MCTA – 301(D) Bio Informatics

Unit 1

Introduction to Bioinformatics, algorithm design and computational complexity aspects in bioinformatics,

paradigms for algorithm design like greedy, divide and conquer, dynamic programming, exhaustive search

and randomization help in obtaining useful bioinformatics algorithms,

Unit 2

Genome rearrangement, bock alignment, global sequence alignment, finding regulatory motifs in DNA

sequences, finding minimum energy conformation in drug molecules respectively exemplifying the uses of

these paradigms.

Unit 3

Application of computational learning in bioinformatics, the learning of probabilistic finite automata

(Hidden Markov Models)

Unit 4

Several important problems in computational biology, like protein folding which turnout to be NP-hard,

study some of these problems and corresponding approximation algorithms that address the issue of

intractability.

Reference Books:

1. Neil Jones and P Pevzner; An introduction to Bioinformatics Algorithms, MIT Press

2. Peter Clote and R Backofen, Computational Molecular Biology, J Wiley

3. R. Durbin, Eddy etc; Biological sequence analysis, probabilistic models of protein

and nucleic acids; Cambridge Univ Press.