What is it?
Computational biology involves application of mathematical modeling and computational simulation techniques to the study of biological systems. Thus, its applications can be very broad, covering many applications such as genomics, neuroscience, ecology and evolutionary biology, and biological systems in general. Systems biology focuses on complex interactions within biological systems. Its aim can be to model and discover properties of cells, tissues and organisms functioning as a system. Computational models are also helping attain the goals of systems biology.
How we use it?
At a smaller scale, systems and computational approach can help us understand and control phenomena within a cell with broad implications to plant and human systems. At a larger scale, it can help us understand how climate change will affect food production and ecosystems, and help design systems to remedy such effects. One can build models of evolutionary systems in order to predict changes that could occur in the future in areas such as disease susceptibility. Transmission of bacterial and viral outbreak can be better predicted and thus more effective preventive measures taken. Computer models of industrial food and bioprocesses will allow their design to be optimized better and faster for increased efficiency and sustainability
Industrial career in bioprocess, food and medical industry can be in systems analysis, software development, process and product design, and device development. Many of these careers can also be appropriate for regulatory and research agencies such as FDA and NIH. Systems biology is a very active area of research where one can pursue higher studies. Likewise, computational biology higher studies can be in, for example, computational genomics, computational neuroscience, and cancer computational biology.
Core courses to help you prepare
- BEE 3310 – Bio-Fluid Mechanics
Focus Area courses to help you prepare
- BEE 4530 – Computer-Aided Engineering: Applications to Biomedical Processes
- BEE 4600 – Determininstic and Stochastic Modeling for Biological Engineering
- BME 3300 – Introduction to Computational Neuroscience
- BME 5400 – Biomedical Computation
- CHEME 5940 – Biomolecular Engineering Logic and Design
- CS 4220: Numerical Analysis: Linear and Nonlinear Problems
- CS 4820 – Introduction to Analysis of Algorithms
- ECE 3530 – Introduction to Systems and Synthetic Biology
- ECE 3200 – Networks and Systems
- MAE 3260 – System Dynamics
- ORIE 4350 – Introduction to Game Theory
- ORIE 4580 – Simulation Modeling and Analysis
- SYSEN 5100 Applied Systems Engineering