Boban Stojanovic

Computer Modeling in Bioengineering: Theoretical Background, Examples and Software

Contributors. Preface. Part I: Theoretical Background of Computational Methods. 1. Notation - Matrices and Tensors. 2. Fundamentals of Continuum Mechanics. 3. Heat Transfer, Diffusion, Fluid Mechanics, and Fluid Flow through Porous Deformable Media. Part II: Fundamentals of Computational Methods. 4. Isoparametric Formulation of Finite Elements. 5. Dynamic Finite Element Analysis. 6. Introduction to Nonlinear Finite Element Analysis. 7. Finite Element Modeling of Field Problems. 8. Discrete Particle Methods for Modeling of Solids and Fluids. Part III: Computational Methods in Bioengineering. 9. Introduction to Bioengineering. 10. Bone Modeling. 11. Biological Soft Tissue. 12. Skeletal Muscles. 13. Blood Flow and Blood Vessels. 14. Modeling Mass Transport and Thrombosis in Arteries. 15. Cartilage Mechanics. 16. Cell Mechanics. 17. Extracellular Mechanotransduction: Modeling Ligand Concentration Dynamics in the Lateral Intercellular Space of Compressed Airway Epithelial Cells. 18. Spider Silk: Modeling Solvent Removal during Synthetic and Nephila clavipes Fiber Spinning. 19. Modeling in Cancer Nanotechnology. Index.

Adaptive system for dam behavior modeling based on linear regression and genetic algorithms

Most of the existing methods for dam behavior modeling require a persistent set of input parameters. In real-world applications, failures of the measuring equipment can lead to a situation in which a selected model becomes unusable because of the volatility of the independent variables set. This paper presents an adaptive system for dam behavior modeling that is based on a multiple linear regression (MLR) model and is optimized for given conditions using genetic algorithms (GA). Throughout an evolutionary process, the system performs real-time adjustment of regressors in the MLR model according to currently active sensors. The performance of the proposed system has been evaluated in a case study of modeling the Bocac dam (at the Vrbas River located in the Republic of Srpska), whereby an MLR model of the dam displacements has been optimized for periods when the sensors were malfunctioning. Results of the analysis have shown that, under real-world circumstances, the proposed methodology outperforms traditional regression approaches.

Derivation of a finite-element model of lingual deformation during swallowing from the mechanics of mesoscale myofiber tracts obtained by MRI

To demonstrate the relationship between lingual myoarchitecture and mechanics during swallowing, we performed a finite-element (FE) simulation of lingual deformation employing mesh aligned with the vector coordinates of myofiber tracts obtained by diffusion tensor imaging with tractography in humans. Material properties of individual elements were depicted in terms of Hills three-component phenomenological model, assuming that the FE mesh was composed of anisotropic muscle and isotropic connective tissue. Moreover, the mechanical model accounted for elastic constraints by passive and active elements from the superior and inferior directions and the effect of out-of-plane muscles and connective tissue. Passive bolus effects were negligible. Myofiber tract activation was simulated over 500 ms in 1-ms steps following lingual tip association with the hard palate and incorporated specifically the accommodative and propulsive phases of the swallow. Examining the displacement field, active and passive muscle stress, elemental stretch, and strain rate relative to changes of global shape, we demonstrate that lingual reconfiguration during these swallow phases is characterized by (in sequence) the following: 1) lingual tip elevation and shortening in the anterior-posterior direction; 2) inferior displacement related to hyoglossus contraction at its inferior-most position; and 3) dominant clockwise rotation related to regional contraction of the genioglossus and contraction of the hyoglossus following anterior displacement. These simulations demonstrate that lingual deformation during the indicated phases of swallowing requires temporally patterned activation of intrinsic and extrinsic muscles and delineate a method to ascertain the mechanics of normal and pathological swallowing.

Pneumatic osteoarthritis knee brace.

Knee osteoarthritis is a chronic disease that necessitates long term therapeutic intervention. Biomechanical studies have demonstrated an improvement in the external adduction moment with application of a valgus knee brace. Despite being both efficacious and safe, due to their rigid frame and bulkiness, current designs of knee braces create discomfort and difficulties to patients during prolonged periods of application. Here we propose a novel design of a light osteoarthritis knee brace, which is made of soft conforming materials. Our design relies on a pneumatic leverage system, which, when pressurized, reduces the excessive loads predominantly affecting the medial compartment of the knee and eventually reverses the malalignment. Using a finite-element analysis, we show that with a moderate level of applied pressure, this pneumatic brace can, in theory, counterbalance a greater fraction of external adduction moment than the currently existing braces.

Three-dimensional stochastic model of actin–myosin binding in the sarcomere lattice

Models of cellular contraction, for example, in striated muscle, usually involve mass action kinetics. Mijailovich et al. implement spatially explicit actomyosin interactions in the Monte Carlo platform MUSICO and show the extent to which myosin tethering affects other biological parameters.

A self-tuning system for dam behavior modeling based on evolving artificial neural networks

We proposed a self-tuning system for a dam behavior modeling.The system performs near real-time generation of the optimized ANN dam model.Optimized model is adapted to currently available measurements and input parameters.The system is based on artificial neural networks and genetic algorithm.Case study showed advantages and disadvantages of this system compared to MLR/GA. Most of the existing methods for dam behavior modeling presuppose temporal immutability of the modeled structure and require a persistent set of input parameters. In real-world applications, permanent structural changes and failures of measuring equipment can lead to a situation in which a selected model becomes unusable. Hence, the development of a system capable to automatically generate the most adequate dam model for a given situation is a necessity. In this paper, we present a self-tuning system for dam behavior modeling based on artificial neural networks (ANN) optimized for given conditions using genetic algorithms (GA). Throughout an evolutionary process, the system performs near real-time adjustment of ANN architecture according to currently active sensors and a present measurement dataset. The model was validated using the Grancarevo dam case study (at the Trebisnjica river located in the Republic of Srpska), where radial displacements of a point inside the dam structure have been modeled as a function of headwater, temperature, and ageing. The performance of the system was compared to the performance of an equivalent hybrid model based on multiple linear regression (MLR) and GA. The results of the analysis have shown that the ANN/GA hybrid can give rather better accuracy compared to the MLR/GA hybrid. On the other hand, the ANN/GA has shown higher computational demands and noticeable sensitivity to the temperature phase offset present at different geographical locations.

Elastic grid resource provisioning with WoBinGO

In this paper, we present the WoBinGO (Work Binder Genetic algorithm based Optimization) framework for solving optimization problems over a Grid. It overcomes the shortcomings of earlier static pilot-job frameworks, by: (1) providing elastic resource provisioning thus avoiding unnecessary occupation of Grid resources; (2) providing friendliness towards other batching queue users thanks to adaptive allocation of jobs with limited lifetime. It hides the complexity of the underlying Grid environment, allowing the users to concentrate on the optimization problems. Theoretical analysis of possible speed-up is presented. An empirical study using an artificial problem, as well as a real-world calibration problem of a leakage model at the Visegrad power plant were performed. The obtained results show that despite WoBinGOs adaptive and frugal allocation of computing resources, it provides significant speed-up when dealing with problems that have computationally expensive evaluations. Moreover, the benchmarks were performed in order to estimate the influence of the limited job lifetime feature on the queuing time of other batching jobs, compared to a static pilot-job infrastructure. Framework for optimization using parallel GA over Grid and HPC resources.Provides elastic resource provisioning avoiding unnecessary occupation of resources.Automatic adaptive allocation of jobs with limited lifetime.Limited job lifetime provides friendliness towards other batching queue users.The complexity of underlying Grid infrastructure is hidden from the user.

Computer modeling of high-pressure leaching process of nickel laterite by design of experiments and neural networks

Due to the complex chemical composition of nickel ores, the requests for the decrease of production costs, and the increase of nickel extraction in the existing depletion of high-grade sulfide ores around the world, computer modeling of nickel ore leaching process became a need and a challenge. In this paper, the design of experiments (DOE) theory was used to determine the optimal experimental design plan matrix based on the D optimality criterion. In the high-pressure sulfuric acid leaching (HPSAL) process for nickel laterite in “Rudjinci” ore in Serbia, the temperature, the sulfuric acid to ore ratio, the stirring speed, and the leaching time as the predictor variables, and the degree of nickel extraction as the response have been considered. To model the process, the multiple linear regression (MLR) and response surface method (RSM), together with the two-level and four-factor full factorial central composite design (CCD) plan, were used. The proposed regression models have not been proven adequate. Therefore, the artificial neural network (ANN) approach with the same experimental plan was used in order to reduce operational costs, give a better modeling accuracy, and provide a more successful process optimization. The model is based on the multi-layer neural networks with the back-propagation (BP) learning algorithm and the bipolar sigmoid activation function.

Coupling finite element and huxley models in multiscale muscle modeling

In this paper we present a novel approach in multi-scale muscle modeling based on finite element method and Huxley crossbridge kinetics model. In order to determine the mechanical response of a muscle, we implement basic mechanical principles of motion of deformable bodies using finite element method. Constitutive properties of muscle are defined by the number of molecular interconnections between the myosin and actin filaments. To account for these effects, we used Huxleys micro model based on sliding filament theory to calculate muscle active forces and instantaneous stiffnesses in FE integration points. In order to run these computationally expensive simulations we have also developed a special parallelization strategy which gives speedup of two orders of magnitude. Results obtained using presented multi-scale model are compared to those obtained by Hills phenomenological model.

Multiscale model predictions of X-ray diffraction patterns from nonuniformly stretched actin filaments

In order to explain time-resolved X-ray diffraction data from striated muscle we explored the feasibility of using dynamic 3D models of muscle contraction to predict X-ray diffraction patterns. This approach differs radically from previous attempts, which merely aimed to provide a “best fit” structure for defined quasi-static states, by providing a tool to generate families of structures that evolve in time that explains both the structural (X-ray) and the mechanical data simultaneously. Specifically, we exploit the computational platform MUSICO (Muscle Simulation Code), which was developed originally to model muscle mechanics data, by extending this framework to simulate X-ray diffraction patterns using 3D multiscale models. The platform is conceived primarily as a hypothesis-testing tool in which model predictions are tested against the best available mechanical and X-ray diffraction data on the same system. Our preliminary simulations provided dynamic X-ray diffraction patterns during force development and relaxation in skeletal muscle. The simulated patterns generally predicted well the changes in repetitive molecular spacings and were otherwise similar to the experimental data. Once fully developed, this tool will enable extraction of maximum information from the X-ray patterns, in combination with the physiological data, and therefore provide a template to test hypotheses concerning crossbridge and regulatory protein action in working muscle. Our approach can be extended to any muscle system, and it could ultimately provide an interpretive framework for studying the mechanisms of inherited or acquired diseases.