Intermolecular forces which govern self-organization of biological and synthetic nanostructures. Thermodynamic aspects of strong (covalent and coulomb interactions) and weak forces (dipolar, hydrogen bonding). Self-assembling systems: micelles, bilayers, and biological membranes. Computer simulations for ôhands-onö experience with nanostructures.

Materials for biomedical applications; synthetic polymers, metals and composite materials as biomaterials; biopolymers, dendrimers, hydrogels, polyelectrolytes, drug delivery systems, implants, tissue grafts, dental materials, ophthalmic materials, surgical materials, imaging materials.

Bioengineering, engineering materials, medicine, polymers, metals, smart materials, surgical implants, surgical instruments, cell and tissue mechanics, microsurgery, self-expanding stents, physical therapy, phase transformations, shape memory alloys.

A graduate level introduction to matrix-based computing. Stable and efficient algorithms for linear equations, least squares and eigenvalue problems. Both direct and iterative methods are considered and MATLAB is used as a computing environment.

Development and analysis of numerical methods for ODEs, an introduction to numerical optimization methods, and an introduction to random numbers and Monte Carlo simulations. The course starts with a short survey of numerical methods for ODEs. The related topics include stability, consistency, convergence and the issue of stiffness. Then it moves to computational techniques for optimization problems arising in science and engineering. Finally, it discusses random numbers and Monte Carlo simulations. The course combines the theory and applications (such as programming in MATLAB) with the emphasis on algorithms and their mathematical analysis.

The advanced methodology used for modern biological science research. Topics include the interpretation of data gained from both hypothesis-driven and high-throughput experiments from research articles focusing on DNA repair, DNA replication, transcription, cell cycle, organelle biogenesis, proteomics and genetics.

This course will discuss the applications of mechanics to biological systems. We will cover the basic principles of mechanics (force-moment, stress-strain, work, energy, rigid body dynamics), analysis of human movement, musculoskeletal mechanics, tissue mechanics, motor control system, sports biomechanics, and rehabilitation engineering.

Modeling, simulation and identification of physical systems. Instrumentation. Sensors and transduscers. Hardware components. Pneumatic, hydraulic, mechanical and electrical actuators. Programmable logic controllers (PLC). Signals, systems, and controls. Real time interfaceing and programing. Microprocessor-based electro-mechanical control applications and projects for factory automation, manufacturing and machine systems.

Bioengineering, engineering materials, medicine, polymers, metals, smart materials, surgical implants, surgical instruments, cell and tissue mechanics, microsurgery, self-expanding stents, physical therapy, phase transformations, shape memory alloys.

Provides hands-on teaching experience to graduate students in undergraduate courses. Reinforces students' understanding of basic concepts and allows them to communicate and apply their knowledge of the subject matter.

Classical thermodynamics: enthalpy, entropy, free energies, equilibria; introduction to statistical thermodynamics to describe the properties of materials; kinetic processes; diffusion of mass, heat, energy; fundamentals of rate processes in materials, kinetics of transformations.

The principles and computational methods to study the biological data generated by genome sequencing, gene expressions, protein profiles, and metabolic fluxes. Application of arithmetic, algebraic, graph, pattern matching, sorting and searching algorithms and statistical tools to genome analysis. Applications of Bioinformatics to metabolic engineering, drug design, and biotechnology.

Key aspects of microbial physiology; exploring the versatility of microorganisms and their diverse metabolic activities and products; industrial microorganisms and the technology required for large-scale cultivation.

Recombinant DNA, enzymes and other biomolecules. Molecular genetics. Commercial use of microorganisms. Cellular reactors; bioseparation techniques. Transgenic systems. Gene therapy. Biotechnology applications in environmental, agricultural and pharmaceutical problems.

Reconstruction of metabolic network from genome information and its structural and functional analysis, computational models of biochemical reaction networks; system biology in drug discovery and proteomics, flux balance analysis; modeling of gene expression; system biology in artificial intelligence. These concepts will be supported with statistic, thermodynamic, structural biology and learning machine

Topics will be announced when offered.

The fundamentals of tissue engineering at the molecular and cellular level; techniques in tissue engineering; problems and solution in tissue engineering; transplantation of tissues in biomedicine using sophisticated equipments and materials; investigation of methods for the preparation of component of cell, effect of growth factors on tissues.

Principles of molecular modeling in chemical engineering applications; fundamentals for molecular simulation of adsorption and diffusion processes in nanoporous materials; molecular dynamics methods for gas transport in nanopores; Monte Carlo methods for equilibrium based gas separations; molecular modeling of zeolites and metal organic frameworks for gas storage.

An introduction to interactive Python and Jupyter Notebooks, Python built-in data structures, conditional statements, loops, functions, strings and basic input/output, basics of data manipulation and visualization with relevant Python libraries, different types of plots, vector/matrix representations, linear algebra operations, probability/statistics operations, data analysis applications

Relationship between structure, function and dynamics in biomolecules. Overview of the biomolecular databases and application of computational methods to understand molecular interactions; networks. Principles of computational modeling and molecular dynamics of biological systems.

Entropy, Relative Entropy and Mutual Information; Asymptotic Equipartition Theory; Entropy Rates of a Stochastic Process; Data Compression; Kolmogorov Complexity; Channel Capacity; Differential Entropy; The Gaussian Channel; Maximum Entropy and Spectral Estimation; Rate Distortion Theory, Network Information Theory.

Fundamental concepts of concurrency, non-determinism, atomicity, race-conditions, synchronization, mutual exclusion. Overview of parallel architectures, multicores, distributed memory. Parallel programming models and languages, multithreaded, message passing, data driven, and data parallel programming. Design of parallel programs, decomposition, granularity, locality, communication, load balancing. Patterns for parallel programming, structural, computational, algorithm strategy, concurrent execution patterns. Performance modeling of parallel programs, sources of parallel overheads.