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:
- Developing computational pipelines to assemble, annotate and analyze whole genome sequences, transcriptomes of different tissues and developmental stages, and protein-protein interaction networks.
- Predicting biological functions of genes and their proteins using machine-learning methods.
- Predicting gene and protein structures using statistical methods such as Hidden Markov Models and homology information.
- 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|
|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|
|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|
|BIOL-UA 0038||Genome Biology|
|BIOL-UA 44||Microbiology and Microbial Genomics|
|BIOL-UA 0103||Bioinformatics in Medicine and Biology|
|BIOL-UA 0124||Fundamentals of Bioinformatics|
|BIOL-UA 0031||At the Bench: Laboratory in Genetics and Genomics|
|BIOL-UA 0036||At the Bench: Applied Molecular Biology|