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Bioinformatics and Computational Biology
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; Biochemistry, Biophysics and Molecular Biology; Chemical and Biological Engineering; Chemistry; Computer Science; Ecology, Evolution, and Organismal Biology; Electrical and Computer Engineering; Genetics, Development and Cell Biology; Industrial and Manufacturing Systems Engineering; Mathematics; Mechanical Engineering; Physics and Astronomy; Plant Pathology; Statistics; Veterinary Microbiology and Preventive Medicine.
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.
During the first year, all BCB students complete background coursework in calculus, molecular genetics, computer science, statistics and discrete structures, with specific courses determined by prior training. The total course requirements for Ph.D. students include at least one core course in Computational Molecular Biology (BCB 594 and/or BCB 548), one core course in Molecular Genetics (e.g., Gen 411, GDCB 511, BBMB 501), and at least 12 credits of advanced coursework in the areas of Molecular Biology (6 credits) and either Computer Science or Mathematics/ Statistics (6 credits in one area). 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: 484, 495.
Courses primarily for undergraduate students
BCB 495. Molecular Biology for Computational Scientists. (Same as Gen 495.) (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 539. Statistical Methods for Computational Biology. (Same as GDCB 539.) (2-0) Cr. 2. Alt. S., offered 2006. Prereq: BCB 594. 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. (Same as GDCB 542.) See Genetics, Development and Cell Biology.
BCB 548. Fundamental Algorithms in Computational Biology. (Same as Com S 548, Cpr E 548.) (3-0) Cr. 3. S.Prereq: Com S 311 and some knowledge of programming. Introduction, design and analysis of fundamental algorithms and methods for molecular biology. Topics include pairwise sequence alignment, alignment heuristics, biological database and retrieval systems, multiple sequence alignment, phylogenetic trees, physical mapping, genome rearrangements, DNA-chips, fragment assembly, protein folding, and genetic networks.
BCB 549. Advanced Algorithms in Computational Biology. (Same as Cpr E 549, Com S 549.) See Computer Engineering or Computer Science.
BCB 550. Evolutionary Problems for Computational Biologist. (Same as Com S 550.) (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. (Same as Com S 551.) (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. (Same as Pl P 565.) See Plant Pathology.
BCB 590. Special Topics. Cr. var. Prereq: Permission of instructor.
BCB 593. Workshop in Bioinformatics and Computational Biology. (1-0) Cr. 1, each time taken. 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 594. Computational Molecular Biology. (Same as Com S 594, GDCB 594, Math 594.) (3-0) Cr. 3. S. Prereq: BCB 484, BCB 495, Stat 432 or equivalent courses and programming experience (C, C++, or Perl). State-of-the-art introduction to bioinformatics with emphasis on concepts and principles, combined with hands-on (keyboard) applications. Topics typically include: molecular databases, score-based sequence analysis, amino acid substitution scoring matrices, query search problems, dynamic programming and other methods for pairwise sequence alignment, motif identification, multiple sequence alignment, construction of phylogenetic trees from sequence data, gene structure prediction, protein structure prediction.
BCB 596. Genomic Data Processing. (Same as Com S 596, GDCB 596.) Cr. 3. F. Prereq: Some knowledge of programming. Chou. Practical aspects of genomic data processing. Emphasis on projects that carry out major steps in data processing using important bioinformatic 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.
BCB 597. Introduction Computational Structural Biology. (Same as Math 597.) (3-0) Cr. 3. S. Prereq: Math 265 and some knowledge of programming. 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. var. Course for Graduate Students
BCB 690. Student Seminar in Bioinformatics and Computational Biology. Cr. 1, each time taken. S. Student research presentations.
BCB 691H. Faculty Seminar in Bioinformatics and Computational Biology. (Same as GDCB 691H.) (1-0) Cr. 1, each time taken.Faculty research series.
BCB 697. Graduate Research Rotation. Cr. var. each time taken. F.S.SS. Graduate research projects performed under the supervision of selected faculty members in the Bioinformatics and Computational Biology major.
BCB 699. Research.