Curriculum
Through the University of Maryland's Biocomputational Engineering degree program, students are given the skills and experience they need across a range of disciplines to be able to tackle today's biggest challenges in public health.
The curriculum offers junior- and senior-level courses within the state-of-the-art Biomedical Sciences and Engineering (BSE) education facility at the Universities at Shady Grove. Graduates of the program will be well-positioned for rewarding career opportunities in the emerging biopharma, biotech, and biomedical industries centered in Maryland and throughout the mid-Atlantic region.
Fundamentals Included in the Instruction
- Mathematics and Statistics for Engineers
- Molecular Biology
- Computational Systems Biology
- Synthetic Biology
- Fluid Dynamics, Mass Transfer
Skills Taught by the Program
- Computer Programming (Python, C++, R)
- Machine Learning
- Data Visualization
- Computer Modeling
- Molecular Lab Technologies
Students will take 60 credits of coursework at the 300- and 400-level to complete the University of Maryland B.S. degree in Biocomputational Engineering. Courses will be offered within the state-of-the-art Biomedical Sciences and Engineering (BSE) education facility at the Universities at Shady Grove.
Learn more about the Biocomputational Engineering faculty and staff.
Course descriptions and a sample schedule are provided below:
| COURSE | TITLE | DESCRIPTION | CREDITS |
|---|---|---|---|
| ENBC301 | Introduction to Biocomputational Engineering | Provides practical tools to help Biocomputational Engineering majors to think critically about their goals and career paths and to utilize their major to set their career trajectory. | 1 |
| ENBC311 | Python for Data Analysis | Provides an introduction to structured programming, computational methods, and data analysis techniques with the goal of building a foundation allowing students to confidently address problems in research and industry. Fundamentals of programming, algorithms, and simulation are covered from a general computer science perspective, while the applied data analysis and visualization portion makes use of the Python SciPy stack. | 3 |
| ENBC312 | C++ | Provides a solid foundation for object-oriented programming using the C++ programming language. It introduces fundamental conceptual tools and their implementation of object-oriented design and programming such as: object, type, class, implementation hiding, inheritance, parametric typing, function overloading, polymorphism, source code reusability, and object code reusability. Fundamental principles of object-oriented design and programming are stressed while covering the language details. | 3 |
| ENBC321 | Machine Learning for Data Analysis | Instructs students in the fundamentals of machine learning methods through examples in the biological phenomenon and clinical data analysis. This course is designed to share knowledge of real-world data science and aid to learn complex machine learning theory, algorithms, and coding libraries in a simple way. Students will learn the machine learning theory, but they will also get hands-on practice building their models using programming tools such as Python and R. | 3 |
| ENBC322 | Algorithms | Utilizing the Python proramming language for a systematic study of the complexity of algorithms related to sorting, graphs and trees, and combinatorics. Algorithms are analyzed using mathematical techniques to solve recurrences and summations. | 3 |
| ENBC331 | Applied Linear Systems and Differential Equations | Applications of linear algebra and differential equations to bioengineering and biomolecular systems. Designed to instruct students to relate mathematical approaches in bioengineering to their physical systems. Examples will emphasize fluid mechanics, mass transfer, and physiological systems. | 3 |
| ENBC332 | Statistics, Data Analysis, and Data Visualization | This course will instruct students in the fundamentals of probability and statistics through examples in biological phenomenon and clinical data analysis. Data visualization strategies will also be covered. | 3 |
| ENBC341 | Biomolecular Engineering Thermodynamics | A quantitative introduction to thermodynamics analysis of biomolecular systems. The basic laws of thermodynamics will be introduced and explained through a series of examples related to biomolecular systems. | 3 |
| ENBC342 | Computational Fluid Dynamics and Mass Transfer | Principles and applications of fluid mechanics and mass transfer with a focus on topics in the life sciences and an emphasis on computational methods and modeling. Content includes conservation of mass, momentum, and energy, as well as the application of these fundamental relations to hydrostatics, control volume analysis, internal and external flow, and boundary layers. Applications to biological and bioengineering problems such as tissue engineering, bioprocessing, imaging, and drug delivery. | 3 |
| ENBC351 | Quantitative Molecular and Cellular Biology | Provides a quantitative analysis of the behavior of cellular and molecular systems. The focus will be the construction and application of mechanistic models of biomolecular interaction rate processes, which form the foundation of most biological functions. The course will also provide in-depth, practical exploration into data analysis of key bioengineering techniques. | 3 |
| ENBC352 | Molecular Techniques Laboratory | Provides students with the opportunity to learn how biology and engineering can synergistically contribute to our understanding of biological and biomedical problems. Students will gain hands-on experience through wet lab experiments in basic techniques relevant to bioengineering. | 2 |
| ENBC353 | Synthetic Biology | Students are introduced to the scientific foundation and concepts of synthetic biology and biological engineering. Current examples that apply synthetic biology to fundamental and practical challenges will be emphasized. The course will also address the societal issues of synthetic biology, and briefly examine the interests to regulate research in this area. | 3 |
| ENBC425 | Imaging and Image Processing | Examines the physical principles behind major biomedical imaging modalities, including X-Ray, CT, MRI. Instructs students in mathematical tools for extracting information from images. Provides an introduction to the use of machine learning for interpreting images. Matlab and/or Python utilized for image processing exercises. | 3 |
| ENBC431 | Finite Element Analysis | An introduction to the theory, programming and application of the finite element method that is used to solve problems in engineering analysis and design. Modeling, analysis, and design using the FEA software SolidWorks. The objective of the course is to teach the fundamentals of the finite element method with emphasis on the underlying theory, assumption, and modeling issues as well as providing hands-on experience using finite element software to model, analyze, and design systems. | 3 |
| ENBC441 | Computational Systems Biology | Introduction to building computer models that analyze dynamic functions within a cell, organ, tissue, or organism. | 3 |
| ENBC491 | Senior Capstone Design in Biocomputational Engineering | Senior design project, in which students work in teams to utilize the skills acquired through the major to identify and solve quantitative problems in bioengineering. Ethics in bioengineering and biotechnology will also be covered. | 3 |
| ENGL393 | Technical Writing | The writing of technical papers and reports. | 3 |
| ENBC423 | Applied Computer Vision (Elective) | Introduction to the basics and modern deep learning models in the Artificial Intelligence field of computer vision. The course emphasizes applications of computer vision in medical imaging. Computer vision techniques will be demonstrated using software packages implementing bioimage informatics methods. | 3 |
| ENBC403 | Research Methods in Biological Data Mining (Elective) | An introduction to the fundamentals of conducting research projects utilizing a general understanding of quantitative/qualitative research, clinical data analysis, and multiple examples of different research approaches in the biological phenomenon. The course includes an overview of design strategies to implement various data mining technologies. | 3 |
| Coming soon | Bioinformatics Engineering (Elective) | Introduces students to the core principles of bioinformatics while encouraging students to apply their programming skills to real-world biological problems. Students will learn to utilize Python to process data sets. | 3 |
| Program Elective | 3 | ||
| TOTAL REQUIRED COURSE CREDITS | 60 |
| SEMESTER 1 | ||
|---|---|---|
| ENBC301 | Introduction to Biocomputational Engineering | 1 |
| ENBC311 | Python for Data Analysis | 3 |
| ENBC331 | Applied Linear Systems and Differential Equations | 3 |
| ENBC332 | Statistics, Data Analysis, and Data Visualization | 3 |
| ENBC341 | Biomolecular Engineering Thermodynamics | 3 |
| ENBC322 | Algorithms | 3 |
| TOTAL CREDITS | 16 |
| SEMESTER 2 | ||
|---|---|---|
| ENBC312 | Object Oriented Programming in C++ | 3 |
| ENBC321 | Machine Learning for Data Analysis | 3 |
| ENBC351 | Quantitative Molecular and Cellular Biology | 3 |
| ENBC342 | Computational Fluid Dynamics and Mass Transfer | 3 |
| ENBC352 | Molecular Techniques Laboratory | 2 |
| TOTAL CREDITS | 14 |
| SEMESTER 3 | ||
|---|---|---|
| ENBC353 | Synthetic Biology | 3 |
| ENBC425 | Imaging and Image Processing | 3 |
| ENBC423 | Applied Computer Vision (Elective) | 3 |
| ENBC431 | Finite Element Analysis | 3 |
| ENGL393 | Technical Writing | 3 |
| TOTAL CREDITS | 15 |
| SEMESTER 4 | ||
|---|---|---|
| ENBC403 | Research Methods in Biological Data Mining (Elective) | 3 |
| ENBC441 | Computational Systems Biology | 3 |
| ENBC491 | Senior Capstone Design in Biocomputational Engineering | 3 |
| Bioinformatics Engineering (Elective) | 3 | |
| Elective 4 | 3 | |
| TOTAL CREDITS | 15 |
Program Educational Objectives
3-5 years after graduation, our graduates will:
- Be successful in Biocomputational Engineering careers or post-graduate educational pursuits by applying scientific depth, technical skills, and knowledge gained through practical experiences.
- Address the data-driven computational biomedical challenges facing society in both the near and long term by demonstrating an awareness of their field and an ability for lifelong learning.
- Serve their profession, promote equity and justice through technology, and positively impact society by drawing upon a foundation of professional ethics.
Student Learning Outcomes
The Biocomputational Engineering programs use the following ABET learning outcomes:
- an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics
- an ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors
- an ability to communicate effectively with a range of audiences
- an ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts
- an ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives
- an ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions
- an ability to acquire and apply new knowledge as needed, using appropriate learning strategies.
The current overall undergraduate enrollment in Biocomputational Engineering is 10 students distributed over all four years of study (as of fall 2024). We have seven new incoming juniors, with ten total students. We have a total of ten awarded Bachelor of Science degrees.