Courses Offered

BCB Course Offerings and Requirements

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Listed below is a list of all BCB Course Requirements for PhD students in the BCB degree program.

Before reading about BCB Course Requirements, here is information on some courses offered for those interested in fundamental skills.  Students from other disciplines might want to take these courses.


BCB546X, Biology Data Skills, will teach students the fundamental skills of data processing, management, and analysis that are essential for working with large data sets.  The course will include modules on basic UNIX commands, scripting in Python and R, version control using Git and GitHub, use of high performance computing clusters and writing effective data management plans.  The book "Bioinformatics Data Skills" by Vince Buffalo, part of the O'Reilly computational series, will be the primary text for the course.  This will be a 3-credit course meeting twice a week for 1.5 hours.  Topics will be taught in class using a combination of lectures and computational exercises.

Time:     1:10 - 2:30 p.m.;  Location: 0205 Bessey
Instructor:    Matthew Hufford

BCB546X is intended to be a service course directed primarily toward graduate students outside of the BCB Graduate Program (e.g., IGG, Animal Science, Agronomy, EEB), but should also prove useful for new BCB students from a biological background who need to develop basic computational skills.

Math/ComS/CprE 424 and 525

These are two High Performance Computing Courses.  The purpose of math 424 and 525 is to train students to be able to effectively use on-campus High Performance Computing (HPC) machines. Students without Fortran or C programming experience should take 424 and then 525. Students with Fortran or C programming experience should take only 525. More information is here.

Spring 2018 Course Announcements

PLP 512, Lifestyles of Plant Pathogenic Fungi and Oomycetes

2 credits, is being offered in Spring Semester 2018.

  • It is a 10-week-long course, beginning on February 12, 2018.
  • Class times are Mondays and Wednesdays from 3:10-4:30 pm.
  • Here is a syllabus from Spring 2016 for your information; content will be similar in 2018.

INSTRUCTOR: Mark Gleason (

  • Office phone 294-0579
  • Cell phone: 231-4925
  • Office: 313 Bessey Hall


BCB Course Requirements for PhD Students

The curriculum for BCB's graduate program at Iowa State University has developed over the last ten+ years to incorporate and keep pace with the expanding body of knowledge associated with this dynamic discipline.  Some curriculum updates were recently developed by the BCB curriculum committee and approved by the full BCB faculty.  New students will be given the opportunity to take part in background courses to prepare them for BCB's rigorous core courses.  Descriptions of these courses can be found on the Academic Preparation page under the Future Students tab.

Fewer courses overall and more flexibility in meeting course requirements are hallmarks of further curricular changes for BCB PhD students. Refer to the Degree Requirements page and our BCB student handbook for further details about the BCB degree program. 

Required background course:

Many students do take this background course, but some may be directed to take a series of Statistics courses such as Stat 401 and 447.  Talk with your advisor to determine the best path for you and your academic background. 

Stat 430. Empirical Methods for Computer Science. (3-0) Cr. 3. F. Prereq: Stat 330 or an equivalent course, Math 166, knowledge of linear algebra. Programs and systems as objects of empirical studies; 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 performance assessment - comparison of multiple systems; communicating results of empirical studies. Statistical software: R.

Advanced Biology Core Requirement (Examples):

GDCB 511. Molecular Genetics. (Cross-listed with MCDB). (3-0) Cr. 3. S. Prereq: Biol 313 and BBMB 405. The principles of molecular genetics: gene structure and function at the molecular level, including regulation of gene expression, genetic rearrangement, and the organization of genetic information in prokaryotes and eukaryotes. (An equivalent or more advanced course may be substituted with approval of student's POS Committee.)

AN S 556: Current Topics in Genome Analysis (3-0) Cr. 3. Alt. S., offered even-numbered years. Prereq: BBMB 405 or GDCB 510 Introduction to principles and methodology of molecular genetics useful in analyzing and modifying large genomes.

EEOB 561: Evolutionary and Ecological Genomics (3-0) Cr. 3. S. Prereq: Permission of instructor; BCBIO 444 recommended. Use of genomic and other "omic" data in evolution and ecology. Review of data-generation platforms, computational methods, and examples of how phylogenomics, metagenomics, epigenomics, and population genomics are transforming the disciplines of evolution and ecology.

EEOB 563: Molecular Phylogenetics (2-3) Cr. 3. F. Prereq: BIOL 313 and BIOL 315 An overview of the theory underlying phylogenetic analysis and the application of phylogenetic methods to molecular datasets. The course emphasizes a hands-on approach to molecular phylogenetics and combines lecture presentations with computer exercises and discussion of original scientific literature.

Core courses in computational biology:

BCB 567. Bioinformatics I (Bioinformatics Algorithms). (Cross-listed with Com S, Cpr E). (3-0) Cr. 3. F. Required Prerequisites: Com S 228; Com S 330; Biol 313; credit or enrollment in Biol 315, Stat 430. Biology as an information science. A review of the algorithmic principles that are driving the advances in bioinformatics and computational biology. Syllabus: BCB_567_2070015_Eulenstein_F15.pdf

BCB 568. Bioinformatics II (Statistical Bioinformatics). (Cross-listed with GDCB, Stat, Com S). (3-0) Cr. 3. S. Prereq: BCB 567, Biol 315, Stat 430, credit or enrollment in Gen 409. Statistical models for sequence data, including applications in genome annotation, motif discovery, variant discovery, molecular phylogeny, gene expression analysis, and metagenomics.  Statistical topics include model building, inference, hypothesis testing, and simple experimental design, including for big data/complex models. Syllabus: BCB_568_2081005_Dorman_S15.pdf

BCB 570. Bioinformatics IV (Systems Biology). (Cross-listed with Com S, GDCB, Stat, Cpr E). (3-0) Cr. 3. S. Prereq: BCB 567 or Com S 311; Com S 228, Gen 409, and Stat 430. Algorithmic and statistical approaches in computational functional genomics and systems biology. Analysis of high throughput biological data obtained using system-wide measurements. Topological analysis, module discovery, and comparative analysis of gene and protein networks. Modeling, analysis, and inference of transcriptional regulatory networks, protein-protein interaction networks, and metabolic networks. Dynamic systems and whole-cell models. Ontology-driven, network based, and probabilistic approaches to information integration. Syllabus: BCB 570_2125005_Dickerson_S15.docx

Advanced Group Requirements

In addition to the four core courses, students must complete at least six credits of advanced coursework. The table below provides a list of some of the courses that can be used to fulfill this depth requirement. Not all listed courses are suited for all programs of study. Students should consult with their POS committees to determine which courses from this list, or not from the list, are most appropriate.

Courses That Fulfill Advanced Requirements

Students should select advanced requirements in consultation with their POS Committee. This is a partial list of suggestions. Advanced courses should be selected with POS committee consultation and approval.


Category I. Molecular Biology
An Sci 556 Current Topics in Genome Analysis 3 cr - Alt S
BBMB 404 Biochemistry I 3 cr. - F
BBMB 405 Biochemistry II 3 cr. - S
BBMB 461 Molecular Biophysics 2 cr. - S
BBMB 501 General Biochemistry 3 cr. - F
BBMB 502 General Biochemistry 3 cr. - S
C, D, E, F
Introduction to Molecular Biology Techniques 1 cr. per module - F, S
BBMB 561 Molecular Biophysics 2 cr. - S
BBMB 607 Plant Biochemistry 2 cr. - F
BBMB 653 Protein Chemistry - Physical Methods 1 cr. - S
BBMB 660 Membrane Biochemistry 2 cr. - F
BBMB 675 Nucleic Acid Structure and Function 2 cr. - F
Gen 462/EEOB 562 Evolutionary Genetics 3 cr. - S
GDCB 520 Genetic Engineering 3 cr. - Alt. F
EEOB 561X Evolutionary and Ecological Genomics 3 cr. - S
EEOB 563 Molecular Phylogenetics 3 cr. - F
EEOB 566 Molecular Evolution 3 cr. - Alt. F
Category II. Computer Science
*BCB 567 Bioinformatics I (Fundamentals of Genomic Informatics) 3 cr. - F
*BCB 568 Bioinformatics II (Advanced Genome Informatics) 3 cr. - S
BCB 596 Genomic Data Processing 3 cr. - F
BCB 569 Structural Bioinformatics 3 cr. - F
Com S 363 Introduction to Database Management Systems 3 cr. - F S
Com S 461/561 Principles and Internals of Database Systems 3 cr. - F
Com S 472/572 Principles of Artificial Intelligence 3 cr. - F
Com S 474 Elements of Neural Computation 3 cr. - Alt F
Com S 511 Design and Analysis of Algorithms 3 cr. - F
Com S 526 Intro to parallel Algorithms and Programming 4 cr. - F
Com S 549 Advanced Algorithms in Computational Biology 3 cr. - Alt S
Com S 550 Evolutionary Problems for Computational Biologists 3 cr. - Alt F
Com S 551 Computational Techniques for Genome Assembly and Analysis 3 cr. - Alt F
Com S 573 Machine Learning 3 cr. - S
Com S 574 Intelligent Multiagent Systems 3 cr. - Alt F
Com S 611 Advanced Topics in Analysis of Algorithms 3 cr. - Alt S
Com S 672 Advanced Topics in Computational Models of Learning 3 cr. - Alt S
Com S 673 Advanced Topics in Computational Intelligence 3 cr. - Alt S
EE 547 Pattern Recognition 3 cr. - F
Category III. Mathematics & Statistics
*BCB 568 Advanced Genome Informatics 3 Cr. - S
BCB 660 Applications of NGS data processing software in genomics 3 Cr. - F
Math 304 Introductory Combinatorics 3 Cr. - F
Math 407 Applied Linear Algebra 3 Cr. - F
Math 314 Graphs and Networks 3 Cr. - S
Math 554 Introduction to Stochastic Processes 3 Cr. - F
Stat 500 Statistical Methods 4 Cr. - F
Stat 501 Multivariate Statistical Methods 3 Cr. - S
Stat 536 Statistical Genetics 3 Cr. - Alt F
Stat 542 Theory of Probability and Statistics I 4 Cr. - F
Stat 543 Theory of Probability and Statistics II 3 Cr. - S
*F = Fall semester; S = Spring semester; SS = Summer Session
*BCB 567 and 568 cannot be used to meet core course requirements AND Advanced Course Requirements.

Ethics Requirement:

GRST 565. Responsible Conduct of Research in Science and Engineering.(1-0) Cr. 1. F.S. Prereq: Graduate classification. Ethical and legal issues facing researchers in the sciences and engineering.

Workshop and Seminars:

More information on these courses can be found here.

BCB 593. Workshop in Bioinformatics and Computational Biology. (1-0) Cr. 1. Repeatable. F.S. Current topics in bioinformatics and computational biology research. Lectures by off-campus experts. Students read background literature, attend preparatory seminars, attend all lectures, meet with lecturers.

BCB 690. Student Seminar in Bioinformatics and Computational Biology. Cr. 1. Repeatable. S. Student research presentations.

BCB 691. Faculty Seminar in Bioinformatics and Computational Biology. (1-0) Cr. 1. Repeatable. F. Faculty research series. Fall 2017 Schedule.


BCB 697. Graduate Research Rotation. Cr. arr. F.S.SS. Graduate research projects performed under the supervision of selected faculty members in the Bioinformatics and Computational Biology major.

BCB 699. Research. Cr. arr. Repeatable. (BCB 699 reference numbers.)