Semester A – Masters in "Bioinformatics – Computational Biology " – Department of Biology, National & Kapodistrian University of Athens

Molecular Biology and Genomics

Structure and organization of prokaryotic and eukaryotic cells – DNA, Chromosomes – Transfer of sequential information (introductory concepts, aimed at educating students without a background in Biology, some concepts will be covered in greater detail).
Principles of DNA replication, replication origins and initiators, trans-factors, Principles of DNA Transcription, regulation of transcription in eukaryotes.
Genetic Code – structure and function of tRNAs and ribosomes in mRNA translation.
Principles of mRNA translation in prokaryotic and eukaryotic cells.
Recombinant DNA, molecular cloning – Southern, Northern, DNA-sequencing, PCR.
Mechanims of DNA repair.
DNA-sequencing. Modern approaches and sequencing methods.
Sequence repeats – chromosome telomers – cell aging – cell lines – Model organisms.
Post-translational modifications – protein dissociation and proteolysis (proteasome and lysozomes)
The Human Genome Project – Genetic probes – microarray technologies.
Applications of DNA analysis in forensic sciences.
Genetically Modified Organisms, Methods – applications – risks
Animal Cloning, methods – applications – risks

Atomic Representation of Molecular Structure.
Biological macromolecules: Structure and conformations, Non-bonded interactions and their importance, Biological macromolecules in aqueus and non aqueus environment, Structure Determination Methods, Crystal Symmetry.
Protein architecture: sequence, secondary structure, structure motifs, structural and functional domains, tertiary and quaternary structure, protein-protein interactions.
Membrane proteins: hydrophobicity, protein-lipid interactions, energy and signal transduction, molecular engines, examination of selected, “model” membrane protein structures: (e.g. Rhodopsin, Bacteriorhodopsin, Ion channels etc).
Structural and fibrous proteins, collagen, super-helices and coiled-coil structures, polymers, amyloid aggregates and amyloidoses, biopolymers and biomaterials.
DNA binding proteins, protein-DNA recognition and interactions in eukaryotic and prokaryotic cells, DNA structure and helicity, nucleosome structure.
Carbohydrates (chitin, cellulose), protein-carbohydrate interactions in complex systems (e.g. insect cuticle)
Protein mechanics, prediction of structure, modeling, rational protein design.
Protein folding and stability.
Structural genomics

Introduction
Data Types: Fundamental data types, definition of octal and hexadecimal constants, types of variables, arrays and strings, enumeration constants.
Operators: Substitution operator, numeric operators, increment and decrement operators, comparison operators, Boolean logical operators, digital logical operators, hypothetical operator, explicit data type conversion- the cast operator, the sizeof operator, Priorities of operators, mixture of different data types in expressions.
Control Structures: if structure, while structure, for structure, switch/case structure, continue structure, goto structure, recursion
Pointers: Definition, pointer variables, applications, Dynamic memory allocation functions.
Derived data types: Data structures, Bit fields, Unions, Definition of new data type: typedef
The C pre-processor: Definition of constants: The #define directive, File inclusion: the #include directive, Conditional compilation
Input/Output and Library Functions: Input/Output functions, other library functions
The structure of C programs: Functions, Arguments of the main function
Applications (Simple Bioinformatics Programs)

Sample space and events. Probability of events. Basic counting rules. Permutations, combinations. Conditional Probability. Theorem of total probability and Bayes’ theorem. Stochastic independence.
Random variable. Probability density function and Cumulative density function. Discrete random variables. Continuous random variables. Distribution of random variables. Mean and variance.
Discrete and continuous distributions. Univariate and Multivariate distributions. Central Limit Theorem and applications.
Statistical tables.Measures of central tendency and variability. Coefficient of variation.
Introduction to the STATA statistical package.
Frequency distributions of qualitative and quantitative characteristics. Hypothesis testing.
Parametric and non-parametric approaches ( t-test, Wilcoxon rank test κτλ). Type I and II error rates. 2×2 tables.
Comparison of mean value with a constant. Comparison between two mean values. T-test, Paired T-test.
T-test and paired t-test using STATA.
Analysis of Variance (ANOVA) and linear regression. Multivarate Analysis. Pearson correlation coefficient.
ANOVA and linear regression using STATA.
Non parametric approaches: Sign test, Wilcoxon, Mann-Whitney and Kruskal – Wallis tests. Spearman correlation coefficient. Logistic regression.
Non parametric approaches and logistic regression using STATA.
Evaluation of laboratory/clinical/epidemiological data.

What is Bioinformatics – Basic Definitions
Principles of Informatics – Computer Applications in Biological Sciences
Biological Databases – Annotation principles and limitations
Protein and Genome Information Resources and Analysis
Genome Projects
Principles of Protein folding
Protein-protein interactions – Metabolic pathways – protein self-assembly, structure and function
Large scale analysis problems – High throughput methods – Principles of Systems Biology – Challenges in experimental determination of protein structure and function – Structural Genomics
Computational Analyis as a means to unveil the relationship between sequence, structure and function
Data Base Management Systems
Principles of Data Mining
Introduction to Machine Learning Methods and Bioinformatics applications