Cold Spring Harbor Laboratory  
Contact Us | Faculty & Staff Directory

Scientific Resources

Centralized resources and services for CSHL scientists.

See Resources
Simons Center for Quantitative Biology

simons graphs intropictThe Simons Center for Quantitative Biology is dedicated to the development of new mathematical tools and techniques for the analysis of biological data. Researchers at the Center work on a wide variety of applications, including autism, cancer, neuroscience, plant biology, gene regulation and evolution.

Research at the Simons Center for Quantitative Biology is funded by a major donation from the Simons Foundation as well as gifts from the Starr Foundation and Lavinia and Landon Clay.

External Advisory Committee

Quantitative Biology Fellows Program

graph abouttab topAs technologies for data generation have become steadily more efficient and inexpensive, the interpretation of vast quantities of biological data has emerged as a rate-limiting step in advances in the biological sciences. This challenge cuts across research areas, from genomics, neuroscience, and human diseases to the plant sciences. Making sense of the “big data” that is now ubiquitous in biology requires the development of innovative new quantitative tools and techniques, grounded in classical theory yet adapted for powerful modern technologies. The central idea behind the Simons Center for Quantitative Biology is to place researchers trained in mathematics, physics, computer science, and other quantitative fields on the front lines in biology, working shoulder to shoulder with experimentalists. In addition to collaborative work, SCQB researchers pursue independent research in algorithms, machine learning, statistical genetics, evolution, and other areas. The ultimate goal of the Center is to promote interdisciplinary approaches that can break new ground on problems of central importance in both fundamental biology and applications in human health, agriculture, and the environment.

 

graph abouttab bottom

 

qb graphs main

siepel RGB thumbI’d like to welcome you warmly to the Simons Center for Quantitative Biology (SCQB), Cold Spring Harbor Laboratory's home for mathematical, computational, and theoretical research in biology. The SCQB is a blend of old and new: it extends a long history of quantitative research at CSHL, yet leverages new technologies and ideas from the quantitative sciences to enable groundbreaking research across a wide variety of biological domains, including human genetics, cancer, plant biology, and neuroscience. Members of the SCQB maintain close collaborative ties across CSHL as well as with many other groups in the New York area, including Computer Science and Applied Mathematics at Stony Brook University and the New York Genome Center. Enabled by generous donations from the Simons Foundation and other sources, the SCQB is currently undergoing a major expansion, with several new hires of faculty and staff. I am deeply honored by the opportunity to lead this unique center of excellence in quantitative biology, embedded in the world-class research environment of Cold Spring Harbor Laboratory.

Adam Siepel, Chair
February, 2015

 

qb welcome collage

Researchers at Cold Spring Harbor Laboratory have long been interested in the use of quantitative methods in genetics, biophysics, neuroscience and other areas, but for most of the history of the Laboratory, quantitative biology was not considered a distinct area of focus. These circumstances changed with the creation of a Center for Quantitative Biology in 2008. Not long afterward, the Center was renamed the Simons Center for Quantitative Biology in recognition of generous donations from the Simons Foundation. The Simons Foundation made additional donations in 2014, enabling further growth of the Center.

During the long history of the Laboratory, a number of prominent quantitative biologists have been associated with CSHL, either as permanent staff members or as summer visitors. Examples include Charles Davenport, Sewall Wright, Max Delbruck, Bruce Wallace, and Claude Shannon. Additional details can be found in this Short History of Quantitative Biology at Cold Spring Harbor Laboratory.

short-history image

(left to right) Sewall Wright, Claude Shannon, Max Delbruck and Salvador Luria, and the modern Hillside Campus at CSHL, which is home to the Simons Center for Quantitative Biology

 

qb history collage

Core, L. J., Martins, A. L., Danko, C. G., Waters, C. T., Siepel, A., Lis, J. T. (2014) Analysis of nascent RNA identifies a unified architecture of initiation regions at mammalian promoters and enhancers. Nat Genet, 46(12) pp. 1311-20.

Gulko, B., Hubisz, M. J., Gronau, I., Siepel, A. (2015) A method for calculating probabilities of fitness consequences for point mutations across the human genome. Nat Genet, 47, 276-83.

Iossifov I, O'Roak BJ, Sanders SJ, Ronemus M, Krumm N, Levy D, Stessman HA, Witherspoon KT, Vives L, Patterson KE, Smith JD, Paeper B, Nickerson DA, Dea J, Dong S, Gonzalez LE, Mandell JD, Mane SM, Murtha MT, Sullivan CA, Walker MF, Waqar Z, Wei L, Willsey AJ, Yamrom B, Lee YH, Grabowska E, Dalkic E, Wang Z, Marks S, Andrews P, Leotta A, Kendall J, Hakker I, Rosenbaum J, Ma B, Rodgers L, Troge J, Narzisi G, Yoon S, Schatz MC, Ye K, McCombie WR, Shendure J, Eichler EE, State MW, Wigler M. (2014) The contribution of de novo coding mutations to autism spectrum disorder. Nature.Nov13;515(7526):216-21.

Kinney JB, Atwal GS. (2014) Equitability, mutual information, and the maximal information coefficient. Proc Natl Acad Sci U S A. Mar 4;111(9):3354-9.

Kinney JB, Atwal GS. (2014) Parametric inference in the large data limit using maximally informative models. Neural Comput.  Apr;26(4):637-53.

Krasnitz A, Sun G, Andrews P, Wigler M. (2013) Target inference from collections of genomic intervals. Proc Natl Acad Sci U S A. Jun 18;110(25):E2271-8.

Levy D, Wigler M.(2014) Facilitated sequence counting and assembly by template mutagenesis. Proc Natl Acad Sci U S A. Oct 28;111(43):E4632-7.

Narzisi, G, O'Rawe, JA, Iossifov, I, Fang, H, Lee, YH, Wang, Z, Wu, Y, Lyon, G, Wigler, M, Schatz MC (2014) Accurate de novo and transmitted indel detection in exome-capture data using microassembly. Nature Methods, 11:1033-36

Rasmussen, M. D. and Hubisz, M. J. and Gronau, I. and Siepel, A. (2014) Genome-wide inference of ancestral recombination graphs. PLoS Genetics, 10(5) pp. e1004342.

Schatz MC, Maron, LG, Stein, JC, Wences, AH, Gurtowski, J, Biggers, E, Lee, H, Kramer, M, Antoniou, E, Ghiban, E, Wright, MH, Chia, JM, Ware, D, McCouch, S, McCombie, WR. Whole genome de novo assemblies of three divergent strains of rice (O. sativa) documents novel gene space of aus and indica. (2014) Genome Biology 15:506

Wei Y, Koulakov AA. Long-term memory stabilized by noise-induced rehearsal. (2014) J Neurosci. Nov 19;34(47):15804-15.

Neanderthals mated with modern humans much earlier than previously thought, study finds
February 17, 2016

Genetic analysis supports prediction that spontaneous rare mutations cause half of autism
September 22, 2015

The biggest beast in the Big Data forest? One field's astonishing growth is, well, 'genomical'!
July 7, 2015

CSHL Quantitative Biologist Michael Schatz awarded 2015 Sloan Foundation Research Fellow
February 23, 2015

Harnessing data from Nature's great evolutionary experiment
January 21, 2015

Re-learning how to read a genome
November 10, 2014

New study casts sharpest light yet on genetic mysteries of autism
October 29, 2014

A shift in the code: new method reveals hidden genetic landscape
August 18, 2014

CSHL quantitative biologist Michael Schatz wins prestigious NSF Early CAREER Award
July 29, 2014

CSHL receives $50 million to establish Simons Center for Quantitative Biology
July 7, 2014

Researchers propose new way to make sense of 'Big Data'
February 15, 2014

Dr. Adam Siepel, Cornell University - Similarity in Primate DNA
August 26, 2013

Analysis of 26 networked autism genes suggests functional role in the cerebellum
July 17, 2013

Mathematical technique de-clutters cancer-cell data, revealing tumor evolution, treatment leads
June 5, 2013

Genome sequencing's big fix
Harbor Transcript, Spring 2012

An error-eliminating fix overcomes big problem in '3rd-gen' genome sequencing
June 29, 2012

A striking link is found between the Fragile-X gene and mutations that cause autism
April 25, 2012

Autism study validates importance of spontaneous casual mutations and sheds new light on gender skew
June 8, 2011

Current job openings in the SCQB include a faculty position at the assistant professor level, and several staff and postdoctoral positions.  Details are available at the CSHL Careers website.  In addition, we are accepting nominations for the QB Fellows Program.
Gurinder
Gurinder Atwal - Associate Professor
Population genetics; bioinformatics; cancer; stochastic processes; statistical mechanics and information theory
 
Molly
Molly Hammell - Assistant Professor
Gene regulatory networks; integrated genomic analysis; bioinformatics; RNA biology; small RNAs
 
Ivan
Ivan Iossifov - Assistant Professor
Computational biology; molecular networks; human genetics; human disease; applied statistical and machine learning; biomedical text-mining; molecular evolution
Justin
Justin Kinney - Assistant Professor
Sequence-function relationships; machine learning; biophysics; transcriptional regulation
 
Alexei
Alexei Koulakov - Professor
Theoretical neurobiology; quantitative principles of cortical design; computer science; applied mathematics
Alexander
Alexander Krasnitz - Associate Professor
In silico genomics of cancer; single-cell genomics; inference from noisy biological data; large-scale numerical computing
 
Dan
Dan Levy - Assistant Professor
Computational biology; human genetics; phylogenetics; copy number variation
 
Partha
Partha Mitra - Professor
Neuroscience and theoretical biology
Michael
Michael Schatz - Adjunct Associate Professor
Genomics; DNA sequencing; genome assembly & validation; sequence alignment; high performance and multicore computing; parallel algorithms; cloud computing
 
Adam
Adam Siepel - Professor
Computational biology, population genetics, computational genomics, molecular evolution, gene regulation
Michael
Michael Wigler - Professor
Human genetic disorders; population genetics; cancer genomics