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Semester C

 

Application of Informatics in the Study and Preservation of Biodiversity

Semester: C’

Course Type: Compulsory

Course Curriculum

  • Biodiversity Data and datasets: structure, categories and utilization
  • Methods of biodiversity analysis: Databases, statistixal analysis, timeline analysis, spatial analysis, analytical models, GIS
  • Bioinformatics applications in biodiversity: Biogeography and evolution (scientific nomenclature and classification, species description, clavicles and phylogenetic data, data sampling and species taxonomy);  Spatial distribution of organisms (software applications, correlation analysis of population and ecological factors, biological community analysis and statistics); Behavioral analysis; manipulation of biodiversity datasets and collections; metadata analysis and manipulation; visualization of biodiversity data; biodiversity network analysis; parsing biodiversity datasets for the non-experts; uncertainty analysis in biodiversity; manipulation of incomplete data; text mining; automatic indexing

Administrator

  • Assoc. Prof. A. Legakis

 Instructors

  • Assoc. Prof. A. Legakis
  • Assist. Prof. A. Parmakelis
  • Assoc. Prof. P. Megalofonou
  • Dr. S. Faulwetter

 

Special Topics in Bioinformatics I: Data Types – Databases – Biological Database Design

Semester: C’

Course Type: Elective

Course Curriculum

  • Data Types: Abstract Data Types (ADT). Arrays, hashes, strings; Stack and Heap memory; Recursive algorithms, lists, tree structures (binary trees, binary search trees) self-balancing AVL tees, graphs (nodes and edges, algorithms, implementations); Hashing and hash tables; ADT implementations in programming (C, Java)
  • Databases: Database models; Database management systems;  Database schemes (hierarchical, relational etc); Relational algebra; Relational design, database normalization; the Structured Query Language (SQL); programming complex SQL queries; Queries by Example (QBE) in SQL; Modern SQL database approaches (object oriented programming, database taxonomy, chronological classification, metadata, multimedia etc); principles of database design
  • Biological Database Design: applications of previous subjects to the design of databases with biological interest.

Administrator

  • Assist. Prof. I. Varlamis

 Instructors

  • Assist. Prof. I. Varlamis
  • Assist. Prof. D. Michail

 

Special Topics in Bioinformatics II: Architecture of Internet Applications and Bioinformatics

Semester: C’

Course Type: Elective

Course Curriculum

  • Architecture of Internet Applications: Client/Server architectures and the World Wide Web; n-tier architecture; the role of the web server; OSF/DCE architectures; DNA architectures; WAP and wap servers; application servers; middleware – corba -activeX; transaction servers; message passing; message queues.
  • Application Building: design and implementations, protocols and programming (Client side programming: HTML, DHTML, XML, scripting languages; Server side programming: JSP, ASP, CGI); access to legacy builds; database connections and multimedia; transaction implementations;
  • Internet security: secure access; symmetric and asymmetric encryption; digital signatures and certificates; trustworthy sources; the X509.3 protocol; private and public keys; PKI and PKIX structures; server/client authentification, connection protocols (SSL, TSL, S/MIME, PGP, IPSEC)
  • Biological Application design: development tools, Internet applications in Bioinformatics

Administrator

  • Prof. C. Vorgias

 Instructors

  • Prof. C. Vorgias
  • Dr. I. Hamodrakas

 

Special Topics in Bioinformatics III: Complex Adaptive Systems

Semester: C’

Course Type: Elective

Course Curriculum

  • Behavioral models: Models of motion, control and regulation of behaviour of real and artificial animals, Motion control, Learning Problems, Learning models (spatial, associative, etc)
  • Population models: Ecological models, Social insect societies, Models of groups
  • Evolutionary models: A. Techniques: Genetic and evolutionary algorithms, Genetic programming, Evolution of hierarchical structures and classifiers, B. Problems: Evolutionary phenomena at the population level, Evolutionarily stable strategies, The problem of cooperativity, Species creation, Symbiosis and symbiogenesis.
  • Developmental models: Morphogenesis, generative grammars and L-systems, arbitrary rule-based systems.
  • Molecular models: Metabolic pathways and cellular regulation modeling.
  • Cellular Automata: One-dimensional and two-dimensional cellular automata, Self-reproducted forms, Adaptative forms.
  • Dynamic Systems: Fundamentals, Describing systems and phenomena as dynamic systems, Topics in the analysis of dynamic systems, Graphical criteria, Deterministic chaos.

Administrator

  • Assoc. Prof. E. Tzafestas

 Instructors

  • Assoc. Prof. E. Tzafestas

Special Topics in Bioinformatics IV: Microarray Technology & Applications

Semester: C’

Course Type: Elective

Course Curriculum

  • Introduction to DNA Micro-arrays technology: DNA Microarray types, microarray technologies (printed/spotted vs on-site synthesis), cDNA vs oligo arrays, one dye vs two dye experiments, biomedical questions that can we answered with microarrays.
  • Experimentation I: Design for high throughput DNA micro-arrays: Gene-Clustering dogma, replicate experiments, reproducibility, sources of noise and biological variation.
  • Experimentation II: from sample preparation to gene expression profiles: Sample preparation, RNA isolation and labeling, Chip Hybridization, Scanning, Gridding, Normalization / Data row, Gene Annotation, Validation (Quantitative PCR), TMEV-4, Go-Miner, Future directions and technology expansion.
  • From Microarray Measurements to Data Analysis: Measures of expression, microarray image analysis, normalization, scaling and filtering, fold calculation and significance.
  • Dissimilarity and Similarity Measures: Linear correlation, entropy and mutual information, dynamics.
  • Genomic Data-Mining Techniques: Supervised vs Unsupervised, Data reduction and filtering, Clustering, Classification, PCA, Regression Analysis, Self Optimizing Maps, Support Vector Machines, Determining the Significance of Findings.
  • Integrating Microarray Data with other sources of Information: Genotype, Phenotype, Genomic Sequences, Annotation, Bio-ontologies [Gene Ontology, EC Nomenclature], systems biology.
  • Microarray Standards, Databases (MIAME-MGED) and related resources: Necessity for microarray standards, MGED and gene-ontology, description of MIAME ((Minimum Information About a Microarray Experiment), related inter-WEB sources, Bioconductor.
  • Application of bioinformatic tools in mouse genome-wide DNA microarrays: Analysis of the liver transcriptome, in old mice and a mouse model of a DNA-damage related progeroid syndrome. Human genome-wide microarray applications in biomedicine.
  • Gene expression profiling and signatures: Molecular carcinogenesis and chemo-resistance development in cancer cells, functional analysis of human diseases biomarkers in human cells.
  • Micro-arrays for proteomics – Proteins arrays / Cell arrays: Types of protein-arrays, Sample preparation and hybridization, technological drawbacks and challenges, future applications.

Administrator

  • Assoc. Prof. I. Trougakos

 Instructors

  • Assoc. Prof. I. Trougakos
  • Dr. I. Michalopoulos
  • Dr. A. Polyzos
  • Dr. A. Chatziioannou