Tissue engineering, bio- and nano-materials, prosthetics, biomechanics, genetics, clinical trials, ethics

Columbian law, electrostatic field, potential, electric flux and Gauss's law. Vectors and concepts of electromagnetic, electrostatic, magnetostatic field, electromagnetic fields in different materials; Maxvel equations and Lorentz force law as integral and derivative for time-changing fields; potential functions; energy accumulation, stationary and quasi-stationary areas; Solutions of Poisson and Laplace equations in spherical and cylindrical coordinate systems; dielectric material and shelter concept. Stationary electrical energy and forces. Regular electric currents, Ohm and Kirchhoff laws, electric charge avoidance and continuity equation. Joule's law. BiotSavart law and stationary magnetic field. Ampere's law and vector magnetic potential. Magnetic materials, forces and cycles. Faraday laws, magnetic energy, displacement currents and Maxwell equations. Lumpy and distributed circuits. Reflection, transmission, weakening, dispersion and spreading events in transmission lines.

The course covers Bioelectronic devices, Potentiometric, amperometric, Optical, Fluorescent, colorimetric and other biosensor systems, Enzyme electrodes, substrate electrodes, immunosensors, aptamer sensors, new approaches in biosensors.

The course covers biomedical data, standards in medical informatics, electronic record systems, health organisations, patient care systems

The course covers terminology used in bioinformatics, introduction to online and offline tools and databases, basic analysis techniques of biological sequences, comparison methods.

The course covers algorithms used for sequence alignment, motif discovery in DNA and protein sequences, clustering protein networks, protein complex discovery using interaction networks, phylogenetic tree construction and next generation sequencing.

This course covers binding interactions and forces determining the biomolecules/biopolymers, structural-functional and dynamical features of biomolecules (nucleic acids, proteins, lipids etc.), enzyme mechanisms, cell membrane dynamics, membrane proteins, transport, physics of neurons, thermodynamics, bioenergetics, radiation processes, biomedical spectroscopy, infrared spectroscopy, and novel approaches in nanobiomedicine and biomedicine.

This course explains the time and frequency time domain analysis of random biological signals, the concepts of time and group mean and high degree moment, modeling of random signals, the concept of estimation, filtering, stationary random processes.

Principles of acquisition, storage, visualization, and processing of medical image data. Sampling and quantization. Picture archiving and communication systems, Medical image formats. Basic and advanced image processing algorithms.

The course contains lectures and practical case studies on various light sources as well as optical elements commonly used in bimedical engineering field.

The course covers the definition and history of biomechanics and robotics, biologic and robotic actuators, sensor systems, actuator control, principles of locomotion, evolution of robotics, use of robotics in modern medicine.

This course includes introduction to artificial organs, the importance of mass transfer in these systems, basic reactions in biological systems and knowledge of biomaterials, basic principles and applications of artificial organ design.

Introduction to polymer science, properties of polymers, polymerisation techniques, characterisation of polymers, biomedical applications of polymers, Biopolymer processing, drug delivery systems, biodegredation and biodeterioration, chemical synthesis of biopolymers, synthesis of natural biopolymers, and future of biomedical applications of polymers.

This course covers basic knowledge in design tools and development methods, test and analysis, regulations for manufacturing and documentation methods, in a perspective of application in industry.

Radioactivity, types of radioactive decay, half-life calculations, x-ray sources, radiation measurement, dosage calculations, radiation safety equipment and gamma cameras are among the topics to be covered within the discipline

The course covers structure of bionanoparticles and properties of bionanotechnology, application areas of this technology, processing and trends.

This course includes the importance of characterization and usage areas of different characterization methods, points to be considered in characterization and current studies on the subject.

The course covers the definition and history of polymerase chain reaction (PCR) method and how this method evolved through the years. The course covers principles of modern PCR methods and basic application of a PCR protocol starting from sample preparation to evaluation of the results obtained from PCR instruments. The course covers the introduction to common genetic and communicable diseases and PCR applications for their diagnosis in modern medicine.

This course includes the basic concepts and types of biostatistics, the classification of data, the selection of the appropriate statistical analysis for the problem, the application of the test after the hypothesis, and the evaluation of the results.

This course covers an introduction and modeling of random processes that generates random biological signals, stationary processes, linear optimum (Wiener) filtering, linear adaptive filtering, steepest descent, LMS and RLS learning algorithms and Kalman filter theory.

This course covers the implementation of digital signal processing methods and algorithms using MATLAB software in computer environment. It covers simulations of discrete time signals and systems, frequency analysis, system analysis and implementation, digital filter designs and biomedical engineering applications.

The course covers basic definitions and principles, design characteristics, manufacturing technologies for microfluidic and micro-total-analytic-system (µTAS) structures, micro electrical, micro mechanical and micro optical sensors, MEMS, bioMEMS and their applications for biomedical purposes including Lab-on-a-Chip analysis, implantable sensors and drug delivery.

The course covers recommendations of World Health Organization, framework policies and resolutions and regulations about medical devices in Turkey and globally; also the principles of Medical Device Directive, quality management system for medical devices and similar legal regulations and directives for medical device manufacturers and importers.

Learning and adoption, Bayesian decision theory, discriminant functions, parametric techniques, maximum likelihood estimation, Bayesian estimation, sufficient statistics, non-parametric techniques, linear discriminants, algorithm independent machine learning, classifiers, unsupervised learning, clustering.

This course discusses various aspects of the most important component of a computer, the microprocessors. The topics include the fundamental concepts of microprocessors and the relationship between assembler and basic components of a computer, 80x86 family architecture, 80x86 based assembly language programming, computer organization and architecture of the PC.

Course discusses several advanced techniques such as training data collection, learning in order to extract statistical structure from data, over-fitting, parametric models and parameter selection, validation, regression, classification, nonparametric models, clustering.

Machine learning is concerned with computer programs that automatically improve their performance with past experiences. Machine learning draws inspiration from many fields, artificial intelligence, statistics, information theory, biology and control theory. The course will cover the following topics;concept learning,decision tree learning ,artificial neural networks , instance based learning,evolutionary algorithms ,reinforcement learning ,Bayesian learning , computational learning theory.

The following topics will be included: sampling and information theory, digital filters and discrete Fourier transform, basics of vector and matrix manipulations, basics of numerical optimization, principles of statistical learning theory.

The following topics will be included: feed-forward neural networks, back-propagation, convolutional neural networks, recurrent neural networks, recursive neural networks, regularization, optimization.

The following topics will be included: image formation, image processing, feature detection and matching, segmentation, feature-based alignment, structure from motion, dense motion estimation, image stitching, computational photography, stereo correspondence, 3D reconstructions, image-based rendering, and recognition.

The following topics will be included in the course: The main neural network architectures and learning algorithms, perceptrons and the LMS algorithm, back propagation learning, radial basis function networks, support vector machines, Kohonen’s self organizing feature maps, Hopfield networks, artificial neural networks for signal processing, pattern recognition and control.

The following topics are included: Digital images as two-dimensional signals; two-dimensional convolution, Fourier transform, and discrete cosine transform; Image processing basics; Image enhancement; Image restoration; Image coding and compression.

This course includes subject areas such as analysis and synthesis of continuous and sample data linear feedback control systems. properties and advantages of feedback systems, time-domain and frequency-domain performance measures, stability and degree of stability, the Nyquist criterion, frequency-domain control system design, the Root-locus method and application to a wide variety of physical systems.

Topics covered in class mainly include principles and applications of digital signal processing. Representation, analysis, and design of digital signals and systems. Discretetime processing of continuoustime signals. Frequency domain representations: Fourier series and transforms. Decimation, interpolation, and sampling rate conversion. Time and frequencydomain design techniques for recursive (IIR) and nonrecursive (FIR) filters. Discrete Fourier transform (DFT) and fast Fourier transform (FFT). Shorttime Fourier analysis and filter banks.

This course covers the introduction of 8 and 32 bit microcontrollers and their peripherals, registers, serial communication with other microcontrollers and main computer; design of a microcontroller based device; design of printed circuit board; coding and uploading the firmware and all steps including the testing of device for the application.

Solutions of system of linear equations, iterative methods, interpolation, cubic splines, numerical differentiation, numerical integration, numerical solution of nonlinear equations, initial value problems, numerical solution of ordinary differential equations, finite difference method, engineering application problems.

In this course, the following topics will be covered, with a special focus on practical applications: the importance of optimization, basic definition and facts on optimization problems, theory of linear programming, nonlinear programming (constrained and unconstrained optimization problems), numerical methods for constrained and unconstrained problems, numerical solution of partial differential(elliptic and parabolic) equations.

The internal organization of the eukaryotic cell, organelle and membrane function, cell-cell signaling, cell transport and secretion, the extracellular matrix, cell division and apoptosis will be introduced. Also, stem cells, tissue regeneration and repair and iPSCs technology will also be explained.

This course covers the basic concepts of biotechnology and the methods used in the manipulation of nucleic acids (DNA and RNA). Bioethical issues relating to this new technology will also be discussed.

Embryonic stem cells, Haematopoietic stem cells, Adult stem cells, tissue engineering, cell therapy, drug discovery.

This course covers the description of molecular mechanisms of cell death including apoptosis, autophagy, necrosis, necroptosis, lysosome-dependent cell death, entosis, anoikis, ferroptosis, pyroptosis, anastasis, mitotic death and immunogenic cell death.

The course will explore the ethics in science and research with an emphasis on biological sciences. The students will become familiar with common ethical debates in science including the topics listed as: misconduct in research, conflicts of interest and scientific objectivity, publication and peer review, intellectual property, ethical decision-making, dual-use of research, principles of ethics in science for model organisms and human beings.

This course covers the formation of microbial biofilms, the structure and functions of the extracellular polymeric substance synthesized in biofilm formation, environmental signals and intracellular and intercellular signaling networks regulating the biofilm formation and dispersion processes, and biofouling on surfaces, chronic infections and implant-associated infections caused by biofilms. In addition, this course also provides information on the current strategies developed to combat biofilms.

Basic principles of cell signaling. Characterization of signalling components: signalling molecules, receptors, second messengers, effectors, signalling complexes. Basic classification and characterization of membrane receptors. Intracellular/nuclear receptors. Major signalling pathways.

This course will include discussion of the properties of innate and adaptive immunity, the cells of the immune system. Additionally, students will learn about antibody structure and function, antigen recognition, lymphocyte activation, and immunity to microbes. Immunity that relates to diseases will also be covered including autoimmunity, transplantation, immunity to viruses and tumor immunology.

The mechanisms of vertebrate regeneration, animal models and techniques used in regenerative therapies will be introduced.

Cell culture, Sterile conditions, Primary cell culture, Cell lines, Culture of stem cells and tumor cells, Three-dimensional cell culture

This course explains the gene control mechanisms that affect development and deteriorate in disease. The course covers epigenetic markers and related techniques to identify differences in gene regulation.

Definition and application areas of genomics and proteomics approaches, structural genomics, functional genomics and usage of comparative genomic, analysis of genomes of livings, proteomic analyses. Recombinant DNA methods, genetically modified plants and animals and their use will be introduced.

This course covers the description of fundamentals of gene therapy, viral and non-viral gene delivery techniques, and use of gene therapy as a treatment strategy.

This course describes the managerial process of pharmaceutical product development with examples and case studies by highlighting the technical and regulatory challenges.

HFE is the part of engineering most closely concerned with humans. HFE is also called Ergonomics. HFE deals with the capabilities and limitations of human beings as they relate to the design, improvement, and operation of equipment, tools, machinery, computers, automobiles, airplanes, working and living environment, organizational structures, communication systems, etc.

This course covers topics related with IE372 and ISE 380

Biological applications of difference and differential equations. Biological applications of nonlinear differential equations. Biological applications of graph theaory.

This course provides several basic methods for analyzing statistical data appear in various fields of science.

The main topics included in this course are elements of interface mechanics-electronics (sensors and actuators), circuits for supplying actuators, circuits for conditioning signals from sensors, physical values and role of sensors and actuators in measurement.

This course covers the fundamental concepts of fluid mechanics, properties of fluids, hydrostatic pressure force on plane and curved surfaces, pressure changes in fluid movement, the Bernoulli's equation, momentum, mass and energy balances, dimensional analysis, viscous flow in pipes, laminar and turbulent flows, and major and minor losses.

Within this course, the key elements of scientific thinking and key points for writing a scientific project along with ethical issues in research will be covered in detail within the scope of Biomedical Research. The key elements of scientific thinking, will cover observation, asking critical questions, developing a testable hypothesis, collecting and analysing data and making a logical conclusion. The key points for writing a scientific project will cover the title, aim, literature review, project uniqueness, methods, project and risk management, common domains and reporting the findings. Ethical issues will also be covered within the scope of the course.

This course provides an introduction to Artificial Intelligence (AI). In this course we will study a number of theories, mathematical formalisms, and algorithms, that capture some of the core elements of computational intelligence. We will cover some of the following topics: search, logical representations and reasoning, automated planning, representing and reasoning with uncertainty, decision making under uncertainty, and learning.