Computational Biology

With the introduction of new technologies such as next-generation sequencing, it has become easier and cheaper to generate high-throughput biological data. Data are being generated at such a fast rate that the bottleneck for scientific discovery is data analysis. The main goal of the field of Computational Biology is to develop and apply mathematical, statistical, and computational methods to efficiently process and analyze large-scale biological data. In the Department of Biology and within the Center for Genomics and Systems Biology, a rigorous curriculum has been developed to train future computational biologists in the areas of genomics, mathematics, statistics, and computer science.

Some of the computational methods being developed in the Department to answer biological questions in a range of organisms from yeast and viruses, to plants and parasites, include:

  1. Developing computational pipelines to assemble, annotate and analyze whole genome sequences, transcriptomes of different tissues and developmental stages, and protein-protein interaction networks.
  2. Predicting biological functions of genes and their proteins using machine-learning methods.
  3. Predicting gene and protein structures using statistical methods such as Hidden Markov Models and homology information.
  4. Developing tools to enable biologists with no computational background to analyze their data.

Faculty and students have access to cutting-edge research facilities including the Sequencing (GenCore) Facility for high-throughput sequencing and data generation, and NYU’s high performance compute clusters. Students are taught in the sub-disciplines of computer programming (e.g., shell and python scripting), statistical programming languages such as R, and how to use high performance computing systems.

 

NYU Biology Faculty in this research area:

Ken Birnbaum* Cell identity, pluripotency and regeneration in plants.
Richard Bonneau* Network inference and protein structure design and prediction.
Jane Carlton* Comparative genomics and evolution of protists.
Gloria Coruzzi* Plant systems biology: From predictive network modelling to trait evolution.
Patrick Eichenberger* Transcriptional regulatory networks in spore-forming bacteria.
Sevinc Ercan* Regulation of transcription by chromatin structure.
David Fitch Gene-interaction networks regulating sexually dimorphic morphogenesis & its evolution.
Elodie Ghedin* Viral evolution and host-pathogen interactions.
David Gresham* Systems biology of cell growth and RNA degradation.
Kris Gunsalus* Developmental systems biology.
Manpreet S. Katari Translational plant systems biology: From model organisms to crops.
Edo Kussell* Stochastic processes in adaptation and evolution.
Alex Mogilner Computational modeling of cell motility and mitosis.
Michael Purugganan* Evolutionary genomics of plants.
Matthew Rockman* Systems genetics of gene expression in C. elegans.
Mark Siegal* Robustness and evolution of complex phenotypes.
Daniel Tranchina Computational neuroscience, stochastic gene expression, statistics of genomic data.
Christine Vogel* Proteomics and regulation of protein expression.

*Faculty with a primary appointment in the Center for Genomics and Systems Biology.

 

Sample course curriculum in this research area:

Course Number(s) Course Name
Computational Biology
Graduate Level
Core
BIOL-GA 1007 Programming for Biologists
BIOL-GA 1128 Systems Biology
BIOL-GA 1009 Biological Databases & Datamining
BIOL-GA 1127 Bioinformatics & Genomes
BIOL-GA 1130 Applied Genomics
BIOL-GA 2030 Statistics in Biology
  
Computational Electives
BIOL-GA 1129 Evolutionary Genetics and Genomics
BIOL-GA 1131 Biophysical Modeling of Cells & Populations
BIOL-GA 1501 Math in Medicine/Biology
BIOL-GA 1502 Computers in Medicine & Biology
BIOL-GA 2015
Genomics and Global Public Health
 
Undergraduate Level
Core
BIOL-UA 0038 Genome Biology
BIOL-UA 0042 Biostatistics
BIOL-UA 44 Microbiology and Microbial Genomics
BIOL-UA 0103 Bioinformatics in Medicine and Biology
BIOL-UA 0124 Fundamentals of Bioinformatics
 
Electives
BIOL-UA 0031 At the Bench: Laboratory in Genetics and Genomics
BIOL-UA 0036 At the Bench: Applied Molecular Biology