** Courses and Syllabus **

**Mathematical Modeling in biology (6 Credit Units)**

Structure and Construction of biological models, Translation of a biological question into a mathematical model, Qualitative and quantitative models, Deterministic models, Analysis of results, validation and verification, Technical identification of mathematical models of data, Recursive algorithms, Selection of the appropriate model for different biological questions, Mathematical formulation of the problem.

**Programming Languages and Biological Databases (6 Credit Units)**

Design of biological databases, Python or Antha (biological programming language), Relational database models, Indexing in biological databases, Big data analysis in biological databases, Complexity of biological data, Mapping databases.

**Biostatistics (6 Credit Units)**

Calculation data probabilities, Interpretation of different distributions of data analysis techniques with the use of SPSS/SPATA, Variance, non-parametric statistical methods, Linear models, Design of biological experiments, discrete random variables, probability distributions.

**Biophysics and Biochemistry (6 Credit Units)**

Energy, entropy, equilibrium, Molecular structure, physiology of the cell, synapses of cytoskeleton, membrane proteins, Macromolecules, Catabolism, Metabolism, Amino

Acid structure and function, Protein Folding, Chaperon, degradation, proteasomes.

**Use of Biological Laboratory Equipment (6 Credit Units)**

**Recording and Processing of Brain Signals (6 Credit Units)**

Use of Acknowledgement Software, Recording Techniques, Processing Techniques, Methodology, Explain the fundamentals of neural information processing. Mathematically model the electrophysiological behavior of neurons. Extract information from neural data.

**Intelligent Systems and Applications of Artificial Intelligence in Medicine (6 Credit Units)**

Machine learning, Logic Programming, Information retrieval, Applications of monitoring techniques for patient imaging, diagnosis, choice of treatment. Artificial Neural Networks, Genetic Algorithms, Evolutionary neural networks, intelligent systems, patient scenarios modelling, cognitive patient basis, handling uncertain, unclear and incomplete knowledge.

**Algorithms in Molecular Biology and Structural Bioinformatics (6 Credit Units)**

Algorithms and complexity, Theory of Computational Complexity, Algorithms for biological databases, Clustering algorithms, Dynamic programming algorithms, Combinatorial algorithms, Greedy and Randomized Algorithms, Brute Algorithms, Evolutionary Trees , Graph Algorithms, Genetic Algorithms, Theoretical Foundations of Genetic Algorithms, Artificial Neural Networks.

**Comparative and Evolutionary Genomics (6 Credit Units)**

Genome biology, Evolutionary Geriatrics, Comparative Genomics, Genetic mapping, Representation of Phylogenetic Networks, Tree and network construction, Gene prediction function, Microarray Data Analysis and Metabolic reconstructions.

**Proteomics and Genomics Analysis (6 Credit Units)**

Gene expression analysis, Biological sequence analysis, Genome scale data, statistical data analysis, design an experiment, storage and analysis of data, Grouping and data visualization, Genomics classification, Apply knowledge of data structure algorithms, analysis to computational genomics problems.

**Human – Computer Interaction in Neurosciences (6 Credit Units)**

Introduction to Brain – Computer Interfaces (BCI), BCI Design, Implementation and Operation, BCI Signal Processing, BCI applications

**Molecular Diseases and Structural Drug Design (6 Credit Units)**

Methodology, Pharmaceutical technology, Analytical methods of pharmaceutical science. Molecular Recognition: stabilization methods of encapsulated ionic charges, Entropy terms in complex creation, Degrees of Molecular Recognition, Identification of High Fidelity, Evolution and molecular recognition. Structural elements of biomolecules: Size and environment of biomolecules, interactions between biomolecules. Categories of interactions between proteins and nucleic acids. Protein folding. Composition and structure of biological membranes, membrane proteins, membrane protein structure.

**Medical Protocols (6 Credit Units)**

**Nanotechnology Applications in Biomedicine (6 Credit Units)**

Introduction to Nanotechnology, Nanotechnology Applications in biomedicine, Nanomedicine, Stem cell Technologies, Imaging and Treatment (visualization methods), Tissue Engineering, Treatment methods, Diagnostic methods, Nanomaterials, Toxicity of Nanomaterials in medicine, Types of nanoparticles, Applications of Nanomedicine in vitro and in vivo Nano – neurology, Nanotechnology and Bioethics, Applications of Nanotechnology in regenerative medicine.

**Neurobiology and Cellular Systems Modeling (6 Credit Units)**

Neurotransmitters receptors, Channel Transporters, Synaptic Transmission, Synaptic Plasticity, Cellular Systems, Modeling and Simulation, Cellular Automata, Cancer Modeling, Neurological Assessment and specific diseases.

**Medical Imaging Systems, Analysis and Image Processing (6 Credit Units)**

Basic concepts of Imaging. Imaging Methods in Biomedicine. Computational and mathematical methods for solving problems related to medical images and their use for biomedical research and clinical care.

**Neuronal Rehabilitation Engineering** **(6 Credit Units)**

Structure and function of the nervous system – neural circuits. Degenerative changes and responses of the nerve cell mechanisms in response to nervous system injuries or changes in use patterns and their uselessness. Learning and memory: systems principles and models. Operation of Plasticity in the Central Nervous System. Plasticity after the injury of the Central Nervous System. The non-invasive brain stimulation in cognitive rehabilitation. Nerve Regeneration and Restoration – Basic Cell and Molecular Processes. Determining factors for the regeneration of nervous system trauma.

**Thesis (30 Credit Units)**

Thesis can be either written in Greek or English following the student’s selection in agreement with the supervisor.