| Designing Your Course Sample Curriculum An interdisciplinary undergraduate bioinformatics curriculum for biological scientists
Dan Krane, Title: Associate Professor of Biological Science; Address: Wright State University, Dayton, OH 45435-0001; email: dan.krane@wright.edu, Office: (937) 775-2257; Home: (937) 374-2462; FAX: (937) 775-3320.
Travis Doom, Title: Assistant Professor of Computer Science; Address: Wright State University, Dayton, OH 45435-0001; email: travis.doom@wright.edu, Office phone: (937) 775-5105; FAX: (937) 775-5133.
Michael Raymer, Title: Assistant Professor of Computer Science; Address: Wright State University, Dayton, OH 45435-0001; email: mraymer@cs.wright.edu, Office phone: (937) 775-5110; FAX: (937) 775-5133.
Oscar Garcia Title: Professor and NCR Endowed Chair of Computer Science; Address: Wright State University, Dayton, OH 45435-0001; email: ogarcia@cs.wright.edu, Office phone: (937) 775-5134; FAX: (937) 775-5133.
Word count: 3,434
Index Terms – Bioinformatics, curriculum, genomics, incorporation of research in undergraduate education.
Abstract: The interdisciplinary nature of bioinformatics research has caused academia to be slow to respond to strong demands for training in this area. We present an undergraduate-level bioinformatics curriculum in biology. This program is easily tailored to the needs and resources of various departments and institutions.
Introduction
Bioinformatics is an inherently interdisciplinary and rapidly evolving field that has emerged from the fields of molecular biology and biochemistry, and the application of artificial intelligence, database, pattern recognition, and algorithms, disciplines of computer science. Bioinformatics research explores the functional relationships between the composition of the genes within the context of the genome and the structure and function of the proteins encoded by these genes. Because the interaction of those proteins largely determines metabolism, reproduction, form, and health, the implications of bioinformatics studies are far reaching. Recent advances in the experimental techniques of molecular biology have resulted in an explosive growth in the availability of molecular data. As a result, current bioinformatics research is often focused on the organization, representation, analysis, annotation and mining of large databases of genome sequence information of different species. In the future, the focus will shift to a functional analysis of the proteins produced by these genes and their interactions in the context of biochemical pathways.
Academia has been slow, due to the difficulties of interdisciplinary work, to respond to strong student interest as well as industry and government needs for training that facilitates the development and application of novel bioinformatics techniques to the rapidly-growing repositories of genetic and proteomic data. Some institutions are responding to this demand by establishing graduate programs in bioinformatics. However, the entrance barriers for graduate programs in bioinformatics are high, largely as the result of the significant amount of prerequisite knowledge in the disparate fields of biochemistry and computer science.
The demand for bioinformaticians is already high and will continue to grow. The genomic information available at the National Center for Biotechnology Information (NCBI) currently doubles every 14 months (NCBI 2002). Industry analysts forecast that the market for genomic information alone (and the technology to use it) will reach US $2 billion annually by 2005 (Moore 2000). Both industry and the National Institute of General Medical Sciences (NIGMS) are having difficulty finding qualified individuals from other disciplines to investigate the kind of modeling and data analysis that researchers in the biological sciences now require (Henry 2001). The educational opportunities available to undergraduate students wishing to participate in this exciting enterprise are not adequate either present or anticipated demands – by as much as fifty-fold (Henry, 2001; Schachter, 2002; Doom et al, 2002a; Doom and Garcia, 2001). The development of undergraduate opportunities in bioinformatics is essential to meeting future needs worldwide.
The authors have developed an undergraduate-level bioinformatics program that is unencumbered by the high entrance barriers associated with post-graduate bioinformatics education. We combine early training in the fundamental material necessary for a strong grasp of bioinformatics concepts and algorithms with junior- and senior-level bioinformatics research. The undergraduate research component of our program is oriented towards application of existing bioinformatics methods to investigate current problems in molecular biology, and, for more advanced students, development of novel techniques.
The author's goal in the development of this model is to provide exposure to the fundamental concepts, laboratory skills and vocabularies required to use and develop new bioinformatics techniques and tools. Students will learn the algorithms at the core of current bioinformatics analyses. They will also learn how to implement these algorithms at the same time as they are exposed to the laboratory-based techniques used by molecular biologists to gather data. By the end of their course of study, students will be ready to directly enter the bioinformatics job market or participate in on-going research projects involving analyses of molecular data.
Problem
Graduate programs in bioinformatics are beginning to emerge at universities worldwide (Schachter 2002). Entrance requirements for such programs, however, require students with a specific prerequisite program of undergraduate study that is rarely made available as part of an organized program. Graduate bioinformatics programs must currently accept students with undergraduate degrees in either biology or computer science and have sequences of remedial or prerequisite courses designed to complement the knowledge already acquired by the students as undergraduates.
Students holding an undergraduate degree in biology generally spend the majority of their first year of graduate study in course work covering introductory computer programming and data structures, entity-relationship modeling, databases, and artificial intelligence. Students holding an undergraduate degree in computer science generally need to spend the majority of their first year of graduate study (and possibly longer) taking remedial courses in basic chemistry, biochemistry, molecular biology, and genetics.
The second year of a graduate bioinformatics program is generally dominated by pre-existing graduate courses in biology and computer science. From biology, a course sequence providing specialization in genetics, molecular biology, physiology, or ecology is common. From computer science, courses in artificial intelligence, database, pattern recognition, and genetic algorithms are fundamental. Students from either background are only then also exposed to a required course sequence covering contemporary algorithms and research techniques in bioinformatics. It is unlikely for this amount of material to be accommodated in a two-year course of study without significant and deliberate preparation at the undergraduate level.
New undergraduate programs must be developed that incorporate a more specific (and shorter) biology sequence with a more focused computer science foundation. As bioinformaticians must be equally versed in the languages and practices of biology and computer science, this effort will require a fundamentally interdisciplinary approach. Furthermore, basic research in the field of bioinformatics is progressing rapidly. Professionals in fields, such as bioinformatics and computational molecular biology, must possess not only a strong grasp of computer science fundamentals, but also an equally comfortable understanding of the fundamentals of biology and biochemistry, in order to recognize and appreciate the results of their analyses.
Integration of biology core material
From the discipline of biology, a bioinformatics professional should have working knowledge of several life sciences fields, including genetics, environmental biology, physiology, and biochemistry. Of these many possibilities, the specific curriculum the authors propose below focuses on the area of molecular bioinformatics. A professional in this field of study should understand genetics, molecular and cellular biology, chemical and physical aspects of the flow of genetic information from DNA to proteins, gene expression, replication, recombination and repair, and the experimental tools of molecular biology and eventually functional cell simulation. Fairly easy modifications would allow the creation of curricula with different foci such as physiological or environmental bioinformatics. The amount of practical laboratory experience that is needed by an undergraduate bioinformatician is becoming a point of debate due primarily to the large glut of data already available as well as the growing opportunities for computationally generated results (in silico experimentation and simulation) (Moore 2000). While we hold that hands-on laboratory experience is an important component of training in any area of biology, it is possible that other institutions may choose to place a greater emphasis on more abstract types of processes and results such as those that arise from simulations and the mining of existing databases.
Integration of computer science core material
Bioinformatics methods are becoming an essential aspect of the evaluation, validation and analysis of experimental data in the increasingly data-and simulation-driven science of biology. Computational modeling and prediction methods, such as comparative modeling of protein structure, are now reaching a level of integrative sophistication that allows some experimentation to take place entirely within a computational framework. The July 2002 issue of IEEE Computer showcases the emergence of bioinformatics as a discipline in its own right (IEEE Computer 2002).
Classically, computer science has focused on the study of computer hardware and software. A more contemporary view of information technology, however, must recognize that efficient storage, transmission, and distribution of data make up a significant portion of the future demand on the discipline and on future computer professionals. This view mandates a program of study emphasizing contemporary topics in databases and artificial intelligence. From the discipline of computer science, a baccalaureate bioinformatics professional should have knowledge of introductory programming, entity-relationship models, data structures, AI algorithms (search, optimization, list processing, pattern recognition, etc.), databases, formal and comparative languages, complexity, and specialized algorithm topics, such as those explained in (Baldi and Brunak 1998). Additionally, a baccalaureate bioinformatics professional should have strength in at least one elective field from modeling and simulation, probability and statistics, visualization, pattern recognition, human-computer interaction, the development of complex bioinformatics systems (distributed systems), or evolutionary computation. Mastery of all of these techniques is beyond the scope of even a computer science baccalaureate degree but is within the scope of post-graduate bioinformatics education.
Incorporating research into the curriculum
Basic research in the field of bioinformatics is progressing rapidly. Students will be best served by a program of study that focuses primarily on the fundamental biological and algorithmic principles that give rise to bioinformatics techniques as a means of understanding and developing current and future analytical approaches. For these reasons, training in bioinformatics requires a strongly integrated program of undergraduate and graduate student research activities that employ state-of-the-art algorithms and methods on biologically relevant data. The incorporation of substantive research activities into a bioinformatics tract for biology students is both necessary and attainable for several reasons. - The discipline of bioinformatics is still in its infancy, thus many of the state-of-the-art solutions to important problems use direct approaches that are easily conveyed even to under-graduate students with limited background.
- There exists a clear demarcation between research problems that utilize already developed bioinformatics algorithms and the development of new algorithms. This demarcation facilitates a multidisciplinary approach to the understanding of bioinformatics methods.
- World-wide collaborations in genome sequencing, protein structure determination, and other areas have helped to bring about the sharing of bioinformatics research data and applications as a standard practice. Few other fields allow free access to largely unexplored research data and unperfected techniques as they are being developed. This availability of data affords invaluable opportunities for integrating the discovery of new knowledge into the core course work of the program.
Methods
Bioinformatics professionals must be capable of communicating in both the language of biology and the languages of computer science. Both disciplines are rich in technical terminology. The defining characteristic of a successful baccalaureate bioinformatician is not necessarily complete mastery of both fields, but rather a traditional mastery of one field and a comfortable familiarity with the other (Krane and Raymer 2002).
The model proposed is designed to provide students with a traditional mastery of biological sciences through course work available in existent biology programs. Additionally, the authors recommend course work common in contemporary baccalaureate computer science programs designed to provide students with opportunities to develop a "comfortable familiarity" with the language and concepts of computation crucial to bioinformatics. Finally, specialized bioinformatics training is recommended at an introductory level (during the sophomore year) and as a capstone (during the senior year) to provide students with opportunities to become familiar with the use and development of the tools of bioinformatics.
The introductory course, "Introduction to Bioinformatics", is offered to sophomore-level students. This course, offered early in their program of study, presents the fundamental concepts of bioinformatics and provides a tools-oriented approach toward solving informatics problems. This course has minimal prerequisites and is designed for students with at least one course in biology or computer science. Student projects in the course are completed on a team basis, such that each team has at least one "expert" in biology and one in computer science. This collaboration fosters the ability to communicate concepts between the two disciplines – the hallmark of well-trained bioinformaticians. This introductory course is designed for students with little or no experience in bioinformatics. In-class lectures for this course focus on pen-and-paper implementation of algorithms, so that prerequisite experience in programming languages and strategies is eliminated.
In contrast, the capstone course, "Algorithms for Bioinformatics," is offered to students in their senior year. This course assumes that the incoming student is well versed in both the fundamentals of biology and the fundamentals of computer science and focuses on the application of algorithmic techniques to biologically driven problems in bioinformatics.
The development and instruction of these two new courses are designed to be the major additional resources needed to implement a bioinformatics tract in existent biology or computer science departments. It is critical, in the authors opinion, that these courses be co-taught by faculty from both the department of computer science and the department of biological sciences in order to provide the appropriate interdisciplinary exposure. At Wright State University (Dayton, Ohio), these courses are co-listed as BIO/CS courses and open to students majoring in either discipline. The objectives of these two new courses are summarized below.
Figure 1 presents suggested learning modules for an introductory course in bioinformatics. The goal of this four-quarter credit hour (three-semester credit hour) introductory bioinformatics course is to present a tools-oriented approach to bioinformatics emphasizing data structure in DNA, data searches, pairwise alignments, substitution patterns, protein structure prediction and modeling, proteomics, and the use of existing web-based bioinformatics tools. This course also introduces students to beginning programming skills in Perl, the most common computer language for biological data analysis. The lectures focus on common classes of problems in bioinformatics, and in-class solutions are implemented using a language-independent, pencil-and-paper approach. Course objectives include development of a solid understanding of Perl basics, familiarity with existing computational approaches to solving problems in bioinformatics, and the skills necessary to continue towards advanced bioinformatics training.
Figure 2 presents suggested learning modules for a capstone course. The goal of this four-quarter credit hour (three-semester credit hour) capstone course in bioinformatics is to provide a theory-oriented approach to the application of contemporary algorithms to bioinformatics. Graph theory, complexity theory, dynamic programming and optimization techniques are introduced in the context of solving specific computational problems in molecular genetics.
The material presented in these two courses serves as a core, the unifying element for a bioinformatics program, otherwise consisting of existing course work in computer science and biological science. Although designed in the framework of a biology degree with a bioinformatics focus, these courses can also be used to provide students majoring in computer sciences with similar bioinformatics opportunities.
To maximize student learning, these courses are taught using an active/cooperative learning approach (Johnson et al 1998a; Johnson et al 1998b; Millis and Cottell 1998). Integration of these courses into existing curricula is substantially aided by the creation of inter-disciplinary project teams in which students with a stronger biology background take the lead in experimental design and data interpretation while students with a stronger computer science back-ground take the lead in the development and implementation of representation methodologies and optimized solution-finding algorithms. Additionally, as part of the capstone course, student teams are asked to complete a formative project with research application. Students are afforded the opportunity to apply their own expertise on a project of their choosing or provided an opportunity to work on one of the numerous basic research projects being conducted by faculty members.
Implementation
This model program we propose was accepted as an official option in the Biological Sciences Degree Program at Wright State University in 2002. Prior to this adoption, undergraduate students wishing to study bioinformatics worked with individual faculty to develop tailored programs of study that required a successful petition for graduation. Concurrently, a related bioinformatics degree program has been implemented for the department of Computer Science.
Although the two related four-year courses of study differ significantly in their upper-division course work, they were designed so that requirements are satisfied in tandem during their first two years in order to allow students to defer specialization in Biology or Computer Science until their junior year. During the first two years of study, both curriculums include a two-year sequence of biology (including molecular genetics and cell biology), a two-year sequence of computer science course work (including programming and data structures), a two-year chemistry sequence (including organic chemistry), and a two-year sequence in mathematics (including calculus, discrete mathematics and statistics). Upper-division course work in the related programs differs but retain major areas of commonality, including advanced topics in biochemistry and molecular cell biology, operating systems, artificial intelligence, and database design, and the capstone course in bioinformatics developed for this option.
A model biology curriculum for undergraduate bioinformatics
The authors now present a curriculum proposal for biology (Figure 3) that is based on courses common to most undergraduate institutions, yet incorporates specific sequences in computer science and mathematics to which most biology majors are not usually exposed. In order to meet these objectives, it was necessary to significantly limit the freedom of choice of upper division electives that undergraduate biology majors usually enjoy. This curriculum is best suited for training molecular bioinformaticians. As a result, students have relatively little opportunity to take advanced courses in aquatic ecology, for instance, though such a course could easily take the place of a cell signaling elective for those interested in environmental bioinformatics.
To facilitate the implementation of this program, only two new courses have been introduced (described above). Both courses are co-taught by faculty from both the Department of Biological Science and the Department of Computer Science. This first course, Introduction to Bioinformatics, is designed not only for students in the bioinformatics program, but as an elective for all biology or computer science students who wish some exposure to the field. Thus, this sophomore-level course has a relatively wide appeal, and its enrollment far exceeds the number of students entering the bioinformatics tracts proper.
Most existing biology (and Computer Science) departments can successfully adopt this model program in bioinformatics. The additional resources required for this program are small – only two new courses are introduced as regular offerings. Furthermore, the authors recommend that the courses be co-taught with both computer science and biological sciences faculty, thus sharing the additional overhead costs between two academic units.This model bioinformatics program can be reasonably modified to meet CAC/CSAB/ABET requirements for a bachelor of science degree in computer science at most universities (CSAB 2000; Doom et al. 2002b).
Conclusion
There are few area where there is a greater need or more opportunities for scientists to hone their methods of questioning than in bioinformatics. The competitive pressure and rewards for progress in bioinformatics are substantial, and students can use them to prepare themselves to join this sought-after work force. The creation of an undergraduate bioinformatics option in computer science and engineering is of utmost importance for global health, economic development, and the success of the students.
The central argument that is presented for an undergraduate bioinformatics option within a Biology BS degree can be summarized as follows: (1) Students holding undergraduate degrees in Biology or Computer Science are generally required to remediate course work from the other discipline if accepted to a postgraduate bioinformatics program. The number and chain of prerequisites that must be satisfied in either case require about two years of course work because course dependencies are such that they cannot be taken in parallel. (2) An assumption of two years of remedial course work, in addition to the two years to obtain the MS degree, implies eight years of preparation could be required for a student to obtain an MS degree in bioinformatics. (3) The alternative that is proposed would lead to a BS degree in bioinformatics in four years and an MS degree in bioinformatics in the standard six-year time frame.
The authors believe that complementing existing degree options in biology with the appropriate existing computer science and mathematics course work is the most realistic way to implement programs in bioinformatics. The model presented requires relatively few additional resources: two additional courses offered as infrequently as once every other year may suffice for small programs. Thus, this model not only meets the needs of research universities (such as Wright State University), but also provides potential direction for the many small, primarily liberal arts, colleges interested in providing bioinformatics education (Dyer and LeBlanc 2002).
The model that is presented is just that – an evolving model. The content decisions for this model are based upon the contemporary needs of the bioinformatics industry towards providing the academic background required for meeting the entrance requirements of postgraduate programs in bioinformatics. As is already the case with biological sciences, it is impossible to include all potential fields of relevance within the scope of a baccalaureate degree. Each institution will need to address the specific implementation appropriate to its strengths and needs. The integration of bioinformatics resources, such as the two courses that are proposed, with those resources already present within the biology and computer science programs at existing institutions will require the participation of faculty from both biology and computer science. This model will serve as a framework for initial dialogues and will help guide faculty towards the rapid development of bioinformatics programs that capitalize on the strengths of their specific institution.
* This work was supported by the National Science Foundation (NSF) under an Educational Innovation grant from the Computer and Information Science and Engineering (CISE) directorate, award #EIA-0122582.
References
Baldi, P. and S. Brunak (1998). Bioinformatics: The machine learning approach, Cambridge, MA: MIT Press.
CSAB (Computing Sciences Accreditation Commission,) (2000). "Criteria for accrediting programs in computer science in the United States." Available online at: http://www.csab.org.
Doom T. E. and O. N. Garcia (2001). "Bioinformatics: An option in computer science," in 2001 Midwest Artificial Intelligence and Cognitive Science (MAICS) Conference, Miami, OH.
Doom, T., M. Raymer, D. Krane, and O. Garcia (2002). "A proposed undergraduate bioinformatics curriculum for computer scientists," in Proceedings of the 2002 ACM Special Interest Group on Computer Science Education (SIGCSE 2002), Covington, KY.
Doom, T., M. Raymer, D. Krane, and O. Garcia (2002). "Crossing the interdisciplinary barrier: A baccalaureate computer science option in bioinformatics," IEEE Transactions on Education, in press.
Dyer, B. and M. LeBlanc (2002). "NSF workshop: Incorporating genomics research into the undergraduate curricula." Available online at: http://genomics.wheatoncollege.edu/, NSF DUE-0126643, Wheaton College, Norton, MA.
Henry, C. M. (2001). "The hottest job in town," Chemical and Engineering News, vol. 79, no. 1, pp. 47 – 55.
[IEEE Computer, 2002] "About this issue," IEEE Computer, vol. 35, no. 7, p. 3, July 2002.
Johnson, D., R. Johnson, and K. Smith (1998). Active learning: Cooperation in the college classroom, Edina: Interaction Book Co.
Johnson, D., R. Johnson, and K. Smith (1998). "Maximizing instruction through cooperative learning," American Society for Engineering Education (ASEE) Prism, vol. 7, pp. 24 – 29.
Krane, D. E. and M. L. Raymer (2003). Fundamental concepts of bioinformatics, San Francisco: Benjamin Cummings.
Millis, B. and P. Cottell (1998). Cooperative learning for higher education faculty, Westport: Oryx Press.
Moore, S. K. (2000). "Understanding the human genome," IEEE Spectrum, vol. 37, no. 11, pp. 33 – 35.
[NCBI, 2002] National Center for Biotechnology Information (NCBI). Available online at: http://www.ncbi.nlm.nih.gov/Database/index.html, August 2002.
Schachter, B. (2002). "Bioinformatics moves to the head of the class," Bio-IT World, pp. 62 – 67.
FIGURES
Module One- (From Biology) major biological issues that define the discipline: information storage in DNA, protein structure and function, the tools of molecular biology
- (From Computer Science) programming environment: command line Unix, redirection, pipelines for STDIN and STDOUT, introduction to the Perl programming language
- (Lab) DNA isolation and gel electrophoresis
Module Two- (From Biology) simple pairwise alignments: simple searches, gaps, and techniques for scoring, strategies for efficient searches
- (From Computer Science) programming primitives: Scalar and array variables in Perl, basic I/O, program control
- (Lab) DNA sequencing demonstration and counting nucleotides
Module Three- (From Biology) substitution patterns and phylogenetics: causes of variation in genes and lineages, molecular phylogenetics
- (From Computer Science) programming decomposition: functions and subroutines, parameter passing
- (Lab) PCR demonstration and finding open reading frames
Module Four - (From Biology) statistical and parsimony-based approaches to phylogenetics: distance matrix methods, cluster analysis and multiple sequence alignments, inferred ancestral sequences
- (From Computer Science) programming tools for bioinformatics: string manipulation in Perl, strategies for efficient searches (exhaustive, heuristic, and branch and bound)
- (Lab) sterile techniques demonstration and parsing molecular data repositories
Module Five- (From Biology) gene recognition: gene structure and density, introns and exons, transposition and repetitive elements, introduction to microarrays
- (From Computer Science) proteomics: predicting RNA secondary structure, tools for molecular visualization and structural modeling, tools for ligand screening, inhibition, and drug design
- (Lab) final research project
Figure 1: Learning modules an introductory-level course "Introduction to Bioinformatics". |
Module One- (From Biology) review of the limitations of molecular biology tools; transcription, translation, protein synthesis
- (From Computer Science) review of data structures and complexity: elementary data structures, review of analyzing and designing algorithms, recurrence relations, polynomial and non-polynomial growth, introduction to the concept of NP-completeness, decision, and optimization problems
- (Lab) graph structures
Module Two- (From Biology) sequence comparison and searching: global, local, and semi-local comparison, gap penalty functions, comparing multiple sequences, BLAST and FASTA
- (From Computer Science) introduction to dynamic programming: elements of dynamic programming, optimal string alignment using dynamic programming, double dynamic programming
- (Lab) sequence comparison
Module Three- (From Biology) fragment assembly of DNA: base call errors, orientation, repeated sequences, incomplete data
- (From Computer Science) optimization algorithms: elements of greedy strategy, algorithms for shortest common super-string, reconstruction, and multi-contig
- (Lab) fragment assembly
Module Four- (From Biology) protein structures and functions: secondary structures, structural motifs, enzyme kinetics
- (From Computer Science) molecular structure prediction: recurrence relations for determining total free energy of a structure, branch and bound techniques for protein threading
- (Lab) protein threading
Module Five- contemporary algorithms for bioinformatics research: introduction to the use of classifiers, genetic algorithms, Markov models, and other contemporary tools for research computation
- (Lab) final research project
Figure 2: Learning modules for a capstone course "Algorithms for Bioinformatics". |
I Humanities and General Education (42)
– English (11)
– Humanities (31)
II Biological Science (58)
Core (38)
– One year principles of Biology sequence (12)
– Molecular biology (4)
– Molecular genetics (4)
– Cell biology (4)
– Microbiology (5)
– Vascular plants/Lower plants (5)
– Invertebrate biology (5)
– Intro to Bioinformatics (4)
Advanced (20)
– Molecular cell laboratory (4)
– Algorithms for Bioinformatics (4)
– Senior seminar (2)
– Electives (10)
III Computer Science (32)
– One year programming sequence (12)
– Intro to CompInformation Sys(4)
– Data Structures and Software Design (4)
– Database Systems (4)
- Bioinformatics electives (8)
IV Other Math and Science (67)
Chemistry (33 hours)
– One-year inorganic chemistry sequence (15)
– One-year organic chemistry sequence (18)
Mathematics (19)
– Two-quarter calculus sequence (10)
– Elementary Matrix Algebra (3)
– Discrete Mathematics (3)
– Statistical methods (3)
Physics (15)
– One-year physics sequence (15)
Figure 3: A model curriculum for a bioinformatics tract in a standard biological sciences training program. The courses below limit the upper division elective option available to students largely due to the need for upper level computer science and mathematics classes. This curriculum has been in place at Wright State University since 2002 and can be completed in a four-year course of study with a total of 199 quarter credit hours. |
|
