Design and implementation of an interdepartmental bioinformatics program across life science curricula



Over the past 10 years, there has been a technical revolution in the life sciences leading to the emergence of a new discipline called bioinformatics. In response, bioinformatics-related topics have been incorporated into various undergraduate courses along with the development of new courses solely focused on bioinformatics. This report describes the design and implementation of an interdepartmental bioinformatics program throughout several life science programs. Using elements of the backward curricular design process, nine faculty members from the Biology, Microbiology, and Chemistry Departments at the University of Wisconsin – La Crosse incorporated bioinformatics in a coordinated manner into 10 courses. Key molecular biology concepts were first identified followed by development of bioinformatics exercises that centered on these concepts. An overview of how the program was constructed and implemented and a summary of the exercises that were designed will be presented.

The emergence of bioinformatics has changed the face of biological research. It has influenced the types of questions that are asked, increased the pace at which knowledge is obtained, and clearly illustrated the need for biologists to have some prowess in computer science and mathematics. The influence of bioinformatics affects many biological disciplines including biomedicine, ecology, evolution, and organismal biology. Accordingly, the bioinformatics revolution is dynamically changing undergraduate education in the sciences. Indeed, there have been many calls for educational reform based on pedagogical research and studies on cognitive thinking, as well as technological advances in research [1–5]. For instance, the BIO 2010 report advocates the inclusion of more “quantitative biology” in undergraduate curricula, one aspect of which is the use of computers [4]. Effective computer use includes knowledge of the data and respective databases that are readily available and proficiency in using and manipulating the different programs to perform the necessary functions. Similarly, the ASBMB recommended biochemistry and molecular biology undergraduate curriculum lists the use of computer databases and bioinformatics as core content items and the “ability to use computers as information and research tools” as one of 15 specific skills that biochemistry and molecular biology students should master before receiving their baccalaureate degree [2].

The incorporation of bioinformatics into undergraduate curricula has been well documented in the literature. There are multiple examples of specific exercises that have been designed and implemented into existing undergraduate courses in biology or biochemistry or provide the foundation for new courses in genomics, bioinformatics, or computational biology [6–15]. The introduction of bioinformatics as a field and initial hands-on experience have begun to permeate the introductory biology classroom and even span multiple classes within a single department [6, 12]. At the University of Wisconsin-La Crosse (UW-L),1 we sought a more comprehensive infusion of bioinformatics through the life sciences curricula. What emerged was the strategic incorporation of bioinformatics throughout 10 courses spanning three departments. Here we describe the organization, design, and implementation of our Bioinformatics across the Life Science Curriculum (BLSC) program. An accompanying paper describes the assessment of the BLSC program [16].


UW-L is a four-year comprehensive university that is part of the University of Wisconsin System. UW-L enrolls 8,500 students, 7,850 of whom are undergraduates. The life science programs are well populated with over 750 declared biology majors, ∼80 biochemistry majors, and 70 microbiology majors. Students with declared biology and microbiology majors have the option of focused study within specialized curricular tracks called concentrations. In biology, 308 of the majors have chosen to pursue the biomedical concentration, and another 40 are in the cell and molecular biology concentration. Within microbiology, 30 students are following the biomedical science concentration. The tactical placement of some of the newly developed bioinformatics exercises within lower level required courses (Table I, Tiers 1 and 2) ensures multiple exposures to bioinformatics for all students within each major. However, those students participating in the biomedical or molecular-based programs will have additional and more sophisticated experiences with bioinformatics in higher level courses (Table I, Tiers 3 and 4). The stratified design provides for a natural progression from simple exposure to bioinformatics, to the ability to execute a basic program, to developing proficiency with multiple programs that allow increasingly complex questions to be addressed.

Table I. The progression of key concepts in a tiered bioinformatics program
 CourseBiologyaBiochemistryaMicrobiologyaNo. of students per yearKey course concept(s)
  • a

    Required (R) or Elective (E) Course for Biology, Biochemistry, or Microbiology programs

Tier 1General BiologyRRR900Transcription, translation, mutations
Tier 2Fundamentals of MicrobiologyE R300Transcription, mutations
 GeneticsRR 260Transcription, translation, mutations, evolutionary conservation
Tier 3Survey of BiochemistryE E120Protein structure/function, enzyme kinetics
 Cell BiologyRR 180Evolutionary conservation, phylogeny, protein structure/function
 Microbial Genetics  R25Data base searches, protein sequence alignments, conserved protein domains
 Biochemistry IERE50Protein structure/function tied to evolution
Tier 4Molecular BiologyR/ER 40Genomics
 Developmental BiologyEE 30Mutations, evolutionary conservation, model organisms/comparative genomics, protein structure/function, development
 Bacterial Diversity  E14–20Diversity of morphologies, physiologies and ecological niches throughout the microbial phylogenetic tree

There were four major justifications for the incorporation of bioinformatics across life sciences curricula.

(1) To Expose Students to Bioinformatics for the Sake of Learning Bioinformatics Itself—

As bioinformatics has permeated the life sciences and become a mainstay in the researcher's toolbox, we wanted to instill an appreciation for the vast amount of data available and an understanding of the types of questions that can be addressed by bioinformatics. An additional aim was for students to develop some proficiency with running common bioinformatics programs to search for and obtain sequences by name, search for orthologous sequences, identify domains within proteins, perform multiple sequence alignments, generate evolutionary trees, and view molecular structures. In the upper level Bioinformatics course, which was offered at UW-L prior to the BLSC program, students revisit some of the specific programs encountered in earlier courses but now learn how to manipulate program parameters to customize and maximize the data output. This course provides a launching pad for subsequent bioinformatics training in graduate school or the workforce where programming, database development, and large scale data mining skills will be expected to a greater extent from biologists as superficial bioinformatics skills become insufficient [17, 18].

(2) To Facilitate Student Learning of Key Molecular Biology Concepts—

Faculty participating in the project identified concepts, some specific and some broad, that students needed to have a firm understanding of. Many of the identified concepts span multiple courses, and they are often challenging for students, in part because the teaching and learning of them is not facilitated by traditional classroom pedagogies. Using bioinformatics in a lecture demonstration or a hands-on activity allows students to visualize and probe structural, spatial, and temporal relationships between atoms, functional groups, and molecules. Examples of the developed bioinformatics exercises ranged from having students simply translate a DNA sequence into a polypeptide and explore the possible reading frames to reinforce the central dogma to using Protein Explorer to explore the importance of particular amino acids in the active site of alkaline phosphatase and make connections between amino acid substitutions and resulting changes in enzyme kinetics.

(3) To Support Faculty Development—

Faculty development is necessary to refresh the knowledge and skills of any academician, and it is a key component of undergraduate science education as detailed in BIO 2010 [4]. Bioinformatics had become an essential component of the research programs of participating faculty, and some faculty members were more experienced in selecting, executing, and manipulating programs than others. In addition, some of the faculty members had routinely used bioinformatics during their graduate and post-doctoral careers where their supporting laboratories purchased expensive software, but due to budget limitations at a comprehensive university, these faculty now needed to familiarize themselves with available freeware that offered similar functionality. During the initial meetings, faculty members who actively used bioinformatics in their courses and research provided a brief overview of some common software and answered questions. While developing their respective exercises for courses, all faculty members involved had an opportunity to delve further into a subset of programs and gain useful experience that could not only affect their teaching but translate into more productive research. Furthermore, each faculty member peer-reviewed two projects so that they had an opportunity to extend their knowledge and practice of bioinformatics.

(4) To Foster Collegiality among Faculty from Multiple Departments—

Although the three departments represented by the participating faculty are housed in one building, the hectic teaching, research, and service schedules prohibit routine discussions of what we teach, how we teach, and our research needs and goals. The interactions demanded by the BLSC program provided time to specifically discuss these issues and allowed relationships to develop on a personal and professional level. The BLSC initiative sparked changes in curriculum and new partnerships in research. This unified effort to include bioinformatics throughout curricula continues to be revised and assessed, thereby maintaining a continued connection between these faculty members.

An interdepartmental approach was taken for multiple reasons. Bioinformatics is a fundamental component of research within the subdisciplines of biology, chemistry, and microbiology; thus, students in these departments would be best served by developing proficiency in bioinformatics. In addition, a coordinated multidepartmental effort would allow multiple exposures to bioinformatics that built on each other and increased in complexity. The same background information would not be repeated over and over again, wasting valuable time that could be spent increasing students' breadth and depth of knowledge of bioinformatics. Importantly, the multidepartmental approach was pursued to help dispel the notion that bioinformatics is specific to one subdiscipline or even that it refers to one task or one program, a common misconception held by students prior to implementation of the BLSC program.


Timeline and Logistics—

The design of the BLSC program at UW-L was carried out during a three-month summer period, whereas the implementation phase occurred during the following academic year. Initially, the participants met for a three-day workshop during which time the program goals, format, and implementation scheme were determined. The workshop was followed by periodic meetings throughout the next several months where updates on progress, review of exercises, and further discussion of the program took place. The timeline of the design process is summarized in Table II with details provided below.

Table II. Timeline for the Design and Implementation of the Bioinformatics Program
Initial three-day workshop
Day 1, Part 1Update on the format and curriculum of the current Bioinformatics course
Demonstration of bioinformatics tools related to databases, phylogenetics, genomics and proteins
Day 1, Part 2Discussion on “What should students know when they leave?”
Generation of Key Concepts list
Day 2, Part 1Demonstration of two bioinformatics exercises currently integrated into courses
Day 2, Part 2Discussion of “When should student be exposed to different materials”
Discussion of which bioinformatic tools could be used to enhance a particular course concept
Day 3Rough draft of possible exercises for various courses
Following 2 monthsIndividual development of bioinformatics exercises
Peer-review of exercises and revision
Three half day formal meetings every three weeks to discuss progress and review finished exercises

Two factors deemed critical for the BLSC program to be instituted in a timely and successful manner were: 1) compensation for the participating faculty members who were making a significant time commitment to the project and 2) adequate technology in the classrooms. A University of Wisconsin System Undergraduate Teaching and Learning Grant provided funding for the development of the BLSC program. These grants are specifically designed to aid in the development of projects and programs aimed at improving undergraduate teaching and student learning. Nine faculty members participated in the BLSC program, five from the Department of Biology, two from the Department of Chemistry, and two from the Department of Microbiology. From the grant, each participant received the equivalent of one month of summer salary as an incentive and reward for their work.

UW-L has been committed to infusing technology throughout the campus; thus, all of the lecture rooms were equipped with computers and projection systems at the initiation of the project. Subsequently, one of the key teaching laboratories has obtained new laptops (one per four students) and a projection system, which facilitates laboratory-based bioinformatics exercises. In addition, multiple general computer access classrooms are available on campus, and these are reserved during the regularly scheduled laboratory periods for exercises that students must complete individually.

The Design Process—

Many aspects of the backward curricular design process described by Wiggins and McTighe [19] were applied to the development of a comprehensive bioinformatics program at UW-L [19]. In backward design, there are three distinct stages that are followed to plan curriculum: stage 1, identification of desired results; stage 2, determination of acceptable evidence; and stage 3, planning of learning experiences and instruction. This backward design process requires that desired curricular results are identified before learning exercises are generated. The desired results focus on what students should know, understand, and be able to do. In the BLSC project, faculty members worked together to first define key concepts and related bioinformatics applications and then design exercises to address these concepts. The following is an overview of each stage in the backward curricular design process and a description of how our group adapted a modified version of each stage to achieve our unique programmatic goals.

Stage 1: Identification of Desired Results—

During an initial three-day workshop, faculty spent a significant amount of time discussing and identifying the desired curricular results of the BLSC project. It was determined that the overarching goal was for students to have an understanding of the bioinformatics tools that are available to them and an understanding of how to utilize these tools to gain the knowledge needed to answer a scientific question. Specific bioinformatics applications that are considered as basic, fundamental skills were discussed and linked to core concepts that overlapped multiple courses. Examples of core concepts include the flow of information in gene expression, the relationship between protein structure and function and the effects of mutations, evolutionary conservation of proteins, and the power of comparative genomics (Table III). Such concepts are “big picture” ideas that transcend any single class, whose understanding is foundational to modern day science, and that often prove difficult for students to fully grasp, especially with traditional classroom techniques. Wiggins and McTighe [19] classify such concepts as worthy of having “enduring understanding,” which refers to what students will retain after they have forgotten many of the details they have learned. For each key concept, a list of subconcepts was generated that aided in the eventual development of exercises.

Table III. Concepts associated with bioinformatics that are important for life science curriculums
Central DogmaDNA replication: antiparallelism, semi conservation
Coding and noncoding strands
Transcription: structural RNA (tRNA and rRNA)
Processing: intron/exon splicing, polyA
ORG/reading frame
Definition of a gene/operonGene organization: promoter, intron/exon, polyA, CpG islands
Operon organization: promoter, operator, terminator
DNA binding proteins
Restriction mapping
Regulation of gene expressionTranscription- promoters, enhancers, operator, repressor
Promoters-consensus: strong/weak
DNA binding proteins
Evolutionary ConservationGene/protein families
 Gene duplication/exon shuffling
 Selection (coding, noncoding, active sites)
 New motifs/proteins
Gene OrganizationViruses, prokaryotes, eukaryotes: composition, size, structure, RNA/DNA, % of GC
HOX: gene duplication
Centimeres/telomeres/Autonomous Replicating Sequence
 Repetitive (Alu)
Phylogeny/TaxonomyThree domains
Accumulation of mutations over time
Generation of tree & evaluate the tree
Applications to other courses
Rates of mutations in different areas of DNA
Model Organisms/Comparative GenomicsUse to identify disease genes
Use to identify biochemical pathways
Use to model human disease
Identify new important genes
Minimal model of life
Gene organization
Protein Structure/FunctionActive site/binding site
Structures (primary to quaternary): globular and fibrous
Properties of MacromoleculesProteins: amino acid properties, pI, molecular weight, Km, slab view, S–S bonds, H bonds
H bonding, binding proteins
Relative size of macromolecules
DNA packaging: chromatin
Lipid structure
DevelopmentHOX genes
Gene regulation
Virus life cycle/sporulation
Genomics/ProteomicsApplications of bioinformatics: drug design, vaccines, arrays, yeast two-hybrid, phage display, two-dimensional gels

Stage 2: Determination of Acceptable Evidence—

Assessment of student learning outcomes is necessary to determine the level of student understanding. The backward design process works to mesh assessment more closely in the process of curriculum design [19]. Before developing bioinformatics exercises, faculty determined what types of assessment evidence would be collected to document and validate that student understanding was achieved. Assessment played a role in the curricular design process on many levels. First, in some cases, prior assessment drove the development and incorporation of a bioinformatics exercise into a course. Past assessment of student learning via exam questions or homework problem sets showed a need for improvement of student understanding of particular course concepts. To this end, a bioinformatics exercise was used as an alternate teaching method to augment student learning. Second, faculty-determined mechanisms of assessment for the individual course exercises that they planned to develop. Contrary to backward design theory, these assessments were not completely fleshed out prior to the last phase of the process, and instead, mostly represented notions of what students should know or do but not exactly how this would be determined. Finally, the faculty participants collectively identified knowledge and skills that all students served by the BLSC program should attain upon completion of the program, with completion of the program defined by enrollment in a large subset of participating courses during a student's baccalaureate career. The knowledge and skill set included the ability to properly select and execute bioinformatics programs, analysis of information obtained by bioinformatics, and the ability to relate bioinformatics findings to big picture concepts. The level of proficiency and the complexity of the bioinformatics applications were established for each tier of the program. The need to assess student understanding at various stages of a multidepartmental program that spans a student's entire undergraduate career provided a unique opportunity. To fulfill the need for assessment, new assessment tools were developed, and they are described in an accompanying article [16], along with the data that were obtained using them.

Stage 3: Planning of Learning Experiences and Instruction—

After identifying the desired curricular results of the BLSC project and discussing measures that would demonstrate understanding, the project entered Stage 3, which involved planning of learning experiences and instruction. To aid in this portion of the design process, time was spent on an instructional phase for faculty members involved in the project. The level of expertise in bioinformatics varied greatly among the faculty participants. Some were currently teaching sections in the newly developed Bioinformatics course at UW-L, whereas others only used a specific bioinformatics tool for their research. Overall, most of these faculty members were not current on the full array of bioinformatics tools available. Initially, all participants were updated on the format and curriculum of the current Bioinformatics course, including a discussion of perceived strengths and weaknesses of the course. Then, faculty who were experienced in various bioinformatics tools related to databases, phylogenetics, genomics, and proteins demonstrated software programs that are available free of cost. At the conclusion of this half-day meeting, all participants were updated on a wide array of bioinformatics tools that are available for incorporation into course curriculum and research. Next, faculty who had already incorporated bioinformatics within their courses shared their experiences with the rest of the group. They focused on presenting the logistics of their course exercises and then discussing the mistakes and successes they had seen. The presentations helped the entire group gain a better understanding of the type of exercise that could be accomplished within a particular time constraint and the level of student knowledge and expertise required.

Before the actual development of specific bioinformatics exercises, several issues were considered to ensure that the end result was a synchronized program with a logical progression of ideas and skills and minimal overlap. The participants first discussed when during their academic career students should be exposed to different bioinformatics concepts/skills, and they worked to coordinate the order in which students would encounter different bioinformatics tools with the typical sequence of courses taken by students. Given that all students served by the BLSC program enroll in General Biology, it was determined that there must be a firm, initial exposure to bioinformatics in this course. For students in the Biology and Biochemistry programs, the Genetics course was established as a key junction point, and for students in the Microbiology program, the Fundamentals of Microbiology course was established as a key junction point. Therefore, exposure to bioinformatics in these two courses was deemed critical. Because similar tools might be used in multiple courses, other discussions focused on the degree of complexity with which a particular bioinformatics concept/skill should be addressed in different levels of courses to maximize student learning. For instance, students might be exposed to a sequence alignment program via a demonstration in an introductory course and then work independently to obtain scientific information using the same alignment program in an upper level course. Another way that certain bioinformatics tools were “assigned” to particular courses was to determine which tools could best enhance understanding of a particular concept that was already a main focus within a course.

There was substantial group input and support into the development of each exercise. Each participant first formulated a plan or outline for a potential exercise to be incorporated within the lecture or laboratory portion of their course(s). Individuals were given an opportunity to present the plan to the entire group, and feedback was offered. Individuals then spent time over the next two months developing their exercises. Many of these exercises required faculty members to delve into the scientific literature of a subject, develop new expertise in specific bioinformatics programs, and write new lecture notes/slides or even entire laboratory units complete with background information, instructions, explanation, screen captures, etc. Another aspect of backward curricular design that was manifested in many of the exercises developed for the BLSC program was the notion of “uncoverage” [19]. Content uncoverage is simply the opposite of content coverage, which implies that the instructor talked about a topic in a class of passive listeners. Alternatively, uncoverage requires that students grapple with difficult concepts and discover the content in more independent and experiential ways. Although uncoverage is not a universal feature of the exercises that comprise our bioinformatics program, many of the more in-depth projects did use this method in an attempt to maximize enduring understanding.

One of the most useful features of our process was that each new exercise was required to go through peer review by two other members of the group. These reviewers went through the exercises first with the mindset of students completing the exercise with no prior exposure to determine any technical flaws or points of confusion. Reviewers went through their assigned exercises a second time as experienced faculty to suggest changes that might improve student learning. The group continued to meet once every 2–3 weeks, at which time each new exercise, either fully developed or an outline of the exercise, was presented to all participating faculty.

Implementation and Subsequent Review—

The newly developed bioinformatics exercises were incorporated into the lecture or laboratory during the following academic year. Table IV summarizes the exercises that were developed for each course, which range from a demonstration within a large lecture section, to a one-page homework assignment, to a multiple week laboratory project. Following the first year, most instructors further modified their individual projects both in content and in the mode of delivery or execution (i.e. changing from a group project to individual assignment). Several exercises have been subsequently disseminated to the scientific community through publication or presentations at national meetings [20].

Table IV. Summary of the exercises presented throughout the bioinformatics program
CourseDescription of exercise
General BiologyA demonstration of how researchers use bioinformatics programs to identify the β-hemoglobin gene, translate the DNA into protein, and then look for mutations in β-hemoglobin that cause sickle cell anemia. Molecular models of the mutated form of β-hemoglobin and restriction fragment length polymorphism maps are also shown. The following tools are used for this exercise: Biology Workbench (6Frame translation, restriction enzymes, CLUSTAL W alignments) and Protein Explorer.
Fundamentals of MicrobiologyStudents use programs to search for open reading frames and prepare reverse complement sequences of an assigned DNA sequence. Prepared DNA and protein sequence alignments are also used to identify and characterize various types of mutations (missense nonsense, frameshift, etc.).
GeneticsIn the laboratory, students work on individual and group assignments to familiarize them with resources available through NCBI. Students learn to find sequence data, use BLAST and interpret BLAST results, investigate primary literature related to specific genes, and use linked databases such as OMIM, TAIR, and FLYBASE. One laboratory is entirely devoted to bioinformatics; taking the students from a mutant phenotype to genetic mapping to finding putative genes (using GENSCAN) and investigating gene expression using the Stanford Microarray Database. As a homework assignment, students use programs to search for a gene for which they have been assigned a mutant fruit fly culture. They find the coding sequence of this gene and use that sequence to find the top non-fly match. They then analyze that match as to whether or not it is biologically relevant based on the E value and/or any primary literature they can find about the gene. This work is done individually.
Survey of BiochemistryIn the laboratory, students perform a bioinformatics exercise that utilizes Protein Explorer to examine the active site of alkaline phosphatase. Students first explore the structure of alkaline phosphatase, identifying subunits, active sites, and active site residues. Using the protein structure model, students predict probable functions for certain amino acid residues. Students are then presented with published kinetic data of mutant alkaline phosphatase enzymes and the general mechanism of alkaline phosphatase. Using kinetic data and the protein structure, specific functions for various active site amino acid residues are proposed. The exercise facilitates student understanding of the correlation between the enzyme mechanism and the effects that active site residue changes have on the kinetics of the enzyme.
Cell BiologyIn the lecture, the instructor uses bioinformatics to perform a multiple sequence alignment of G-protein superfamily members. In an in-class problem, students are provided information regarding the function of the proteins and compare that information with the alignment and resulting unrooted tree to draw relationships between protein structure and function. In the laboratory, students perform a bioinformatics investigation similar to one that laid the foundation for a primary literature paper. Specifically, students obtain a Drosophila protein sequence, determine what, if any, domains are present, perform a BLASTP search to identify a potential human ortholog, and determine the number and type of domains present in that sequence. Finally, the fly and human sequence are aligned, and the region(s) of highest similarity is discussed. The following week, the “benchwork” experiments and resulting data from the paper are discussed. This exercise not only familiarizes students with some common bioinformatics programs but helps illustrate the interplay between bioinformatics and traditional laboratory research.
Microbial GeneticsIn a 3-h computer laboratory, students use a sequence derived in the laboratory as a query to compare with sequences in the GenBank™ data base. An alignment is made to identify discrepancies in the sequence analysis. The luxA and luxB gene sequences are aligned to look for conserved functional domains. Prosite is then used to identify the sequences involved in catalysis. Students prepare a written report analyzing their output.
Biochemistry IStudents perform a series of two computer laboratory exercises that address: 1) fundamental protein structure and 2) relationship between the evolution of protein structure and maintenance of function. In particular, the students utilize Biology WorkBench to access a variety of protein sequence databases and analysis tools. Students also utilize Protein Explorer to analyze protein structure and perform a comparative protein sequence alignment. The ultimate objective is to have students understand the importance of protein structure during the preservation of function throughout evolution.
Molecular BiologyStudents read six primary literature articles on how the BRCA1 gene was cloned and studies to try to determine the function of the protein. They then follow the articles by examining the gene and protein. The following tools are used for this exercise: Biology Workbench (BLAST, restriction enzymes, CLUSTAL W alignments, motif predictions).
Developmental BiologyStudents perform the reverse genetics technique RNA-mediated interference (RNAi) to reduce levels of a specific protein in developing Caenorhabditis elegans. After characterizing the resulting phenotype, students use bioinformatics to examine the protein structure, analyze the domain architecture, identify orthologous proteins in other organisms, including humans, and link out to literature that describes genetic and molecular analysis of the respective protein and the role of the protein in development or disease. They submit a formal report that describes their phenotypic analysis and the bioinformatics data and proposes a model for protein function that is supported by evidence from multiple species.
Bacterial DiversityEach group devises an isolation protocol for a particular organism, collects source material, and attempts to isolate the organism. Their final isolate is analyzed by amplifying the 16 S rRNA gene and sequencing a fragment of the gene. The students must edit the sequence output and do a database search to identify their sequence or its closest relative. The results are then analyzed to determine the success of the isolation procedure. Students learn to use the “Sequence Match” program at the Ribosomal Database Project site at Michigan State University.


Here we describe the justification for and approach to the coordinated integration of bioinformatics into 10 key courses that span three departments. To our knowledge, this represents the first documentation of an interdepartmental effort to develop a bioinformatics curriculum that spans the baccalaureate career of life science undergraduate students. Participating faculty members designed new exercises for their course lecture or laboratory for the purposes of engaging students in bioinformatics programs routinely used by life scientists and to provide an alternative pedagogical tool to reinforce key course concepts. The backwards curricular design theory provided a mechanistic framework for the development of the BLSC program, although the theory was not followed exactly as prescribed. To fulfill the curricular goals of the BLSC program, faculty members invested significant time expanding their own bioinformatics skills. Furthermore, the incorporation of bioinformatics in a systematic manner throughout multiple courses housed in three departments required a large amount of interaction and communication among faculty members, which naturally contributed to improved collegiality. In closing, the process described here represents an effective and efficient method for developing a comprehensive bioinformatics program where students encountered new bioinformatics exercises of increasing complexity within existing courses of their baccalaureate curriculum.


We thank Drs. Bonnie Bratina, Scott Cooper, Anne Galbraith, Marc Rott, and Todd Weaver for their significant contribution to development of the BLSC program.

  • 1

    The abbreviations used are: UW-L, University of Wisconsin-La Crosse; BLSC, Bioinformatics across the Life Science Curriculum.