The objective of this course is to provide an introduction to the foundations of biology including the chemistry of life, cellular mechanisms, genetics and mechanisms of evolution.
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 discussion's.
The aim of this basic core course is to provide students a decent background in cell biology and biochemistry. The major focus is to a) cover most important concepts in cell biology like structure and functions of cell, proteins, and signal transduction mechanisms b) cover biological thermodynamics, enzyme kinetics and metabolism and finally c) solve qualitative and quantitative problems.
The aim of this basic core course is to provide students a good background in genetics and molecular biology. The major focus is to a) cover most important concepts in genetics like Mendelian laws, linkage and recombination b) cover molecular mechanisms of gene expression and c) solve qualitative and quantitative problems in population and evolutionary genetics.
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.
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 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 objective of this course is to provide introduction to network science, an emerging interdisciplinary discipline with applications to various disciplines including social sciences, security and biomedical sciences. The student will be provided with understanding of fundamentals of network science 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 network science.
The objective of this course is to provide knowledge about concepts and methods of statistical analysis. Data can be generated by machine or manually collected during surveys. All kinds of data-sets need analysis to the point such that we can make conclusions about the trend in the data. Hence statistical modelling and inference is often needed. Recently multiple kinds of statistical modelling approach have been suggested. Some statistical methods have been implemented as tools while some are implemented by users according to need of data-analysis. This course would provide the basics of statistical inference and methods introducing some computational techniques to perform modelling of systems. Through this course student will also learn about weakness and strength of such statistical modelling methods which could guide them to distinguish or develop suitable analysis techniques.