BY4202 Computational Biology Syllabus:
BY4202 Computational Biology Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVE:
1 To improve the programming skills of the student in the field of Biological research
2 Make students know the recent sequence analysis methods
3 To educate the students about recent advancements involved in drug delivery systems
4 To develop depth knowledge in Molecular Dynamics and Molecular Modeling
5 To develop depth knowledge in Systems Biology Networks
UNIT I ALGORITHMS
Dynamic Programming Algorithms: Needleman-Wunsch, Smith-Watermann – Heuristic Algorithms: FASTA, BLAST; statistical and Biological significance; Clustering: Hierarchical Clustering, k-Means Clustering; Phylogeny; Tree Construction: Distance-based (Neighbour Joining, Unweighted Pair Group Method with Arithmetic Mean) and Character-based methods (small and large parsimony algorithm).
UNIT II SEQUENCE ANALYSIS
Nucleic acid: Reading frames, Codon Usage analysis, Translational and transcriptional signals, Splice site identification, Gene prediction methods, RNA fold analysis; Protein: Compositional analysis, Hydrophobicity profiles, Amphiphilicity detection, Moment analysis, Transmembrane prediction methods, Secondary structure prediction methods.
UNIT III COMPUTER AIDED DRUG DESIGN
Drug discovery process: Target identification and validation, lead optimization and validation. Analog-Based drug design: Pharmacophores (3D database searching, conformation searches, deriving and using 3D Pharmacophore, constrained systematic search, Genetic Algorithm, clique detection techniques, maximum likelihood method) and QSAR; Structure-based drug design: Docking, De Novo Drug Design (Fragment Placements, Connection Methods, Sequential Grow), Virtual screening.
UNIT IV MOLECULAR DYNAMICS AND MACHINE LEARNING
Molecular Dynamics & Simulation: Molecular Modeling (QM / MM and Mixed), thermodynamic and chemical transformations, classical force-field, bonds, bond Angles, degrees of freedom, solvation, molecular surfaces, equations of motion, properties of simulations, applications of MD simulations. Hidden Markov Models: Hidden Markov Models, Machine learning techniques, Profile HMMs, Hidden Markov Models for gene finding, Artificial Neural Networks: Historic evolution – Perceptron, NN Architecture, supervised and unsupervised learning, Viterbi Algorithm, Artificial Neural Networks in protein secondary structure prediction; Decision trees, Support Vector Machines.
UNIT V SYSTEMS BIOLOGY
Systems Biology Networks – basics of computer networks, Biological uses and Integration. Micro array – definition, Applications of Micro Arrays in systems biology. Self- organizing maps and Connectivity maps – definition and its uses. Networks and Pathways – Types and methods. Metabolic networks.
TOTAL : 45+15 = 60 PERIODS
COURSE OUTCOMES
After completion of the course the students will be able to
CO1 Gain depth knowledge in programming skills of the student in the field of Biological research
CO2 Learn the current trends developed in sequence analysis methods
CO3 Gain depth knowledge about recent advancements involved in drug delivery systems
CO4 Gain depth knowledge in Molecular Dynamics and Molecular Modeling
CO5 Gain depth knowledge in Systems Biology Networks
REFERENCES
1. Gusfield D. et al., “Algorithms on Strings Trees and Sequences.” Cambridge University Press, 1st Edition, 1997.
2. Mount, David W. “Bioinformatics: Sequence and Genome Analysis”, CBS, 2nd Edition, 2004.
3. Lesk, Arthur M., “Introduction to Bioinformatics”, Oxford University Press, 2nd Edition, 2010.
4. Leach A. R., “Molecular Modeling Principles and Applications”, Pearson, 2nd Edition, 2010.
5. Madsen, U. “Textbook of Drug Design and Discovery”, Taylor & Francis, 3rd Edition, 2002.
6. Baldi P., Brunak S., “Bioinformatics: The Machine Learning Approach.” East West Press, 2nd Edition, 2003
7. Baxevanis A.D., Oullette, B.F.F., A Practical Guide to the Analysis of Genes and Proteins” John Wiley, 2nd Edition, 2002.
8. Durbin R. Eddy S., Krogh A., Mitchison G., “Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids”, Cambridge University Press, 1st Edition, 1998.
9. Alon, U., “An Introduction to Systems Biology: Design Principles of Biological Circuits.”, Chapman & Hall/CRC, 1st Edition, 2006.
Extensive Reading:
1. “https://journals.plos.org/ploscompbiol”, Public Library of Science.
2. “https://academic.oup.com/bioinformatics” , Oxford University Press