Bioinformatics Centre/A.Y. 2020-21
Pathways and Networks
VIJAY BALADHYEPAYEL GHOSH

Pathways and Networks

Pathways and Networks course is about biochemical reactions, metabolic pathways, metabolic networks and metabolic system of an organism. This course will enable the students to:

 Understand the basic biochemical concepts of metabolic pathways.

 Understand metabolic pathway databases and its uses.

 Understand metabolic pathway networks to understand metabolic systems.

BIM 411 (T+P): Software Testing (1T + 1P)
Sanjay Londhe

BIM 411 (T+P): Software Testing (1T + 1P)

BIM 411 (T+P): Software Testing (1T + 1P)
Objectives:
 Enable students to become aware of errors and inaccuracies in software programs and
software routines.
 To learn the process of making error free new or existing computing software
programming systems/packages.
 To optimize the performance of software programming systems/packages.
Aim:
 To enhance the quality of source code, software program, web-enabled databases
released in open source or charged environment.
Syllabus:
 Principles of testing- test-case, test scenarios, different methods in testing. (1)
 Principle of Automation Testing (1)
 JUnit testing (Brief introduction) (1)
 Types of testing: black box testing, white box testing. (1)
 Defect life cycle, STLC (1)
 Different methodologies for: V model, water fall, Agile, continuous delivery, test driven
development, extreme programming. (3)
 Databases Testing (1)
 Fundamentals of Server-side Testing: What is server, server types, types of testing
servers (2)
 Web-services testing. (1)
 Algorithmic Testing (1)
 Some existing tools used in Software Testing industry (2)
Practicals:
 Perform testing of existing well known General Purpose applications, Life Sciences/
Bioinformatics applications. Store and analyze the results.
 Testing of Application of languages/interpreters (Perl, Java, etc) and other standard
services / desktop programs. (15)

Biology of Diseases
ROHAN MESHRAM

Biology of Diseases

This course is intended to make you aware of common diseases. You will learn the basic biology behind these diseases like the life cycles of pathogens and better understand their etiology.  We also aim to teach you about current therapeutic options avilable for the management of these diseases. For example, what drugs are currently used to treat the diseases and their mechanism of action. The major objective is understanding the very host-pathogen interactions at the molecular level that will enable you to identify the chinks in the armour of these pathogens.

You will be trained to understand and appreciate the role of bioinformatics as a descipline and eventually identify drug targets, disease markers, developing vaccines, and learn drug design strategies.

By the end of the course, we hope that you will become an independent thinker on using existing knowledge and bioinformatics tools that will enable you to think towards designing alternative strategies against these diseases.

Comparative Genomics
VIJAY BALADHYEPAYEL GHOSHUrmila Kulkarni-KaleSANGEETA SAWANT

Comparative Genomics

This course will enable the students to:

understand and explore the field of comparative genomics

explore computational tools for genome alignments & analysis

understand applications of comparative genomics

Scientific Data Mining and Visualization
PAYEL GHOSHSanjay Londhe

Scientific Data Mining and Visualization

BIM 304 (T+P): Scientific Data Mining and Visualization (2T + 2P)
Theory
Objectives:
The course will enable the students to:
 Handle large amounts of data generated in experiments
 Organize, perform, and write-up data analyses
 Mine the data to make sense out of it
 Get familiar with concepts of visualizing data
 Visualize the biological data using various tools/techniques & derive knowledge from it
Syllabus:
 Concepts of Shannon Entropy and Information Theory (2)
 Optimization techniques:
o Random walk (1)
o Monte Carlo (2)
o Simulated Annealing (1)
o Genetic Algorithm and Ant colony optimization (2)
 Introduction to Statistical & Machine Learning techniques
o Supervised and unsupervised Classification (1)
o Hierarchical and k-means clustering (1)
o K Nearest Neighbour classifier (1)
o Support vector Machines & Attribute Selection methods (2)
o Decision Tree (1)
o Random Forest (2)
o Linear Regression (1)
o Performance measures for classification and regression (1)
o PCA (1)
o ANOVA (one-way and two way) (2)
o Markov models and Hidden Markov Models (3)
o Artificial Neural Networks (3)
 Big Data: Concepts, Sources, Technological advances (2)
 Introduction to data visualization: (1)
o What is data visualization?
o Need for scientific data visualization
o Advantages and Applications
Practicals Syllabus:
 Programs for Random walk, Monte Carlo and Simulated Annealing (3)
 Use of WEKA for classification techniques (3)
 Protein function prediction case studies with kNN, Decision Tree and Random Forest
with WEKA (3)
 QSAR (As an application of classification Regression) (3)
 Use of ‘R’ for biological data processing and data visualization: (6)
o Introduction to R environment
o Data types and their properties
 Vectors, Factors, Arrays & Matrices, Lists & Data Frames
o File IO
o Data grouping and Control statements
o Functions
 Different statistical plots with R/ R packages: (4)
o Histogram, line, pie, box-whisker etc.
o 3-D interactive plots
o Heat maps, contour plots
Syllabus of M. Sc. Bioinformatics (2-Years Program at Bioinformatics Centre, SPPU) Page 43 of 68
o Contour lines, Streamlines, Streaklines,
 Utilities provided by “grid” package
 Introduction to “lattice” & “ggplot2” packages
 Statistical data analysis with R/ R packages: (8)
o PCA, ANNOVA, Clustering (K-means and hierarchical)
o Regression, correlation, Fitting a regression line, Multiple regression
o Packages:
 Standard Packages in R
 Creating custom packages
 Overview of:
 CRAN project
 BioConductor Project
o Biological Applications:
 Analysis of Sequencing/

Python Programming
Smita Saxena

Python Programming

This course aims to :

  • Learn Python programming syntax and concepts
  • Parse big data using Python
  • Write advanced Python codes to solve biological problems

Molecular Modeling and Simulations
VIJAY BALADHYEPAYEL GHOSHMANALI JOSHIABHIJEET KULKARNISANGEETA SAWANTSmita Saxena

Molecular Modeling and Simulations

This course will give an overview of multiscale modeling for biomolecules.  However, the focus of the course will be on atomistic, molecular mechanics techniques. The course will include: Forcefields, Potential Energy Surfaces, Energy Minimisation techniques, Molecular Dynamics, Docking.