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 courses

Courses (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. (4-0) 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. (3-0) 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
Prereq: STAT 341; MATH 207 or MATH 317
Transformations of random variables; sampling distributions; confidence intervals and hypothesis testing; theory of estimation and hypothesis tests; linear model theory; use of the R statistical package for simulation and data analysis.

Stat 401

STAT 401: Statistical Methods for Research Workers
(3-2) Cr. 4. F.S.SS. Prereq: STAT 101 or STAT 104 or STAT 105 or STAT 201 or STAT 226
Graduate students without an equivalent course should contact the department. Methods of analyzing and interpreting experimental and survey data. Statistical concepts and models; estimation; hypothesis tests with continuous and discrete data; simple and multiple linear regression and correlation; introduction to analysis of variance and blocking.

Stat 447

STAT 447: Statistical Theory for Research Workers
(4-0) Cr. 4. F.S.SS. Prereq: MATH 151 and permission of instructor, or MATH 265
Primarily for graduate students not majoring in statistics. Emphasis on aspects of the theory underlying statistical methods. Probability, probability density and mass functions, distribution functions, moment generating functions, sampling distributions, point and interval estimation, maximum likelihood and likelihood ratio tests, linear model theory, conditional expectation and minimum mean square error estimation, introduction to posterior distributions and Bayesian analysis, use of simulation to verify and extend theory. Credit for both STAT 341 and STAT 447 may not be applied toward graduation.

Stat 430

STAT 430: Empirical Methods for the Computational Sciences
(3-0) Cr. 3. F. Prereq: STAT 330 or an equivalent course, MATH 166, knowledge of linear algebra.
Statistical methods for research involving computers; exploratory data analysis; selected topics from analysis of designed experiments - analysis of variance, hypothesis testing, interaction among variables; linear regression, logistic regression, Poisson regression; parameter estimation, prediction, confidence regions, dimension reduction techniques, model diagnostics and sensitivity analysis; Markov chains and processes; simulation techniques and bootstrap methods; applications to computer science, bioinformatics, computer engineering - programs, models and systems as objects of empirical study; communicating results of empirical studies. Statistical software: R.


Category II. Biological Sciences

Biol 313 or equiv.

Biol 313. Principles of Genetics. (Cross-listed with Gen). (3-0) 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. (3-0) 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
or equiv

COM S 227. Introduction to Object-oriented Programming. (3-2) 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 object-oriented design and programming techniques. Symbolic and numerical computation, recursion and iteration, modularity procedural and data abstraction, and specifications and subtyping. Object-oriented techniques including encapsulation, inheritance and polymorphism. Imperative programming. Emphasis on principles of programming and object-oriented 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. (3-1) Cr. 3. F.S. Prereq: Minimum of C- in 227, credit or enrollment in Math 165. An object-oriented approach to data structures and algorithms. Object-oriented 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 object-oriented design, writing and documenting medium-sized programs. This course is designed for majors.

Com S 230 or

Com S 230. Discrete Computational Structures. (3-1) 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