Contact Information
- Karin Dorman (BCBio Undergraduate Program)
- 2411 Snedecor
- las.iastate.edu/bcbio/
- kdorman@iastate.edu
- 515-294-1457
- Carla Harris (BCB Graduate Program)
- 2014 Molecular Biology
- charris@iastate.edu
- 515-294-5122
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- Bioinformatics and Computational Biology
This is an archived copy of the 2020-2021 catalog. To access the most recent version of the catalog, please visit http://catalog.iastate.edu.
Undergraduate study in BCBio is jointly administered by the Department of Computer Science, the Department of Genetics, Development, and Cell Biology, and the Department of Mathematics. The undergraduate B.S. degree is offered through the College of Liberal Arts and Sciences.
Bioinformatics and Computational Biology is an interdisciplinary science at the interfaces of the biological, informational and computational sciences. The science focuses on a variety of topics. These include gene identification, expression, and evolution; RNA, protein, and genome structure; and molecular and cellular systems and networks. The large group of participating faculty provides students with a multidimensional perspective on bioinformatics and computational biology and presents them with broad range of possibilities to get involved in research.
This major will prepare students for careers at the interfaces of biological, informational and computational sciences. BCBio graduates with a B.S. seeking direct employment will find ready markets for their talents in agricultural and medical biotechnology industries, as well as in academia, national laboratories, and clinics. Although some students find employment directly after their baccalaureate training, many students will continue their education in one of the many excellent graduate programs in bioinformatics and computational biology that now exist.
Participation in this field requires that students achieve a high level of competence not only in biology, but also in mathematics, computer science, and statistics. As a result, the program includes required courses from many different disciplines. Graduates demonstrate an above-average ability to synthesize methods from these different disciplines to solve problems.
In addition to basic degree requirements listed in the Curriculum in Liberal Arts and Sciences, BCBio majors must satisfy the following requirements:
A. Complementary Courses for the BCBio Major
| A minimum of 4 credits from the following: | 4-8 | |
| College Chemistry and Laboratory in College Chemistry | ||
| or | ||
| General Chemistry I and Laboratory in General Chemistry I and General Chemistry II | ||
| or | ||
| Advanced General Chemistry and Laboratory in Advanced General Chemistry | ||
| A minimum of 4 credits from the following: | 4-8 | |
| Elementary Organic Chemistry and Laboratory in Elementary Organic Chemistry | ||
| or | ||
| Organic Chemistry I and Laboratory in Organic Chemistry I | ||
and | ||
| Organic Chemistry II and Laboratory in Organic Chemistry II | ||
| PHYS 111 | General Physics | 5 |
| or | ||
| Introduction to Classical Physics I | ||
| or | ||
| Physics for the Life Sciences and Laboratory in Physics for the Life Sciences | ||
| STAT 330 | Probability and Statistics for Computer Science | 3 |
| STAT 483 | Empirical Methods for the Computational Sciences | 3 |
| BIOL 211 & 211L | Principles of Biology I and Principles of Biology Laboratory I | 4 |
| BIOL 212 & 212L | Principles of Biology II and Principles of Biology Laboratory II | 4 |
| BIOL 314 | Principles of Molecular Cell Biology | 3 |
| or | ||
| Biological Evolution | ||
| or | ||
| Molecular Genetics | ||
| Total Credits | 30-38 | |
Complementary courses note:The following other STAT courses may be substituted for STAT 330 and STAT 483, with permission of the BCBio Major.
STAT 330: STAT 101, 104, 105, 201, 231, 305, or 341
STAT 483: STAT 301, 342, or 432
B. Core Courses Within the BCBio Major
| GEN 313 & 313L | Principles of Genetics and Genetics Laboratory | 4 |
| A minimum of 6 credits from the following: | 6-7 | |
| Object-oriented Programming and Introduction to Data Structures (recommended when developing course plan) | ||
| or | ||
| Fundamentals of Computer Programming and Intermediate Computer Programming (allowed for students entering major who took these courses) | ||
| COM S 230 | Discrete Computational Structures | 3 |
| COM S 311 | Introduction to the Design and Analysis of Algorithms | 3 |
| MATH 165 & MATH 166 | Calculus I and Calculus II (recommended when developing course plan) | 8 |
| BCBIO 110 | BCBIO Orientation | 0.5 |
| BCBIO 322 | Introduction to Bioinformatics and Computational Biology | 3 |
| BCBIO 401 | Fundamentals of Bioinformatics and Computational Biology | 3 |
| BCBIO 402 | Fundamentals of Systems Biology and Network Science | 3 |
| BCBIO 490 | Independent Study | 1-5 |
| or BCBIO 491 | Team Research Projects. | |
| Total Credits | 34.5-39.5 | |
Core courses note: The Com S 227/228 and Math 165/166 core course series is required for BCBio majors. However, students transferring into the major who have already earned credit for Com S 207/208 and/or the Math 181/182 can substitute those courses for the respective Com S 227/228 and/or Math 165/166 series. Students will need permission of the instructors to enroll in any upper level course that requires a pre-req in Com S 227/228 and/or Math 165/166.
C. Support Electives
3-9 credits to be chosen from the following list:
| BBMB 404 | Biochemistry I | 3 |
| BBMB 405 | Biochemistry II | 3 |
| BBMB 461 | Molecular Biophysics | 2 |
| BIOL 328 | Molecular and Cellular Biology of Human Diseases | 3 |
| BIOL 423 | Developmental Biology | 3 |
| BIOL 451 | Plant Evolution and Phylogeny | 4 |
| BIOL 462 | Evolutionary Genetics | 3 |
| BIOL 487 | Microbial Ecology | 3 |
| COM S 252 | Linux Operating System Essentials | 3 |
| COM S 309 | Software Development Practices | 3 |
| COM S 319 | Construction of User Interfaces | 3 |
| COM S 327 | Advanced Programming Techniques | 3 |
| COM S 363 | Introduction to Database Management Systems | 3 |
| COM S 425 | High Performance Computing for Scientific and Engineering Applications | 3 |
| COM S 426 | Introduction to Parallel Algorithms and Programming | 4 |
| GEN 340 | Human Genetics | 3 |
| GEN 410 | Analytical Genetics | 3 |
| MATH 207 | Matrices and Linear Algebra | 3 |
| or MATH 317 | Theory of Linear Algebra | |
| MATH 265 | Calculus III | 4 |
| MATH 266 | Elementary Differential Equations | 3 |
| or MATH 267 | Elementary Differential Equations and Laplace Transforms | |
| MATH 304 | Combinatorics | 3 |
| MATH 314 | Graph Theory | 3 |
| MATH 373 | Introduction to Scientific Computing | 3 |
| MICRO 402 | Microbial Genetics and Genomics | 3 |
| STAT 342 | Introduction to the Theory of Probability and Statistics II | 4 |
| STAT 471 | Introduction to Experimental Design | 3 |
| STAT 474 | Introduction to Bayesian Data Analysis | 3 |
| STAT 475 | Introduction to Multivariate Data Analysis | 3 |
| STAT 486 | Introduction to Statistical Computing | 3 |
| STAT 581 | Analysis of Gene Expression Data for the Biological Sciences | 3 |
D. The communications and English proficiency requirements of the LAS college are met by:
| ENGL 150 | Critical Thinking and Communication | 3 |
| ENGL 250 | Written, Oral, Visual, and Electronic Composition | 3 |
| or ENGL 250H | Written, Oral, Visual, and Electronic Composition: Honors | |
| And one of the following: | ||
| ENGL 309 | Proposal and Report Writing | 3 |
| or | ||
| Biological Communication | ||
| or | ||
| Technical Communication | ||
BCBio majors must earn a minimum grade of C in ENGL 250 Written, Oral, Visual, and Electronic Composition or ENGL 250H Written, Oral, Visual, and Electronic Composition: Honors.
Minor in Bioinformatics and Computational Biology
The administering departments offer a minor in Bioinformatics and Computational Biology, which requires the following courses.
| BIOL 211 | Principles of Biology I | 3 |
| BIOL 212 | Principles of Biology II | 3 |
| GEN 313 | Principles of Genetics | 3 |
| COM S 227 & COM S 228 | Object-oriented Programming and Introduction to Data Structures | 7 |
| or | ||
| Fundamentals of Computer Programming and Intermediate Computer Programming | ||
| STAT 330 | Probability and Statistics for Computer Science | 3 |
| BCBIO 322 | Introduction to Bioinformatics and Computational Biology | 3 |
| BCBIO 401 | Fundamentals of Bioinformatics and Computational Biology | 3 |
| BCBIO 402 | Fundamentals of Systems Biology and Network Science | 3 |
| Total Credits | 28 | |
Note: The following other STAT courses may be substituted for STAT 330, with permission of the BCBio Major: STAT 101, 104, 105, 201, 231, 305, or 341
2. The Com S 227/228 course series is required for the BCBio minor. However, students transferring into the minor who have already earned credit for Com S 207/208 can substitute those courses for the Com S 227/228 series. Students will need permission of the instructors to enroll in any upper level course that requires a pre-req in Com S 227/228.
Most students pursuing a minor in Bioinformatics and Computational Biology will be biology, genetics, computer science, computer engineering, statistics, or mathematics students who have already taken some of these courses for their major. The minor must include at least 9 credits that are not used to meet any other department, college, or university requirement.
Bioinformatics and Computational Biology B.S.
| Freshman | |||
|---|---|---|---|
| Fall | Credits | Spring | Credits |
| BCBIO 110 | 0.5 | BIOL 212 | 3 |
| BIOL 211 | 3 | BIOL 212L | 1 |
| BIOL 211L | 1 | CHEM 231 | 3 |
| CHEM 163 | 4 | CHEM 231L | 1 |
| CHEM 163L | 1 | MATH 166 | 4 |
| MATH 165 | 4 | LIB 160 | 1 |
| ENGL 150 | 3 | Humanities choice | 3 |
| 16.5 | 16 | ||
| Sophomore | |||
| Fall | Credits | Spring | Credits |
| BIOL 313 | 3 | COM S 228 | 3 |
| BIOL 313L | 1 | BIOL 314 | 3 |
| BCBIO 322 | 3 | PHYS 115 | 4 |
| COM S 227 | 4 | PHYS 115L | 1 |
| ENGL 250 | 3 | Social Science choice | 3 |
| International Perspectives or U.S. Diversity | 3 | ||
| 17 | 14 | ||
| Junior | |||
| Fall | Credits | Spring | Credits |
| COM S 230 (or Cpr E 310) | 3 | COM S 311 | 3 |
| STAT 330 | 3 | STAT 483 | 3 |
| ENGL 309 (or ENGL 312 or ENGL 314) | 3 | Bioinformatics Support Elective | 3-9 |
| MATH 265 (or other Support Elective) | 4 | Humanities choice | 3 |
| Humanites Choice | 3 | Social Science choice | 3 |
| 16 | 15-21 | ||
| Senior | |||
| Fall | Credits | Spring | Credits |
| BCBIO 401 (or BCBIO 444) | 3 | BCBIO 402 | 3 |
| Humanities choice | 3 | BCBIO 490 or 491 | 1-5 |
| World Language if neeeded | 4 | World Language if needed or elective | 4 |
| COM S 363 (Recommended or other support elective) | 3 | International Perspectives or US Diversity | 3 |
| Social Science choice | 3 | ||
| 16 | 11-15 | ||
Graduate Study
Work is offered for the master of science and doctor of philosophy degrees with a major in Bioinformatics and Computational Biology (BCB). Faculty are drawn from several departments: Agronomy; Animal Science; Astronomy and Physics; Biochemistry, Biophysics and Molecular Biology; Biomedical Sciences; Chemical and Biological Engineering; Chemistry; Computer Science; Ecology, Evolution, and Organismal Biology; Electrical and Computer Engineering; Entomology, Genetics, Development and Cell Biology; Materials Science and Engineering; Mathematics; Plant Pathology; Statistics; Veterinary Microbiology and Preventive Medicine; and Veterinary Pathology.
The BCB program emphasizes interdisciplinary training in nine related areas of focus: Bioinformatics, Computational Molecular Biology, Structural and Functional Genomics, Macromolecular Structure and Function, Metabolic and Developmental Networks, Integrative Systems Biology, information Integration and Data Mining, Biological Statistics, and Mathematical Biology. Additional information about research areas and individual faculty members is available at: www.bcb.iastate.edu.
BCB students are trained to develop an independent and creative approach to science through an integrative curriculum and thesis research projects that include both computational and biological components. First year students are appointed as research assistants and participate in BCB 697 Graduate Research Rotation, working with three or more different research groups to gain experience in both “wet” (biological) and “dry” (computer) laboratory environments. In the second year, students initiate a thesis research project under the joint mentorship of two BCB faculty mentors, one from the biological sciences and one from the quantitative/computational sciences. The M.S. and Ph.D. degrees are usually completed in two and five years, respectively.
Before entering the graduate BCB program, prospective BCB students should have taken courses in mathematics, statistics, computer science, biology, and chemistry. A course load similar to the following list would be considered acceptable:
| MATH 265 | Calculus III | 4 |
| STAT 341 | Introduction to the Theory of Probability and Statistics I | 4 |
| COM S 207 | Fundamentals of Computer Programming | 3 |
| COM S 208 | Intermediate Computer Programming | 3 |
| COM S 230 | Discrete Computational Structures | 3 |
| CPR E 310 | Theoretical Foundations of Computer Engineering | 3 |
| CHEM 163 | College Chemistry | 4 |
| CHEM 231 | Elementary Organic Chemistry | 3 |
| BBMB 301 | Survey of Biochemistry | 3 |
| BIOL 313 | Principles of Genetics | 3 |
| BIOL 315 | Biological Evolution | 3 |
During the first year, BCB students are required to address any background deficiencies in calculus, molecular genetics, computer science, statistics and discrete structures, with specific courses determined by prior training. Among the total course requirements for Ph.D. students are four core courses in Bioinformatics, three of which are mandatory in the BCB program:
| BCB 567 | Bioinformatics Algorithms (mandatory) | 3 |
| BCB 568 | Statistical Bioinformatics (mandatory) | 3 |
| BCB 569 | Structural Bioinformatics | 3 |
| BCB 570 | Systems Biology (mandatory) | 3 |
| And also should include | ||
| Advanced Molecular Genetics | ||
| Student Seminar in Bioinformatics and Computational Biology | ||
| Faculty Seminar in Bioinformatics and Computational Biology | ||
| Workshop in Bioinformatics and Computational Biology | ||
M.S. students take the above background and core courses, take at least 6 credits of advanced coursework, and may elect to participate in fewer seminars and workshops. Additional coursework may be selected to satisfy individual interests or recommendations of the Program of Study Committee. All graduate students are encouraged to teach as part of their training for an advanced degree. (For curriculum details and sample programs of study, see: www.bcb.iastate.edu.)
Courses
Courses primarily for undergraduates:
(1-0) Cr. 0.5. F.
First 8 weeks. Orientation to the area of bioinformatics and computational biology. For students considering a major in BCBIO. Specializations and career opportunities.
Offered on a satisfactory-fail basis only.
(Cross-listed with BIOL, GEN). (3-0) Cr. 3. F.
Prereq: BIOL 212
Genome sequencing, assembly, structural and functional annotation, and comparative genomics. Investigating these topics will develop skills in programming and scripting (Perl and/or Python), the use of biological databases, sequence alignment, similarity search, identification of sequence patterns, construction of phylogenetic trees, and comparative genomics.
(3-0) Cr. 3. F.
Prereq: BCBIO 322 and basic programming experience (e.g. COM S 207, COM S 227 or permission of instructor)
Application of computer science and engineering to molecular biology. String algorithms, sequence alignments, data structures, homology search methods, pattern recognition, fragment assembly, genome annotation, construction of bioinformatics databases, and gathering and distribution of biological information with the Internet.
(3-0) Cr. 3. S.
Prereq: BIOL 212
Technologies: transcriptome, proteome, metabolome; Networks: Gene regulatory network, Protein-protein interaction network, Literature network; Theories: Graph theory, random network, scale-free network, evolving network, network robustness; Tools: Jmol, MeV, Cytoscape, Citespace.
(0.2-0.5) Cr. 0.5. Repeatable, maximum of 2 credits. S.SS.
Prereq: BIOL 314 recommended
Modular minicourses consisting of guided tutorials and hands-on computer software exercises focused on fundamental problems, approaches, and software applications in bioinformatics and computational biology.
Offered on a satisfactory-fail basis only.
(0.2-0.5) Cr. 0.5. Repeatable, maximum of 2 credits. S.SS.
Prereq: BIOL 314 recommended
Modular minicourses consisting of guided tutorials and hands-on computer software exercises focused on fundamental problems, approaches, and software applications in bioinformatics and computational biology.
Offered on a satisfactory-fail basis only.
(0.2-0.5) Cr. 0.5. Repeatable, maximum of 2 credits. S.SS.
Prereq: BIOL 314 recommended
Modular minicourses consisting of guided tutorials and hands-on computer software exercises focused on fundamental problems, approaches, and software applications in bioinformatics and computational biology.
Offered on a satisfactory-fail basis only.
(0.2-0.5) Cr. 0.5. Repeatable, maximum of 2 credits. S.SS.
Prereq: BIOL 314 recommended
Modular minicourses consisting of guided tutorials and hands-on computer software exercises focused on fundamental problems, approaches, and software applications in bioinformatics and computational biology.
Offered on a satisfactory-fail basis only.
(0.2-0.5) Cr. 0.5. Repeatable, maximum of 2 credits. S.SS.
Prereq: BIOL 314 recommended
Modular minicourses consisting of guided tutorials and hands-on computer software exercises focused on fundamental problems, approaches, and software applications in bioinformatics and computational biology.
Offered on a satisfactory-fail basis only.
(Cross-listed with BCB, BIOL, COM S, CPR E, GEN). (4-0) Cr. 4. F.
Prereq: MATH 165 and Introductory Statistics (STAT 101, STAT 104, STAT 105, STAT 201, or STAT 330).
Broad overview of bioinformatics with a significant problem-solving component, including hands-on practice using computational tools to solve a variety of biological problems. Topics include: bioinformatic data processing, Python programming, genome assembly, database search, sequence alignment, gene prediction, next-generation sequencing, comparative and functional genomics, and systems biology.
Cr. 1-5. Repeatable, maximum of 9 credits. F.S.SS.
Prereq: BCBIO 322, junior or senior classification, permission of instructor
Independent research projects for undergraduate students in bioinformatics and computational biology.
Students in the College of Liberal Arts and Sciences may use no more than 9 credits of BCBIO 490 and 491 toward graduation.
Cr. 1-5. Repeatable, maximum of 9 credits. F.S.SS.
Prereq: BCBIO 322, junior or senior classification, permission of instructor
Research projects in bioinformatics and computational biology done by teams of students.
Students in the College of Liberal Arts and Sciences may use no more than 9 credits of BCBIO 490 and 491 toward graduation.
Courses
Courses primarily for undergraduates:
(Cross-listed with BCBIO, BIOL, COM S, CPR E, GEN). (4-0) Cr. 4. F.
Prereq: MATH 165 and Introductory Statistics (STAT 101, STAT 104, STAT 105, STAT 201, or STAT 330).
Broad overview of bioinformatics with a significant problem-solving component, including hands-on practice using computational tools to solve a variety of biological problems. Topics include: bioinformatic data processing, Python programming, genome assembly, database search, sequence alignment, gene prediction, next-generation sequencing, comparative and functional genomics, and systems biology.
Cr. 1-5. Repeatable, maximum of 9 credits. F.S.SS.
Prereq: Permission of instructor
Courses primarily for graduate students, open to qualified undergraduates:
(Cross-listed with COM S, CPR E, GDCB). (4-0) Cr. 4. F.
Prereq: MATH 165 or STAT 401 or equivalent
A practical, hands-on overview of how to apply bioinformatics to biological research. Recommended for biologists desiring to gain computational molecular biology skills. Topics include: sequence analysis, genomics, proteomics, phylogenetic analyses, ontology enrichment, systems biology, data visualization and emergent technologies.
(Cross-listed with EEOB). Cr. 3. F.
Prereq: Graduate student status or permission of the instructor
Computational skills necessary for biologists working with big data sets. UNIX commands, scripting in R and Python, version control using Git and GitHub, and use of high performance computing clusters. Combination of lectures and computational exercises.
(Cross-listed with COM S, GDCB, STAT). (3-0) Cr. 3. S.
Prereq: BCB 567 or (BIOL 315 and one of STAT 430 or STAT 483 or STAT 583), 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.
(Cross-listed with BBMB, COM S, CPR E, GDCB). (3-0) Cr. 3. F.
Prereq: BCB 567, BBMB 316, GEN 409, STAT 430
Molecular structures including genes and gene products: protein, DNA and RNA structure. Structure determination methods, structural refinement, structure representation, comparison of structures, visualization, and modeling. Molecular and cellular structure from imaging. Analysis and prediction of protein secondary, tertiary, and higher order structure, disorder, protein-protein and protein-nucleic acid interactions, protein localization and function, bridging between molecular and cellular structures. Molecular evolution.
(Cross-listed with COM S, CPR E, GDCB, STAT). (3-0) Cr. 3. S.
Prereq: BCB 567 or COM S 311, COM S 228, GEN 409, STAT 430 or STAT 483 or STAT 583
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.
(Cross-listed with GDCB, M E). Cr. 4. F.
Principles of engineering, data analysis, and plant sciences and their interplay applied to predictive plant phenomics. Transport phenomena, sensor design, image analysis, graph models, network data analysis, fundamentals of genomics and phenomics. Multidisciplinary laboratory exercises.
Cr. arr. Repeatable.
Prereq: Permission of instructor
(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.
Cr. R. Repeatable. F.S.SS.
Prereq: Permission of the program chair
Off-campus work periods for graduate students in the field of bioinformatics and computational biology.
Cr. arr.
Courses for graduate students:
(3-0) Cr. 1-4. Repeatable, maximum of 4 times. F.S.SS.
Prereq: Permission of Instructor
Topics of interest in the major research areas of computational molecular biology, including genomics, structural genomics, functional genomics, and computational systems biology.
Cr. 1. Repeatable. S.
Student research presentations.
(1-0) Cr. 1. Repeatable.
Faculty research series.
Cr. arr. Repeatable. F.S.SS.
Graduate research projects performed under the supervision of selected faculty members in the Bioinformatics and Computational Biology major.
Cr. arr. Repeatable.