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From the beginning, computational biology and quantitative modeling has been an intimate part of the field of genomics. Advances in data acquisition and advances in algorithms and data models galvanized mutual advancement. The data generation rate of high-throughput methods is such that less than 1/10000 of the primary data will be seen by human eyes and the complexity of the data is such that quantitative modeling is essential to understanding biological processes. Recently, the development of the next-generation sequencing (NGS) platforms in the last 3-4 years has led to a truly remarkable explosion of primary data and creative application of sequencing. In 2014, the NGS platforms have achieved the $1,000 genome and are capable of generating 600 Gigabases of data per day. The sequencing technology is yielding not only DNA sequence information at an unprecedented scale, but the technology has been creatively adopted to many functional assays including analyses of the transcriptome, the epigenome, RNA structure, RNA-protein interactions, DNA-protein interactions, transcriptional activity, etc. Novel problem domains have been generated such as the investigation of memory and storage efficient algorithms (e.g., compressive algorithms). Large-scale functional genomics data has greatly increased the need for techniques to handle such data including high-dimensional data visualization, complex network analysis, and machine learning over big data. Lastly, the new data is revealing an unprecedented level of complexity in biological systems, including molecular mechanisms, evolutionary dynamics, and disease risk. All of this is requiring application of mathematical, statistical, and computational techniques to decipher the fundamentals of living systems.


Year 1:

GCB 536/BIOL437: Introduction to computational biology and biological modeling

GCB 537: Advanced Computational Biology

CIT 590: Programming Languages and Techniques

CIT 594: Programming Languages and Techniques II


BIOL 421: Molecular Genetics

BIOL 540: Genetic Systems

BIOL 410: Advanced Evolution

GCB 534: Experimental Genome Science


Year 2:

BE 567: Mathematical and Computational Modeling of Biological Systems

CIS 502: Analysis of Algorithms

CIS 519: Introduction to Machine Learning or CIS520 Machine Learning

CIS 550: Database and Information Systems

STAT 510: Probability

STAT 512: Mathematical Statistics

BSTA 785: Statistical Methods for Genomics Data Analysis


CAMB 550: Genetic Principles

BIOM 555: Control of Gene Expression


Year 3: 

BE 520: Computational Neuroscience and Neuroengineering

BE 530: Theoretical Neuroscience

BE 539: Neural Networks, Chaos, and Dynamics

BE 559: Multiscale Modeling of Biological Systems

BE 567: Mathematical Computation Methods for Modeling Biological Systems

STAT 531: Stochastic Processes

STAT 542: Bayesian Methods and Computation

STAT 953: Bioinformatics

BSTA 787: Methods for Statistical Genetics in Complex Human Disease