Teaching second‐year biochemistry students the principles of an enzyme‐catalyzed spectrophotometric assay with an online lab simulator

Abstract

The teaching of laboratory skills to undergraduate students is central to all experimental sciences. In this setting, students must understand the experimental procedures as well as the fundamental principle(s) being demonstrated, all while learning within a limited time. Other limiting factors include access to equipment and reagents, resulting in students frequently working in pairs or small groups to complete experiments, and consequently, students gain limited experience in the practical techniques. In addition, due to competing subjects and a filled curriculum, there is typically limited or no opportunity for students to repeat a laboratory exercise. As a result, the time pressure forces students to focus on completing the laboratory exercise without engaging at a deeper level with the concepts and principles being demonstrated. This can result in deficiencies in student understanding principles as well as developing the required proficiency in hands‐on skills with equipment. To overcome these issues, we implemented an online laboratory data simulator to replicate the laboratory quantification of ethanol in simulated driver blood samples. This approach allowed students to attempt the exercise as often and whenever they chose, while still faithfully replicating a traditional laboratory setting. We implemented this approach across two Australian universities using a mixed‐methods approach and assessed the impact of the simulator on student learning of biochemistry lab‐related concepts. Based on our findings, we suggest that this online approach can effectively teach fundamental laboratory concepts to students while eliminating many of the constraints of hands‐on laboratory classes.

1. INTRODUCTION

Practical laboratory exercises are essential components of all experimental disciplines in STEM. In Biochemistry and Molecular Biology, laboratory classes provide an opportunity for students to visualize foundational principles and concepts while also developing the required skills to correctly use laboratory equipment relevant to their discipline. A well‐designed set of practical activities that align closely with the concepts covered in lectures and tutorials is considered the gold standard approach to reinforce knowledge and understanding.^{1, 2} Students learn to generate, analyze and interpret experimental data, develop accurate record‐keeping skills, write laboratory reports, and communicate with peers and instructors using the specific language and ‘culture’ of the discipline.^{3, 4}

There are, however, significant limitations to a face‐to‐face laboratory approach that can impact the quality of student learning. Biochemistry teaching laboratories are limited by staff and student time, cost, and availability of reagents, space, and equipment, especially when teaching large cohorts. It is, therefore, uncommon for students to have opportunities to repeat an individual laboratory exercise, perform experiments individually, deviate from defined protocols, or test new hypotheses. Consequently, students have limited opportunities to learn how to effectively troubleshoot and optimize experimental protocols, and to develop skills that are essential for academic or professional careers within the discipline. ⁵

Due to these constraints, student cognitive processes focus on ‘what’ needs to be done to complete the experiment ‘correctly’, rather than ‘how’ or ‘why’ specific steps are taken, or the consequences of changes to a standardized protocol. A student's attention is primarily focused on carefully following the provided method, often with a level of anxiety about making mistakes, to ensure that successful results are achieved on their first (and only) attempt at the experiment. ⁶ A well‐designed laboratory simulation can reduce cognitive load by providing a controlled, focused learning environment to help students to focus on core biochemical processes and principles without being overwhelmed by the complexity of the real‐world lab setting. ⁷

Unsuccessful experiments or students unable to attend the physical teaching laboratory are commonly addressed by providing curated datasets for analysis and interpretation. Often, these datasets present an ideal‐world scenario, with expected outcomes, and require students to undertake a standardized approach to data analysis and interpretation. This approach limits the opportunity for troubleshooting, discussion of concepts, and the requirement for critical evaluation of real‐world experimental data. ⁵

This approach was commonly used during the height of the COVID pandemic when face‐to‐face teaching shifted to an online mode. ⁸ However, this approach omits student learning associated with physically performing all steps and hence, any discussion regarding less‐than‐ideal results can only be speculative as to the potential causes. Virtual laboratories provide opportunities for experiential learning by simulating real‐world biochemical experiments, allowing students to engage in trial‐and‐error, observe outcomes, and refine their understanding through reflection.^{9, 10}

Laboratory simulators allow students to work in a physically safe environment ¹¹ ; they are not limited to completing all tasks during timetabled class hours, the exercise can be attempted multiple times, and there are no ethical concerns related to the use of simulated human or animal biological samples. ¹² They also enable students to experience experiments that may be prohibitively expensive, take days or weeks to complete in a physical laboratory, or require access to specialized, low throughput equipment.^{13, 14}

While a substantial number of virtual laboratory tools currently exist (Labster, PraxiLabs, McGraw Hill Connect), with some available as open educational resources (HHMI Biointeractive, LabXchange), they all have limitations that do not permit sufficient scope for inquiry‐based learning. Most are constrained to a limited number of scenarios, permit limited deviation from predetermined (and often passive) protocols, and do not give students sufficient opportunity to influence the results outputs or to analyze, interpret, and reflect upon unexpected experimental data.

An ideal online laboratory simulation should provide students with the opportunity to perform techniques and generate realistic data that aligns with scientific principles, experimental actions, and produce observations and data based on student actions while using the simulator. There is a significant body of evidence showing that virtual laboratory simulations lead to similar increases in student knowledge and understanding of disciplinary knowledge compared to traditional face‐to‐face laboratory activities.^{15, 16, 17} Similarly, there is little to no difference observed between final exam results when online laboratory simulations and traditional wet labs are compared.^{18, 19}

To address the limitations of face‐to‐face and existing virtual laboratories, we have developed a laboratory simulation built within the Unity® software game engine (unity.com) to generate realistic datasets that adhere to scientific principles and laboratory processes, resulting in changes in the data output based on experimental actions and decisions made by students within the program.

By providing opportunities to explore cause and effect relationships in experimental protocols, this laboratory simulation can prepare students for a future where they will be making decisions around the planning of experiments and discernment of the validity and limits of interpretation of the outcomes. It provides an opportunity for students to engage in the experimental process beyond the physical laboratory, to generate their own data, establish a meaningful link between experimental steps and the results output, allowing for authentic troubleshooting scenarios and post‐lab discussion of the practical context.

Due to the inbuilt variability in data generated and unique identifiers associated with each experiment, students can work collaboratively on authentic laboratory tasks ²⁰ that ensure academic integrity and are more resistant to the current influence of generative‐AI.

1.1. The experiment: An enzyme‐linked spectrophotometric assay

In this manuscript, we report the implementation of a laboratory simulation in which each student was responsible for estimating the concentration of ethanol within four fictitious driver samples using an enzyme‐linked metabolite assay (ELMA) with a blood alcohol limit of 0.05% (w/v). The assay uses alcohol oxidase to catalyze the oxidation of ethanol into acetaldehyde and hydrogen peroxide (H₂O₂), which then reacts with phenol and 4‐aminoantipyrine (4‐AAP), catalyzed by peroxidase ²¹ to produce a red product, phenyl‐aminoantipyrine, detected spectrophotometrically at 500 nm.

$$\begin{array}{l}\mathsf{\text{1st reaction}}:{\mathsf{CH}}_{\mathsf{3}}{\mathsf{CH}}_{\mathsf{2}}\mathsf{OH}\mathsf{\text{(colorless)}}+{\mathsf{O}}_{\mathsf{2}}\\ \to {\mathsf{CH}}_{\mathsf{3}}\mathsf{CHO}+{\mathsf{H}}_{\mathsf{2}}{\mathsf{O}}_{\mathsf{2}}\end{array}$$

$$\begin{array}{l}\mathsf{\text{2nd reaction}}:\mathsf{\text{Phenol}}+\mathsf{4}-\mathsf{AAP}+{\mathsf{\text{2H}}}_{\mathsf{2}}{\mathsf{O}}_{\mathsf{2}}\\ \to \mathsf{\text{phenyl}}-\mathsf{\text{aminoantipyrine complex}}\left(\mathsf{red}\right)+{\mathsf{\text{4H}}}_{\mathsf{2}}\mathsf{O}\end{array}$$

1.2. The lab simulator Interface

The online laboratory was accessed via Github where students would click on ELMA Ethanol to start their experiment. On loading, the student is greeted by a virtual laboratory bench, which includes access to all tubes, chemicals, and equipment necessary to carry out the procedures and export results. Based on the data generated, they then perform the required calculations, plot and interpret the data, and formulate conclusions as to whether the individual drivers are below or above the legal blood alcohol limit (Figure 1).

FIGURE 1.

Starting screenshot of the Unity‐based simulation with each of the primary functions enabled. The callout boxes explain the function of each icon.

The key learning outcome of this work was for students to learn through experience how to develop, adapt, and troubleshoot experiments and to analyze, interpret, and draw conclusions from laboratory data. In the process, students should have learned the principles for specifically quantifying individual metabolites within complex biological samples via spectrophotometry.

2. MATERIALS AND METHODS

2.1. Learning objectives

On completion of the simulation and practical activity, students will be able to:

1. Understand how to quantify ethanol in a biological sample using an enzymatic assay.

2. Be able to perform biochemical calculations to analyze and present experimental data.

3. Understand the validity of their results in determining if an individual is above or below the legal blood alcohol limit for driving.

2.2. Context and student cohorts

At University A, Biochemistry and Molecular Biology (BCMB2X01/MEDS2003) is a second‐year course that is a core requirement for students enrolled in Bachelor of Science degrees majoring in Medical Science, Applied Medical Science, Biochemistry and Molecular Biology, Genetics and Genomics, Nutrition Science, and an elective for students majoring in Microbiology, Infectious Diseases, or Virology. Total enrolments are approximately 800 students per year. Students at University A have previously attended a practical laboratory class in which they complete a hands‐on experiment to quantify blood glucose concentration in samples obtained from an oral glucose tolerance test. Consequently, when they are introduced to the blood ethanol simulation, they have recently experienced a similar experimental protocol and already have physical experience with pipetting techniques and spectrophotometry equipment.

At University B, Biochemistry (BIOL 2014) is a core 2nd‐year undergraduate course for students enrolled in Laboratory Medicine, Medical Science, and Nutrition & Food Sciences programs and an elective for Human Movement, Science, Science and Education, Pharmaceutical Science, and Pharmaceutical Science double degree students. Total enrolments are approximately 130 students per year. Students at University B complete the online simulation prior to an enzyme kinetics practical class in which they gain hands‐on experience with laboratory equipment and liquid manipulations to monitor enzyme kinetics.

In 2023, students enrolled in Biochemistry and Molecular Biology (BCMB2X01 and MEDS2003, n = 716) at University A or Biochemistry (BIOL 2014, n = 125) at University B used the simulation to quantify the level of alcohol in 4 simulated blood samples.

Students analyzed the data generated and submitted their final calculated ethanol concentrations for their experiment into the Learning Management System (Canvas for University A), or as a paper‐based report (University B).

Ethical clearance for this study was granted by University A Human Research Ethics Committee (#2023/064) and University B Human Ethics Committee (#204123). Students were provided with details of the research project and given 4 weeks to opt out of inclusion. Students that opted out of the study were identified and their data (assignment answers, marks and feedback responses) were removed from the final dataset (n = 716 students) prior to the analyses. All research was in accordance with the National Statement on Ethical Conduct in Human Research (2018), ²² the Australian Code for the Responsible Conduct of Research (2018), applicable legal requirements, and with university policies, procedures, and governance requirements.

2.3. Programming of the simulation

The simulation was programmed within the Unity Real‐Time development platform (Unity Editor Version 2021.3.11f1). The simulation purposely replicated a biochemistry laboratory setting with a lab bench and spectrophotometer visible on loading (Figure 1). Access to additional features were available through shortcut icons (Figure 1). The simulation was made available by selecting ‘ELMA Ethanol’ at this URL (https://garethdenyer.github.io/BCMB3X01/).

The underpinning programming designed into the simulator enabled a realistic student experience and outputs consistent with the scientific principles of the assay, actions of students, detection limits of the instrument, and systematic and random errors that would be present in a face‐to‐face laboratory. Examples include (1) a small degree of random variation in each liquid transfer (that adhere to the specifications provided by the pipette manufacturer), (2) a small volume carried over from one tube to the next if the student did not use a new pipette tip prior to each liquid transfer, (3) biological variation included within each driver sample, and (4) a variable background absorbance reading added to the baseline (zero) reading for each of the samples when read in the spectrophotometer.

2.4. Implementation of the simulation

The rationale behind the development of the simulation was explained to students in an email communication in Week 6 (University B) or Canvas announcement in Week 11 (University A) of the semester. Students were advised that the simulation should take approximately 30 min to complete.

Students at both universities were provided with detailed written instructions (see Data S1) and short instructional videos about the experimental rationale, simulation controls, and data analysis via their online learning management systems (Canvas and Moodle respectively). On each accession of the simulator, each student was provided with a set of four unique driver samples. If they re‐started the session, a new set of samples were generated within the program. Students were able to work with their peers or receive assistance from instructors, but they were each required to perform their own experiment, gather and analyze their own unique data, and report their own experimental results and conclusions.

Students at University A were introduced to the simulator in a 2‐hour face‐2‐face class and had one week to complete additional experiments before submitting the report. During the face‐to‐face activity, observations were made on how students engaged with the online activities within their small groups and how they worked with their peers and instructors to complete the tasks. Students at University B were required to complete the simulation at home, individually, during the mid‐semester break.

2.5. Analysis of student performance

Students completed a laboratory report assessment task (contributing 10% towards their final grade for University A, and 13% for University B) that required them to report the concentrations of their unknown samples, to state their confidence in the calculated concentrations based on variability seen within replicates, and to answer a set of post‐lab questions that challenged them to reflect on the limitations of their experimental design and to consider several troubleshooting scenarios. The laboratory report required students to answer 6 questions related to the experiment:

1. Report your values and comment on your confidence. You will need to report the concentration in % (w/v). Determine whether each of your 4 suspect drivers are ‘guilty’ or ‘not guilty’ of driving over the legal blood alcohol concentration (BAC) of 0.05% (w/v). Importantly, you need to consider how confident you are in your estimate of each driver's BAC. (4 marks for answers, 2 marks for confidence)

2. Why might you get very different estimates for an unknown? What might have contributed to this variation? How would you decide which estimate (which of your three replicate samples) was most accurate? (1 mark)

3. What would happen if you made a standard curve using 0–30 μL instead? You can test this within the simulator to help answer this question if you like. (1 mark)

4. What might happen if you added vodka (37% w/v) to your assay plate instead of one of your blood samples? What additional steps would you need to include to be able to measure the concentration? You can test this in the ELMA simulator to help answer this question if you like. (1 mark)

5. What additional steps could be taken to give you more confidence in your estimates? You can test this to help answer this question if you like. (1 mark)

6. You notice that one of your patient samples has turned a red color due to hemolysis (lysis of red blood cells). What effect do you think this will have on your assay? What steps could you take to correct this? (2 bonus marks).

Student‐reported concentration values, confidence, and critical reflections were considered as primary evidence of the success of the simulator in achieving the learning outcomes. Student submissions were marked by their small‐group laboratory demonstrators (University A) or by the course coordinator (University B). A rubric that covered aspects of experimental design and evaluation, and interpretation of experimental data was used by markers to grade assessments, and final marks were used in analyses to assess students' understanding of fundamental biochemistry concepts.

2.6. Student feedback

On completion of the virtual laboratory practical, students were invited to complete a Likert scale questionnaire. The questions related to their perceived knowledge, experience and benefit of using the computer simulation to enhance their understanding of alcohol determination. Results were scaled 1, strongly disagree (SD); 2, disagree (D); 3, neutral (N); 4, agree (A) and 5, strongly agree (SA) (Figure 4,5). Three free text questions relating to the ‘best aspects’, ‘improvements’ and ‘any other comments’ related to the simulation were also included. The students were also asked to rate whether the online simulation helped in their understanding of the entire process.

FIGURE 4.

Responses to the reflective student experience questionnaire that was provided for students to answer after they had completed the task. Data presented are combined totals from both universities.

FIGURE 5.

Likert scale responses to the student experience questionnaire that was completed at the end of the learning activities.

2.7. Statistical analysis of results

The responses for the Likert style questions were scored as described above and the statistics were calculated using GraphPad Prism (Version 8.04). Studies have shown that for five‐point Likert items, either parametric t‐tests or non‐parametric Mann–Whitney tests have been used previously to assess changes in responses. ²³ Results of the survey Likert responses were analyzed using a Mann–Whitney test wherein a p‐value of <0.05 was considered statistically significant.

2.8. Analysis of free text student feedback

Themes from the free text questions were analyzed using NVivo (Version 14, release 14.23.2). Themes and their underlying codes were created inductively during the process of categorizing student comments following the process described by Braun and Clarke. ²⁴ The NVivo software was used to manually assign each student feedback comment or partial comment to a code. These codes were collated into a set of themes encompassed by five broad areas: (1) Flexibility, (2) Resources, (3) Task Activities, (4) Support, and (5) Evaluation. Since many individual student comments mentioned more than one theme, the entire comment or partial comment was treated as a reference that was mapped back to each of the codes. Indicative examples are provided for clarity (for summary tables and examples of student responses to the open‐text questions, see Data S2).

3. RESULTS

3.1. Student results

The primary aim of the experiment was for students to learn the process for estimating ethanol concentration in a set of 4 fictitious driver serum samples that were randomly assigned in the simulation. In total, students generated 22,096 fictitious serum samples. Given that each experiment produced a set of 4 tubes for analysis, this shows that 764 students completed a total of 5524 online experiments, with an average of 7 experiments per student.

To determine whether students were able to accurately estimate the concentration of ethanol in samples provided in each of their unique experiments, we compared the student estimates of concentration to the ‘actual’ concentration of their assigned set of unknown samples (Figure 2).

FIGURE 2.

Student Estimates of ethanol concentration in samples randomly assigned in the simulation. Graph shows means and SD of student estimates versus ‘actual’ concentrations provided to each student in the online experiment. Student estimates of >100 mM were omitted (19 out of 2542 estimates).

The data represents the means and standard deviation of student estimates submitted for each unknown concentration sample that was provided. The line of best fit (blue) was plotted by considering each replicate student estimate (y‐value) as an individual point. The graph shows 2542 concentration estimates from 645 student experiments plotted against the ‘actual’ concentration after 19 concentration estimates that were above 100 mM were omitted as outliers.

The line of best fit was given by the equation y = 0.8682x + 1.433 (R² = 0.7012), indicating that on average, students overestimated the lower ethanol concentration samples and underestimated those with higher ethanol concentrations. The most accurate student estimates were approximately 10.9 mM (the interception between the line of best fit and with y = x). This is equivalent to 0.05% (w/v), or the blood alcohol limit that students were testing for this task.

3.2. Student estimates of concentration

In the context of blood alcohol concentrations, the highest physiological concentrations observable were expected to be 0.3%(w/v), or 65 mM. Most submissions (2470 out of 2561) fell between 0 and 65 mM. Twenty‐three concentrations over 65 mM were submitted by 12 out of 645 students (1.9%). These are likely to have resulted from errors in the calculations. Sixty‐one students submitted a total of 68 sample concentration estimates that were negative numbers, which are physically impossible. One of these students had also submitted a sample estimate greater than 65 mM. The negative values may have been due to calculation errors or, more likely, they were a consequence of inappropriately correcting for the background absorbance of the simulated blood samples provided in the simulation. In total, 71 students submitted concentrations that were not physiologically possible. That is, some students did not critically evaluate their experimental results by considering whether the numerical values were reasonable, or even possible, within the biological context of the experiment.

Students were assessed on their experimental approach and calculations (4 marks) and the reasoning given for their confidence in the estimates of blood ethanol concentration in the samples (2 marks). That is, marks were assigned for their process, rather than the numerical concentrations provided for each sample. If no details of biochemical calculations or spreadsheets containing calculation formulae were submitted, students were awarded zero marks out of 4 for their approach (69 out of 716; 9.6%), whereas students that had shown their raw absorbance values, standard curve, regression analysis, and calculations were awarded full marks (410 out of 716 students; 57.3%). Confidence reported as achieved through repeated measurement, replicates, controls, or raw absorbance measurements that were within the range of their standard curve were awarded marks. Students scored a mean of 1.25 out of 2 for this question, with 245 out of 716 (34%) scoring 2 out of 2, indicating that a significant proportion of students reflected appropriately on the validity of their experimental results.

3.3. Haemolysis

Since the samples being tested were blood serum samples, the simulation represented varying degrees of hemolysis in the samples. Given that the end product of the assay is also red, it was important for students to appreciate the impact this could have on their results and how it could be accounted for in the experimental procedure. This question was included as bonus marks, with students not directly taught appropriate methods to work with samples containing compounds that absorb light at the target wavelength. Consequently, the question proved to be the most challenging for students, with common yet ineffective solutions including centrifugation of the sample to remove the hemoglobin (106 students out of 716; 14.8%). A word frequency analysis using NVivo software revealed the word ‘centrifuge’ (including stemmed words for example, centrifugation, centrifuged) appeared 207 times in student answers. More suitable approaches such as ‘blank’ (including blanks, blanking and blanked) appeared 120 times and ‘background’ was mentioned in 84 responses. At University A, 94 out of 716 students scored full marks for this question, with a mean of 0.52 out of 2. At University B, just 23 students scored more than 0 for this question, with 9 scoring 2 out of 2.

3.4. Student lab reports

The median marks were 63% and 73% for University A and University B, respectively, with the overall distribution of marks shown in Figure 3.

FIGURE 3.

Student lab reports were marked based on their answers to 6 questions related to the concentration estimates, confidence, sample variation, linear range of the assay, and background absorbance. The graph shows the distribution of total marks for each student expressed as %.

The mark distributions indicate qualitatively that the majority of students were able to gather, analyze, and report data obtained within the simulation and to propose strategies for dealing with difficult samples (high concentration or high background absorbance) (Figures 4, 5).

3.5. Likert‐scale student experience evaluations

To gain insight into the student experience with the online computer simulation, students were asked to complete a questionnaire that asked them to reflect on the difficulty of the task, their engagement levels, and confidence in applying the skills learned, see Figure 4.

To evaluate students' perceptions of the online laboratory simulation, a brief Likert‐scale survey was administered following completion of the activities. The survey consisted of five statements addressing the usability of the simulation, clarity of scientific principles, adequacy of instructions, overall enjoyment, and perceived value of the approach for broader application, see Figure 5.

3.6. Free‐text responses

The cohort of 764 University A students provided a total of 577, 552, and 370 responses respectively to three questions about the ‘best aspects’, ‘suggested improvements’, and ‘other comments.’ The cohort of 125 University B students provided 180, 125, and 37 responses respectively to the same three questions. Student free text responses were manually assigned to a set of codes as described in the methods Section 2.8. Summary tables of the results and a thematic analysis of the emerging themes are provided in Data S2.

Briefly, students found the ‘best aspects’ of the simulation to be the short time required to complete the experiment, the ability to repeat, make mistakes, modify variables and not be concerned about limitations of reagents or equipment. In response to how the simulation could be improved, students most frequently commented that it should include an undo feature, functionality to save progress and resume an experiment later and that the manual liquid handling (pipetting) steps were tedious and time‐consuming and suggested the inclusion of a multichannel pipette or automation to address this. Some also mentioned that the visual interface with plastic plates and clear liquids made it challenging to see whether reagents had been added to specific wells of the plate. Some wanted clear instructions regarding the volume of every unknown driver blood sample to be added to the assay plate. Overall, students feedback was positive regarding the online laboratory simulation although many noted that it should complement a hands‐on laboratory experience rather than replace it.

4. CONCLUSION

This study aimed to explore the potential benefits and disadvantages for students performing experiments in an online laboratory simulation. It was hypothesized that the online laboratory would increase opportunities for exploration beyond the prescribed protocols and allow students more scope to focus on the principles of enzyme kinetics, spectrophotometry, linear regression, reflection on results, and confidence in conclusions.

Whilst most students simply followed the protocols necessary to gather the data for their submitted work, there was evidence that many students had tested the upper and lower detection limits, and discussion in class revealed that students were comparing results and strategizing to achieve the most accurate possible predictions of sample concentrations in their individual experiments. Tracking the data from the simulator showed that students, on average, commenced the experiment 7 times, generating a total of 22,096 sample tubes for estimation. This level of repetition, exploration, and associated learning would have been completely unfeasible in a hands‐on laboratory class.

The laboratory simulation also provided a platform for educators to evaluate understanding of the foundational concepts that were required for students to be able to design and troubleshoot experiments. For example, to complete all the tasks successfully, students needed to understand the concept of linearity and how to use equations, be able to evaluate their results, identify limitations, and identify opportunities for optimization.^{25, 26}

Whilst the entire experiment could be completed online, the student experience was enormously improved by working through the activities in a classroom or laboratory with computers. Students were able to engage with instructors and peers, to discuss approaches and results in a supportive environment.

Finally, the estimation of blood ethanol concentration was provided as a relatable and authentic scenario for students to work within. However, given that the simulator is programmed to generate absorbance values proportionally related to the concentration of any target metabolite within a biological sample, the experiment itself can be adapted to numerous scenarios without the need to modify the program in any way. For example, the exact same simulation could be used to measure blood glucose concentration, catalyzed by glucose oxidase (rather than ethanol oxidase) to produce a colored product that is detected by spectrophotometry.

Supporting information

Data S1. Supporting Information.

Data S2. Supporting Information.

Clemson M, Huang A, Denyer G, Costabile M. Teaching second‐year biochemistry students the principles of an enzyme‐catalyzed spectrophotometric assay with an online lab simulator. Biochem Mol Biol Educ. 2025;53(4):370–380. 10.1002/bmb.21903

DATA AVAILABILITY STATEMENT

The data that supports the findings of this study are available in the supplementary material of this article.