Course Outcome:
After completion of the course the students will be able to (ONLY 5)
CO1: To develop basic understanding about computational techniques used in biological studies.
CO2: To understand the algorithms used in Computational Biology.
CO3: To understand the use of methods in analyzing biological data.
CO4: Apply python in writing the Computational Biology programs.
CO5: Apply machine learning algorithms in python.
Details of the Course:
Syllabus Computational Biology
Unit – I
Introduction to Computation and Programming, What is computation? Types of Computational Problems, biological computational problems.
Introduction to programming – programming language, syntax and semantics, doing simple computations, writing programs, compiling, testing and debugging.
Programming in Python- features, functionalities, keywords, data types, sequence.
Unit – II
Problem Solving Techniques – Algorithms, What is an algorithm? Types of algorithms, computational methods (e.g., algorithms), algorithms design techniques – greedy, divide and conquer, dynamic programming.
Unit – III
Algorithms for Computational Biology Applications of algorithms in solving problems of molecular biology, principles of algorithm design for biological data, Computational Biology Algorithms – Global Matching – Needleman-Wunsch problem, Local Sequence Matching- Smith-Waterman problem.
Unit – IV
Sequence analysis – methods, Hidden Markov Models – Markov chain, modelling sequences. Population genetics- Fisher-Wright Model, Evolutionary Trees- distance-based trees and sequence-based trees.
Unit – V
Machine learning and Data Mining in Computational biology, Introduction to Machine learning (data mining, computational intelligence, or pattern recognition), basic algorithms.
Introduction – Machine learning for computational biology problems, steps of classification and clustering machines learning models.
9. |
Unit – III | Algorithms for Computational Biology Applications of algorithms in solving problems of molecular biology, principles of algorithm design for biological data Computational Biology Algorithms – Global Matching – Needleman-Wunsch problem, Local Sequence Matching- Smith-Waterman problem. | 9 |
Unit – IV | Sequence analysis – methods, Hidden Markov Models – Markov chain, modelling sequences. Population genetics- Fisher-Wright Model, Evolutionary Trees- distance-based trees and sequence-based trees. | 10 |
Unit – V | Machine learning and Data Mining in Computational biology Introduction to Machine learning (data mining, computational intelligence, or pattern recognition), basic algorithms. Introduction – Machine learning for computational biology problems, steps of classification and clustering machines learning models. | 9 |
TOTAL | 44 |
- Suggested Books:
S. No. | Name of Authors/Books/Publishers | Year of Publication / Reprint |
Text Books (At least 2 and has to be most recent) | 2020 | |
1. | Michael S. Waterman, “Introduction to Computational Biology: Maps, Sequences and Genomes”, Waterman. Edition 2 (2012) Chapman and Hall/ CRC Press ISBN:143986131. | 2019 |
2. | Ralf Blossey, “Computational Biology: A Statistical Mechanics Perspective,” Second Edition (Chapman & Hall/CRC Computational Biology). 2021. | |
Reference Books (At least 2 and has to be most recent/ Journals) | ||
1. | S. K Randske and Manan Choudhary, Bioinformatics And Computational Biology: Bioinformatics And Computational Biology, 22 April 2021 | |
2. | Ross Carlson, Herbert Sauro, “Methods in Computational Biology,”, 2019. | |
e- Resources (as per AICTE,MHRD e.g. e-pathshala (ePathshalaepathshala.nic.in), | ||
1. | Programming for everybody (Python), conducted by University of Michigan. https://www.coursera.org/course/pythonlearn |