Mathematics Majors

Within the advent of genomics and proteomics, the biological sciences are evolving from mainly experimental sciences performed at the bench to one in which large databases of information, probabilistic models and statistical analysis techniques play a significant role. Typical probabilistic models include variance components, hidden Markov models, Bayesian networks, and coalescent. Typical statistical methods include maximum likelihood, Bayesian inference, Monte Carlo Markov chains, and some methods of classification and clustering. Stochastic modeling and statistical methods are applied to a wide range of problems from Biology, such as mapping quantitative trait loci, analyzing gene expression data, sequence alignment, and reconstructing evolutionary trees. Thus, we propose a new option within the Mathematics major to provide students with a working knowledge of computing and the biological sciences in order to pursue research in applied mathematical and statistical methodology.

There is a need at Hunter College to prepare and train a new generation of mathematicians and statisticians capable of using computational analysis to solve important engineering problems arising from the new frontiers of biology and medicine at the molecular level. The quantitative biology curriculum will give Mathematics students the necessary background in Biology and Computer Science that they need for a working understanding of the subject-matter, while putting a strong emphasis on a rigorous methodological training. These students will be well prepared for bioengineering careers in bioinformatics, the pharmaceutical industry, and the biotechnology industry. They will also be well prepared for graduate studies in the mathematics and statistics of bioinformatics.

Mathematics QuBi concentrators will be able to develop and implement numerical algorithms as well as define and apply stochastic and deterministic mathematical models for the study of biological processes.

The Department of Mathematics & Statistics also has an MA program in Statistics and Applied Mathematics with track III in Bioinformatics

MATH 156 Introduction to Mathematical Proof Workshop (1 cr.)
MATH 250 Calculus with Analytic Geometry III (4 cr.)
MATH 254 Ordinary Differential Equations (3 cr.)
MATH 255 Vector Analysis (3 cr.)
MATH 260 Linear Algebra (4 cr.)
MATH 311 Abstract Algebra I (3 cr.)
MATH 351 Mathematical Analysis I (3 cr.)
STAT 213 Introduction to Applied Statistics (3 cr.)
STAT 311 Probability Theory (3 cr.)

Symbolic Computation Proficiency Requirement – As a requirement for graduation with a BA in mathematics, students must demonstrate entry level proficiency in symbolic computation. This requirement can be met in any of the following ways:

a) passing any one of MATH 126, MATH 151, MATH 154, MATH 385;

b) passing a departmental exam in a computer algebra system (currently we use MATHEMATICA or the equivalent)

In order to pursue the Bioinformatics concentration (option 5), students are required to complete the following sequences, in addition to the core courses listed above:

STAT 319 Bayesian Statistical Inference in the Sciences (3 cr.) (NEW)

CHEM 102  General Chemistry I (3 cr.)
CHEM 104  General Chemistry II (3 cr.)
CHEM 106  General Chemistry Lab (3 cr.)
CHEM 222  Organic Chemistry (3 cr.)

Note that CHEM 102 and CHEM 104-106 are GER/2/E courses. 
The GER/2/E block consists of at least 7 credits.

BIOL 100  Principles of Biology I (4.5 cr.)
BIOL 203  Molecular Biology and Genetics (4.5 cr.)
BIOL 425  Computational Molecular Biology (3 cr.) (NEW)

Acknowledgments
National Institutes of Health (NIH)/MARC Program
Howard Hughes Medical Institute (HHMI)
Center for the Study of Gene Structure and Function