OPTIONAL COURSES
1. Data Types - Databases - Biological Database Design
Lecturers: Assist. Prof. H. Varlamis | Assist. Prof. D. Michail
Optional Module
- Data Structures: Introduction. The concept of Abstract Data Type (ADT). Tables, Records, Strings. Stacks, Recursion, Lists, Trees (Binary Trees, Binary Search Trees), Balanced Trees (AVL). Graphs (implementation, algorithms). Search with hashing. Implementation of ADT with an object-oriented programming language (C++ or JAVA)
- Databases: Conceptual database modeling. Database management system architectures, Data Models (Hierarchical, Network, Relational). Relational algebra. Relational calculus. Database Normalization. The Structured Query Language (SQL), programming advanced queries in SQL, Queries by Example (QBE), Modern trends in Databases (object-oriented, distributed, multimedia) database design.
- Design of Biological Databases: Applications to specific examples of designing Biological Databases.
2. Architecture of Internet Application and Bioinformatics
Lecturer: Dr. I. Hamodrakas
- Internet Application Architecture: Client/Server architecture and its correlation with WWW, multi-tier architectures(n-tier), the WEB Server role, OSF DCE architecture, DNA architecture, WAP architecture, WAP servers, Application Servers, middleware (corba, activeX, transaction servers, message passing, message queues).
- Designing applications: System Design and modelling, protocols and programming (Client Side Programming: HTML, DHTML, XML, scripting languages, Server Side Programming: JSP, ASP, CGI), access to legacy systems, communications with DBMSs and multimedia systems, transaction processing (methods and environments).
- Internet Security: secure information access and transport, symmetrical and asymmetrical cryptography, digital signatures, secure third parties, Digital certificates, X509.3 protocol, PKI ? PKIX structures, Server and Client authentication, SSL ? TSL ? S/MIME ? PGP ? IPSPEC protocols.
- Implementing Applications: Development tools, Specialized applications in Bioinformatics systems.
3. Complex Adaptive Systems
You can find more information for this module at the following address: http://users.softlab.ntua.gr/~brensham/CourseCAS/
Lecturers: Assos. Prof. E. Tzafesta
- Introduction
- Behavioural 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:
- Techniques: Genetic and evolutionary algorithms, Genetic programming, Evolution of hierarchical structures and classifiers
- 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.
4. Special Topics in Bioinformatics
Starting from the 2006-2007 academic year, the "Special Topics in Bioinformatics" course will be covered by the topic "Microarray Technologies and Applications".
Microarray Technologies and Applications
Lecturers: Assoc. Prof. I. Trougakos| Lect. V. Prombonas| Dr. I. Michalopoulos
- 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.
- Simulation of biochemical networks reactions: Libraries of biochemical pathways, Methodologies for the in-silico delineation/description of biochemical pathways, (SBML, CellML, BioPAX), Mathematical methodologies for biochemical reaction networks simulation, Mathematical methodologies of metabolic engineering (Metabiloc Flux Analysis, Metabolic Control Analysis), Biochemical reaction networks simulation software (Gepasi,Copasi, CellDesigner, Matlab), Studying the biosynthetic reaction network of neurotransmitter glutamate in rat cerebral tissue, as an example for a rational study of the regulation of metabolic processes and the formulation of systemic approaches in biochemistry.
For the spring semesters 2003-2004, 2004-2005 and 2005-2006, the module "Special Topics in Bioinformatics" was covered by the topic "Algebraic and Geometric Algorithms in Molecular Biology" (http://www.di.uoa.gr/~erga/AlgGewBio.html). The module will be taught simultaneously with the module under the same title, available for the students of the Postgraduate Programme of the Department of Informatics, with coordinator the Associate Professor I. Emiris.
Computational Methods in Science
Coordinator: Assoc. Prof I. Emiris
Lecturers: Assoc. Prof I. Emiris
- Three-dimensional structure of proteins, protein folding:
- Data: Experimental data from X-Ray Crystallography and NMR.
- Representation: Protein modelling by distance geometry and dimensionality reduction
- Conformational search with combinatorial and probabilistic methods, the road map approach, algorithmic complexity
- Algebraic algorithms: linear algebra, studying and solving polynomial systems, relations between molecular conformations with robotic mechanism positions.
- Optimization algorithms: global or local optimization, integer programming
- Similarities and recognition of molecular structures, docking:
- Geometric models: alpha-shapes, spheres union, Connoly surface
- Molecular surface representation, Delaunay triangulation and meshing
- Data structures for Geometrical data: Geometric hashing, structure similarity metrics, data mining.
- Recognition of Three-dimensional features, move and fit, computer vision techniques
5. Application of Informatics in the Study and Preservation of Biodeversity
Lecturers: Prof. A. Nikolaidou |Assos. Prof. A. Legakis | Assist. Prof P. Megalofonou| Lect. A. Parmakelis
Lectures (In pdf format)
Compulsory Module
Semester: Α
Syllabus:
- Biodiversity data: Forms, Categories, Usage
- Methods for the analysis of biodiversity: Data Bases, Statistical analyses, Temporal analyses, Spatial analyses, Models, GIS
- Applications of Informatics in Biodiversity: Systematics, Biogeography and evolution (binomial species naming and classification, species description, taxonomy keys and phylogenetic data, sampling and observation data and species distribution), spatial distribution of organisms (Software applications for the analysis of territoriality, correlations of population and ecological factors, statistical analysis of biocommunities), Behavioural analysis, management of biological collections, metadata management, visualisation, knowledge networking, data mining. The application of biodiversity data from non-experts: uncertainty analysis, reasoning with incomplete information, automatic summarization.
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