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Bioinformatics and Computational Biology (BCB)400 |Graduate Courses | www.bcb.iastate.edu email@example.com
Interdepartmental Graduate Major
Chair: S. Aluru
Supervisory Committee: S. Aluru, C. Tuggle,
J. Dickerson, D. Dobbs, X. Huang, D. Voytas, Z. Wu,
R. Jernigan (ex-officio), V. Honavar (former chair)
Participating Faculty: R. Ackerman, D. Adams, R. Adolphs, S. Aluru, A. Andreotti, D. Ashlock, M. Bhattacharyya, A. Bogdanove, V. Brendel, A. Bronikowski, S. Carpenter, A. Carriquiry, H-H. Chou, D. Cook, J. Davidson, J. Dekker, J. Dekkers, J. Dickerson, P. Dixon, D. Dobbs, K. Dorman, O. Eulenstein, D. Fernandez-Baca, R. Fernando, S. Gadia, H. Greenlee, X. Gu, M. Hargrove, K-M. Ho, V. Honavar, M. Hong, R. Honzatko, X. Huang, F. Janzen, R. Jernigan, D. Jones, S. Kothari, S. Lamont, D. Lavrov, C. Lawrence, H. Levine, C. Link, G. Macintosh, R. Maddux, S. Mallapragada, J. Mayfield, L. Miller, W. A. Miller, C. Minion, K. Moloney, A. Myers, D. Nettleton, B. Nikolau, M. Nilsen-Hamilton, L. Nolan, J. Peccoud, R. Peters, T. Peterson, G. Philips, E. Pollak, S. Proulx, K. Rajan, J. Reecy, P. Reilly, S. Rodermel, M. Rothschild, D. Sakaguchi, A. Sannier, P. Schnable, J. Shanks, R. Shoemaker, M. Smiley, J. Smith, G. Song, X. Song, A. Travesset, C. Tuggle, N. Valenzuela, D. Voytas, J. Wendel, S. Willson, R. Wise, Z. Wu, E. Wurtele, E. Yu
Courses in bioinformatics and computational biology are offered for undergraduates, but a baccalaureate degree is not offered at this time.
Undergraduates wishing to prepare for graduate study in Bioinformatics and Computational Biology should obtain solid undergraduate training in at least one of the foundation disciplines: molecular biology, computer science, mathematics, statistics, and physics. Undergraduates should elect courses in basic biology, basic transmission and molecular genetics, chemistry, physics, mathematics at least through calculus, statistics, and computer programming.
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; Genetics, Development and Cell Biology; Industrial Manufacturing and Systems Engineering; Materials Science and Engineering; Mathematics; Plant Pathology; Statistics; Veterinary Microbiology and Preventive Medicine; and Veterinary Pathology.
The BCB program emphasizes interdisciplinary training in six related areas of focus: Bioinformatics, Functional and Structural Genomics, Genome Evolution, Macromolecular Structure and Function, Mathematical Biology and Biological Statistics, and Metabolic and Developmental Networks. 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 well-prepared student will have taken calculus (through multivariable calculus, such as Math 265), a calculus-based introduction to probability and statistics (like Stat 341), two semesters of computer programming (like Com S 207 and 208), one semester of discrete structures (like Com S 330 or Cpr E 310), some physical and organic chemistry (like Chem 163 and 231), biochemistry (like BBMB 301), genetics (like Biol 313), and evolution (like Biol 315).
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 (BCB 567, 568, 569, and 570), one core course in Molecular Genetics (Gen 411), and background courses in statistics and computer science. Students make research presentations (BCB 690), attend faculty research seminars (BCB 691), and participate in workshops/symposia (BCB 593). M.S. students take the above background and core courses, take at least 12 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 open for nonmajor graduate credit: 495.
Courses primarily for undergraduate students
BCB 444. Introduction to Bioinformatics. (Dual-listed with 544). (Cross-listed with Com S, Cpr E, Gen, Biol). (4-0) Cr. 4. F. Prereq: Math 165 or Stat 401 or equivalent. 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: database searching, sequence alignment, gene prediction, RNA and protein structure prediction, construction of phylogenetic trees, comparative and functional genomics. Nonmajor graduate credit.
BCB 490. Independent Study. Cr. 1-5. Repeatable. F.S.SS. Prereq: Permission of instructor.
BCB 495. Molecular Biology for Computational Scientists. (Cross-listed with Gen). (3-0) Cr. 3. F. Survey of molecular cell biology and molecular genetics for nonbiologists, especially those interested in bioinformatics/computational biology. Basic cell structure and function; principles of molecular genetics; biosynthesis, structure, and function of DNA, RNA, and proteins; regulation of gene expression; selected topics. Provides biological background for BCB 594. Nonmajor graduate credit.
Courses primarily for graduate students, open to qualified undergraduate students
BCB 538. Computational Genomics and Evolution. (Cross-listed with GDCB). (3-0) Cr. 3. Alt. S., offered 2009. Prereq: Biol 313. Introduction to evolutionary sequence analysis at the genome level. Topics include sequence alignment, phylogenetic inference, molecular clock analysis, ancestral state inference, sequence/structure relation, functional divergence and prediction, evolutionary development, genome duplication, and comparative genomics. Focus will be on data analysis and biological interpretation.
BCB 539. Statistical Methods for Computational Biology. (Cross-listed with GDCB). (2-0) Cr. 2. Alt. S., offered 2008. Prereq: BCB 568. Gu. Advanced discussion about statistical modeling of DNA and amino acid sequences, microarray expression profiles and other genome-wide data interpretation.
BCB 542. Introduction to Molecular Biology Techniques. (Cross-listed with GDCB, BBMB, B M S, FS HN, Hort, NutrS, VDPAM, EEOB). Cr. 1. Repeatable. F.S.SS. Prereq: Graduate classification. Workshops in basic molecular biology techniques and related procedures. Satisfactory-fail only.
BCB 544. Introduction to Bioinformatics. (Dual-listed with 444). (Cross-listed with Com S, Cpr E, GDCB). (4-0) Cr. 4. F. Prereq: Math 165 or Stat 401 or equivalent. 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: database searching, sequence alignment, gene prediction, RNA and protein structure prediction, construction of phylogenetic trees, comparative and functional genomics.
BCB 549. Advanced Algorithms in Computational Biology. (Cross-listed with Cpr E, Com S). (3-0) Cr. 3. S. Prereq: Com S 311 and either 208 or 228. Design and analysis of algorithms for applications in computational biology, pairwise and multiple sequence alignments, approximation algorithms, string algorithms including in-depth coverage of suffix trees, semi-numerical string algorithms, algorithms for selected problems in fragment assembly, phylogenetic trees and protein folding. No background in biology is assumed. Also useful as an advanced algorithms course in string processing.
BCB 550. Evolutionary Problems for Computational Biologists. (Cross-listed with Com S). (3-0) Cr. 3. F. Prereq: Com S 311 and some knowledge of programming. Discussion and analysis of basic evolutionary principles and the necessary knowledge in computational biology to solve real world problems. Topics include character and distance based methods, phylogenetic tree distances, and consensus methods, and approaches to extract the necessary information from sequence-databases to build phylogenetic trees.
BCB 551. Computational Techniques for Genome Assembly and Analysis. (Cross-listed with Com S). (3-0) Cr. 3. F. Prereq: Com S 311 and some knowledge of programming. Huang. Introduction to practical sequence assembly and comparison techniques. Topics include global alignment, local alignment, overlapping alignment, banded alignment, linear-space alignment, word hashing, DNA-protein alignment, DNA-cDNA alignment, comparison of two sets of sequences, construction of contigs, and generation of consensus sequences. Focus on development of sequence assembly and comparison programs.
BCB 565. Professional Practice in the Life Sciences. (Cross-listed with Pl P, Agron, An S, Hort, Micro, V MPM). Cr. 0.5. S. Prereq: Graduate classification. Professional discourse on the ethical and legal issues facing life science researchers. Offered in modular format; each module is four weeks.
BCB 567. Bioinformatics I (Fundamentals of Genome Informatics). (Cross-listed with Com S). (3-0) Cr. 3. F. Prereq: Com S 208; Com S 330; Stat 341; credit or enrollment in Biol 315, Stat 401, and Stat 432. Biology as an information science. Review of algorithms and information processing. Generative models for sequences. String algorithms. Pairwise sequence alignment. Multiple sequence alignment. Searching sequence databases. Genome sequence assembly.
BCB 568. Bioinformatics II (Advanced Genome Informatics). (Cross-listed with GDCB, Stat, Com S). (3-0) Cr. 3. S. Prereq: BCB 567, BBMB 301, Biol 315, Stat 401, Stat 432, credit or enrollment in Gen 411. Advanced sequence models. Basic methods in molecular phylogeny. Hidden Markov models. Genome annotation. DNA and protein motifs. Introduction to gene expression analysis.
BCB 569. Bioinformatics III (Structural Genome Informatics). (Cross-listed with BBMB, Com S, MAth, Cpr E). (3-0) Cr. 3. F. Prereq: BCB 567, Gen 411, Stat 401, Stat 432. Algorithmic and statistical approaches in structural genomics including protein, DNA and RNA structure. Structure determination, refinement, representation, comparison, visualization, and modeling. Analysis and prediction of protein secondary and tertiary structure, disorder, protein cores and surfaces, protein-protein and protein-nucleic acid interactions, protein localization and function.
BCB 570. Bioinformatics IV (Computational Functional Genomics and Systems Biology). (Cross-listed with Com S, GDCB, Stat, Cpr E). (3-0) Cr. 3. S. Prereq: BCB 567, Biol 315, Com S 363, Gen 411, Stat 401, Stat 432. Algorithmic and statistical approaches in computational functional genomics and systems biology. Biological Information Integration - knowledge (ontology) driven and statistical approaches. Qualitative, probabilistic, and dynamic network models. Modeling, analysis, simulation and inference of transcriptional regulatory modules and networks, protein-protein interaction networks. Metabolic networks; cells and systems.
BCB 590. Special Topics. Cr. arr. Repeatable. Prereq: Permission of instructor.
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 596. Genomic Data Processing. (Cross-listed with Com S, GDCB). (3-0) Cr. 3. F. Prereq: Some knowledge of programming. Study the practical aspects of genomic data processing with an emphasis on hand-on projects. Students will carry out major data processing steps using bioinformatics tools. Topics include base-calling, raw sequence cleaning and contaminant removal; shotgun assembly procedures and EST clustering methods; genome closure strategies and practices; sequence homology search and function prediction; annotation and submission of GenBank reports; and data collection and dissipation through the Internet. Important post-genomic topics like microarray data analysis and pathway database will also be covered.
BCB 597. Introductory Computational Structural Biology. (Cross-listed with MAth). (3-0) Cr. 3. S. Prereq: Permission of instructor. Mathematical and computational approaches to protein structure prediction and determination. Topics include molecular distance geometry, potential energy minimization, and molecular dynamics simulation.
BCB 599. Creative Component. Cr. arr.
Courses for graduate students
BCB 690. Student Seminar in Bioinformatics and Computational Biology. Cr. 1. Repeatable. S. Student research presentations.
BCB 691H. Faculty Seminar in Bioinformatics and Computational Biology. (Cross-listed with GDCB). (1-0) Cr. 1. Repeatable. Faculty research series.
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.