Academic Preparation
The foundation disciplines for BCB are genetics, molecular biology, mathematics, computer science, statistics and physics. Students entering the BCB program are expected to have a strong undergraduate background in at least one of these disciplines and additional coursework in another.
The following table summarizes the three areas in which BCB majors must demonstrate basic competence. A flow chart of the background and core courses is also provided.
Students are strongly encouraged to take courses equivalent to the ISU courses listed below prior to enrollment in the BCB program. Where such preparatory coursework has not been taken, students will have the opportunity to take these courses during their first year of BCB graduate training to prepare for BCB core courses.
The temporary advisor or major professor helps each student determine whether additional courses are needed. The student's POS committee will evaluate competence in the three background areas during the student's first POS Committee meeting.
Background Courses for Admission to BCB and as preparation for BCB Core coursesCourses (or equiv.) that should be taken prior to enrollment or during first year unless similar coursework was completed prior to joining the BCB Program 


Category I. Mathematics and Statistics 

Math 265 or equiv. 
Math 265. Calculus III. (40) Cr. 4. F*.S.SS.Prereq: Grade of C or better in 166 or 166H. Analytic geometry and vectors, differential calculus of functions of several variables, multiple integrals, vector calculus. 

Stat 341 
Stat 341. Introduction to the Theory of Probability and Statistics I. (30) Cr. 3. F.S.Prereq: Math 265 (or 265H). Probability; distribution functions and their properties; classical discrete and continuous distribution functions; multivariate probability distributions and their properties; moment generating functions; simulation of random variables and use of the R statistical package. 

Stat 342 
STAT 342. Introduction to the Theory of Probability and Statistics II 

Stat 401 renumbered Stat 587 
STAT 401: Statistical Methods for Research Workers 

Stat 447 renumbered Stat 588 
STAT 447: Statistical Theory for Research Workers 

Stat 430 
STAT 430: Empirical Methods for the Computational Sciences 


Category II. Biological Sciences 

Biol 313 or equiv. 
Biol 313. Principles of Genetics. (Crosslisted with Gen). (30) Cr. 3. F.S.Prereq: 211, 211L, 212, and 212L. Introduction to the principles of transmission and molecular genetics of plants, animals, and bacteria. Recombination, structure and replication of DNA, gene expression, cloning, quantitative and population genetics. 

Biol 315 
Biol 315. Biological Evolution. (30) Cr. 3. F.S.Prereq: 313. The mechanisms of evolution. Topics in microevolution: population genetics, natural selection, genetic variation, and adaptation. Macroevolution: speciation, extinction, phylogeny, and major evolutionary patterns. 


Category III. Computer Science 

Com S 227 
COM S 227. Introduction to Objectoriented Programming. (32) Cr. 4. F.S. Prereq: Placement into Math 143, 165, or higher; recommended: a previous high school or college course in programming or equivalent experience. Introduction to objectoriented design and programming techniques. Symbolic and numerical computation, recursion and iteration, modularity procedural and data abstraction, and specifications and subtyping. Objectoriented techniques including encapsulation, inheritance and polymorphism. Imperative programming. Emphasis on principles of programming and objectoriented design through extensive practice in design, writing, running, debugging, and reasoning. Course intended for Com S majors. Credit may not be applied toward graduation for both Com S 207 and 227. 

Com S 228 or equiv 
COM S 228. Introduction to Data Structures. (31) Cr. 3. F.S. Prereq: Minimum of C in 227, credit or enrollment in Math 165. An objectoriented approach to data structures and algorithms. Objectoriented analysis, design, and programming, with emphasis on data abstraction, inheritance and subtype polymorphism. Abstract data type specification and correctness. Collections and associated algorithms, such as stacks, queues, lists, trees. Searching and sorting algorithms. Graphs. Data on secondary storage. Analysis of algorithms. Emphasis on objectoriented design, writing and documenting mediumsized programs. This course is designed for majors. 

Com S 230 or 
Com S 230. Discrete Computational Structures. (31) Cr. 3. F.S. Prereq: C or higher in 228, C or higher in Math 166 and Engl 250. Concepts in discrete mathematics as applied to computer science. Logic, proof techniques, set theory, relations, graphs, combinatorics, discrete probability and number theory. 
*F = Fall semester; S = Spring semester; SS = Summer Session