| 1. |
Introduction to Bioinformatics Data and Databases
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| 2. |
Sequence Analysis
a) Basic concepts of sequence similarity, identity and homology, definitions of homologues, orthologues, paralogues and xenologues
b) Scoring matrices: basic concept of a scoring matrix, Matrices for nucleic acid and proteins sequences, PAM and BLOSUM series, matrix derivation methods and principles.
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| 3. |
Sequence alignment
a) Measurement of sequence similarity; Similarity and homology.
b) Pairwise sequence alignment: Basic concepts of sequence alignment, Needleman and Wunsch, Smith and Waterman algorithms for pairwise alignments, gap penalties, use of pairwise alignments for analysis of Nucleic acid and protein sequences and interpretation of results.
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| 4. |
Computing Algorithms
a) Computing, analysing algorithms, designing algorithms, Sorting, Searching & Strings Matching Sorting: Binary Search, Fibonacci Search.
b) String Matching: Naïve algorithm, Boyer Moore algorithm.
c) Graphs Representation of Graphs, Breadth First Search, Depth First Search, Topological Sort, Connected Components, Minimum Spanning Tree, Single-Source Shortest Path: Dijkstra’s Algorithm, All-Pairs
d) Shortest Paths, Coloring of Graphs Trees Forests.
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| 5. |
Introduction to Molecular Modelling
a) Molecular Mechanics, Molecular Docking, Molecular Dynamics, Trajectory Analysis, Essential Dynamics, Advanced sampling Techniques, Free energy Calculation
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| 6. |
Drug Discovery
a) Drug discovery process. Target identification and validation, lead optimization and validation. Methods and Tools in Computer-aided molecular Design
b) 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. 2D and 3D QSAR.
c) Structure based drug design: - Docking, De Novo Drug Design (Fragment Placements, Connection Methods, Sequential Grow), Virtual screening.
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| 7. |
Toxicity Prediction ADMET properties
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| 8. |
Systems Biology Networks
a) 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
b) Simulation of pathways: Whole cell: Principle and levels of simulation – E-cell and v-cell, Virtual Erythrocytes. Pathological analysis. Flux Balance Analysis. Biochemical metabolic pathways, Metabolomics and enzymes. Interconnection of pathways, metabolic regulation. Translating biochemical networks into linear algebra.
c) Cellular models. Networks and Motifs: Gene Networks: basic concepts, computational models. Lambda receptor and lac operon as an example. All types of networks and its uses.
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| 9. |
Machine Learning Algorithms
a) Nearest Neighbour Neural Network Applications of Machine Learning and Applications of Python in Machine Learning.
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| 10. |
Business Communication
a) Behavior in interpersonal relationship, Conflict management
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| 11. |
Develop entrepreneurship skills
a) Importance of entrepreneurship
b) Strategy and entrepreneurship
c) Managing global leather markets
d) Market Forecast
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