme Cold Weather Clothing System (ECWCS) is affected during work in cold weather. A review of published reports concerning physiological consequences of wearing NBC protective clothing during cold exposure was completed. The findings reported in the experimental literature were too limited to adequately forecast the effects of adding NBC clothing to ECWCS. To remedy the information gap, simulation modeling was employed to predict body temperature changes during alternating bouts of exercise and rest throughout 8 h of exposure to three different severely cold conditions. Published findings indicate that NBC protective clothing may inadequately protect against hand and finger cooling, especially during rest following strenuous activity. No evidence substantiates suggestions that wearing NBC protective masks increases susceptibility to facial frostbite. Collectively, the limited experimental work and the results of simulation modeling argue against any increased risk of hypothermia associated with wearing NBC protective clothing while working in the cold. However, wearing NBC protective clothing during strenuous activity in cold weather may increase the risk of hyperthermia, and cause sweat accumulation in clothing which may compromise insulation and increase the risk of hypothermia during subsequent periods of inactivity.

Ward, S. A. (2000). "Control of the exercise hyperpnoea in humans: a modeling perspective." Respir Physiol 122(2-3): 149-66.

            Models of the exercise hyperpnoea have classically incorporated elements of proportional feedback (carotid and medullary chemosensory) and feedforward (central and/or peripheral neurogenic) control. However, the precise details of the control process remain unresolved, reflecting in part both technical and interpretational limitations inherent in isolating putative control mechanisms in the intact human, and also the challenges to linear control theory presented by multiple-input integration, especially with regard to the ventilatory and gas-exchange complexities encountered at work rates which engender a metabolic acidosis. While some combination of neurogenic, chemoreflex and circulatory-coupled processes are likely to contribute to the control, the system appears to evidence considerable redundancy. This, coupled with the lack of appreciable error signals in the mean levels of arterial blood gas tensions and pH over a wide range of work rates, has motivated the formulation of innovative control models that reflect not only spatial interactions but also temporal interactions (i.e. memory). The challenge is to discriminate between robust competing control models that: (a) integrate such processes within plausible physiological equivalents; and (b) account for both the dynamic and steady-state system response over a range of exercise intensities. Such models are not yet available.

Tenti, G., J. M. Drake, et al. (2000). "Brain biomechanics: mathematical modeling of hydrocephalus." Neurol Res 22(1): 19-24.

            The considerable amount of literature on mathematical models of hydrocephalus and other brain abnormalities is critically reviewed. These models have various degrees of mathematical sophistication, and have influenced not only the diagnosis of hydrocephalus, but also its treatment with CSF shunts. The mathematical models are classified into two classes, pressure-volume models, and consolidation models. Advantages and disadvantages of both types are pointed out with a view to removing the confusion frequently generated by the technical aspects of the subject. The conclusion is reached that, while none of the current models are good enough to be of immediate use to the neurosurgeon, mathematical models are likely in the future to be a powerful tool for the understanding and the treatment of hydrocephalus, as well as other conditions related to brain biomechanics. The amount of mathematics has been kept to the absolute minimum, but it is cited and appended for those who would like to dig further into this fascinating area of research.

Sanchez, R., U. Pieper, et al. (2000). "Protein structure modeling for structural genomics." Nat Struct Biol 7 Suppl: 986-90.

            The shapes of most protein sequences will be modeled based on their similarity to experimentally determined protein structures. The current role, limitations, challenges and prospects for protein structure modeling (using information about genes and genomes) are discussed in the context of structural genomics.

Ramsey, S. D., M. McIntosh, et al. (2000). "Simulation modeling of outcomes and cost effectiveness." Hematol Oncol Clin North Am 14(4): 925-38.

            Modeling will continue to be used to address important issues in clinical practice and health policy issues that have not been adequately studied with high-quality clinical trials. The apparent ad hoc nature of models belies the methodologic rigor that is applied to create the best models in cancer prevention and care. Models have progressed from simple decision trees to extremely complex microsimulation analyses, yet all are built using a logical process based on objective evaluation of the path between intervention and outcome. The best modelers take great care to justify both the structure and content of the model and then test their assumptions using a comprehensive process of sensitivity analysis and model validation. Like clinical trials, models sometimes produce results that are later found to be invalid as other data become available. When weighing the value of models in health care decision making, it is reasonable to consider the alternatives. In the absence of data, clinical policy decisions are often based on the recommendations of expert opinion panels or on poorly defined notions of the standard of care or medical necessity. Because such decision making rarely entails the rigorous process of data collection, synthesis, and testing that is the core of well-conducted modeling, it is usually not possible for external audiences to examine the assumptions and data that were used to derive the decisions. One of the modeler's most challenging tasks is to make the structure and content of the model transparent to the intended audience. The purpose of this article is to clarify the process of modeling, so that readers of models are more knowledgeable about their uses, strengths, and limitations.

Pierazzo, E. and H. J. Melosh (2000). "Understanding oblique impacts from experiments, observations, and modeling." Annu Rev Earth Planet Sci 28: 141-67.

            Natural impacts in which the projectile strikes the target vertically are virtually nonexistent. Nevertheless, our inherent drive to simplify nature often causes us to suppose most impacts are nearly vertical. Recent theoretical, observational, and experimental work is improving this situation, but even with the current wealth of studies on impact cratering, the effect of impact angle on the final crater is not well understood. Although craters' rims may appear circular down to low impact angles, the distribution of ejecta around the crater is more sensitive to the angle of impact and currently serves as the best guide to obliquity of impacts. Experimental studies established that crater dimensions depend only on the vertical component of the impact velocity. The shock wave generated by the impact weakens with decreasing impact angle. As a result, melting and vaporization depend on impact angle; however, these processes do not seem to depend on the vertical component of the velocity alone. Finally, obliquity influences the fate of the projectile: in particular, the amount and velocity of ricochet are a strong function of impact angle.

Oliveira, S. C., F. M. Pereira, et al. (2000). "Mathematical modeling of controlled-release systems of herbicides using lignins as matrices. A review." Appl Biochem Biotechnol 84-86: 595-615.

            The herbicides applied in soils can be easily lost, owing to leaching, volatilization, and bio- and photodegradation. Controlled-release systems using polymeric matrices claim to solve these problems. The movement of the herbicides in the soil is also an important phenomenon to be studied in order to evaluate the loss processes. The development of mathematical models is a relevant requirement for simulation and optimization of such systems. This study reviews mathematical models as an initial step for modeling data obtained for controlled-release systems of herbicides (diuron, 2,4-dichlorophenoxyacetic acid, and ametryn) using sugarcane bagasse lignin as a polymeric matrix. The release kinetic studies were carried out using several acceptor systems including a water bath, soil, and soil-packed columns. Generally, these models take into account phenomena such as unsteady-state mass transfer by diffusion (Fick's law) and convection, consumption by several processes, and partitioning processes, resulting in partial differential equations with respect to time and space variables.

Nucci, G. and C. Cobelli (2000). "Models of subcutaneous insulin kinetics. A critical review." Comput Methods Programs Biomed 62(3): 249-57.

            Subcutaneous insulin kinetics is a complex process whose quantitation is needed for a reliable glycemic control in the conventional therapy of insulin-dependent diabetes. The major difficulties in modeling include accounting for the distribution in the subcutaneous depot and transport to plasma. A single model describing in detail the various processes for all the commercially available insulin preparations is not available. Several models however have been proposed which vary in the degree of complexity. Virtually all of them handle the regular insulin preparation while a few handle the intermediate acting and the novel insulin analogues. In this paper we critically review these models.

Neptune, R. R. (2000). "Computer modeling and simulation of human movement. Applications in sport and rehabilitation." Phys Med Rehabil Clin N Am 11(2): 417-34, viii.

            Computer modeling and simulation of human movement plays an increasingly important role in sport and rehabilitation, with applications ranging from sport equipment design to understanding pathologic gait. The complex dynamic interactions within the musculoskeletal and neuromuscular systems make analyzing human movement with existing experimental techniques difficult but computer modeling and simulation allows for the identification of these complex interactions and causal relationships between input and output variables. This article provides an overview of computer modeling and simulation and presents an example application in the field of rehabilitation.

Muller, G. (2000). "Towards 3D structures of G protein-coupled receptors: a multidisciplinary approach." Curr Med Chem 7(9): 861-88.

            Current strategies in pharmaceutical research comprise two methodologically different but complementary approaches for lead finding purposes, namely the random screening of compound libraries and the structure-based effort, commonly termed rational drug design. The structure-based approach is aimed to exploit 3D structure data of the molecular components involved in the molecular recognition event that underlies the attempt to therapeutically modulate the biological function of a macromolecular target with proven pathophysiological relevance for a disease state. In this context, G protein-coupled receptors (GPCRs) constitute the most prominent family of validated drug targets within biomedical research, since approximately 60 % of approved drugs elicit their therapeutic effects by selectively addressing members of that target family. From a 3D structure point of view, these transmembrane signal transduction systems represent the most challenging task for structure determination, which is due to the heterogeneous and fine-balanced environment conditions that are necessary for structural and functional integrity of the receptor protein. This contribution will address the different concepts to derive structurally relevant information on the transmemebrane seven-helix protein (7TM) domain of GPCRs with special emphasis laid on the multidisciplinarity of the applied methodologies. The current status of electron-cryo-microscopy on 2D crystals and even high-resolution x-ray crystallography on 7TM proteins will be introduced highlighting the transferability of the emerging structural principles onto the GPCR superfamily. Special techniques from bioinformatics and homology-related molecular modeling in combination with tailor-made protein simulation methodologies complement the experimentally derived data, in that they facilitate the 3D structure generation and structure validation process. This contribution summarises the most recent results of GPCR structure studies with the aim to underline the impact of structure data not only for the purpose of rationalising structure-activity data on low-molecular weight antagonists within the context of a protein binding pocket, but also for a better understanding of e.g. mutagenesis experiments, thus qualifying GPCR structure models as valid communication platforms establishing a functional link between molecular biology, biophysics, bioinformatics and organic chemistry in a highly efficient manner.

McKenzie, F. E. (2000). "Why model malaria?" Parasitol Today 16(12): 511-6.

            The past 30 years have seen little tangible progress in alleviating the worldwide burden of malaria. Ellis McKenzie here discusses some of the history, problems and prospects of mathematical models of malaria, and the contributions that models might make towards progress. He argues that models can be powerful tools for integrating information from different disciplines, and that advances in computer modeling can complement and extend classic approaches.

McCray, J. E. (2000). "Mathematical modeling of air sparging for subsurface remediation: state of the art." J Hazard Mater 72(2-3): 237-63.

            A review of published mathematical models used to simulate air sparging is provided. Applicability of the models, efforts to test the models using experimental data and contributions of modeling efforts to the practice of air sparging are also discussed. Compartmentalized lumped-parameter models and multiphase flow models have dominated air-sparging modeling efforts. In essence, each class of models requires the assumption of a continuum over some model domain. Each approach has significant benefits as well as some inherent disadvantages. Based on the literature, both lumped-parameter modeling and multiphase-flow modeling have been successful in improving our theoretical understanding of the air-sparging process and in facilitating practical development of sparging systems. Lumped-parameter models are simpler to use, and can lend considerable insight to sparging operations. Multiphase flow models have the potential to offer a more realistic simulation of the airflow process, but may require a considerable amount of data collection for model input. The literature suggests that for any air-sparging model to be useful for field applications, detailed model calibration is necessary. It is recommended that models incorporate, in some fashion, the diffusion and dispersion of contaminants to macro-scale air channels, and nonequilibrium interphase mass transfer of contaminants. These mass-transfer-limited processes are frequently listed as causes for the "tailing" of vapor-extraction effluent contaminant concentrations that are frequently observed during field applications. However, time-varying mixing of relatively clean and contaminated vapors in the extraction system may also explain this tailing. Geophysical imaging techniques and inverse modeling combined with air-sparging pilot tests and measurement of traditional hydrogeologic parameters may allow for successful modeling efforts.

Martonen, T. B., C. J. Musante, et al. (2000). "Lung models: strengths and limitations." Respir Care 45(6): 712-36.

            The most widely used particle dosimetry models are those proposed by the National Council on Radiation Protection, International Commission for Radiological Protection, and the Netherlands National Institute of Public Health and the Environment (the RIVM model). Those models have inherent problems that may be regarded as serious drawbacks: for example, they are not physiologically realistic. They ignore the presence and commensurate effects of naturally occurring structural elements of lungs (eg, cartilaginous rings, carinal ridges), which have been demonstrated to affect the motion of inhaled air. Most importantly, the surface structures have been shown to influence the trajectories of inhaled particles transported by air streams. Thus, the model presented herein by Martonen et al may be perhaps the most appropriate for human lung dosimetry. In its present form, the model's major "strengths" are that it could be used for diverse purposes in medical research and practice, including: to target the delivery of drugs for diseases of the respiratory tract (eg, cystic fibrosis, asthma, bronchogenic carcinoma); to selectively deposit drugs for systemic distribution (eg, insulin); to design clinical studies; to interpret scintigraphy data from human subject exposures; to determine laboratory conditions for animal testing (ie, extrapolation modeling); and to aid in aerosolized drug delivery to children (pediatric medicine). Based on our research, we have found very good agreement between the predictions of our model and the experimental data of Heyder et al, and therefore advocate its use in the clinical arena. In closing, we would note that for the simulations reported herein the data entered into our computer program were the actual conditions of the Heyder et al experiments. However, the deposition model is more versatile and can simulate many aerosol therapy scenarios. For example, the core model has many computer subroutines that can be enlisted to simulate the effects of aerosol polydispersity, aerosol hygroscopicity, patient ventilation, patient lung morphology, patient age, and patient airway disease.

Marti-Renom, M. A., A. C. Stuart, et al. (2000). "Comparative protein structure modeling of genes and genomes." Annu Rev Biophys Biomol Struct 29: 291-325.

            Comparative modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. The number of protein sequences that can be modeled and the accuracy of the predictions are increasing steadily because of the growth in the number of known protein structures and because of the improvements in the modeling software. Further advances are necessary in recognizing weak sequence-structure similarities, aligning sequences with structures, modeling of rigid body shifts, distortions, loops and side chains, as well as detecting errors in a model. Despite these problems, it is currently possible to model with useful accuracy significant parts of approximately one third of all known protein sequences. The use of individual comparative models in biology is already rewarding and increasingly widespread. A major new challenge for comparative modeling is the integration of it with the torrents of data from genome sequencing projects as well as from functional and structural genomics. In particular, there is a need to develop an automated, rapid, robust, sensitive, and accurate comparative modeling pipeline applicable to whole genomes. Such large-scale modeling is likely to encourage new kinds of applications for the many resulting models, based on their large number and completeness at the level of the family, organism, or functional network.

Lemmen, C. and T. Lengauer (2000). "Computational methods for the structural alignment of molecules." J Comput Aided Mol Des 14(3): 215-32.

            In drug design, often enough, no structural information on a particular receptor protein is available. However, frequently a considerable number of different ligands is known together with their measured binding affinities towards a receptor under consideration. In such a situation, a set of plausible relative superpositions of different ligands, hopefully approximating their putative binding geometry, is usually the method of choice for preparing data for the subsequent application of 3D methods that analyze the similarity or diversity of the ligands. Examples are 3D-QSAR studies, pharmacophore elucidation, and receptor modeling. An aggravating fact is that ligands are usually quite flexible and a rigorous analysis has to incorporate molecular flexibility. We review the past six years of scientific publishing on molecular superposition. Our focus lies on automatic procedures to be performed on arbitrary molecular structures. Methodical aspects are our main concern here. Accordingly, plain application studies with few methodical elements are omitted in this presentation. While this review cannot mention every contribution to this actively developing field, we intend to provide pointers to the recent literature providing important contributions to computational methods for the structural alignment of molecules. Finally we provide a perspective on how superposition methods can effectively be used for the purpose of virtual database screening. In our opinion it is the ultimate goal to detect analogues in structure databases of nontrivial size in order to narrow down the search space for subsequent experiments.

Jacobs, C. R. (2000). "The mechanobiology of cancellous bone structural adaptation." J Rehabil Res Dev 37(2): 209-16.

            The distinguishing morphological feature of cancellous bone is its high level of porosity relative to cortical bone. This porosity leads to more free surfaces and thus to more of the cellular constituents that inhabit those surfaces. As a result, cancellous bone is often more metabolically active and responsive to stimuli than cortical bone. This extends to the relationship between cancellous bone's internal structure and external mechanical loading. Observational investigations established this relationship as early as the late 19th century. These findings point to the interplay between biology and the cellular mechanical environment, forming the underpinnings of the modern term mechanobiology. Interestingly, it has proven to be more straightforward to assay the biological response than to quantify the precise mechanical environment of cancellous bone and the influence of cancellous bone structure. Despite this concern, significant insights into the nature of cancellous bone mechanobiology can be obtained from computational simulations that allow investigators to determine the morphological consequences of quantitative assumptions about cancellous bone mechanobiology. As the power of computers and the sophistication of these modeling techniques continue to grow, we can expect an increased impact in terms of clinical diagnosis and treatment. The next decade will bring improvements in exercise interventions to prevent and reverse bone loss; improved replacement-joint designs, particularly for those joints currently having poor expected outcomes; and an integration of computer simulation technology with clinical scanners.

Hopfinger, A. J. and J. S. Duca (2000). "Extraction of pharmacophore information from high-throughput screens." Curr Opin Biotechnol 11(1): 97-103.

            Two major advances have been made in the computational perception and utilization of pharmacophores in compound libraries, both real and virtual. Firstly, a hierarchical set of filtering calculations has emerged that can be used to efficiently partition a library into a trial set of pharmacophores. This sequential filtering permits large libraries to be efficiently processed, as well as compounds judged as 'hits' to be analyzed in great detail. Secondly, new and extended methods of QSAR (quantitative structure-activity relationship) analysis have evolved to translate pharmacophore information into QSAR models that, in turn, can be used as virtual high-throughput screens for activity profiling of a library.

Hines, M. L. and N. T. Carnevale (2000). "Expanding NEURON's repertoire of mechanisms with NMODL." Neural Comput 12(5): 995-1007.

            Neuronal function involves the interaction of electrical and chemical signals that are distributed in time and space. The mechanisms that generate these signals and regulate their interactions are marked by a rich diversity of properties that precludes a "one size fits all" approach to modeling. This article presents a summary of how the model description language NMODL enables the neuronal simulation environment NEURON to accommodate these differences.

DeTolla, D. H., S. Andreana, et al. (2000). "Role of the finite element model in dental implants." J Oral Implantol 26(2): 77-81.

            Computer-aided design and finite element methods (FEM) have interested dental researchers because of its use in the computer simulation and design of dental implants, a process greatly facilitated by the development of new computer technology and more accurate modeling technologies. FEM allows for a better understanding of stresses along the surfaces of an implant and in surrounding bone. This will aid in the optimization of implant design and placement of the implant into the bone; it will also help when designing the final prostheses to minimize stresses. The purpose of this review is to elucidate the role of FEM and the impact of this technology in clinical dentistry in the new millennium.

Dao, N., P. J. McCormick, et al. (2000). "The human physiome as an information environment." Ann Biomed Eng 28(8): 1032-42.

            Modern biology is rapidly laying the foundation necessary for integrated modeling of physiological processes in living organisms. The human physiome project attempts to model interactions between biochemicals, cellular organelles, cells, tissues, and organs within whole organisms. One of the first challenges that this project faces is the development of a database environment flexible enough to accommodate the diversity in structure and content of physiological data. This paper reviews the current state of database technology, presents our understanding of the physiome database problem, and proposes a preliminary strategy for addressing it.

Cremer, T., G. Kreth, et al. (2000). "Chromosome territories, interchromatin domain compartment, and nuclear matrix: an integrated view of the functional nuclear architecture." Crit Rev Eukaryot Gene Expr 10(2): 179-212.

            Advances in the specific fluorescent labeling of chromatin in fixed and living human cells in combination with three-dimensional (3D) and 4D (space plus time) fluorescence microscopy and image analysis have opened the way for detailed studies of the dynamic, higher-order architecture of chromatin in the human cell nucleus and its potential role in gene regulation. Several features of this architecture are now well established: 1. Chromosomes occupy distinct territories in the cell nucleus with preferred nuclear locations, although there is no evidence of a rigid suprachromosomal order. 2. Chromosome territories (CTs) in turn contain distinct chromosome arm domains and smaller chromatin foci or domains with diameters of some 300 to 800 nm and a DNA content in the order of 1 Mbp. 3. Gene-dense, early-replicating and gene-poor, middle-to-late-replicating chromatin domains exhibit different higher-order nuclear patterns that persist through all stages of interphase. In mitotic chromosomes early replicating chromatin domains give rise to Giemsa light bands, whereas middle-to-late-replicating domains form Giemsa dark bands and C-bands. In an attempt to integrate these experimental data into a unified view of the functional nuclear architecture, we present a model of a modular and dynamic chromosome territory (CT) organization. We propose that basically three nuclear compartments exist, an "open" higher-order c