Core Courses
All BCB students must take three core courses, which cover the three focus areas of Bioinformatics and Computational Biology: Computer Science, Biological Sciences, and Mathematical Sciences. Students enter our interdisciplinary program with a wide variety of backgrounds, and the core courses provide a common foundation for students to build on. They also create a cohort community, ensuring BCB students interact with each other despite having diverse focus areas for their individual research projects.
The core courses are only offered every other year, so it is important to plan ahead and ensure you fit them into your Study Plan.
Core courses can be taken at any time during a student’s program, but it is strongly recommended you complete them during the first 2-3 years. Because of the centrality of the core courses to the BCB program, substitutions will only be given in exceptional circumstances.
This class takes a rigorous and quantitative approach to understanding the processes of molecular evolution and the analyses available to detect patterns and make strong inferences. It focuses on reading primary scientific literature presenting molecular evolution theory, describing statistics and analytical tools, and implementing those analyses on empirical data.
Students will be best served by having a mastery of the basic concepts of evolution and genetics at the level of upper-level undergraduate courses in those areas, as well as fluency in basic statistical concepts and mathematics through calculus. However, these are not prerequisites if students are willing to put in extra effort to catch up in areas of weakness, and it is expected that students will have a wide range of backgrounds. For example, important concepts include:
- Structure and roles of DNA, RNA, protein; replication, transcription, translation
- Gene regulation and expression; structure of chromosomes; mutational processes
- Population genetics: Hardy-Weinberg equilibrium, genetic drift, mutation, selection, recombination, demography, effective population size, dominance, epistasis, etc.
- Microevolution: sexual selection, coevolution, heritability, biogeography, speciation, phylogenetics
- Statistics: mean, variance, p-values, probability distributions
- Math: differentiation, integration
Resources for background information in these areas are textbooks and notes from undergraduate-level courses, online resources, and your classmates and the instructor.
Design and analyze algorithms that address the computational problems posed by biological sequence data, such as DNA or protein sequences. Topics may include: comparing sequences (from genes to genomes), database searching, multiple sequence alignment, Hidden Markov Models, phylogenetic inferencing, gene discovery and annotation, and genome assembly.
The emphasis in this course is on understanding the details of the algorithms, including computational complexity, time-accuracy trade-offs, and actually coding.
There are two midterms, a comprehensive final exam, and three programming projects.
This course requires a knowledge of calculus and basic probability and statistics. Students should be familiar with the concepts of probability, expected value, variance, covariance, and some of the basic probability distributions (binomial, normal, exponential, Poisson). Some experience with a programming language is also helpful, but some sample programs will be provided to ease the way for those with no programming background or those wishing to try a new coding language. No prior knowledge of biology is presumed. This course is populated by students with very different backgrounds. The key is a willingness to dive in and get your hands dirty (or your feet wet) trying out different ideas.
Recommended Prerequisites: Math 451 (Probability Theory) and Math 452 (Mathematical Statistics)
As an exclusive microbiologist, I was intimidated by taking classes where the pre-req was a four-year degree in math/stat or cs, but it wasn鈥檛 that bad! The instruction was conceptually based and there wasn鈥檛 an expectation of memorizing every possible concept. Ideas were presented and walked through and there was no expectation of perfection. And as a bonus, working with other BCB students who did have degrees in math/stat or cs made each class collaborative, which is one of the reasons I chose the BCB program here at U of I. BCB Ph.D. student Yva Eline