BIOLOGICAL SCIENCES MAJOR

Investigate the range of quantitative and other analytical techniques in biological theory and experimentation.

REQUIREMENTS 

Coding requirement:  CS110, or CS111, or NICO101+102-counts in place of one of the two non-CSB 300-level Bio courses.

Any three of the following courses:

• BIOL_SCI 323-0 Bioinformatics: Sequence & Structure Analysis - Use of informational and modeling techniques to explore evolutionary and other problems related to the genome.Prerequisite: Students must have taken BIOL_SCI 241-0 or BIOL_SCI 301-0 in order to register for this class.
• BIOL_SCI 337-0 Biostatistics - Highly interactive course focused on analysis of ecological data and independent projects using the opensource software program R. Prerequisite: Students must have completed BIOL_SCI 201-0 or ENVR_SCI 202-0, and MATH 218-3 or MATH 220-2 to register for this course.
• BIOL_SCI 338-0 Modeling Biological Dynamics - Mathematical and computational techniques for analyzing and predicting biological dynamics. Techniques include statistical models, discrete- and continuous- time dynamical models, and stochastic models. Applications cover a range of scales, with an emphasis on common mathematical concepts and computational techniques, the interpretation of existing data, and making predictions for new experiments. Prerequisites: at least one of MATH 218-1, MATH 220-1, MATH 240-0, STAT 202-0, BIOL_SCI 337-0, OR equivalent.
• BIOL_SCI 345-0 Topics in Biology:  Principles and Methods in Systems Biology - Life is an inherently dynamic process, and the dynamics at every scale of organization -- from the atomic self-assembly of macromolecular complexes to the interactions of species in an ecology -- can give rise to surprising outcomes. Predicting and modulating those dynamics requires the development of accurate mathematical and computational models. In this class, you will learn about mathematical and computational techniques for analyzing and predicting biological dynamics. Techniques will include statistical models, discrete- and continuous- time dynamical models, and stochastic models. Applications will cover a range of scales, from biomolecules to population dynamics, with an emphasis on common mathematical concepts and computational techniques, the interpretation of existing data, and making predictions for new experiments. Prerequisites: Students must have completed BIOL_SCI 202-0, BIOL_SCI 203-0, and BIOL_SCI 234-0 to register for this course.
• BIOL_SCI 354-0 Quantitative Analysis in Biology - Each module (of which there will be five or six) will end with analysis of a data set acquired from the authors of the studies, with re-analysis and re-plotting of a central result therefrom. En route to this, the course covers the mathematics, physics, and statistics required to produce the plots. The landmark papers include studies in gene regulation, developmental biology, gene sequencing, etc. There will also be aspects of coding, image analysis, random genetic processes, gene expression, cell adaptation, the cell cycle, developmental morphogens, and phylogenomics. Prerequisites: Students must have completed BIOL_SCI 201-0 and BIOL_SCI 202-0 to register for this course.
• BIOL_SCI 359-0 Quantitative Experimentation in Biology - This course is taught as four modules, in which students replicate experiments from some of the landmark papers in the history of quantitative biology. Groups of students will work together and learn how to perform experiments in a wet lab setting, perform live imaging, conduct sequencing-based studies, analyze their own data, and present results in a professional manner. Prerequisites: Students must have completed BIOL_SCI 201-0 and BIOL_SCI 202-0, or BIOL_SCI 354-0 to register for this course.
• BIOL_SCI 378-0 Functional Genomics - Patterns of gene expression and their causes. Prerequisite: Students must have completed BIOL_SCI 202-0 and BIOL_SCI 203-0 to register for this course.
• Chem_Eng_379-0 Computational Biology - Introduction to the development and application of data-analytical and theoretical methods, mathematical modeling, and computational simulation techniques to the study of biological systems. 
• ES_APPM_495-0 Introduction to the Analysis of RNA Sequencing Data - This course will give an introduction to the theory and practice of analyzing high-throughput RNA sequencing through lectures and hands-on exercises.

Concentration Checklist

Computational & Systems Biology Concentration Checklist

2023-24 Course Offerings in this Concentration*

*Course schedule considered tentative until published in Caesar