Academics Computational Biology

Computational Biology

Computation has become essential to biological research. Genomic databases, protein databanks, MRI images of the human brain, and remote sensing data on landscapes contain unprecedented amounts of detailed information that are transforming almost all of 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 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 four subareas of computational biology: biomolecular structure, bioinformatics and data mining, ecology and evolutionary biology, and statistical and computational methods for modeling biological systems. Specific topics of study include DNA databases, protein structure and function, computational neuroscience, biomechanics, population genetics, and management of natural and agricultural systems.

Beyond core skills in mathematics, physical sciences and biology, the computational biology Program of Study 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. The program is also an excellent preparation for graduate study in any area of biology or computational biology.

Required courses for Program of Study in Computational Biology

1. One course in computer programming (CS 1110, CS 1112, CS 1113, CS 1114) Introduction to Computer Programming, or BEE 1510, Introduction to Computing)
    
2. One additional course in mathematics (MATH 2210, Linear Algebra and Calculus; or MATH 2310, Linear Algebra with Applications; or MATH 2940, Linear Algebra for Engineers; or MATH 4200, Differential Equations and Dynamical Systems; or BTRY 4070, Principles of Probability and Statistics; or BTRY 4080, Theory of Probability; or BTRY 4210, Matrix Computation)
    
3. One of the following bridging courses, i.e., a course in mathematical modeling applied to biology:
   
   
   • BIOEE 3620, Dynamic Models in Biology
   • BIOEE 4600, Theoretical Ecology
   • BIONB 3300, Introduction to Computational Neuroscience
   • BTRY 4820, Statistical Genomics
   • BTRY 4830, Quantitative Genomics
   • BTRY 4840, Computational Genomics
   • CS 4520 , Introduction to Bioinformatics
   • NTRES 3100, Applied Population Ecology
   • NTRES 4110, Quantitative Ecology and Management of Fisheries Resources
     
     
4. One course from the following list of advanced courses, or an additional "bridging" course numbered 4000 or above:
   • BIOBM 6310, Protein Structure and Function
   • BIOGD 4810, Population Genetics
   • BIOGD 4840, Molecular Evolution
   • BIOGD 4870, Human Genomics
   • BIONB 4220, Modeling Behavioral Evolution
   • BIOPL 4400, Phylogenetic Systematics
   • BTRY 4070, Principles of Probability and Statistics
   • BTRY 4080, Theory of Probability
   • BTRY 4090, Theory of Statistics
   • BTRY 4790, Probabilistic Graphical Models (also CS 4782)
   • BTRY 6520, Computationally Intensive Statistical Inference
   • CS 2110, Object-Oriented Programming and Data Structures
   • CS 4210, Numerical Analysis and Differential Equations
   • CS 4220, Numerical Analysis: Linear and Non-Linear Equations
   • CS 6522, Biological Sequence Analysis
   • MATH 4200, Differential Equations and Dynamical Systems
   • NTRES 4120, Wildlife Population Analysis: Techniques & Models
   • NTRES 6700, Spatial Statistics
   • OR&IE 3500, Engineering Probability and Statistics II
   • OR&IE 3510, Introductory Engineering Stochastic Processes

Note 1: It is strongly recommended that students in this POS use PHYS 2207/2208 to satisfy the Core physics requirement.

Note 2: It is strongly recommended that students complete the Core organic chemistry requirement using the CHEM 1570/2510 option, and that the time saved be used to take either CS 2110 or a second mathematics course from the list above.

Note 3: MATH 2210 Linear Algebra and Calculus, MATH 2310 Linear Algebra with Applications, or MATH 4200 Differential Equations and Dynamical Systems is recommended for bridging course BIOEE 4600.

Note 4: One course may not be used to simultaneously satisfy two different requirements. For example BTRY 4080 can be used to satisfy either requirement (2) or requirement (4), but not both.

Note 5: Students who use BTRY 4080 to fulfill the additional mathematics requirement should not use OR&IE 3500 Engineering Probability and Statistics II to fulfill the requirement for an advanced course.

Note 6: Biology majors in the College of Agriculture and Life Sciences who select this Program of Study are allowed to take up to 61 credit hours in the endowed colleges due to the high number of required endowed courses for this Program of Study.

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