Graduate Minor in Bioinformatics
Bioinformatics is the field of study that represents the intersection between Molecular Biology (particularly the understanding of the function of genomes) and the computational and mathematical techniques appropriate to organizing and analyzing these data. It has never been more important for biologists to be trained in information technology and computational science, and computational scientists to be trained in biology, because the discoveries yet to be made are tremendously exciting. Bioinformatics is an expanding field that will play a central role in advances in medicine, agriculture, and other technologies in the 21st century.
In light of the importance of this field of study, the Molecular Biology & Interdisciplinary Life Sciences Graduate Program (MOLB-ILS) is offering a bioinformatics minor jointly with the Department of Computer Science. The minor consists of two tracks:
1. Computer Science track: This track is intended for graduate students majoring in a non-biological science related to bioinformatics, most likely Computer Science or the Mathematical Sciences. The minor consists of courses in bioinformatics, biochemistry, molecular genetics, and certain Computer Science topics that are particularly appropriate for Bioinformatics. The goal is to give the non-biology graduate student a solid background in understanding modern Bioinformatics analyses and the biological processes behind the data.
2. Biology track: This track is intended for graduate students majoring in a biological science. The minor consists of courses in bioinformatics and fundamental and more advanced topics in the computer sciences. The goal is to give the molecular biology graduate student a better understanding of modern Bioinformatics analyses and of the computational skills required to develop such analyses.
The minor consists of 9 graduate credit hours for Masters, or 12 credit hours for Ph.D. students. These may require the student to first take a graduate credit background course, which does not count toward the required credits. In addition to required courses, there are elective courses in each track; the student will work with his or her advisor to decide which elective courses are appropriate. Although most of these courses have prerequisites, they can be taken with consent of the instructor for students pursuing this minor. Also, all courses can be taken for graduate credit.
Required courses, both tracks
GENE 452 Applied Bioinformatics. (3 credits) Survey and application of publically available bioinformatic tools that treat genomic DNA, cDNA, and protein sequences, RNA abundance, as well as tools that allow inference based on phylogenetic relationships.
Prerequisite: BIOL 305 or GENE 315.
OR
BIOL 550 Bioinformatics. (3 credits)
MOLB 452. Independent Study in Bioinformatics. (3 credits) In coordination with his or her advisor, the student will work on a Bioinformatics project with a student or faculty member from the complementary field. The project will require that the computer science student learn biological principles and the biology student to learn computational principles, while sharing the work necessary to complete the project. Prerequisites: GENE 452
OR
BIOL 550 Bioinformatics. (3 credits)
MOLB 452. Independent Study in Bioinformatics. (3 credits) In coordination with his or her advisor, the student will work on a Bioinformatics project with a student or faculty member from the complementary field. The project will require that the computer science student learn biological principles and the biology student to learn computational principles, while sharing the work necessary to complete the project. Prerequisites: GENE 452
Computer Science Track
Background Course:
BIOL/AGRO/HORT/ANSC 305. Principles of Genetics. (3 credits) Prerequisites: CHEM 111, 112; BIOL 211 or consent of instructor.
OR
GENE 315. Molecular Genetics. (3 credits) Prerequisites: Chem 111, 112: BIOL 211 or consent of instructor.
Elective Courses:
BCHE 396. Biochemistry and Biotechnology. (3 credits) Prerequisites: BCHE 395
MOLB 486. Intermediate Genetics. Same as AGRO 486, BIOL 486, HORT 486. (3 credits) Prerequisites: BIOL 305; BCHE 341
MOLB 520. Molecular Cell Biology. Same as BIOL 520 (3 credits) Prerequisites: BIOL 377 or equivalent
MOLB 542. Biochemistry I (3 credits) Relationship between macromolecular structure and function. Basic enzymology. Energy metabolism. Same as BCHE 542. Prerequisites: CHEM 431 or CHEM 433.
MOLB 545. Molecular and Biochemical Genetics (3 credits). Same as BCHE 545 and BIOL 545. Prerequisites: BCHE 542 or equivalent
C S 552. Introduction to Computational Science and Engineering (3 credits) Modeling of scientific and engineering problems; computational methods for solving such problems, including data structure design and relevant discrete and numerical algorithms. Prerequisites:
C S 549 Computational Biology (3 credits) Advanced algorithms for the analysis of sequence data . This course is appropriate for Computer Science graduate students. Prerequisites: C S 470
CS 479/CS 579 Special Topics – Bioinformatics (3 credits) This course is designed to introduce the complexity of biological problems and to develop computational approaches. It introduces high-level knowledge of molecular biology background for non-molecular biology students. This course does not focus on the analysis of currently available tools. It combines classical bioinformatics materials and current advances in selected research directions. Students are expected to be able to use one programming language of his/her choice. Prerequisites: CS 372 or consent of instructor
C S 482. Database Management Systems I (3 credits) Database design and implementation; models of database management systems; privacy, security, protection, recovery. This course is appropriate for Computer Science graduate students. Prerequisites: at least C in C S 272 or MATH 279.
C S 490. Parallel Computing (3 credits) Models of parallel computation, performance measures, basic parallel constructs and communication primitives, programming of parallel computers, parallel algorithms for selected problems. Prerequisite: C S 372 or consent of instructor. Corequisite: C S 470.
C S 491. Parallel Programming (3 credits) Programming of shared memory and distributed memory machines; tools and languages for parallel programming; parallelizing compilers; parallel programming environments. Prerequisite: C or better in C S 272 and C S 370.
C S 582. Database Management Systems II (3 credits) Advanced data models and abstractions, dependencies, implementations, languages, database machines, and other advanced topics. This course is appropriate for Computer Science graduate students. Prerequisite: C S 482 or consent of instructor.
BIOL/AGRO/HORT/ANSC 305. Principles of Genetics. (3 credits) Prerequisites: CHEM 111, 112; BIOL 211 or consent of instructor.
OR
GENE 315. Molecular Genetics. (3 credits) Prerequisites: Chem 111, 112: BIOL 211 or consent of instructor.
Elective Courses:
BCHE 396. Biochemistry and Biotechnology. (3 credits) Prerequisites: BCHE 395
MOLB 486. Intermediate Genetics. Same as AGRO 486, BIOL 486, HORT 486. (3 credits) Prerequisites: BIOL 305; BCHE 341
MOLB 520. Molecular Cell Biology. Same as BIOL 520 (3 credits) Prerequisites: BIOL 377 or equivalent
MOLB 542. Biochemistry I (3 credits) Relationship between macromolecular structure and function. Basic enzymology. Energy metabolism. Same as BCHE 542. Prerequisites: CHEM 431 or CHEM 433.
MOLB 545. Molecular and Biochemical Genetics (3 credits). Same as BCHE 545 and BIOL 545. Prerequisites: BCHE 542 or equivalent
C S 552. Introduction to Computational Science and Engineering (3 credits) Modeling of scientific and engineering problems; computational methods for solving such problems, including data structure design and relevant discrete and numerical algorithms. Prerequisites:
C S 549 Computational Biology (3 credits) Advanced algorithms for the analysis of sequence data . This course is appropriate for Computer Science graduate students. Prerequisites: C S 470
CS 479/CS 579 Special Topics – Bioinformatics (3 credits) This course is designed to introduce the complexity of biological problems and to develop computational approaches. It introduces high-level knowledge of molecular biology background for non-molecular biology students. This course does not focus on the analysis of currently available tools. It combines classical bioinformatics materials and current advances in selected research directions. Students are expected to be able to use one programming language of his/her choice. Prerequisites: CS 372 or consent of instructor
C S 482. Database Management Systems I (3 credits) Database design and implementation; models of database management systems; privacy, security, protection, recovery. This course is appropriate for Computer Science graduate students. Prerequisites: at least C in C S 272 or MATH 279.
C S 490. Parallel Computing (3 credits) Models of parallel computation, performance measures, basic parallel constructs and communication primitives, programming of parallel computers, parallel algorithms for selected problems. Prerequisite: C S 372 or consent of instructor. Corequisite: C S 470.
C S 491. Parallel Programming (3 credits) Programming of shared memory and distributed memory machines; tools and languages for parallel programming; parallelizing compilers; parallel programming environments. Prerequisite: C or better in C S 272 and C S 370.
C S 582. Database Management Systems II (3 credits) Advanced data models and abstractions, dependencies, implementations, languages, database machines, and other advanced topics. This course is appropriate for Computer Science graduate students. Prerequisite: C S 482 or consent of instructor.
Biology Track
Background Course:
C S 472. Introduction to Data Structures (4 credits) Same as C S 272 but can be taken for graduate credit by non-majors. Includes some C programming. Prerequisite: at least C in C S 171 and MATH 279; corequisite: MATH 330; or consent of instructor
Elective Courses:
C S 371, Software Development (4 credits) Prerequisite: at least C in C S 171 and MATH 279; corequisite: MATH 330
C S 372, Data Structures and Algorithms (4 credits) Prerequisites: at least C in C S 272 and MATH 330
C S 467. C Programming (3 credits) Programming in the C language. C S 467 is more advanced than C S 167. Prerequisites: graduate standing
C S 470. Analysis of Algorithms (s) (3 credits) Techniques for design and analysis of algorithms; time and space complexity; proving correctness of programs. Particular algorithms such as sorting, searching. NP complete problems. Prerequisites: at least C in C S 372, MATH 330.
C S 482. Database Management Systems I (3 credits) Database design and implementation; models of database management systems; privacy, security, protection, recovery. Prerequisites: at least C in C S 272 or MATH 279.
C S 484. Computer Networks I (3 credits) Introduction to telecommunications: basic terms, concepts, equipment, protocols, standards. Networks: evolution, architecture, public, local, examples, analysis. Data security. Prerequisites: at least C in C S 372, C S 363.
C S 490. Parallel Computing (3 credits) Models of parallel computation, performance measures, basic parallel constructs and communication primitives, programming of parallel computers, parallel algorithms for selected problems. Prerequisite: C S 372 or consent of instructor. Corequisite: C S 470.
C S 491. Parallel Programming (3 credits) Programming of shared memory and distributed memory machines; tools and languages for parallel programming; parallelizing compilers; parallel programming environments. Prerequisite: C or better in C S 272 and C S 370.
C S 552. Introduction to Computational Science and Engineering (3 credits) Modeling of scientific and engineering problems; computational methods for solving such problems, including data structure design and relevant discrete and numerical algorithms.
C S 472. Introduction to Data Structures (4 credits) Same as C S 272 but can be taken for graduate credit by non-majors. Includes some C programming. Prerequisite: at least C in C S 171 and MATH 279; corequisite: MATH 330; or consent of instructor
Elective Courses:
C S 371, Software Development (4 credits) Prerequisite: at least C in C S 171 and MATH 279; corequisite: MATH 330
C S 372, Data Structures and Algorithms (4 credits) Prerequisites: at least C in C S 272 and MATH 330
C S 467. C Programming (3 credits) Programming in the C language. C S 467 is more advanced than C S 167. Prerequisites: graduate standing
C S 470. Analysis of Algorithms (s) (3 credits) Techniques for design and analysis of algorithms; time and space complexity; proving correctness of programs. Particular algorithms such as sorting, searching. NP complete problems. Prerequisites: at least C in C S 372, MATH 330.
C S 482. Database Management Systems I (3 credits) Database design and implementation; models of database management systems; privacy, security, protection, recovery. Prerequisites: at least C in C S 272 or MATH 279.
C S 484. Computer Networks I (3 credits) Introduction to telecommunications: basic terms, concepts, equipment, protocols, standards. Networks: evolution, architecture, public, local, examples, analysis. Data security. Prerequisites: at least C in C S 372, C S 363.
C S 490. Parallel Computing (3 credits) Models of parallel computation, performance measures, basic parallel constructs and communication primitives, programming of parallel computers, parallel algorithms for selected problems. Prerequisite: C S 372 or consent of instructor. Corequisite: C S 470.
C S 491. Parallel Programming (3 credits) Programming of shared memory and distributed memory machines; tools and languages for parallel programming; parallelizing compilers; parallel programming environments. Prerequisite: C or better in C S 272 and C S 370.
C S 552. Introduction to Computational Science and Engineering (3 credits) Modeling of scientific and engineering problems; computational methods for solving such problems, including data structure design and relevant discrete and numerical algorithms.
(Updated: 4/2013)
