Computational BiologyComputational Biology
Computation has become essential to biological research. Technology for collecting high-throughput data, such as genomic technology, mass spectrometry, and MRI imaging, and the development of large-scale databases, such as those for genomes, epidemiology, and compilations of biological information types, have made available unprecedented amounts of detailed information that require computationally intensive methodologies to access and analyze. These data and computational methods are transforming almost all of the biological research.
Problems investigated by computational biologists include topics as diverse as the genetics of disease susceptibility; comparing entire genomes to reveal the evolutionary history of life; predicting the structure, motions, and interactions of proteins; designing new therapeutic drugs; modeling the complex signaling mechanisms within cells; predicting how ecosystems will respond to climate change; and designing recovery plans for endangered species. The computational biologist must have skills in mathematics, statistics, machine learning, and the physical sciences as well as in biology. A key goal in training is to develop the ability to relate biological processes to computational models. Cornell faculty work primarily in six subareas of computational biology: 1. computational and statistical genomics, 2. population, comparative, and functional genomics, 3. bioinformatics, 4. proteomics, 5. ecology and evolutionary biology, and 6. statistical and computational methods for modeling biological systems.
Beyond core skills in mathematics, physical sciences and biology, the computational biology concentration requires additional coursework in mathematics and computer programming, a “bridging” course aimed at connecting biology to computation, and an advanced course where the theoretical/computational component of one aspect of biology is studied. Students should enroll in the more rigorous courses in the physical and mathematical sciences, and may wish to take additional courses in these areas.
Computational biology has applications as broad as biology itself. The problems of interest and the tools available to study them are constantly evolving, so students are encouraged to gain fundamental skills that will serve them throughout their careers. There is great, and increasing, demand for research scientists and technical personnel who can bring mathematical and computational skills to the study of biological problems. This concentration is also an excellent preparation for graduate study in any area of biology or computational biology.
Problems investigated by computational biologists include topics as diverse as the genetics of disease susceptibility; comparing entire genomes to reveal the evolutionary history of life; predicting the structure, motions, and interactions of proteins; designing new therapeutic drugs; modeling the complex signaling mechanisms within cells; predicting how ecosystems will respond to climate change; and designing recovery plans for endangered species. The computational biologist must have skills in mathematics, statistics, machine learning, and the physical sciences as well as in biology. A key goal in training is to develop the ability to relate biological processes to computational models. Cornell faculty work primarily in six subareas of computational biology: 1. computational and statistical genomics, 2. population, comparative, and functional genomics, 3. bioinformatics, 4. proteomics, 5. ecology and evolutionary biology, and 6. statistical and computational methods for modeling biological systems.
Beyond core skills in mathematics, physical sciences and biology, the computational biology concentration requires additional coursework in mathematics and computer programming, a “bridging” course aimed at connecting biology to computation, and an advanced course where the theoretical/computational component of one aspect of biology is studied. Students should enroll in the more rigorous courses in the physical and mathematical sciences, and may wish to take additional courses in these areas.
Computational biology has applications as broad as biology itself. The problems of interest and the tools available to study them are constantly evolving, so students are encouraged to gain fundamental skills that will serve them throughout their careers. There is great, and increasing, demand for research scientists and technical personnel who can bring mathematical and computational skills to the study of biological problems. This concentration is also an excellent preparation for graduate study in any area of biology or computational biology.
Computational Biology Requirements:
a. One course in computer programming:
- CS 1110 - Introduction to Computing Using Python
- CS 1112 - Introduction to Computing Using MATLAB
- CS 1114 - [Introduction to Computing Using MATLAB and Robotics]
- BEE 1510 - Introduction to Computer Programming
b. One additional course in mathematics:
- MATH 2210 - Linear Algebra
- MATH 2310 - Linear Algebra with Applications
- MATH 2940 - Linear Algebra for Engineers
- MATH 4200 - Differential Equations and Dynamical Systems
- BTRY 3080 - Probability Models and Inference (crosslisted)
c. One of the following bridging courses:
i.e., a course in mathematical modeling applied to biology:
- BIOEE 3620 - [Dynamic Models in Biology] (crosslisted)
- BIONB 3300 - Introduction to Computational Neuroscience (crosslisted)
- BTRY 4820 - Statistical Genomics: Coalescent Theory and Human Population Genomics
- BTRY 4830 - Quantitative Genomics and Genetics
- BTRY 4840 - [Computational Genomics]
- NTRES 3100 - Applied Population Ecology
- NTRES 4110 - [Quantitative Ecology and Management of Fisheries Resources]
d. One course from the following list of advanced courses:
or an additional "bridging" course numbered 4000 or above:
- BIOMG 6310 - Protein Structure and Function
- BIOMG 4810 - Population Genetics
- BIOMG 4840 - Molecular Evolution
- BIOMG 4870 - Human Genomics
- BIONB 4220 - Modeling Behavioral Evolution
- BIOPL 4400 - [Phylogenetic Systematics]
- BTRY 3080 - Probability Models and Inference (crosslisted)
- BTRY 4090 - Theory of Statistics (crosslisted)
- BTRY 6790 - [Probabilistic Graphical Models] (crosslisted)
- BTRY 3520 - Statistical Computing (crosslisted)
- CS 2110 - Object-Oriented Programming and Data Structures (crosslisted)
- CS 4210 - Numerical Analysis and Differential Equations (crosslisted)
- CS 4220 - Numerical Analysis: Linear and Nonlinear Problems (crosslisted)
- MATH 4200 - Differential Equations and Dynamical Systems
- NTRES 4120 - Wildlife Population Analysis: Techniques and Models
- NTRES 6700 - Spatial Statistics
- ORIE 3500 - Engineering Probability and Statistics II
- ORIE 3510 - Introduction to Engineering Stochastic Processes I (crosslisted)
Note:
- Many of the “bridging” and “advanced” courses listed above (items c and d) are offered only in alternate years or irregularly, and many have prerequisites that are not required for the biology major or this concentration. Students therefore need to plan well in advance how they will satisfy these requirements, and verify when course offerings will occur.
- It is strongly recommended that students in this concentration use PHYS 2207/PHYS 2208 to satisfy the core physics requirement.
- It is strongly recommended that students complete the core organic chemistry requirement using the CHEM 1570/CHEM 2510 option, and that the time saved be used to take either CS 2110 or a second mathematics course from the list above.
- One course may not be used to satisfy two different requirements simultaneously. For example, BTRY 3080 can be used to satisfy either requirement (b) or requirement (c), but not both.
- Students who use BTRY 3080 to fulfill the additional mathematics requirement should not use ORIE 3500 - Engineering Probability and Statistics II to fulfill the requirement for an advanced course.