• Bioinformatics;
  • new curriculum;
  • assessment


  1. Top of page
  2. Abstract
  5. Acknowledgements
  7. Appendix

The Department of Biological Sciences at the University of Wisconsin-Parkside has developed and implemented an innovative, multidisciplinary undergraduate curriculum in Molecular Biology and Bioinformatics (MBB). The objective of the MBB program is to give students a hands-on facility with molecular biology theories and laboratory techniques, an understanding of mathematical and physical concepts, an ability to apply these concepts to MBB, and a proficiency with the computational tools and skills related to bioinformatics. We hypothesized that a greater exposure to bioinformatics methods, more rigorous requirements in math and computer science, and a constant demand for integrating information in hands-on laboratory courses would help students develop better analytical skills. Indeed, the assessment data support these predictions. Interestingly, 80% of MBB majors apply and are accepted into graduate schools.

Recent advances in both biology and computer science have greatly impacted the fields of Molecular Biology and Bioinformatics (MBB)—of particular importance are new genomics and proteomics methods and concepts. As a result of these changes, a new curriculum in undergraduate biology is needed to incorporate these new concepts and methods while maintaining a broad training in biology. At the University of Wisconsin-Parkside (UWP)1, the Department of Biological Sciences reorganized the existing Molecular Biology undergraduate major into a completely new undergraduate major called MBB, with partial support from a National Science Foundation Course and Curriculum Improvement (NSF-CCLI) grant. The new MBB curriculum was greatly altered across all 4 years (Table AI, Appendix). The new curriculum includes a subset of courses that also serve a second general Biological Science major in the department, other courses that were adjusted to fit the MBB curriculum, and five new courses. In addition, the curriculum emphasizes inquiry-based learning and problem solving and adds a minimum of 1 year (two semesters) required independent research culminating in a research seminar and written thesis. The latter component was included based on studies that suggest a positive impact of undergraduate research on student learning outcomes and student retention [1–3].

The goal for the MBB program was to develop and deliver an innovative, interdisciplinary undergraduate curriculum in MBB that involves both conceptual and practical tools for generating, processing, and understanding biological information. The objective of the MBB program is to give students hands-on competence in fundamental molecular biology theories and laboratory techniques, an understanding of mathematical and physical concepts relevant to biology, and proficiency in computational tools so that our graduates can meet emerging needs in the newer pharmaceutical/biotechnology industries or in the academic and medical research workforce. The MBB curriculum was built on the original molecular biology courses, but with expanded computer-based biology-learning activities and training. To implement the new curriculum, key elements of infrastructure, equipment and continued faculty development in bioinformatics were identified as needs and were successfully dealt with through retraining of faculty and purchasing of new equipment.


  1. Top of page
  2. Abstract
  5. Acknowledgements
  7. Appendix


The Department of Biological Sciences has two undergraduate degree programs: a traditional Biological Sciences major and the new MBB major. There is an overlap between these two degrees at the introductory level (Table AI, Appendix). Both require Bioscience (BIOS 101), Organismal Biology (BIOS 102), Biostatistics (BIOS 210), General Genetics (BIOS 260), General Chemistry I & II (CHEM 101 and CHEM 102), Organic Chemistry I and II (CHEM 321 and CHEM 322), Calculus I (MATH 221), and College Physics I and II (PHYS 105 and PHYS 106). Course material from electives in the Biological Sciences major, Biochemical Metabolism (BIOS 307), and Cell Biology (BIOS 301) has been integrated into a single course for MBB majors, Macromolecular Structure and Function (BIOS 240). However, the core of the MBB curriculum, which separates this major from Biological Sciences, contains a series of required inquiry-based laboratory courses, Microbiology (BIOS 303), MBB Laboratory I: DNA (BIOS 453), MBB Laboratory II: RNA (BIOS 454), MBB Laboratory III: Proteins (BIOS 455), Bioinformatics Programming (BIOS 480), and MBB Senior Thesis (BIOS 489). Although all of these courses can be taken by non-MBB majors, the MBB-specific courses are highly integrated in content and learning goals.

These integrated courses focus on a common set of biological problems (e.g., structural topology, synthesis, metabolism, and function of macromolecules) and build a general set of tools and techniques that students use throughout the program with an emphasis on problem solving and critical thinking. To make the integration a success, the current faculty share a common set of tools and theories. This requires our faculty to function as an interdisciplinary team and learn from each other. The courses are taught in a studio approach, where the “studio” allows free movement between lecture, discussion, computer terminal, and laboratory bench. This approach reflects the actual research environment in that it provides the flexibility to address complex topics using multiple methodologies and creates a more effective learning process by using inquiry or problem-based learning. Students work in groups of two or three to provide a team approach to problem solving and learning skills critical to success in the modern workplace. For example, students work as teams in MBB III: Proteins (BIOS 455) to address multifaceted problems highlighting protein structure and function, using structural bioinformatics tools at a computer terminal and also implementing laboratory exercises in protein purification, enzyme characterization, and experiments of their own design to examine potential function in previously uncharacterized DNA-binding proteins.

In MBB I: DNA (BIOS 453), students learn to conduct DNA sequencing on plasmid DNA using an automated sequencer (LiCor NEN Global IR2) purchased using funds obtained from our NSF-CCLI grant. Students also learn to edit and manipulate the raw sequencing data using e-seq 2.0, a LiCor software package. Once the DNA sequence has been assembled, students are asked to deduce the amino acid sequence, identify the potential coding region, and use their DNA sequence or their deduced amino acid sequence to search the databases to identify genes and/or gene products similar to their sequencing data. A number of alignment programs, including BLAST (Basic Local Alignment Search Tool), and other web-based programs (e.g., Genefinder and ClustalW) are used, showing students the advantages and disadvantages of each program. At the end of these searches and analyses, students are required to provide a hypothetical identity for their DNA sequence and/or their gene product. In doing so, students gain an appreciation of how sequences are edited and learn to use knowledge acquired in their molecular biology, genetics, and biochemistry classes to analyze DNA sequences. Following these exercises, the students are also asked to annotate an unknown genomic sequence and post their findings on a website.

In addition, as part of the NSF-CCLI grant, students in MBB II: RNA (BIOS 454) design and conduct microarray experiments using a microarray scanner (Axon GenePix 4000B). Initially, students use GenBank database files and PCR primer designing software to design gene-specific probes and make their own microarray slides to learn the concepts of slide production. Groups of two students conduct literature reviews and develop a microarray research proposal. The proposals are critiqued by the instructor for their design and reviewed regularly throughout the course of the experiments. Students then design and conduct microarray experiments initially using slides obtained from the Boyce Thompson Institute for Plant Research at Cornell University; more recently, slides have been obtained through the Chlamydomonas Genetics Center. Students perform the entire set of experiments, evaluate the data, write a laboratory report, and present their work in a 15-minute seminar. Data analysis involves statistical evaluation of data quality to determine genes for which there is a significant change in expression due to the individual experimental treatment. In addition, students are trained in hierarchical clustering analysis, gene finding, exon–intron predictions, database searching for regulatory sequences, as well as determining RNA structure using energy minimization algorithms and three-dimensional RNA modeling programs.

While these courses highlight the integration of benchwork and computer-aided predictions, Bioinformatics Programming (BIOS 480) emphasizes in silico approaches in modern biology. Students gain experience using multiple software packages (HMMer, BLAST, etc.) on diverse platforms (PC and Unix) and designing their own utilities in different programming languages (Perl and PHP) to examine biological data. This is a team-taught course in which the instructors (a biochemist from Biological Sciences and a computer scientist from the School of Business and Technology) have expertise in the distinct areas that bridge biological and computer applications. This class uses project-based learning, in which the students develop and analyze relational biological databases to address their own research question. Both within and subsequent to this course, MBB students participate in the development of several web applications in this course—including applications to scan for metallothioneins (, predict prophage sequence in prokaryotic genomes (∼phage/ProphageFinder.php) [4], or identify conserved intergenic sequence elements in prokaryotic genome sequences (∼PIGED/home.htm) [5]. All these applications are accessed openly.

Complementary to these activities, a new Unix-based Sun Microsystems Solaris server with six SunBlade workstations was obtained for the bioinformatics computer laboratory, providing students with hands-on training in three different computer platforms (Unix, PC, and Mac) within these bioinformatics courses. The Unix server allows more use of on-site applications and student training in server-based databases and analysis.

These developments enhanced the general Biological Sciences courses as well, enriching the education of the Biological Sciences majors. In General Genetics (BIOS 260), which is required for all biology students, introductory lectures and computer exercises in BLAST and DNA/protein databases were added. This involved using β-hemoglobin as a model gene to search DNA and protein databases for genes/proteins that are orthologues and paralogues as well as identifying single-nucleotide polymorphisms (SNPs) that cause sickle-cell anemia. Continuation of this exercise will expose all students, Biological Science and MBB majors, to a few fundamental concepts in bioinformatics. In the last few years, we introduced genomics and proteomics technology to the students in our cell biology course (BIOS 301) and asked these students to attend “bioinformatics” seminars on campus.

The curriculum required for the MBB major is demanding (Table AI, Appendix). In addition to the MBB courses, required courses in mathematics, physics, chemistry, and computer sciences total to 85 credits for the major. Some of the unique requirements of the program are MBB laboratory courses (MBBI to MBBIII), courses that emphasize laboratory techniques, critical thinking, problem solving, and experience in writing laboratory reports and oral presentation; Discrete Mathematics (MATH/CSCI 231) and Introduction to Computer Science I (CSCI 241), both providing a foundation in computational techniques and tools for use in bioinformatics; and a year-long “capstone” independent research program (BIOS 489: MBB Senior Thesis) where technique and theory are combined in a research project with emphasis on problem-solving, critical, and innovative thinking. The capstone research experience is culminated with a written thesis and a departmental research seminar.

Given the innovative and rapidly changing nature of bioinformatics, key support for initiating and maintaining this curriculum have come from the institution, department, and our external NSF-CCLI grant. This support has brought in guest speakers who have interacted with faculty and students on bioinformatics-related topics and funded faculty participation in numerous bioinformatics-related workshops and conferences.

Degree Recipients

One of the hallmarks of the MBB program is that graduates are among the most accomplished and successful in the university. During its short history, MBB graduates were given in 3 different years the university's top undergraduate academic award, and, as part of this award, addressed the graduating class at the commencement ceremonies. The majority of our graduates have gone to graduate and medical schools. Our graduates have entered the M.S. in Applied Molecular Biology at UWP (47% of total MBB graduates), medical school (13%), and Ph.D. programs in the life sciences (33%, Northwestern University, Harvard University, Indiana University, Mayo Clinic, University of California-Irvine, University of Virginia, and University of British Columbia, Canada). The remaining MBB graduates have entered directly into the pharmaceutical/biotechnology industries or the academic and medical research workforce.


When the program first began, we designed an assessment protocol to test both student learning and program outcomes, using six-assessment criteria to help the MBB committee create and implement needed changes to the program. We decided our desired student-learning outcomes to be: 1) learning fundamental MBB theories; 2) becoming proficient with hands-on MBB practical applications; 3) gaining an understanding of mathematical and physical concepts relevant to biology; 4) developing proficiency in computational tools related to bioinformatics; and 5) developing good problem-solving and critical thinking skills. We believe that if students achieve these outcomes, they would be viewed by industry and graduate or professional schools as well prepared for cutting-edge molecular biology and bioinformatic careers. The assessment criteria (AF below) included both a formative assessment through feedback from students while in the program and a summative assessment of students after they had graduated.

Assessment of Learning Outcomes:

  • A
    Grade point averages;
  • B
    Student evaluations for each class, each semester;
  • C
    Yearly (2001–2005) testing of student-learning outcomes with an assessment exam;
  • D
    Focus group interviews with graduating or recently graduated MBB students; assessment of program outcomes;
  • E
    Placement of students in MBB-based jobs, graduate schools, and professional programs;
  • F
    Evaluation by an independent, external reviewer.

In addition to student evaluations, which were reviewed by both the individual instructor and the Department, the MBB faculty committee received the assessment data directly and used them to identify strengths and weaknesses of the program and make needed adjustments. Because the field of bioinformatics is changing so rapidly, it was particularly important to assess bioinformatics content and student-learning outcomes to assure relevance to current concepts and methods. We summarize the data we obtained below and provide a section on the use of the data at the end.

A. Grade Point Averages

The cumulative career GPAs for all MBB students from 1999 to 2005 were assessed as a rough numerical estimate of academic success and abilities (Table I). The MBB GPAs were compared with those of graduates from the Biological Sciences undergraduate major (same department). Despite having what could be described as a “challenging” curriculum, the MBB students' GPAs are, on average, as high as those of the Biological Sciences majors. From this, we conclude that MBB graduates are academically strong, and the GPA scores are a partial measure of the program's quality. The observations that MBB students tend to be highly motivated, do independent research, and be competitive for graduate programs at well-recognized research institutes leads us to believe that these high GPA scores are accurate and reflect these students' academic abilities and the success of the MBB major.

Table I. Career cumulative GPAa scores for MBB versus Biological Sciences majors, 1999–2005
 MBB majorsBiological sciences majors
  • a

    GPA based on an A = 4.0, and includes “+” and “−” grade categories.

Average GPA ± Standard Deviation3.3 ± 0.53.0 ± 0.6
Median GPA3.33.0

B. Student Evaluations

Each semester, students are asked to evaluate each course and its instructor, and these evaluations are used to continuously monitor all courses and instructors. This is a Departmental and University requirement, and it has been used as a tool to assess both new and ongoing MBB courses. These anonymous student evaluations have two components, multiple-choice questions with numerical answers ranking the course/instructor and a written comments section in which students can comment on student-perceived strengths and weaknesses in a given course. Such comments have resulted in several changes in different courses ranging in degree from simple adjustments to more dramatic changes in format, discussed in detail below.

C. Testing of Student Learning Outcomes with a Yearly Assessment Exam

Intensive evaluation of the program was done for both formative and summative outcomes. Two faculty members in the Sociology Department on our campus designed a comprehensive-assessment process. They helped develop a yearly (2001–2005) assessment exam, a major assessment tool for the MBB program and the NSF-Course, Curriculum, and Laboratory Improvement grant that has supported the MBB program (Appendix). This exam was designed to assess general student-learning outcomes in MBB as well as the development of critical thinking skills covering four main “dimensions of learning:”

  • 1
    Proficiency in MBB;
  • 2
    Devising and analyzing experiments and experimental data;
  • 3
    Ability to apply information from other disciplines (chemistry, physics, mathematics, and general biology) to MBB programs;
  • 4
    Critical thinking using old concepts in new problems, making connections, and drawing logical conclusions.

Exam questions tested students' theoretical understanding, practical applications, and their abilities to integrate different disciplines. The test was given in required biology courses to MBB students at all levels (freshman through seniors) and over 5 years (2001–2005) to look at trends over time (Table II). As a comparison group, students in the unrevised Biological Science degree program were also tested. Each year, a sample of the Biological Sciences students who had taken the exam was drawn, matched as closely as possible to the MBB students based on GPA, ACT scores, gender, and the introductory biology course grades (BIOS 101). To ensure unbiased grading, coded tests were “graded” by MBB faculty, so that the identity of exam takers was not known. Differences of means tests were used to determine if the two groups were significantly different in scores in the proficiency areas identified in Table II.

Table II. Ability scores (based on test questions measuring each ability) by major: t test results—2001–2005
Proficiency Area2001 MBB/BIOSa2002 MBB/BIOSa2003 MBB/BIOSa2004 MBB/BIOSa2005 MBB/BIOSa
  • a

    Mean score: Molecular Biology-Bioinformatics majors/Traditional Biology majors. Differences, which are statistically significant at the 0.05 level according to a t test, are starred (*). Those significant at the 0.10 level are designated with a plus sign (+).

Devising experiments8.7/8.110.8/6.4*10.93/7.58+9.89/8.9511.11/7.50+
Analyzing data12.5/8.9+20.3/11.0*17.3/9.94*17.84/10.11*22.28/14.83
Ability to apply the disciplines36.4/28.7+67.4/34.1*58.3/35.0*60.53/38.74*56.56/37.11*
Critical thinking—all areas41.4/32.9+88.0/40.4*80.5/40.58*74.74/47.11+81.78/44.06*
Using old concepts in new problems11.8/9.321.1/9.5*17.76/9.45*17.16/13.4719.39/12.00+
Making connections15.6/13.525.3/13.3*24.02/11.94*22.32/10.16*24.39/12.06*
Drawing logical conclusions28.8/21.2*41.6/17.6*38.72/19.19*34.26./23.47+38.00/20.00*

In the first evaluation (2001, before the full implementation of the revised MBB program), a selected group of 17 MBB and 17 Biological Sciences majors were used in a base-line test. The students in the MBB program showed marginally greater proficiency in bioinformatics (19.4 for MBB vs. 14.5 for Biological Science, p = 0.09), and ability to apply other disciplines to solve biological problems (36.4 for MBB vs. 28.7 for Biological Science, p = 0.08) as well as draw logical conclusions (28.8 for MBB vs. 21.2 for Biological Science, p = 0.05) than their colleagues in the Biological Sciences program.

In the second evaluation (2002, after the first completed year of the revised MBB program), we repeated the evaluation process with 25 MBB and 25 Biological Sciences majors and found that the differences between the two groups were greater. Students in the MBB program showed a significantly higher proficiency in bioinformatics (27.0 for MBB vs. 8.6 for Biological Sciences, p = 0.000) were better at applying other disciplines to solve biological problems (67.4 for MBB vs. 34.1 for Biological Sciences, p= 0.000) and drew better logical conclusions (20.3 for MBB vs. 11.0 for Biological Sciences, p = 0.001). The MBB students also seemed to be better at devising experiments (10.8 for MBB vs. 6.4 for Biological Sciences, p = 0.002).

In the third evaluation (2003, after the second completed year of the revised MBB program), we repeated the evaluation process with 27 MBB and 31 Biological Sciences majors and found that the difference between the two groups remained significant. Students in the MBB program showed a significantly higher proficiency in bioinformatics (27.4 for MBB vs. 11.0 for Biological Sciences, p = 0.001), were better at applying other disciplines to solve biological problems (58.3 for MBB vs. 35.0 for Biological Sciences, p = 0.012), and drew better logical conclusions (38.7 for MBB vs. 19.2 for Biological Sciences, p = 0.002). In the third year, the MBB students also out performed their peers in three other areas not seen in previous years: analyzing data (17.3 for MBB vs. 9.9 Biological Sciences, p = 0.004), using old concepts in new problems (17.8 for MBB vs. 9.5 Biological Sciences, p = 0.008), and making connections among different disciplines as well as within disciplines (24.02 for MBB vs. 11.9 Biological Sciences, p = 0.003).

In the fourth evaluation (2004, after the third year of the revised MBB program), we repeated the evaluation process with 19 MBB and 19 Biological Sciences majors and found that the differences between the two groups again remained significant. Students in the MBB program showed a significantly higher proficiency in bioinformatics (22.8 for MBB vs. 9.2 for Biological Sciences, p = 0.023). The MBB students also were more able to apply other disciplines to solve biological problems (60.5 for MBB vs. 38.7 for Biological Sciences, p = 0.044), analyze data (17.8 for MBB vs. 10.1 Biological Sciences, p = 0.014), and make connections among different disciplines as well as within disciplines (34.26 for MBB vs. 23.47 Biological Sciences, p = 0.005). More interesting is the finding that we have also improved the Biological Sciences program in the process of improving some of the core courses (introductory biology, genetics, and microbiology) for both the MBB and the Biological Science programs. Students in the Biological Sciences program have improved their ability to apply other disciplines to solve biological problems (28.7 in 2001 to 38.7 in 2004).

In the fifth year (2005), we repeated the evaluation process with 18 MBB and 18 Biological Sciences majors and found that the differences between the two groups remained significant. The MBB students retained their advantage in proficiency in bioinformatics (30.28 vs. 14.83, p = 0.011), ability to apply disciplines (56.56 vs. 37.11, p = 0.032), making connections (24.39 vs. 12.06, p = 0.004). In five areas, the differences between groups increased, compared to year 4: devising experiments (11.11 vs. 7.5, p = 0.084), using Math (15.17 vs. 7.67, p = 0.019), critical thinking (81.78 vs. 44.06, p = 0.009), using old concepts in new problems (19.39 vs. 12.00, p = 0.051), and drawing logical conclusions (38.00 vs. 20.00, p = 0.011). Significant differences in these five areas had appeared in the past years. In three areas, the differences between groups decreased: analyzing data, using chemistry, and using biology. In these two areas, there were marked increase among the Biological Science majors, perhaps reflecting the impact this program also had on them.

D. Focus Group Interviews with Graduating and Recently Graduated MBB Students

A second aspect of the intensive assessment process involved focus groups. The graduating MBB class of 2003 and 2004 participated in separate groups either shortly before or after they had graduated, as did Biological Sciences majors, for comparison purposes. Questions were asked to determine students' perspectives about 1) their general motivation toward course material and the MBB program, 2) nature of the MBB program and its learning and teaching processes, 3) material learned and curricular content in the MBB program when compared with the students in other programs or professional colleagues, for those who have entered the work or postgraduate training, 4) strengths of the MBB program, and 5) weakness of the MBB program. Because of space, only a summary with select, representative comments will be provided here.

In general, students felt the MBB program is very strong, citing opportunities for hands-on wet laboratory and computer-laboratory training in teaching laboratories and independent research as some of the outstanding aspects of the program. Most students felt the program provided them with a “well rounded” learning experience and felt positively about being exposed to “real world” biological problems, methods, and concepts. They appreciated how the program helped them develop a strong work ethic in having to “work hard” through “tough courses” and that course material was based on “real world situations” and not just “class work out of textbooks.” Students commented positively on the fact that they were taught to solve biological problems and that there were high expectations of the students by the faculty. They mentioned that small classes gave students a lot of personal attention, and students felt that the faculty worked hard to help each student and noticed if students were having any problem. Finally, students felt that they had learned a great deal in the program, and that they were very well trained compared to peers, specifically in areas of problem solving, hands-on training, reading of scientific papers, and written and oral communication. Several commented that they had been able to move into certain laboratory positions without the extensive training usually required of new undergraduates. In addition to the earlier comments, students noted the caliber and commitment of faculty to students and the program as additional strengths of the program.

Areas of weakness and challenges were expressed in student focus group interviews as well. Students generally felt the lack of options in course choices was a problem, and they would like to have had more elective courses in the curriculum. They also mentioned that, for some well-prepared incoming students, the General Biology (BIOS 101/102) and General Chemistry (CHEM 101/102) courses may not be necessary. An early suggestion was made to have the bioinformatics programming course (BIOS 480) as a team-taught course with a biologist and a computer scientist to help fully integrate programming and biology. This was in fact done and the team-taught course (as previously described) has been offered for several years. Some students indicated that the curriculum is very demanding and that some students were not able to adapt to the high expectations.

E. Placement of Students in MBB-based Jobs, Graduate Schools, and Professional Programs

As stated earlier in Degree Recipients Section, degree recipients have done well in terms of graduate school and job placement.

F. Evaluation by an Independent, External Reviewer

To obtain an independent perspective of the MBB program assessment tools, Dr. Jacob Silver was recruited to provide a professional and nonbiased opinion of the assessment methods and conclusions. Dr. Silver, President of the Huron Mountain Research Services, reported that an analysis of the focus group transcripts of professors and graduated students, as well as of ability score data of MBB majors in comparison with General Biology majors, revealed that the MBB program had a successful implementation and administration. He also found that, from 2002 to 2004, the skills relevant to scientific investigation of the MBB majors had improved significantly when compared with those of their fellow general Biological Sciences majors. Dr. Silver also used the data to evaluate a small cohort group of six MBB students in depth over 3 years, from 2001 to 2003. These six students showed progressive improvement in their performance skills over the 3-year period. Dr. Silver looked at these students' problem-solving skills in the annual exam and compared their ability to the control group (Biological Sciences majors with similar class standing and grade point average). The testimony of professionally employed MBB graduates expressed strong appreciation of their education in the program. Their positive comments were particularly addressed to the three laboratory-course sequence and the quality of teaching in the program.

Dr. Silver identified two problems evidenced since early in the life of this program. The first is the overstructuring of the program, allowing for only one elective. But put in the perspective of a dozen other undergraduate programs in bioinformatics within the United States, this characteristic of a heavy load of course requirements and few electives is typical of all of these programs. The second is the lower than initially anticipated enrollment. We currently have ∼40 MBB majors versus the initially anticipated 60 majors. Thus, we have significantly increased our efforts in recruitment. Specifically, we have sent our faculty to many college fairs at local high schools and 2-year colleges. We have also started a high school recruitment program called “DNA Day” where we bring AP biology students from Southeastern Wisconsin to campus and go through several AP exercises with them. Our goal is that a positive experience at our university will put UWP in contention when these students think about which college to attend.

In addition, the MBB program went through the University of Wisconsin System Joint Review process in 2005. The three external reviewers for the program (academic experts in molecular biology, bioinformatics, genomics, and undergraduate biology curriculum) all commented that the MBB undergraduate program at the UWP is a “strong” and “successful” program. The reviewers felt that the MBB graduates are well trained and highly competitive in industry, graduate programs, and medical schools nationwide. One commented that the “undergraduate achievements are impressive.” Furthermore, there was consensus that this is a valuable program “ahead” of most universities, and that the need for students like the MBB graduates from UWP is pressing and will continue. As stated by one reviewer, there is a “strong national need for biotechnology and bioinformatics education.” Another mentioned that the UWP program is “one of the leaders” in this field for undergraduate training.

All of the reviewers echoed the concern about a lower-than-anticipated enrollment. Other concerns raised by the external reviewers were as follows: the need to maintain up-to-date knowledge base of the MBB faculty in this rapidly changing field of bioinformatics; the possibility that bioinformatics may switch over time from industry to academic and medical research, so that students might need additional training in computer programming as a result of this change; and the concern about not requiring an evolutionary biology course in the MBB curriculum.


  1. Top of page
  2. Abstract
  5. Acknowledgements
  7. Appendix

Over the first 5 years of this program, many adjustments were made in response to concerns raised by students, faculty, or through the use of the assessment tools described earlier. Some of these changes have been within individual classes in response to student evaluation surveys conducted in each course each semester. For example, in the molecular biology course (BIOS 309), early offerings and student evaluations indicated that the course material was extensive and testing of students through three unit exams, and one final was difficult. To encourage students to keep up with reading exercises on a daily bases, weekly quizzes were added in 2001. This improved students' understanding, as demonstrated through exam scores, and students' attitudes, as indicated through student evaluations. Similarly, for the MBB Laboratory III: Proteins (BIOS 455) course, more exams that tested practical, hands-on aspects of this laboratory course were included, as requested in student surveys. Finally, practical exams in early offerings (2000–2001) of MBB laboratory I: DNA (BIOS 453) course revealed that students did not fully understand the aspects of bioinformatics related to DNA function. Thus, the instructor expanded bioinformatics exercises into a semester-long case study for each student that culminated in a student research project, which is presented as a student-developed, web-page report.

Based on findings from the 4-year long NSF-CCLI assessment exams, several changes were made to improve student-learning outcomes. First-year evaluations showed no clear difference in the ability of MBB students to apply understanding from different disciplines (math, physics, and chemistry) toward problem-solving skills, when compared with Biological Sciences majors. We reasoned that this was likely due to the students being at an early stage in the program, but could also be due, in part, to the time between taking the math, chemistry, and physics courses and when they are asked to apply these interdisciplinary skills to biology-research problems. In response to this, an effort was made in the MBB laboratory courses (BIOS 453, 454, and 455) to review basic skills and concepts, in particular chemistry. Student exercises were adjusted to emphasize preparation of chemical solutions plus physics and mathematical components of MBB methods. In the third and fourth year evaluations, students showed statistically significant improvements in their ability to apply different disciplines to MBB problems. A second significant change was made in the senior bioinformatics programming course (BIOS 480) to improve the integration of computer programming directly into the bioinformatics course; the course was changed to a team-taught course, as previously described. This also addresses concerns raised by reviewers about integrating more computer programming into the curriculum.

The results seen in this MBB program at the UWP are in agreement with those obtained from an assessment performed on a similar program at the University of Wisconsin-La Crosse, where bioinformatics was integrated into pre-existing core courses [6]. At the University of Wisconsin-La Crosse, students' performance on bioinformatics problems increases significantly with an expanded exposure to the field of bioinformatics. Our assessment further indicates that the more rigorous requirements in math and computer science for the MBB program drive our majors to perform better in problem-solving situations, and the constant demand for integrating information in our newly developed, hands-on laboratory courses forces our majors to develop better analytical skills. We attained a higher than expected percentage of MBB majors pursuing graduate schools. We think that this interest in attending graduate school is cultivated by the year-long, capstone research project. Our assessment suggests that the intensive laboratory courses and the 1-year long research capstone project give our students both the competence and self-reliance to succeed in their eventual careers.

In conclusion, we believe that our MBB program provides the skills essential for today's biologists to function in the new omics era, which calls for competency in processing and analyzing large biological information databases. We also believe that we have fulfilled the original goals of making our majors proficient in MBB, applying information from other disciplines (chemistry, physics, math, and general biology) to the MBB program, and using old concepts in new problems, making connections, and drawing logical conclusions.


  1. Top of page
  2. Abstract
  5. Acknowledgements
  7. Appendix

The authors thank the participating faculty: R. D. Barber, S. Chalasani, G. C. Mayer, M. P. MacWilliams, C. G. Ruffolo, S. M. Thomson, E. P. Wallen, and G. M. Wood. We also thank Dr. D. B. Stern (Boyce Thompson Institute for Plant Research, Cornell University) for providing microarray chips. This work was partially supported by NSF CCL10088089.

  • 1

    The abbreviations used are: UWP, University of Wisconsin-Parkside; MBB, Molecular Biology and Bioinformatics; NSF-CCLI, National Science Foundation Course and Curriculum Improvement.


  1. Top of page
  2. Abstract
  5. Acknowledgements
  7. Appendix
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  • 4
    M. Bose,R. Barber( 2006) Prophage finder: A prophage loci prediction tool for prokaryotic genome sequences. In Silico Biol. 6, 223227.
  • 5
    M. Bose,D. Slick,M. J. Sarto,P. Murphy,D. Roberts,J. Roberts,R.D. Barber( 2006) Identification of SmtB/ArsR cis elements and proteins in archaea using the Prokaryotic InterGenic Exploration Database (PIGED). Archaea. 2, 3949.
  • 6
    D. R. Howard,J. A. Miskowki,S. K. Grunwald,M. L. Abler( 2007) Assessment of a bioinformatics across life science curricular initiative. Biochem. Mol. Biol. Educ. 35, 1623.


  1. Top of page
  2. Abstract
  5. Acknowledgements
  7. Appendix

Credits in MBB Major

  • 1
    Core courses in major
    • Biology courses = 42 credits

    • Biology elective, minimum = 3 credits

    • Chemistry courses = 18 credits

    • Math courses = 8 credits

    • Physics courses = 10 credits

    • Computer sciences = 4 credits

    • Sub-total = 85 credits

      Note: MBB students must attain a minimum UWP cumulative GPA of 2.50 in all courses required of the major.

  • 2
    General education requirements = 24 credits
  • 3
    Unrestricted electives = 11 credits
    Total Credits to Graduate, University minimum = 120 credits
Table AI. Description of molecular biology and bioinformatics graduation requirements
Fall semesterSpring semester
  1. MBB curriculum, core courses in major: courses in bold lettering are specific for the MBB major.

 Freshman Freshman
Bioscience, BIOS 101Organismal Biology, BIOS 102
General Chemistry I, CHEM 101Quantitative Biology, BIOS 210
Calculus I, MATH 221General Chemistry II, CHEM 102
General Education RequirementsDiscrete Math, MATH 231
 Sophomore Sophomore
General Genetics, BIOS 260Macromolecular Struct & Func, BIOS 240
Organic Chemistry I, CHEM 321Molecular Biology, BIOS 309
Physics I, PHYS 105Organic Chemistry II, CHEM 322
Computer Science I,CSCI 241Physics II, PHYS 106
 Junior Junior
MBB Lab I: DNA, BIOS 453MBB Lab III: Proteins, BIOS 455
MBB Lab II: RNA, BIOS 454Microbiology, BIOS 303
General Education RequirementsGeneral Education Requirements
 Senior Senior
Bioinformatics Programming, BIOS 480MBB Senior Research Project, BIOS 489
MBB Senior Research Project, BIOS 489Independent Research, BIOS 499
Independent Research, BIOS 499Biology elective
Biology electiveGeneral Education Requirements
General Education Requirements 


Samples of the Questions Asked

Section I

  • 4
    Draw a rooted tree showing how to sort numbers having a three-digit zip code, which uses the digits 1 and 2. (111, 112, …, 221, 222)

Section II

  • 5
    Given your knowledge of protein structures and amino acids, explain why glycine is a highly conserved amino acid residue in the evolution of proteins.

Problems 5–6

Consider the following pedigree that shows the incidence of a rare genetic disease (assume that all the spouses do not carry the disease gene). Individuals who have the disease are shown with filled-in symbols.

  • thumbnail image
  • 6
    (10 points) Is the allele associated with this disease dominant or recessive?
  • 7
    (10 points) Suppose the female in generation III has four children. What is the probability that none of the children have the disease?
  • 8
    As a researcher studying E. coli pathogenesis, you have sequenced a key, albeit small, gene involved in infection and you need to accurately predict the encoded protein. Below is the very simple bacterial mRNA sequence from that gene. Scan the sequence to identify the correct start site. Use the genetic code provided to translate this coding region into the hypothetical protein, indicate N- and C-termini. Estimate the molecular weight of this theoretical protein in daltons.5′-AGCUAUAUGGAGGUGUAACUGUAUGGCUUUC CACUGAUAAGC-3′
    • thumbnail image
  • 9
    The following data are from a Sanger DNA sequence experiment. Interpret these data to determine the sequence and write it down in the 5′–3′ direction. In the sequence, indicate the standard translation start site, if present.
    • thumbnail image

Section III

  • 1
    You are given a protein sequence from E. coli. To identify a similar protein in humans, what BLAST algorithms could you use? [circle correct response(s)]
    • blastn

    • blastp

    • blastx

    • tblastn

    • tblastx

      • thumbnail image
  • 2
    Which frame contains the open reading frame (ORF)? (5 points)
    • a
    • b
    • c
    • d
    • e
    • f
  • 3
    • thumbnail image
    • a
      Explain the significance of an E-value in the above search
    • b
      Would you have obtained similar results using PSI-BLAST? Explain the difference between BLAST and PSI-BLAST in your answer.

Section IV

  • 1
    Describe what is meant by specific activity and total activity when discussing protein purification.
    Fill in the missing numbers in the purification table shown below where the protein's activity is measured simply in units after several chromatographic steps shown in the right most column:
  • 2
    Rank these enzymes in terms of efficiency (at low substrate concentration) (1. for most efficient, 3. for least). Explain the criteria used for this assessment. Also, explain the significance of the kinetic parameters kcat and Km.
  • 3
    Given the following data matrix, which sites are informative about common ancestry
    • a
      Which sites show unique derived character states?
    • b
      Which sites show shared derived character states?
    • c
      Make a distance matrix from the above data.
  • 4
    Below is a small microarray dataset from Chlamydomonas that indicates the genes in this studyand Cy5/Cy3 ratio of medians where the wild-type control cDNA was labeled with Cy3 and a nonphotosynthetic mutant (ncc1) that carries a mutation in the nuclear nccl gene was labeled with Cy5. The ratios are averages of three independent experiments in which the standard deviations are significantly low for all features, i.e., you have a reasonable confidence in these data.
    • a
      Highlight those “interesting genes” that have equal to or more than a twofold change in RNA levels in the mutant and indicate if there is an increase or decrease in RNA levels in the mutant.
    • b
      Present a biological hypothesis as to why some genes increase and some decrease in this mutant.
Table  . 
StepTotal activitySpecific activityProtein (mg)Recovery (%)Purification fold
Crude extract20,000 2,0001001
Q sepharose18,00030600 3
Phenyl sepharose 300507530
Superose 1215,0006002575 
MonoQ10,000 550200
Table  . 
 kcat (s−1)KmM)
HIV protease6.015
Table  . 
Species 1Agcctgg
Species 2Agcctcg
Species 3Ctgctat
Species 4Ggcgtat
Table  . Microarray data (ratio of medians, Cy5/Cy3)
GeneFunctionRatio of medians
rrn16Gene expression1.04
rrn18Gene expression1.11
rrn23Gene expression0.95
nad1ATP synthesis2.54
nad2ATP synthesis4.03