BB 621: Biostatistics
The scope of statistics in biomedical data analysis; Statistical design of experiments for clinical and laboratory data: random allocation, methods of allocation without random numbers; Volunteer bias; Crossover designs; Selection and distribution of experimental unit; Case-control analysis. Applications of probability and standard distributions: Estimation, standard error and confidence interval, testing of hypotheses; Correlation and regression; Analysis of variance; Factor analysis; Statistical method oriented and problem-oriented illustrations for computer-aided inferencing.
Texts & References:
- Martin Bland: An Introduction to Medical Statistics, Oxford University Press, 1987.
- G. Eason, C.W. Coles &G. Gittinby: Mathematics and Statistics for the Biosciences. Ellis Horwood, 1980.
- O. Kempthorne: Design and Analysis of Experiments, Wiley Eastern, 1967.
- Wayne Daniel : Biostatistics: Foundation for Analysis in the Health Sciences, 5th ed., John Wiley & Sons, New York, 1987.
Introduction to Biostatistics is also available as an online course from NPTEL
BS 637: Cell Mechanics and Mechanobiology
Mechanical forces are known to play an increasingly important role during development, normal function as well as in disease. This course will focus on the physical interactions between cells and their surroundings. Students will learn how cells sense and respond to external forces and cues, and how these mechanical inputs influence subcellular biochemistry and cell behavior. They will also study various experimental techniques that have been developed for probing cell structure, manipulating cells, and measuring their mechanical properties.
Introduction to mechanobiology is also available as an online course from NPTEL
BB 604: Bio Mathematics
Mathematical and computational problems in the context of biology and medicine with emphasis on deterministic models. Models from epidemiology, cell design, enzyme kinetics, genomics, drug kinetics and design, and sports medicine.
Review of sets, number system and operators, sequences and series, exponential, logarithm, and trigonometric functions, limits, derivatives, and graphing of functions. Polynomials, roots, approximation to functions. Function maxima and minima. Integration. Linear ordinary differential equations. Second-order partial differential equations. Transform techniques. Difference approximations, discrete models, and numerical solutions, error analysis. Introduction to algorithmics for biology and medicine. Examples for modeling and analysis from population growth, spread and arrest of diseases, tumour growth, biomechanics, and theoretical neuroscience. Solutions using Matlab and SciLab.
Texts & References:
- J. D. Murray, Mathematical biology: an introduction, 3rd ed., New York, Springer 2002.
- R. W. Shonkwiler, Mathematical Biology: An Introduction with Maple and Matlab, Springer 2009.
- E. Kreyszig. Advanced Engineering Mathematics, 9th Ed., Wiley, 2006
BM 633: Biomechanics
This course will introduce students to the field of biomechanics. Students will learn basic concepts of statics, solid mechanics, and their applications towards measuring and quantifying mechanical properties of biological fluids, solids, tissues, and organs. Students will be exposed to various applications of biomechanics from the fields of bioengineering, physiology, and medicine.
BB 101: Biology
Quantitative views of modern biology. Importance of illustrations and building quantitative/qualitative models. Role of estimates. Cell size and shape. Temporal scales. Relative time in Biology. Key model systems – a glimpse. Management and transformation of energy in cells. Mathematical view – binding, gene expression, and osmotic pressure as examples. Metabolism. Cell communication. Genetics. Eukaryotic genomes. Genetic basis of development. Evolution and diversity. Systems biology and illustrative examples of applications of Engineering in Biology.