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Improvement of Bioengineering Courses through Systems Biology and Bioprocess Modeling

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2014 ASEE Annual Conference & Exposition


Indianapolis, Indiana

Publication Date

June 15, 2014

Start Date

June 15, 2014

End Date

June 18, 2014



Conference Session

Biological & Agricultural Division Technical Session 1

Tagged Division

Biological & Agricultural

Page Count


Page Numbers

24.713.1 - 24.713.12



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Paper Authors


Kirk D. Dolan Michigan State University

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Kirk Dolan earned degrees in agricultural engineering at U. of FL (B.S.), UC Davis (M.S.), and Michigan St. U. (Ph.D.). He spent 6 years working in China as the Asian Director for Pharmaceutical and Food Specialists, San Jose, CA, a food safety consulting company and process authority. He has been assistant (2000)/associate (2005) professor of food engineering at Michigan State University, with joint appointments in the Department of Food Science and Human Nutrition, and Department of Biosystems and Agricultural Engineering. His extension appointment to assist the MI food industry gives opportunities to visit many food factories and hold workshops on various food safety issues. His research and teaching are in thermal processing, inverse problems, and parameter estimation under dynamic conditions. He teaches an undergraduate engineering class on biological fluid processing and a graduate engineering class on numerical techniques and parameter estimation using MATLAB.

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Yinjie J. Tang Washington University

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I did my PhD in chemical engineering at University of Washington. I worked on DOE GTL projects during my postdoctoral period in Lawrence Berkeley National Laboratory (with Dr. Jay Keasling). Since moving to Washington University in St. Louis, my research focuses on characterizing and engineering environmental microorganisms. Milestones reached include 13C-metabolic pathway analysis, metabolic flux modeling, and systems genetic engineering of E.coli and cyanobacteria for chemical productions. I have received NSF CAREER Award (2010) and Ralph E. Powe Junior Faculty Enhancement Award (2010). I teach Process Dynamics and Control, Fluid Mechanics, Bioprocess Engineering, and Metabolic Engineering at Washington University. I also co-taught Advanced Energy Laboratory (2011) and International Experience in Bioenergy (2012). I received a Department Chair’s Award for Outstanding Teaching in 2013.

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Wei Liao Michigan State University

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Improvement of bioengineering courses through systems biology and kinetic fermentation process modelingAbstract This joint project applied MATLAB and Simulink to improve courses for MetabolicEngineering, Parameter Estimation for Engineering, Process Dynamics and Control, and ProcessControl Laboratory in the ____________ at _________ in 2011. The project also improved theexisting bioengineering courses in the _______ at __________, specifically Microbial SystemsEngineering and Engineering Analysis and Optimization of Biological Systems in 2012. BothMATLAB and Simulink were used in these courses. More than 100 undergraduate and graduatestudents per year from both universities have been enrolled in the classes in the past two years. The teaching approach was to introduce MATLAB and Simulink to bioengineeringcourses. Using computational modeling tools, students developed the equations for variousapplications in systems biology and bioengineering, solved the forward and the inverse problems,used Simulink to perform process control/design of engineering projects, and finally optimizedbioprocesses (both static and dynamic modes) using MATLAB tool boxes. Moreover, studentswere exposed to real experiments in the bio-reaction lab where data were collected. For allcourses, each student had a MathWorks-supplied license to use all necessary toolboxes. Assessment was made through homeworks, projects, exams on MATLAB/Simulink, andcomments from students and other instructors. Several of the students have now implementedMATLAB/Simulink in their research, introducing new methods to their advisors. Outcomesincluded a webpage with slides and notes posted for public access; two journal articles published,one book chapters published and two submitted, and one graduate course on MATLAB/Simulinkthat became a required course for graduate students at ______. 2Introduction Modeling is used to summarize data, optimize, allow rapid predictions, and to designprocesses, such as “what-if” scenarios. Over the past decade, computer modeling and simulationhave become much more widely used in the industry. The explosion of interest in modeling isdue in part to access to better and lower-cost software, and to the potential savings in effort, time,and cost in experimental work. Both undergraduate and graduate engineering students need tohave some proficiency in using modeling and technical computing software before they enter thejob market. Therefore, the goal of this project was to improve the existing engineering coursesin Chemical and Biosystems Engineering by using MATLAB and Simulink. The students’experience of writing code in MATLAB and arranging a system in Simulink was excellentpreparation for understanding the computational algorithms. Ultimately, students will developsystems engineering skills to solve problems in the nascent biotechnology industry.Background and Course Description Industrial biotechnology often uses microorganisms and enzyme catalysts to synthesizeuseful products. The benefits of biological reactions in large quantities cannot be realizedwithout using systems biology, bioprocess control and modeling theory. The critical frontiers forbioprocess engineering students are direct experience with systems biological analysis,bioreactor operations and the modeling of dynamic behavior of metabolic reactions undercontrolled variables. The MathWorks grant-funded project of systems biology and kineticprocess modeling relied on fundamental knowledge in biology, chemistry, mathematics, statistics,kinetics, and chemical process engineering, which was integrated into the curriculum for fourmajor courses at ________University and ________ University. 1. Metabolic engineering (ChE596) at _________ University focuses on analysis of complex interactions in biological systems and introduction of metabolic changes to achieve desired cellular properties [1]. Currently, numerous chemical compounds, ranging from pharmaceuticals to biofuel, have been produced with the aid of biological tools. The ability to efficiently synthesize natural or synthetic products requires a systems-level understanding of metabolism. This class teaches molecular tools for pathway modifications, systems biology, and metabolic modeling. There are total of 30 graduate/undergraduate students who participated in the 2011 and 2012 classes. 2. Process Control (ChE 462) and Process Control Laboratories (ChE 463) at ______ University, teaches chemical engineers process control theory and educates the students on control techniques employed in industry. Process control (CheE462) focuses on the control dynamics and model simulation of chemical processes [2]. The Control 3 laboratories (ChE463) consist of 5 control experiments using a state-of-the-art EMERSON electronic controller and workstations to control processes such as flow, level, pressure and temperature. Real time process data are available in EXCEL and MATLAB (including Simulink). A bioprocess laboratory (a sixth control experiment) is set up to focus on the bioreactor operation and modeling (~30 graduate/undergraduate students/year). Students use the control theory and modeling skills to resolve the problems in industrial biotechnology. 3. Microbial Systems Engineering (BE 360) at _______ University trains biosystems engineers how to design, model, and simulate bioengineering processes. Topics include application of engineering fundamentals, biological principles, and computational tools to the analysis of microbial processes; kinetic analysis of biological processes, modeling of microbial processes, unit operations and scale-up. Applications to biofuel and food production are given. MATLAB and Simulink were used in this course. Development of a fermentation laboratory exercise, and more extensive modeling experience with MATLAB and Simulink enhanced the student experience. There were 30 undergraduate students in 2011 and 42 students in 2012. 4. Engineering Analysis and Optimization of Biological Systems (BE 835) at _______University. This graduate-level course is in two parts: 1) Numerical techniques and the forward problem; and 2) Parameter estimation and inverse problems. Other topics within these two include optimal experimental design, sequential parameter estimation, model discrimination, and Monte Carlo simulation. Students are tested on being able to use MATLAB to solve systems of ODEs when all parameters are given (the forward problem), and to estimate these parameters when experimental data are given (the inverse problem). There were 15 students in 2011 and 17 students in 2012.Teaching Approach Both MATLAB and Simulink were used in these courses to estimate parameters forfermentation kinetics (a group of ordinary differential equations), to numerically solve kineticmodels (ode functions), to simulate the bioethanol/biomass production (Systems BiologyToolbox), and to model a bioreactor combining mass and heat transfer (Basic Simulink). In each course, students were taught the principles and equations before using the MATLABand Simulink, to avoid a “black-box” approach. MATLAB and Simulink were presented as toolsthat make the solving of the equations faster and more convenient. Parameters were given tosolve forward problems first. For the inverse problem, simulated or real data were given toestimate the parameters. Simulink was used to solve systems of ODEs, and then to optimize theprocess based on selected parameters or variables. The details for each course are given below. 1. Metabolic engineering (ChE596) 4 Students at ______ University had team projects to use the bioreactor for ethanol andbutanol fermentations, and developed models to describe the biomass and alcohol productiondata (Figure 1a). For both undergraduate and graduate courses, computer labs were set up forstudents so that MATLAB/Simulink could be demonstrated and used throughout. For example,flux analysis is an important systems biology tool for physiological prediction of enzymatic ratesin metabolic networks, and allows knowledge-based design of cellular functions. The cell-widequantification of intracellular fluxes can be performed via Flux Balance Analysis (FBA), whichuses the stoichiometry of the metabolic reactions and a series of biological constraints to obtainthe feasible fluxes. In this class, students have learned how to develop the FBA model and useMATLAB to solve the underdetermined flux model using the function (fmincon).a) Ethanol fermentations using yeast. b) Kinetic model for ethanol fed-batch fermentation Figure 1: Fermentation lab and modeling at ________ University 2. Process Dynamics and Control and Process Control Laboratories In both classes, steady and unsteady-state behavior of chemical processes, fundamentalfeedback and feedfoward control strategies, and modern control theory and applications weretaught. After taking this course, students not only understand process control theory andlaboratory operations, but also learned the skills for developing models to analyze and predict theprocess dynamics. During the semester, student learned both Simulink and MATLAB in thecomputer lab for about one month. They completed a computer project on alcohol fermentationusing the actual experimental data provided by the instructor. Students developed the kineticmodels (using ordinary differential equations) and perform the parameter fitting and statisticalanalysis using MATLAB (ode45 coupled with nlinfit functions) (Figure 1b). There are four tofive variables in the model including glucose, biomass, alcohol, acetate (as the inhibitorybyproducts) and nitrogen sources (yeast extract). Students have to compare the parametersobtained from different fermentation conditions to estimate the alcohol production underinfluences of oxygen level, substrate concentration, and chemical inhibitions. The project will 5also ask students to incorporate the control loop (PID control) to simulate the operation ofbioreactor fermentation under different oxygen conditions. According to students’ feedback, thecomputer lab is the most value part of this class since it develops their computational skills fortheir future academic and industrial jobs. 3. Microbial Systems Engineering (BE 360)The approach this course took was integrating the introduction of microbial processes withmathematical modeling of microbial kinetics. The course started with lecturing applications ofmicrobial systems in environmental, food, and energy industries. After discussion offundamental microbial physiology and mass/energy balance of microbial processes, microbialkinetics was introduced in the class. Due to the complicated nature of microbial kinetics, a groupof differential equations were required to describe the kinetics. Often, there are no algebraicsolutions for such kinetics. Students used mathematical modeling to connect the engineering andmicrobiology components. The MATLAB function ode45 and a solution approach for a groupof differential equations were introduced to students. A demonstration fermentation lab wasgiven to apply the modeling. All parameters such as specific growth rate constant, maintenancecoefficients, product/biomass yield, and inhibition coefficient that were needed for the modelingwere obtained from the demonstration lab. Students were required to construct a model todescribe a complicated microbial process using the parameters from the demonstration lab.According to the students’ responses, this approach enhanced the students’ understanding of howto use mathematical tools to find solutions for real-world applications. 4. Engineering Analysis and Optimization of Biological Systems (BE 835). All lectures were taught in a computer lab where each student had two monitors. Class washeld Tuesday and Thursday, 4:10-5:30pm. The first half of the course, the forward problem,was taught using this textbook [3]. The main topics taught were numerical techniques tointegrate the area under functions; to do root-finding, and to solve systems of ODEs for initial-value and boundary-value problems for linear and nonlinear ODEs, using ode45 and the finite-difference method. The instructor’s screen was shared live on the student’s screen, so that students could see theinstructor’s code and simultaneously test it on their other screens using MATLAB. The lectureswere based on powerpoint slides (created by Michael Gustafson, Duke University) for eachchapter, supplied by Mathworks. The first 4 lectures were an introduction to coding inMATLAB, Chapters 2-4. After the students had a working knowledge of MATLAB coding, atypical lecture would consists of the instructor giving a powerpoint lecture explaining theconcept for that day, such as how initial-value ODEs are set up and solved in MATLAB. Theinstructor would stop at appropriate intervals to demonstrate how to run the MATLAB code sothe students could try it and ask questions. The instructor had uploaded all slides and codeexamples so that students would have all materials when each class started. 6 After the students had already mastered how to use ode45 to solve an initial-value ODE,Simulink was introduced. Students were expected to know how to use Simulink to solve asystems of initial-value ODEs. The second half of the course, Parameter Estimation, was based on [4] and an update ofChapter 6 that has MATLAB code in it [5]. The instructor developed his own notes andpowerpoint slides to give lectures and show how to run the MATLAB code. The main topicswere parameter estimation by ordinary least squares (OLS) with ode45 and nlinfit, sequentialestimation; matrix formulation and statistics for the parameter errors; model discrimination, andoptimal experimental design. Because the MATLAB code for these topics was fairly long andcomplicated, the instructor supplied all of these as generic codes, and went through each cell inthe code to make sure the students understood it. Students were expected to know how tomodify the code for any homework or exam problem.Assessment Assessment was made through homeworks, projects, exams on MATLAB/Simulink,required numerical student evaluations, and comments from students and other instructors. Basedon student class evaluations, all our courses received very positive feedback. Several of thestudents have now implemented MATLAB/Simulink in the research, introducing new methodsto their advisors. Outcomes included a webpage with slides and notes posted for public access; anew website on parameter estimation using MATLAB; one journal article submitted by a studentbased directly on his data and what he learned in the course with instructor _____; and onegraduate course on MATLAB/Simulink that became a required course for graduate students at___ University. The instructors from ____ and _____ are preparing two book chapters (Onechapter is on Enzyme kinetics, and the other chapter is on Metabolic and Bioreactor Models) fora new text book “Bioenergy: Principles and Applications”. The aim of the book will bridge thegap between bioenergy education and industrial applications. A third book chapter based on theparameter estimation section of BE 835 was recently published.Some details are given below. 1. Metabolic engineering (ChE596)One homework was dedicated to the flux modeling component of the course. Students wereasked to develop a simple FBA model with 20 reactions in the central metabolisms to describethe alcohol fermentation pathways. 2. Process Control and Process Control Laboratories (ChE 462/463)Two homework and two computer projects were assigned for students to practice modeling skillsusing MATLAB and Simulink. They learned parameter estimation using Excel and theMATLAB curve fitting Toolbox; kinetic modeling (using ode45, ode23 and ode15s funcitons);Simulink (building and running simulations); Parameter fitting and Process Optimization (using 7fmin function and nlinfit). One class project example was posted on Youtube by students: 3. Microbial Systems Modeling (BE 360)Two homeworks were dedicated to the modeling component of the course. Students were askedto independently derive a group of differential equations to describe the kinetics of two microbialprocesses, a bacterial denitrification process and a yeast ethanol fermentation process. Thestudents were also required to use MATLAB to find numeric solutions for them. The instructorfound that the students were intrigued by using the mathematical tools they just learned to modelreal-world applications. 4. Engineering Analysis and Optimization of Biological Systems (BE 835). Weekly homeworks counted for 50% of the class, because students can learn MATLABbest by doing many example problems. Some difficulties doing in-class exams on the computerincluded unplanned technical difficulties with certain computers, and the time required to debugcodes. After teaching the course twice, the instructor found that if an in-class midterm exam(25%) is used, it should be either short-answer principle-based, or have a limited number ofstraightforward coding questions, or both. A take-home midterm is being considered as a betteroption. For the final exam (25%), both a take-home exam and a project were tried in twoseparate classes. The instructor preferred the project, because students will use the inverseproblems methods to estimate parameters for the students’ data or data selected from theliterature, giving the students real-world experience.Finally, students from these bioengineering classes at WUSTL and MSU helped submit severalresearch articles and book chapters:  Kinetic modeling and isotopic investigation of isobutanol fermentation by two engineered Escherichia coli strains. Industrial & Engineering Chemistry Research. 2012. 51 (49): 15855–15863.  Construction of a parsimonious kinetic model to capture microbial dynamics via parameter estimation. 2013. Inverse Problems in Science and Engineering. Accepted.  Book Chapter 14. Microbial metabolisms and metabolic modeling for biofuel production in “Bioenergy: Principles and Applications”. Under review.  Book Chapter 15. Enzymatic hydrolysis in “Bioenergy: Principles and Applications”. Under review.  "Chapter 7: Parameter Identification Under Dynamic Temperature Conditions in Inactivation Kinetics", Progress on Quantitative Approaches of Thermal Food Processing, New York, New York: Nova Science Publishers, 2012. Published.Outcomes 8 1. Both undergraduate and graduate engineering students are proficient in MATLAB and Simulink, making them more competitive for jobs. 2. Slides and course syllabus are posted for free use at the website: 3. Journal articles published (on Industrial & Engineering Chemistry Research, Inverse Problems in Science & Engineering) or ) by student using the methods learned in the course. 4. BE 835 selected in 2012 as a required course for graduate students in the department. 5. Increased use of MATLAB and Simulink in undergraduate projects and graduate research, and improvement of the quality of the academic research.Conclusions All four classes were significantly improved by teaching and hands-on problem solving withMATLAB and Simulink. The individual student licenses provided by Mathworks ensured thattoolboxes could be used at all times.Acknowledgments This project was supported by Mathworks’ Curriculum Development Education Grant.Bibliography[1] Stephanopoulos, G., A.A. Aristidou, and J.H. Nielsen, Metabolic engineering : principles and methodologies, San Diego: Academic Press, 1998.[2] Seborg, D.E., T.F. Edgar, D.A. Mellichamp, and F.J. Doyle, III, Process Dynamics and Control, Hoboken, NJ: John Wiley and Sons, 2011.[3] Chapra, S.C., Applied numerical methods with MATLAB for engineers and scientists, 2nd ed., Boston: McGraw-Hill Higher Education, 2008.[4] Beck, J.V., and K.J. Arnold, Parameter estimation in engineering and science, New York: Wiley, 1977.[5] Beck, J.V., and K.J. Arnold, Parameter Estimation in Engineering and Science, Revised Chapter 6,, 2007.

Dolan, K. D., & Tang, Y. J., & Liao, W. (2014, June), Improvement of Bioengineering Courses through Systems Biology and Bioprocess Modeling Paper presented at 2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana. 10.18260/1-2--20605

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