Programme Structure – old

• Standard MSc study program: This is the standard programme of two semesters of courses counting jointly 30 ECTS per semester, and one (the last) semester devoted to the diplom thesis, counting for 30 ECTS, having in toto 90 ECTS.
• MSc by research study program: This program differs from the standard MSc study program in the second semester. Instead of having 30 ECTS from courses, only 10 ECTS are from courses and the rest 20 ECTS is for a research thesis, which continues in the third semester and counting totally for 50 ECTS, having in toto 90 ECTS.

For both programs, there are 6 core (compulsory) courses in the first semester and 2 core courses in the second semester. The rest are elective courses to be selected in the standard MSc study program, whereas in the MSc by research study program the research thesis replaces the elective courses of 20 ECTS.

In the first semester, there are two core courses for building and developing the necessary background as students have diverse education at graduate level. These two courses are organized for two groups of students and are adapted accordingly, namely for students of life science education and students of science / engineering education.

The courses, given by the sign ‘C’ for core courses and ‘E’ for elective courses are listed below.

Systems Biology involves the mathematical and computational modeling of complex biological systems. The field is the result of convergence and synergy of three scientific areas: 1) Rapid accumulation of detailed biological data at the submolecular, molecular, cellular and physiological levels, 2) Technological development that permits analysis of biological systems in vivo using sensors, imaging techniques, and biomarker expression profiles, 3) Combined evolution of mathematical, physical and computational techniques that are more powerful and available to most of the scientific community than ever before. It represents an interdisciplinary scientific field that focuses on complex interactions within biological systems using a holistic approach to biological research. The contents of the course include: 1) description of complex biological systems, 2) mathematical modeling of biological systems, 3) stochastic processes in Biology (with emphasis on the simulation and analysis of stochastic phenomena in biological systems), 4) static network models, 5) cellular response models, gene and protein systems/networks, 6) metabolic systems  structural analysis of metabolic networks, 8) dynamic analysis of metabolic network flows, 9) analysis of signaling pathways/networks, 10) population systems, 13) systems biology in medicine/physiology and drug development, 14) systems biology in personalized medicine approaches prevention and therapy, 15) new horizons in systems biology, 16) from neurons to the brain 17) multi-step models of carcinogenesis, 18 ) multifactorial diseases, inflammation and trauma, 19) interactions between environment and health. The course will be adapted to the background of the life sciences students. Students will have the opportunity to work in small groups with students who attend course 1b (project).

Systems Biology involves the mathematical and computational modeling of complex biological systems. The field is the result of convergence and synergy of three scientific areas: 1) Rapid accumulation of detailed biological data at the submolecular, molecular, cellular and physiological levels, 2) Technological development that permits analysis of biological systems in vivo using sensors, imaging techniques, and biomarker expression profiles, 3) Combined evolution of mathematical, physical and computational techniques that are more powerful and available to most of the scientific community than ever before. It represents an interdisciplinary scientific field that focuses on complex interactions within biological systems using a holistic approach to biological research. The contents of the course include: 1) description of complex biological systems, 2) mathematical modeling of biological systems, 3) stochastic processes in Biology (with emphasis on the simulation and analysis of stochastic phenomena in biological systems), 4) static network models, 5) cellular response models, gene and protein systems/networks, 6) metabolic systems  structural analysis of metabolic networks, 8) dynamic analysis of metabolic network flows, 9) analysis of signaling pathways/networks, 10) population systems, 13) systems biology in medicine/physiology and drug development, 14) systems biology in personalized medicine approaches prevention and therapy, 15) new horizons in systems biology, 16) from neurons to the brain 17) multi-step models of carcinogenesis, 18 ) multifactorial diseases, inflammation and trauma, 19) interactions between environment and health. The course will be adapted to the background of the students of sciences or engineering. Students will have the opportunity to work in small groups with students who attend course 1a (project).

The contents of the course are cell physiology, autonomous nervous system, neurophysiology, cardiovascular and respiratory physiology, kidney physiology, gastrointestinal physiology, endocrine system physiology and reproductive system physiology. In addition to the physiology issues covered, issues regarding modeling systems will be raised, presenting: a) in-silico modeling approaches, and b) methods and indicators for quantifying the operation of systems. Applications will be presented and students’ hands-on experience with in-silico approaches will be sought. The course will be adapted to the background of the life sciences students. In parallel with the course, students will have the opportunity to work in small groups with students who attend course 2b (project).

The contents of the course are cell physiology, autonomous nervous system, neurophysiology, cardiovascular and respiratory physiology, kidney physiology, gastrointestinal physiology, endocrine system physiology and reproductive system physiology. In addition to the physiology issues covered, issues regarding modeling systems will be raised, presenting: a) in-silico modeling approaches, and b) methods and indicators for quantifying the operation of systems. Applications will be presented and students’ hands-on experience with in-silico approaches will be sought. The course will be adapted to the background of the students of sciences or engineering. In parallel with the course, students will have the opportunity to work in small groups with students who attend course 2a (project).

The course has two objectives: I. Overview of natural biological materials and substitute biomaterials. It includes, but is not limited to: categories of materials, methods of study and characterization, physical, chemical and biological properties, biomaterial interactions with physical structures of the body, principles of biomaterial design, uses in biological and medical applications, case studies. II. Introduction to biomaterial mechanical properties. It includes but is not limited to: general principles of engineering, ways of study, modelling, understanding of the action of mechanical forces at the molecular and cellular level, as well as the level of tissues, organs as well as the whole organism, mechanical properties of biological materials and biomaterials, association of mechanical biology and disease treatment, histomorphology, and mechanical design and orthopedic movement. The course is based on a combination of theory and corresponding laboratory practice.

The scope of the course is to introduce the basic principles of digital signal processing and system modelling as practiced in biomedical research and clinical medicine. It covers methodologies and algorithms for the registration and visualization of biosignals, the use of filters and transforms (Fourier, wavelet, PCA), the coding of biomedical data, nonlinear analysis, feature extraction and biomedical systems modelling. It focuses on understanding the theoretical foundation of various biomedical signal processing techniques, as well as their practical advantages and limitations for the purpose of identifying the most promising approach according to the problem at hand. In addition, the implementation of selected signal processing algorithms will be demonstrated in specific tasks that concern real-life biosignals and biomedical systems. The course includes programming projects based on signals from e.g. cardiology, neurology and medical imaging.

The aim of this course is to introduce students to the concept of biomedical technology products and medical devices in specific and emphasise the different prerequisites/stages from their idea conception and design to their use and exploitation in healthcare. The course covers the basic principles of design systems / technology and biomedical research, the basic principles that technological systems must meet in all stages of health care; thus, the course covers elements of specifications and compliance to guidelines and recommendations for equipment procurement, maintenance and equipment management in general (small and large scale), as well as, elements of security, risk management, quality control/assurance. Principles and methodologies of healthcare technology assessment and evaluation are also covered together with the fundamental role they play in decision-making and health policy practice.

The course comprises a series of seminars given by the affiliated lecturers and invited lecturers on timely topics of biomedical engineering. Students are required to deliver a specific report in a topic relevant to the topics of the seminar.

This course is intended to introduce students to basic physics principles pertaining to medical image formation and image processing. The course covers image acquisition by means of ionizing and non-ionizing radiation methods as well as the utilization of magnetic resonance methods, other optical-based and spectroscopic methods but also other newer methods used for imaging living organisms. The course also addresses issues and topics on image processing by means of modern mathematical and algorithmic methodologies.

The aim of the course is to provide students with the basic principles of scientific methodology and research in the field of biomedical engineering through the use of contemporary examples. Τhe course material covers a wide range of methodological approaches in the field of biomedical engineering. Starting with the design, use and control of medical devices and biomedical products, the design and conduct of pilot tests, data collection and analysis, the writing of scientific and technical reports and the organization of “lab-to-market” procedures. In addition, related bioethics issues and concerns are also raised and discussed, along with the concept of innovation and entrepreneurship and the concept of regulatory mechanisms, standardization and patent submission.

The course introduces the field of biomedical informatics and topics of medical technology. In particular, it focuses on the use of information and communication technology (ICT) on application areas, such as the health care, preventive care, care for the elderly and home. On the practical side, the topics of the course cover processing of physiological signals and development of software systems, wireless sensors and applications to smart phones in health. Tools of signal processing and machine learning are combined with artificial intelligence systems in applications in medical engineering and life and health care. The course extends also to the introduction to molecular and nanoscale communications: a) nanomachines and nanonetworks, b) communication by molecular diffusion, c) applications of these. 

The scope of the course is to introduce the concepts of (a) decision support systems and the basic related methodologies (expert systems, fuzzy systems, learning systems) and (b) automated medical diagnosis. The use of these methodologies will be presented in the context of clinical practice (risk assessment, stratification, medical prognosis, care pathway along with the strategies adopted for their evaluation. The contents of the course include: 1) decision making and optimization 2) knowledge-based decision systems, 3) expert/fuzzy decision making 4) data-learning systems. Specific medical examples will be included. The associated ethical issues will be covered along with the novel concepts of trustworthy and explainable AI as these apply to biomedicine.

The course aims at introducing the students in biofabrication, regenerative medicine and histomechanics. It includes, but is not limited to: I. production, on a small and large scale, of cells, biochemical agents, hybrid biomaterials, biocomposites, scaffolds, 3D printing, II. design and production of tissue substitutes, including soft and hard tissue histomechanics products, use of stem cells, development of 3D tissue models and real-time testing of histomechanical processes, legal issues, bioethics, and case studies. The course is based on a combination of theory and corresponding laboratory practice.

New technologies like Virtual Reality and Robotics currently play a major role in health care. Clinically Certified, powerful medical simulators are now available and used all over the world. Advanced general surgery and neurosurgery systems make use of augmented reality and image-guided surgery to improve outcomes and efficiency. Robotics have been used in orthopedics and cardiology, as well as, general practice. In recent years, medical robotics together with advanced extended reality systems are expected to shape the future of mental health, anesthetics, and emergency medicine. So, this course covers the basics aspects of medical robotics, virtual reality and cyber-physical systems and their contemporary applications in healthcare.

Τhe module aims at introducing students to the scientific field of nanoscience and nanotechnology in the context of their applications in medicine (nanomedicine). Indicatively it covers topics such as : types, properties and methods of manufacturing of nanoparticles; technological applications of nanoparticles : nanocoatings, nanospheres, nanomagnets, nanomedicine, nanowires, nanotubes, biochips and biosensors, nanodrug delivery, medical devices, biomimetics, minimally invasive cellular and tissue signal transduction, biomarkers, interactions of nanomaterials with cells and tissues, nanodiagnostics, nanotherapeutics. The course will be delivered as a combination of lectures and complementary hands-on learning.

The course aims to provide basic knowledge and relevant tools for understanding the basic and practical implications of medical precision, its opportunities and challenges as they arise for accurate diagnosis, treatment choices, genetic counseling, public health interventions and biomedical research. Given the use of sensitive personal data required in personalised medicine and precision prevention, bioethics and data science issues will be an integral part. The contents of the course include: 1) Genomic analysis and genetic counseling, 2) Integration of multiple -omics data (analysis of polymorphisms, gene expression profiles, toxicogenomics, proteomics, metabolomics, microbiome analysis). 3) Pharmacogenomics, 4) Cancer biomarkers, 5) Chronic disease risk assessment, 6) Understanding gene-environment interactions, 7) Basic concepts in modern pharmacology, including drug-drug interactions, personalized medicine and drug development, 8) Basic understanding of the unique factors of pathology and pharmacology that affect different population groups and the disease progression as well as, its utilization in precision medicine, 9) Description of the multidisciplinary nature precision medicine development and application of new tools, 10) Application of modern technologies in improving diagnosis, treatment, prevention of disease and the final outcome of patients, 11) Understand key determinants of individual drug responses, 12) Understand how genetics therapeutic outcomes, 13) The ‘exposome’ and its contribution to accurate diagnosis and therapeutic approaches, 14) The role of nutrition in precision prevention,  15) The role of the environmental exposure in precision prevention.

The scope of the course is to introduce the basic principles of computational neuroscience and familiarize the students with the associated research methodologies. This scientific area lies at the crossroad of neurophysiology/neuroanatomy from the side of medicine and machine learning / signal analysis from the side of information theory. The following topics are introduced in this course: a) from neurons to systems (recording, processing, analysis and modelling of neural signals), b) applications to cognitive and clinical neuroscience:  neuroimaging techniques and interpretation of the acquired data, c) brain activity: spectral analysis, nonlinear dynamics, independent component analysis, connectivity analysis, graph-theoretic description, e) examples of translational neuroscience: brain-computer interfaces, neurofeedback, transcranial brain stimulation, neuromimetic intelligence.

Huge advances in large-scale biology have led to achievements such as the sequence of the human genome. At the same time, gene expression research using RNA-seq, microarray platforms and other technologies, as well as the generation of big -omics data (genomics, transcriptomics, metabolomics) have created a wealth of data, the biological interpretation of which is an important tool in both precision and personalised medicine as well as prevention and therapeutic approaches. However, the challenge facing scientists is to analyze/combine and extract useful information regarding the biological system under study. Based on the above, the course focuses on familiarizing students with the use of available bioinformatics resources – mainly online programs and databases – to access the wealth of data and their correct biological interpretation, in order to address problems – questions. Course contents include: 1) sequence alignments, 2) phylogenetics, 3) analysis of gene expression data including information theory, 4) protein interaction networks, 5) interpretation of -omics data (analysis of polymorphisms, toxicogenomic, proteomic, metabolomic), 6) regulatory and metabolic networks, 7) metagenomics, 8) statistical methods in bioinformatics , 10) bioinformatics platforms (R  Bioconductor, GeneSpring), 11) the role of bioinformatics in systems biology and in the study of adverse outcomes.

The aim is to theoretically and experimentally acquaint the students with specialized knowledge and skills related to microscopy, lasers, nano-testing and reverse engineering. A detailed presentation of the various microscopy techniques will be carried out, such as optical microscopy of transmitted and reflected light, fluorescence microscopy, confocal microscopy, scanning electron microscopy (SEM) and transmission electron microscopy (TEM), lasers for characterisation, nano-tests such as nano-indentation and atomic force microscopy (AFM), as well as reverse engineering techniques for 3D geometry reproduction. Understanding their operating principles and using intelligent practices to obtain useful information about the materials-biomaterials, tissues and living organisms is a central objective of the course. In addition, the theory will be coupled with laboratory practice in the above techniques in order to acquire relevant practical experiences and skills in their use.

This multi-disciplinary course aims at introducing the students to the field of drug engineering.  The course will provide detailed knowledge on: basic engineering of bioresponsive materials for the design and implementation of drug delivery systems, development of SMART delivery processes linked to clinical applications, general principles and applications of lab-to-clinic nano/micro-technology transfer, principles of spray drying and freeze drying technologies, encapsulation techniques, engineering drug delivery systems at the nano and micro level, physicochemical and biological characterization of drug delivery systems and their targeted clinical correlations, the pharmacokinetic and pharmacodynamic principles, analytical methods for validation, film coating technology, oral strip manufacturing technology.

Due to the ever increase percentage of the global population living in cities, relevant environmental conditions affect people’s quality of life (QoL). In parallel, IoT-powered sensor technologies allow for personalizing environmental pressures, rendering relevant data as appropriate for the development of QoL information services. Such data may include for example physical, chemical and biological weather conditions as well as personalized symptom recordings, which may be used towards symptom modelling. The expected outcome are services that may provide early warnings to patients in relation to environmental conditions, assist them in receiving medical advice and treatment in a more targeted and effective way, and overall improve aspects of their QoL. Course contents include an introduction to basic analysis of environmental data (working example: weather, air pollution, aeroallergens) and the identification of weather, air pollution and pollen types that may trigger symptoms to sensitive parts of the population; qualitative and quantitative mapping of QoL and symptom data; introduction to Citizen science and crowd-sourced powered sensor and personal report collection along with their methods, tools, limitations, ethical and methodological problems and their linkage with the citizen science hub of AUTh/Thessaloniki; design principles, user requirements and functional specifications of electronic information services for QoL support; and finally hands-on practice on getting familiar with some (i) low cost environmental sensors (AQ for indoor as well as outdoor to be used as an example) and (ii) “low-code” development platforms for a “coding without code” approach. Team-work and group-projects are encouraged on the basis of a real world problem solving scenario.

Large-scale heterogeneous medical data are commonly acquired in diverse health care centers. The size and complexity of these datasets constitute great challenge in analytics and following application in a practical clinical environment. The field of machine learning offers methodologies that match ideally the task of knowledge extraction from such complex data sets. In the frame of the course technical introduction is given to big data analytics (characteristics of the big data, investigation and visualization of big data, knowledge extraction from big data), pattern classification and identification, with emphasis on biomedical data. The particular classical and modern techniques of machine learning is studied, and the areas and approaches of applications is presented. Topics, such as the quantification and diagnosis of disease as well as patient classification is combined with structural data analysis with methods including nonlinear and connectivity analysis and complex networks, as well as unstructured data and text analysis and image analysis (radiomics). In terms of methodology, machine learning techniques for classification and regression are presented (e.g., linear classification and regression, support vector machines, manifold learning as well as ensemble learning, such as random forests, bagging and boosting), including dimension reduction techniques (penalised regression, variable / feature selection). Projects are given in the application of machine learning techniques in clinical practice. The course focuses on the understanding and application of machine learning techniques commonly used in biomedical applications.

Physiology-based biokinetics and biodynamics describe the interaction of human organism with exogenous chemical substances. These may include :  a) industrial substances  which we are exposed to passively in our daily lives through environmental exposure, our nutrition and the use of consumer products, b) medicinal substances that we are purposefully exposed to for therapeutic purposes. Biokinetics describes the process of absorption, distribution, metabolism and excretion (ADME) of a chemical substance in the body, ie what the body does to the chemical substance, whilst biodynamics describes the impact the chemical substance has on the physiology of the body, ie what the substance does to the body. The module syllabus includes : 1) understanding the basic concepts and principles of biokinetics and biodynamics. 2) understanding the mathematical framework of biokinetic models. 3) Generalised biokinetic models. 4) Development and applications of quantitative structure-activity relationship models (QSARs) for the configuration of biokinetic models and the prediction of biodynamic interactions. 5) Biokinetic models that describe the gestational process and the interaction between mother and embryo (transport through placenta, breast-feeding). 6) Biokinetic models that describe changes in physiology from conception all the way through to adulthood. 7) Impact of genetic polymorphism on biokinetics and biodynamics. 8) Influence of blood-brain barrier on the transport of chemical substances in the brain. 9) Cumulative effects of chemicals and medicinal substances. 10) Interactions with respect to biodynamics. 11) Understanding the process and principles of the design of a biokinetics model with applications in a wide range of therapeutic settings (small molecules, proteinaceous drugs and nanoparticles). 12) Data interpretation of human biomonitoring via exposure reconstruction with the use of biokinetic models. 13) Applications of biokinetics and biodynamics models on risk analysis – connection with relevant systems biology models. 14) Applications of biokinetics and biodynamics models on precision medicine. 15) Applications of biokinetics and biodynamics models on real data and chemicals that attract a lot of attention.