Computational Biology Courses
This course consists of two parts: Cell Biology and Biochemistry. This course is designed to provide an quick overview on the biological structures, reactivity, mechanism, thermodynamics, bioenergetics, and kinetics of biological molecules especially the proteins molecules. Further, metabolism of glycolysis will be dealt in detail to illustrate the importance of kinetics, thermodynamics and chemical mechanisms to understand the metabolic process in wholesome. This core course is both qualtitative and quantitative in nature and requires more of paper and pencil type solving of problems rather than any coding or writing programs.
This course will discuss some fundamental aspects of cellular and molecular biology as studied by quantitative approaches. Some simple mathematical/statistical and computational tools will be taken up to carry out quantitative analysis of biological systems. In addition, students will be introduced to systems biology, network biology and biomedical applications. Part of the learning will be achieved through study of relevant papers (and online resources) and class discussions.
The aim of this basic core course is to provide students a good background in genetics, molecular biology. The major focus is to acquaint students with quantitative aspect of different kind of genetic inheritance and horizontal transfers. The course is also to provide introduction about different aspect of molecular aspect of gene regulation.
The objective of this course is to provide introduction to modern biology, data-oriented questions, and training in application of computational techniques for data analysis. Apart from providing conceptual understanding of important topics in bioinformatics, the focus is on hands-on training in implementation of techniques. Through combination of lectures, exercises, assignments and presentations, the student is expected to achieve practical understanding of bioinformatics techniques.
This course introduces undergraduates to the outstanding promise of applied algorithmics in the field of molecular biology and human health. The course aims to bridge the gap between algorithmic thinking and biological problem-solving. As part of this course, the students will be encouraged to design algorithms and apply on real biological datasets to gain fundamental insights into biological systems/diseases. The students will get an opportunity to deep dive into the vibrant realm of high throughput molecular biology through the lense of multi-omics data analysis.
This biophysics course shall delve into concepts of physics acting upon biomolecules, primarily proteins. From understanding protein folding to predicting 3-dimension structures from the 1-dimension sequence, the course shall involve assignments that shall give the students the neccessary knowledge to deal with structural problems.
This course will introduce students to stochastic simulations as used in solving biological probelms. It will emphasize kinetic Monte Carlo simulations that are able to capture dynamical aspects (such as in cellular phenotype generation), complex details in biological problems (such as spatial heterogeneity) and fluctuation effects. We plan to discuss stochastic modeling of important cellular (as mediated by regulatory networks) and immunological processes and biomedical applications.
The aim of this basic core course is to provide students a decent background in cell biology and biochemistry. The major focus is to
The aim of this basic core course is to provide students a good background in genetics and molecular biology. The major focus is to
This is an introductory course on algorithms for computational biology. The goal is to make students familiar with the basics of algorithm designing techniques and their application in solving problems of molecular biology. Students will be trained for developing their own algorithms for solving real life biological problems. Hands on training will be given for commonly used software's for genomic data analysis.
The objective of this course is to provide introduction to chemoinformatics, an interdisciplinary area on the interface of chemistry, informatics and biology. The student will be provided with understanding of fundamentals of chemoinformatics and its applications. Through lectures, hands-on exercises and assignments, the student is expected to achieve a good grasp of the concepts and applications of chemoinformatics.
This course is meant to impart knowledge on medical and biomolecular imaging. The course introduces physical, engineering and signal processing principles needed for medical imaging and image processing. The primary focus of this course will be computational image processing methods. This course covers topics on analysis of medical images from different types of sources such as planar x-ray, x-ray computed tomography (CT), magnetic resonance imaging (MRI), nuclear medicine imaging - positron emission tomography (PET), light and confocal microscopy and electron microscopy. Students will also gain hands on experience with medical image processing software to process CT or MRI scans and construct 3D models from microscopic images.
The aim of this course is to introduce mathematics as applied in quantitative study of biological systems and biological data analysis. Use of ordinary and partial differential equations (ODEs) will be emphasized in this course. Both exact and numerical solutions will be discussed. We also plan to briefly introduce dynamical systems analysis for ODEs.
The objective of this course is to provide introduction to network biology, an emerging interdisciplinary area which aims at graph theoretical modeling of biological complex systems and its applications. The student will be provided with conceptual understanding of complex networks and their application for modeling various biological systems. Through a combination of lectures, exercises, and assignments, the student is expected to achieve understanding of network biology.
This course introduces systems and synthetic biology to students with interest in applying mathematical techniques to biological problem. The main aim of this course is to train students to build mathematical models of complex biological systems and make predictions about the functions of the biological systems in-silico under various conditions. They will also learn conditions to build synthetic circuit in-silico to optimize its functions under various scenarios.
This introductory neuroscience provides basic understanding of neuronal systems and their respective mathematical models that describes the behavior of the neurons under various conditions. The aim of this course is to encourage Computational biology students to diversify into the area of neuroscience. This course in not about neural networks and machine learning, but about the use of the tools of dynamical systems theory to understand oscillatory properties of single cell neurons. Nonlinear ODE and PDE models will be constructed, analyzed and simulated using MATLAB to understand different firing patterns of the neuronal systems under normal and pathological conditions.
The field of Genomics is expanding its horizon with help of high throughput technologies. Scientist are trying to answer fundamental questions related to health, society and human survival outside earth using genomics. With such increase of applications, it constantly needs computational experts for systematic analysis of data and acheiving meaning full insights. Infact the computational experts have now started taking lead in genomic projects. Hence this course is meant to guide students for data analysis approach and steps involved in computational genomics and make them familiar with latest development in genomics.
This course is designed for students having wide range of background like biology, medical science, pharmacology, bioinformatics, and computer science. This course is divided in following three sections; i) Major challenges in the field of biomedical science, ii) Introduction/implementation of machine learning techniques for developing prediction models, and iii) solving biomedical problems using machine learning techniques. This course will be help students in developing novel methods for solving real-life problems in the field of biological and health sciences. Attempt will made to bridge gap between students and world class researchers, students will be exposed to highly accurate methods based on machine learning techniques (research papers).
There is a exponential growth of data in the field of biological, medical and clinical sciences. Aim of this course is to teach implementation of data mining techniques in healthcare, to solve health-related problems. In this course, students will learn techniques for managing and mining big data. It will be broadly divided in four parts; first part will cover various repositories or databases in the field of medical and biological data. In second part, students will be introduced with techniques commonly used to analyze and manage big data. Implementation of techniques using Python and R will be covered in third phase of this course. Finally, students will learn how to solve health-related problems using knowldge based discovery approach.
This course is designed to provide an introduction to the emerging science of Computational Gastronomy, a blend of food, data science and computational techniques. Following are the specific objectives that the course aims to fulfil: (a) Introduction to the science of food; (b) Exposition to culinary data; (c) Analysis and visualization of culinary data (statistical analysis, text mining, natural language processing, machine learning); (d) Exposure to challenges and opportunities.
The main aim of this elective course is to provide students a good background in Quantitative Biology and enabling them to implement various statistical methods in analyzing biological data and its visualization. The course will also provide basic introduction of applied biostatistics.
This objective of this course is to train students in creation of computing solutions that are relevant to medicine. The course will pick timely and relevant topics in computing such as the building blocks of electronic health records, modeling and visualizing diseases, pathogen and human factors involved in spread and the ever increasing role of information management and computing in managing diseases.
TThis course teaches the principles and practice of dug discovery, molecular mechanics and biomolecular simulations with an emphasis on the practical skills needed to perform and interpret simulations of biological macromolecules. In addition, this course will also introduce Molecular Docking technique that is essential for Computational Drug Design.
The objective of this course is to introduce students to the Healthcare Innovation with a specific focus on Digital Health and Entrepreneurship. It covers Biotechnology, Medical Device and Digital Health/IT. The course will aim to generate healthcare startup ideas after identifying the gaps and opportunities in the Indian and US ecosystem.
Metagenomics is the approach to genomically investigate entire microbial communities (referred to as the microbiome) in diverse environmental niches. Metagenomic data is typically huge in terms of volume and extremely complex given that the genomic content originates from a multitude of diverse microbes. This requires the development and application of specialized algorithms, customized databases and advanced statistical and AI-based analytics to not only understand the community-level composition and systems biology of these microbiomes but also relate these aspects to the phenotypic traits of the corresponding environment. This foundation course in metagenomics will provide a conceptual overview and understanding of the different aspects of metagenomic data analysis, along with practical hands-on training and assignments.