B.S. in Biocomputational Engineering

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 will offer junior- and senior-level courses within the state-of-the-art Biomedical Sciences and Engineering (BSE) education facility at the Universities at Shady Grove, starting in Fall 2021. 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. Check back for more information regarding the course schedule for Fall 2021.

Learn more about the Biocomputational Engineering faculty and staff. 

Course descriptions and a sample schedule are provided below:

 

COURSE TITLE CR
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 Object Oriented Programming in C++
Provides an introduction to object oriented programming in the C++ language.
3
ENBC321 Machine Learning for Data Analysis
Provides an introduction to artificial intelligence methods for mining big data sets and for making decisions using data sets.
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
Quantitative analysis of the behavior of cellular and molecular systems.
3
ENBC352 Molecular Techniques Laboratory
Wet lab experiments to observe cellular and molecular processes and phenomenon.
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
Instructs students to use computer tools to analyze the thermal and mechanical properties of devices or systems. The course will focus specifically on the biomechanics of biomedical devices.
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
  Elective Courses
 
12
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
ENBC351 Quantitative Molecular and Cellular Biology 3
  Total credits 16

 

SEMESTER 2
ENBC312 Object Oriented Programming in C++ 3
ENBC322 Algorithms 3
ENBC342 Computational Fluid Dynamics and Mass Transfer 3
ENBC352 Molecular Techniques Laboratory 2
  Elective 1 3
  Total credits 14

 

SEMESTER 3
ENBC321 Machine Learning for Data Analysis 3
ENBC353 Synthetic Biology 3
ENBC431 Finite Element Analysis 3
ENGL393 Technical Writing 3
  Elective 2 3
  Total credits 15

 

SEMESTER 4
ENBC425 Imaging and Image Processing 3
ENBC441 Computational Systems Biology 3
ENBC491 Senior Capstone Design in Biocomputational Engineering 3
  Elective 3 3
  Elective 4 3
  Total credits 15

 

 

You've already got a passion for computers, biology, and solving problems. Now, you're ready to use those interests for the advancement of science, health care, and the greater good. Learn more about the new Biocomputational Engineering degree at the intersection of technology and life science. Reach out to us today!

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